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Phish Chicago 2023 Fall Tour Poster

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Lot poster created for Phish's October 2023 3-night run in Chicago, IL. Inspired by Phish's classic song, Ocelot, and the Chicago Bulls. Original art by Taylor Swope. Signed and individually numbered out of 100 limited edition prints. • 12" x 18" print on 13" x 19" with 1/2" margin • Printed glossy on White 11 pt Futura 100# dull cover card stock • Digitally signed and numbered series of 100 • Shipped in an acid free clear poly bag inside a 3" diameter cardboard tube

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Description.

Lot poster created for Phish's October 2023 three night run in Chicago, IL. Inspired by Phish's song, Ocelot, and a classic Chicago Bulls poster. Design by Taylor Swope. Digitally signed and individually numbered out of 100 limited edition prints. • 12" x 18" print on 13" x 19" with 1/2" margin • Printed glossy on White 11 pt Futura 100# dull cover card stock • Digitally signed and numbered series of 100 • Shipped in an acid free clear poly bag inside a 3" diameter cardboard tube • Phish inspired

Chicago 2023 Fall Tour Poster

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Phish 2023 Posters Signed by the Band up for Auction

Article contributed by the mimi fishm… | published on wednesday, october 25, 2023.

The Mimi Fishman Foundation has unveiled an exclusive online charity auction featuring limited-edition tour posters from Phish's 2023 shows. Each numbered poster is autographed by the entire Phish ensemble: Trey Anastasio, Mike Gordon, Jon Fishman, and Page McConnell.

🔗 Explore the auction and secure a piece of music history: Mimi Fishman Foundation Auction

Auction ends on November 14, 2023. Don't miss out!

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Progressive jam giants Umphrey's McGee‘s return to Las Vegas for the seventh installment of the massively popular UMBowl production was marked once again by a stand-out tour closing dual evening extravaganza where all stops were pulled out and the power given directly to the fans, for better or for worse.

On June 24, Round Records & ATO Records will release GarciaLive Volume Six: July 5, 1973 – Jerry Garcia & Merl Saunders, the latest installment of the celebrated GarciaLive archival series. The three-CD set was recorded at the 200 capacity Lion’s Share club formerly located in the small town of San Anselmo, CA, just 20 miles north of San Francisco. The performance features Jerry Garcia performing with friend, mentor and legendary keyboardist/vocalist Merl Saunders. The duo is joined by drummer Bill Vitt and bassist John Kahn, who soon became a lifelong Garcia collaborator.

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SURRENDER TO THE FLOW FALL 2023 25TH ANNIVERSARY ISSUE

[Courtesy of Christy Articola, one of the Phish community's most generous and gracious fans over the course of twenty-five years, and the Editor and Publisher of STTF. Do not miss user @farmose (formerly @fad_albert )'s "Horoscopes" in this STTF! -Ed.]

Here is Surrender to the Flow' s Fall Tour 2023 issue ! ( www.gum.co/sttf80 )!

It's our 25th Anniversary issue and it's hard to believe---but amazing for us to contemplate---that we have been putting this magazine out to Phish fans for two and a half decades! We are so thankful for your support and readership, and we think you're really going to love this one.

This issue is full of good stuff for you! It includes information about this year's Fall Tour 2023 in Nashville, Dayton, and Chicago---where to eat, things to do, and things you need to know about each area and venue. You can read reviews of the second half of Summer Tour 2023 in this one, too.

Further, we offer articles on a variety of interesting topics that we know you'll just love and so much more, including articles about a new podcast, a new Phish-fan-focused book, a documentary with a Phish-based soundtrack, and more! And this issue also includes our regular features like recipes, My First Show, My Favorite Jam Ever, 20 Years Later, Phish Changed My Life, Everybody Loves Statistics , horoscopes, Celebrations , fan fiction, a puzzle, and other things we think you'll enjoy.

It's free to download, but we also accept donations if you feel so inclined.

Please check out this issue and tell your friends, and have a great time on Fall Tour this year, everyone! Thanks for all your support over these many years.

If you liked this blog post, one way you could "like" it is to make a donation to The Mockingbird Foundation , the sponsor of Phish.net. Support music education for children, and you just might change the world.

Comment by The_Steiner

Comment by markah

Hooray!!! I was hoping there would be a fall tour ed!!! Will there be a hard copy available for a donation to MBF?

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Published: 2023/06/27

Phish Outline Fall 2023 Tour Dates

Today, Phish have announced their fall 2023 tour dates. The impending run is slated to begin in early October. It will consist of three multi-night stands, the first of which will occur in Nashville, Tenn., before follow-ups appearances in Dayton, Ohio, and the final string of shows, which will happen in Chicago.

The initial fall tour dates will be held at Nashville’s Bridgestone Arena, shows will occur nightly on October 6, 7, and 8. In continuation of their brisk jaunt, the Trey Anastasio-led jamband will turn up at Wright State University Nutter Center in Dayton, Ohio, for two back-to-back concerts, scheduled for Oct. 10 and 11.

Phish’s final fall tour dates will be Oct. 13, 14 and 15, when the Vermont-based band arrives at the United Center in Chicago. Before their fall jaunt, the group will participate in their forthcoming summer run, slated to begin at Orion Amphitheater in Huntsville, Ala., on July 11 and 12. Read more about Phish’s summer tour here .

A ticket request period is currently underway at tickets.phish.com and ends Monday, July 10, at noon ET. Tickets go on sale to the general public beginning Saturday, July 15 at 10 a.m. E.T. Specific ticketing information for each show is available at phish.com/tours .

Moreover, travel packages will be available for Nashville and Chicago. Nashville Travel Packages go on sale on July 12 at 11 a.m. E.T./10 a.m. C.T. and Chicago Travel Packages go on sale one hour later at 12 p.m. E.T./11 a.m. C.T. For more info, visit https://phishfall2023.100xhospitality.com/ .

Phish Fall Tour:

6 – Bridgestone Arena – Nashville, Tenn.

7 – Bridgestone Arena – Nashville, Tenn.

8 – Bridgestone Arena – Nashville, Tenn.

10 – Wright State University Nutter Center – Dayton, Ohio

11 – Wright State University Nutter Center – Dayton, Ohio

13 – United Center – Chicago

14- United Center – Chicago

15 – United Center – Chicago

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OUR MINISTER

Dr. Joseph Lozovyy was born into a Christian family in Elektrostal, Moscow Region, and was raised in a pastor’s home. From the age of fifteen, he began actively participating in the music ministry of the Baptist Church in Mytishchi, where his father served as a pastor, and also played in the orchestra of the Central Moscow Baptist Church. From 1989, he participated in various evangelistic events in different cities of Moscow Region and beyond. From 1989 to 1992, as a member of the choir and orchestra “LOGOS,” he participated in evangelistic and charitable concerts, repeatedly performing on the stages of the Moscow State Conservatory, the Bolshoi Theatre, and other concert halls in Russia and abroad. In 1992, his family moved to the United States. In 2007, after completing a full course of spiritual and academic preparation, Joseph moved to Dallas, Texas, to engage in church ministry. In 2008, he founded the Russian Bible Church to preach to the Russian-speaking population living in Dallas, Texas.

– Bachelor of Arts in Music (viola) from the Third Moscow Music School named after Scriabin, Russia (1987-1991)

– Master of Theology (Th.M); Dallas Theological Seminary, Texas (1999-2003);

– Doctor of Philosophy (Ph.D) Hebrew Bible (Books of Samuel): University of Edinburgh, Scotland, United Kingdom (2007).

– Doctoral research (2004-2005) Tübingen, Germany.

– Author of a theological work published in English: Saul, Doeg, Nabal and the “Son of Jesse: Readings in 1 Samuel 16-25, LHBOTS 497 [T&T Clark/Continuum: Bloomsbury Publishing]).

https://www.bloomsbury.com/us/saul-doeg-nabal-and-the-son-of-jesse-9780567027535/

Joseph and his wife Violetta and their son Nathanael live in the northern part of Dallas.

Saul, Doeg, Nabal, and the “Son of Jesse”: Readings in 1 Samuel 16-25: The Library of Hebrew Bible/Old Testament Studies Joseph Lozovyy T&T Clark (bloomsbury.com)

Joseph, his wife Violetta and their son Nathaniel live in North Dallas, Texas where he continues ministering to Russian-speaking Christians and his independent accademic research.

Published Work

1. bloomsbury:, 2. buy at christian book distributors:, 3. buy on amazon:.

research on healthy lifestyle

Research article
Open access
Published: 29 September 2022

A healthy lifestyle is positively associated with mental health and well-being and core markers in ageing

Pauline Hautekiet ORCID: orcid.org/0000-0003-3805-3004 1 , 2 ,
Nelly D. Saenen 1 , 2 ,
Dries S. Martens 2 ,
Margot Debay 2 ,
Johan Van der Heyden 3 ,
Tim S. Nawrot 2 , 4 &
Eva M. De Clercq 1

BMC Medicine volume 20 , Article number: 328 ( 2022 ) Cite this article

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Studies often evaluate mental health and well-being in association with individual health behaviours although evaluating multiple health behaviours that co-occur in real life may reveal important insights into the overall association. Also, the underlying pathways of how lifestyle might affect our health are still under debate. Here, we studied the mediation of different health behaviours or lifestyle factors on mental health and its effect on core markers of ageing: telomere length (TL) and mitochondrial DNA content (mtDNAc).

In this study, 6054 adults from the 2018 Belgian Health Interview Survey (BHIS) were included. Mental health and well-being outcomes included psychological and severe psychological distress, vitality, life satisfaction, self-perceived health, depressive and generalised anxiety disorder and suicidal ideation. A lifestyle score integrating diet, physical activity, smoking status, alcohol consumption and BMI was created and validated. On a subset of 739 participants, leucocyte TL and mtDNAc were assessed using qPCR. Generalised linear mixed models were used while adjusting for a priori chosen covariates.

The average age (SD) of the study population was 49.9 (17.5) years, and 48.8% were men. A one-point increment in the lifestyle score was associated with lower odds (ranging from 0.56 to 0.74) for all studied mental health outcomes and with a 1.74% (95% CI: 0.11, 3.40%) longer TL and 4.07% (95% CI: 2.01, 6.17%) higher mtDNAc. Psychological distress and suicidal ideation were associated with a lower mtDNAc of − 4.62% (95% CI: − 8.85, − 0.20%) and − 7.83% (95% CI: − 14.77, − 0.34%), respectively. No associations were found between mental health and TL.

Conclusions

In this large-scale study, we showed the positive association between a healthy lifestyle and both biological ageing and different dimensions of mental health and well-being. We also indicated that living a healthy lifestyle contributes to more favourable biological ageing.

Peer Review reports

According to the World Health Organization (WHO), a healthy lifestyle is defined as “a way of living that lowers the risk of being seriously ill or dying early” [ 1 ]. Public health authorities emphasise the importance of a healthy lifestyle, but despite this, many individuals worldwide still live an unhealthy lifestyle [ 2 ]. In Europe, 26% of adults smoke [ 3 ], nearly half (46%) never exercise [ 4 ], 8.4% drink alcohol on a daily basis [ 5 ] and over half (51%) are overweight [ 5 ]. These unhealthy behaviours have been associated with adverse health outcomes like cardiovascular diseases [ 6 , 7 , 8 ], respiratory diseases [ 9 ], musculoskeletal diseases [ 10 ] and, to a lesser extent, mental disorders [ 11 , 12 ].

Even though the association between lifestyle and health outcomes has been extensively investigated, biological mechanisms explaining these observed associations are not yet fully understood. One potential mechanism that can be suggested is biological ageing. Both telomere length (TL) and mitochondrial DNA content (mtDNAc) are known biomarkers of ageing. Telomeres are the end caps of chromosomes and consist of multiple TTAGGG sequence repeats. They protect chromosomes from degradation and shorten with every cell division because of the “end-replication problem” [ 13 ]. Mitochondria are crucial to the cell as they are responsible for apoptosis, the control of cytosolic calcium levels and cell signalling [ 14 ]. Living a healthy lifestyle can be linked with healthy ageing as both TL and mtDNAc have been associated with health behaviours like obesity [ 15 ], diet [ 16 ], smoking [ 17 ] and alcohol abuse [ 18 ]. Furthermore, as biomarkers of ageing, both TL and mtDNAc have been associated with age-related diseases like Parkinson’s disease [ 19 ], coronary heart disease [ 20 ], atherosclerosis [ 21 ] and early mortality [ 22 ]. Also, early mortality and higher risks for the aforementioned age-related diseases are observed in psychiatric illnesses, and it is suggested that advanced biological ageing underlies these observations [ 23 ].

Multiple studies evaluated individual health behaviours, but research on the combination of these health behaviours is limited. As they often co-occur and may cause synergistic effects, assessing them in combination with each other rather than independently might better reflect the real-life situation [ 24 , 25 ]. Therefore, in a general adult population, we combined five commonly studied health behaviours including diet, smoking status, alcohol consumption, BMI and physical activity into one healthy lifestyle score to evaluate its association with mental health and well-being and biological ageing. Furthermore, we evaluated the association between the markers of biological ageing and mental health and well-being. We hypothesise that individuals living a healthy lifestyle have a better mental health status, a longer TL and a higher mtDNAc and that these biomarkers are positively associated with mental health and well-being.

Study population

In 2018, 11611 Belgian residents participated in the 2018 Belgian Health Interview Survey (BHIS). The sampling frame of the BHIS was the Belgian National Register, and participants were selected based on a multistage stratified sampling design including a geographical stratification and a selection of municipalities within provinces, of households within municipalities and of respondents within households [ 26 ]. The study population for this cross-sectional study included 6054 BHIS participants (see flowchart in Additional file 1 : Fig. S1) [ 27 , 28 , 29 , 30 , 31 ]. Minors (< 18 years) and participants not eligible to complete the mental health modules (participants who participated through a proxy respondent, i.e. a person of confidence filled out the survey) were excluded ( n = 2172 and n = 846, respectively). Furthermore, of the 8593 eligible participants, those with missing information to create the mental health indicators, the lifestyle score or the covariates used in this study were excluded ( n = 1642, 788 and 109, respectively).

For the first time in 2018, a subset of 1184 BHIS participants contributed to the 2018 Belgian Health Examination Survey (BELHES). All BHIS participants were invited to participate except for minors (< 18 years), BHIS participants who participated through a proxy respondent and residents of the German Community of Belgium, the latter representing 1% of the Belgian population. Participants were recruited on a voluntary basis until the regional quotas were reached (450, 300 and 350 in respectively Flanders, Brussels Capital Region and Wallonia). These participants underwent a health examination, including anthropological measurements and completed an additional questionnaire. Also, blood and urine samples were collected. Of the 6054 included BHIS participants, 909 participated in the BELHES. Participants for whom we could not calculate both TL and mtDNAc were excluded ( n = 170). More specifically, participants were excluded because they did not provide a blood sample ( n = 91) or because they did not provide permission for DNA research ( n = 32). Twenty samples were excluded from DNA extraction because either total blood volume was too low ( n = 7), samples were clothed ( n = 1) or tubes were broken due to freezing conditions ( n = 12). Twenty-seven samples were excluded because they did not meet the biomarker quality control criteria (high technical variation in qPCR triplicates). This was not met for 3 TL samples, 20 mtDNAc samples and 4 samples for both biomarkers. For this subset, we ended up with a final number of 739 participants. Further in this paper, we refer to “the BHIS subset” for the BHIS participants ( n = 6054) and the “BELHES subset” for the BELHES participants ( n = 739).

As part of the BELHES, this project was approved by the Medical Ethics Committee of the University Hospital Ghent (registration number B670201834895). The project was carried out in line with the recommendations of the Belgian Privacy Commission. All participants have signed a consent form that was approved by the Medical Ethics Committee.

Health interview survey

The BHIS is a comprehensive survey which aims to gain insight into the health status of the Belgian population. The questions on the different dimensions of mental health and well-being were based on international standardised and validated questionnaires [ 32 ], and this resulted in eight mental health outcomes that were used in this study. Detailed information on each indicator score and its use is addressed in Additional file 1 : Table. S1. Firstly, the General Health Questionnaire (GHQ-12) provides the prevalence of psychological and severe psychological distress in the population [ 27 ]. On the total GHQ score, cut-off points of + 2 and + 4 were used to identify respectively psychological and severe psychological distress.

Secondly, we used two indicators for the positive dimensions of mental health: vitality and life satisfaction. Four questions of the short form health survey (SF-36) indicate the participant’s vital energy level [ 28 , 33 ]. We used a cut-off point to identify participants with an optimal vitality score, which is a score equal to or above one standard deviation above the mean, as used in previous studies [ 34 , 35 ]. Life satisfaction was measured by the Cantril Scale, which ranges from 0 to 10 [ 29 ]. A cut-off point of + 6 was used to indicate participants with high or medium life satisfaction versus low life satisfaction.

Thirdly, the question “How is your health in general? Is it very good, good, fair, bad or very bad?” was used to assess self-perceived health, also known as self-rated health. Based on WHO recommendations [ 36 ], the answer categories were dichotomised into “good to very good self-perceived health” and “very bad to fair self-perceived health”.

Fourthly, depressive and generalised anxiety disorders were defined using respectively the Patient Health Questionnaire (PHQ-9) and the Generalised Anxiety Disorder Questionnaire (GAD-7). We identified individuals who suffer from major depressive syndrome or any other type of depressive syndrome according to the criteria of the PHQ-9 [ 37 ]. A cut-off point of + 10 on the total sum of the GAD-7 score was used to indicate generalised anxiety disorder [ 31 ]. Additionally, a dichotomous question on suicidal ideation was used: “Have you ever seriously thought of ending your life?”; “If yes, did you have such thoughts in the past 12 months?”. Finally, the BHIS also includes personal, socio-economic and lifestyle information. The standardised Cronbach’s alpha coefficients for the PHQ-9, GHQ-12, GAD-7 and questions on vitality of the SF-36 ranged between 0.80 and 0.90.

Healthy lifestyle score

We developed a healthy lifestyle score based on five different health behaviours: body mass index (BMI), smoking status, physical activity, alcohol consumption and diet (Table 1 ). These health behaviours were defined as much as possible according to the existing guidelines for healthy living issued by the Belgian Superior Health Council [ 38 ] and the World Health Organisation [ 39 , 40 , 41 ]. Firstly, BMI was calculated as a person’s self-reported weight in kilogrammes divided by the square of the person’s self-reported height in metres (kg/m 2 ). BMI was classified into four categories: underweight (BMI < 18.5 kg/m 2 ), normal weight (BMI 18.5–24.9 kg/m 2 ), overweight (BMI 25.0–29.9 kg/m 2 ) and obese (BMI ≥ 30.0 kg/m 2 ). Due to a J-shaped association of BMI with the overall mortality and multiple specific causes of death, obesity and underweight were both classified as least healthy [ 42 ]. BMI was scored as follows: obese and underweight = 0, overweight = 1 and normal weight = 2.

Secondly, smoking status was divided into four categories. Participants were categorised as regular smokers if they smoked a minimum of 4 days per week or if they quit smoking less than 1 month before participation (= 0). Occasional smokers were defined as smoking more than once per month up to 3 days per week (= 1). Participants were classified as former smokers if they quit smoking at least 1 month before the questionnaire or if they smoked less than once a month (= 2). The final category included people who never smoked (= 3).

Thirdly, physical activity was assessed by the question: “What describes best your leisure time activities during the last year?”. Four categories were established and scored as follows: sedentary activities (= 0), light activities less than 4 h/week (= 1), light activities more than 4 h/week or recreational sport less than 4 h/week (= 2) and recreational sport more than 4 h or intense training (= 3). Fourthly, information on the number of alcoholic drinks per week was used to categorise alcohol consumption. The different categories were set from high to low alcohol consumption: 22 drinks or more/week (= 0), 15–21 drinks/week (= 1), 8–14 drinks/week (= 2), 1–7 drinks/week (= 3)and less than 1 drink/week (= 4).

Finally, in line with the research by Benetou et al., a diet score was calculated using the frequency of consuming fruit, vegetables, snacks and sodas [ 43 ]. For fruit as well as vegetable consumption, the frequency was scored as follows: never (= 0), < 1/week (= 1), 1–3/week (= 2), 4–6/week (= 3) and ≥ 1/day (= 4). The frequency of consuming snacks and sodas was scored as follows: never (= 4), < 1/week (= 3), 1–3/week (= 2), 4–6/week (= 1) and ≥ 1/day (= 0). The diet score was then divided into tertiles, in line with the research by Benetou et al. [ 43 ]. A diet score of 0–9 points was classified as the least healthy behaviour (= 0). A diet score ranging from 10 to 12 made up the middle category (= 1), and a score from 13 to 16 was classified as the healthiest behaviour (= 2).

All five previously described health behaviours were combined into one healthy lifestyle score (Table 1 ). The sum of the scores obtained for each health behaviour indicated the absolute lifestyle score. To calculate the relative lifestyle score, each absolute scored health behaviour was given equal weight by recalculating its maximum absolute score to a relative score of 1. The relative lifestyle scores were then summed up to achieve a final continuous lifestyle score, ranging from 0 to 5, with a higher score representing a healthier lifestyle.

Telomere length and mitochondrial DNA content assay

Blood samples were collected during the BELHES and centrifuged for 15 min at 3000 rpm before storage at − 80 °C. After extracting the buffy coat from the blood sample, DNA was isolated using the QIAgen Mini Kit (Qiagen, N.V.V Venlo, The Netherlands). The purity and quantity of the sample were measured with a NanoDrop spectrophotometer (ND-2000; Thermo Fisher Scientific, Wilmington, DE, USA). DNA integrity was assessed by agarose gel electrophoresis. To ensure a uniform DNA input of 6 ng for each qPCR reaction, samples were diluted and checked using the Quant-iT™ PicoGreen® dsDNA Assay Kit (Life Technologies, Europe).

Relative TL and mtDNAc were measured in triplicate using a previously described quantitative real-time PCR (qPCR) assay with minor modifications [ 44 , 45 ]. All reactions were performed on a 7900HT Fast Real-Time PCR System (Applied Biosystems, Foster City, CA, USA) in a 384-well format. Used telomere, mtDNAc and single copy-gene reaction mixtures and PCR cycles are given in Additional file 1 : Text. S1. Reaction efficiency was assessed on each plate by using a 6-point serial dilution of pooled DNA. Efficiencies ranged from 90 to 100% for single-copy gene runs, 100 to 110% for telomere runs and 95 to 105% for mitochondrial DNA runs. Six inter-run calibrators (IRCs) were used to account for inter-run variability. Also, non-template controls were used in each run. Raw data were processed and normalised to the reference gene using the qBase plus software (Biogazelle, Zwijnaarde, Belgium), taking into account the run-to-run differences.

Leucocyte telomere length was expressed as the ratio of telomere copy number to single-copy gene number (T/S) relative to the mean T/S ratio of the entire study population. Leucocyte mtDNAc was expressed as the ratio of mtDNA copy number to single-copy gene number (M/S) relative to the mean M/S ratio of the entire study population. The reliability of our assay was assessed by calculating the interclass correlation coefficient (ICC) of the triplicate measures (T/S and M/S ratios and T, M and S separately) as proposed by the Telomere Research Network, using RStudio version 1.1.463 (RStudio PBC, Boston, MA, USA). The intra-plate ICCs of T/S ratios, TL runs, M/S ratios, mtDNAc runs and single-copy runs were respectively 0.804 ( p < 0.0001), 0.907 ( p < 0.0001), 0.815 ( p < 0.0001), 0.916 ( p < 0.0001) and 0.781 ( p < 0.0001). Based on the IRCs, the inter-plate ICC was 0.714 ( p < 0.0001) for TL and 0.762 ( p < 0.0001) for mtDNAc.

Statistical analysis

Statistical analyses were performed using the SAS software (version 9.4; SAS Institute Inc., Cary, NC, USA). We performed a log(10) transformation of the TL and mtDNAc data to reduce skewness and to better approximate a normal distribution. Three analyses were done: (1) In the BHIS subset ( n = 6054), we evaluated the association between the lifestyle score and the mental health and well-being outcomes (separately). These results are presented as the odds ratio (95% CI) of having a mental health condition or disorder for a one-point increment in the lifestyle score. (2) In the BELHES subset ( n = 739), we evaluated the association between the lifestyle score and both TL and mtDNAc (separately). These results are presented as the percentage difference in TL or mtDNAc (95% CI) for a one-point increment in the lifestyle score. (3) In the BELHES subset ( n = 739), we evaluated the association between the mental health and well-being outcomes and both TL and mtDNAc (separately). These results are presented as the percentage difference in TL or mtDNAc (95% CI) when having a mental health condition or disorder compared with the healthy group.

For all three analyses, we performed multivariable linear mixed models (GLIMMIX; unstructured covariance matrix) taking into account a priori selected covariates including age (continuous), sex (male, female), region (Flanders, Brussels Capital Region, Wallonia), highest educational level of the household (up to lower secondary, higher secondary, college or university), country of birth (Belgium, EU, non-EU) and household type (single, one parent with child, couple without child, couple with child, others). To capture the non-linear effect of age, we included a quadratic term when the result of the analysis showed that both the linear and quadratic terms had a p -value < 0.1. For the two analyses on TL and mtDNAc, we additionally adjusted for the date of participation in the BELHES. As multiple members of one household participated, we added household numbers in the random statement.

Bivariate analyses evaluating the associations between the characteristics and TL, mtDNAc, the lifestyle score or psychological distress as a parameter of mental health and well-being are evaluated based on the same model. The chi-squared tests (categorical data) and t -tests (continuous data) were used to evaluate the characteristics of included and excluded participants. The lifestyle score was validated by creating a ROC curve and calculating the area under the curve (AUC) of the adjusted association between the lifestyle score and self-perceived health. Adjustments were made for age, sex, region, highest educational level of the household, country of birth and household type.

In a sensitivity analysis, to evaluate the robustness of our findings, we additionally adjusted our main models separately for perceived quality of social support (poor, moderate, strong) and chronic disease (suffering from any chronic disease or condition: yes, no). The third model, evaluating the biomarkers with the mental health outcomes, was also additionally adjusted for the lifestyle score.

Population characteristics

The characteristics of the BHIS and BELHES subset are presented in Table 2 . In the BHIS subset, 48.8% of the participants were men. The average age (SD) was 49.9 (17.5) years, and most participants were born in Belgium (79.5%). The highest educational level in the household was most often college or university degree (53.3%), and the most common household composition was couple with child(ren) (37.7%). The proportion of participants in different regions of Belgium, i.e. Flanders, Brussels Capital Region and Wallonia, was respectively 41.1%, 23.3% and 35.6%. For the BELHES subset, we found similar results except for region and education. We noticed more participants from Flanders and more participants with a high educational level in the household. The mean (SD) relative TL and mtDNAc were respectively 1.04 (0.23) and 1.03 (0.24). TL and mtDNAc were positively correlated (Spearman’s correlation = 0.21, p < 0.0001).

We compared (1) the characteristics of the 6054 eligible BHIS participants that were included in the BHIS subset with the 2539 eligible participants that were excluded from the BHIS subset (Additional file 1 : Table S2) and (2) the 739 participants from the BHIS subset that were included in the BELHES subset with the 5315 participants that were excluded from the BELHES subset (Additional file 1 : Table S3). Except for sex and nationality in the latter, all other covariates showed differences between the included and excluded groups. On the other hand, population data from 2018 indicates that the average age (SD) of the adult Belgian population was 49.5 (18.9) with a distribution over Flanders, Brussels Capital Region and Wallonia of respectively 58.2%, 10.2% and 31.6% and that 48.7% were men. The distribution of our sample according to age and sex thus largely corresponds to the age and sex distribution of the adult Belgian population figures. The large difference in the regional distribution is due to the oversampling of the Brussels Capital Region in the BHIS.

Bivariate associations evaluating the characteristics with TL, mtDNAc, the lifestyle score or psychological distress as a parameter of mental health are presented in Additional file 1 : Table S4. Briefly, men had a − 6.41% (95% CI: − 9.10 to − 3.65%, p < 0.0001) shorter TL, a − 8.03% (95% CI: − 11.00 to − 4.96%, p < 0.0001) lower mtDNAc, lower odds of psychological distress (OR = 0.59, 95% CI: 0.53 to 0.66, p < 0.0001) and a lifestyle score of − 0.28 (95% CI: − 0.32 to − 0.24, p < 0.0001) points less compared with women. Furthermore, a 1-year increment in age was associated with a − 0.64% (− 0.73 to − 0.55%, p < 0.0001) shorter TL and a − 0.19% (95% CI: − 0.31 to − 0.08%, p = 0.00074) lower mtDNAc.

Mental health prevalence and lifestyle characteristics

Within the BHIS subset, 32.3% and 18.0% of the participants had respectively psychological and severe psychological distress. 86.7% had suboptimal vitality, 12.0% indicated low life satisfaction and 22.0% had very bad to fair self-perceived health. The prevalence of depressive and generalised anxiety disorders was respectively 9.0% and 10.8%, respectively. 4.4% of the participants indicated to have had suicidal thoughts in the past 12 months. Similar results were found for the BELHES subset (Table 3 ).

Within the BHIS subset, the average lifestyle score (SD) was 3.1 (0.9) (Table 4 ). A histogram of the lifestyle score is shown in Additional file 1 : Fig. S2. 16.6% were regular smokers, and 4.9% reported 22 alcoholic drinks per week or more. 29.7% reported that their main leisure time included mainly sedentary activities, and 18.6% were underweight or obese. 29.2% were classified as having an unhealthy diet score. The participants of the BELHES subset were slightly more active, but no other dissimilarities were found (Table 4 ). The ROC curve shows an area under the curve (AUC) of 0.74, indicating a 74% predictive accuracy for the lifestyle score as a self-perceived health predictor (Additional file 1 : Fig. S3).

Healthy lifestyle and mental health and well-being

Living a healthier lifestyle, indicated by having a higher lifestyle score, was associated with lower odds of all mental health and well-being outcomes (Table 5 ). After adjustment, a one-point increment in the lifestyle score was associated with lower odds of psychological (OR = 0.74, 95% CI: 0.69, 0.79) and severe psychological distress (OR = 0.69, 95% CI: 0.64, 0.75). Similarly, for the same increment, the odds of suboptimal vitality, low life satisfaction and very bad to fair self-perceived health were respectively 0.62 (95% CI: 0.56, 0.68), 0.62 (95% CI: 0.56, 0.68) and 0.56 (95% CI: 0.52, 0.61). Finally, the odds of having depressive disorder, generalised anxiety disorder or suicidal ideation were respectively 0.57 (95% CI: 0.51, 0.63), 0.63 (95% CI: 0.57, 0.69) and 0.63 (95% CI: 0.55, 0.72) for a one-point increment in the lifestyle score.

The biomarkers of ageing

After adjustment, living a healthy lifestyle was positively associated with both TL and mtDNAc (Table 6 ). A one-point increment in the lifestyle score was associated with a 1.74 (95% CI: 0.11, 3.40%, p = 0.037) higher TL and a 4.07 (95% CI: 2.01, 6.17%, p = 0.00012) higher mtDNAc.

People suffering from severe psychological distress had a − 4.62% (95% CI: − 8.85, − 0.20%, p = 0.041) lower mtDNAc compared with those who did not suffer from severe psychological distress. Similarly, people with suicidal ideation had a − 7.83% (95% CI: − 14.77, − 0.34%, p = 0.041) lower mtDNAc compared with those without suicidal ideation. No associations were found for the other mental health and well-being outcomes, and no associations were found between mental health and TL (Table 6 ).

Sensitivity analysis

Additional adjustment of the main analyses for perceived quality of social support, chronic disease or lifestyle score (in the association between the mental health outcomes and the biomarkers of ageing) did not strongly change the effect of our observations (Additional file 1 : Tables S5-S7). However, we noticed that most of the associations between severe psychological distress or suicidal ideation and mtDNAc showed marginally significant results.

In this study, we evaluated the associations between eight mental health and well-being outcomes, a healthy lifestyle score and 2 biomarkers of biological ageing: telomere length and mitochondrial DNA content. Having a healthy lifestyle was positively associated with all mental health and well-being indicators and the markers of biological ageing. Furthermore, having had suicidal ideation or suffering from severe psychological distress was associated with a lower mtDNAc. However, no association was found between mental health and TL.

In the first part of this research, we evaluated the association between lifestyle and mental health and well-being and showed that living a healthy lifestyle was positively associated with better mental health and well-being outcomes. Similar trends were found in previous studies for each of the health behaviours separately [ 11 , 12 , 46 , 47 , 48 ]. Although evaluating these health behaviours separately provides valuable information, assessing them in combination with each other rather than independently might better reflect the real-life situation as they often co-occur and may exert a synergistic effect on each other [ 24 , 25 , 49 ]. For example, 68% of the adults in England engaged in two or more unhealthy behaviours [ 25 ]. Especially, smoking status and alcohol consumption co-occurred, but half of the studies in the review by Noble et al. indicated clustering of all included health behaviours [ 24 ].

To date, the number of studies evaluating the combination of multiple health behaviours and mental health and well-being in adults is limited, and most of them use a different methodology to assess this association [ 50 , 51 , 52 , 53 , 54 , 55 , 56 ]. Firstly, differences are found between the included health behaviours. Most studies included the four “SNAP” risk factors, i.e. smoking, poor nutrition, excess alcohol consumption and physical inactivity. Other health behaviours that were sometimes included were BMI/obesity, sleep duration/quality and psychological distress [ 50 , 53 , 54 , 56 ]. Secondly, differences are found in the scoring of the health behaviours and the use of the lifestyle score. Whereas in this study the health behaviours were scored categorically, studies often dichotomised the health behaviours and/or the final lifestyle score [ 50 , 52 , 53 , 56 ]. Also, two studies performed clustering [ 54 , 55 ]. Health behaviours can cluster together at both ends of the risk spectrum, but less is known about the middle categories. This is avoided by using the cluster method where participants are clustered based on similar behaviours. On the other hand, a lifestyle score can be of better use and more easily be interpreted when aiming to compare healthy versus unhealthy lifestyles, as was the case for this study.

Despite these different methods, all previously mentioned studies show similar results. Together with our findings, which also support these results, this provides clear evidence that an unhealthy lifestyle is associated with poor mental health and well-being outcomes. Important to notice is that, like our research, most studies in this field have a cross-sectional design and are therefore not able to assume causality. Therefore, mental health might be the cause or the consequence of an unhealthy lifestyle. Further prospective and longitudinal studies are warranted to confirm the direction of the association.

Healthy lifestyle and biomarkers of ageing

How lifestyle affects our health is not yet fully understood. One possible pathway is through oxidative stress and biological ageing. An unhealthy lifestyle has been associated with an increase in oxidative stress [ 57 , 58 , 59 ], and in turn, higher concentrations of oxidative stress are known to negatively affect TL and mtDNAc [ 60 ]. In this study, we showed that living a healthy lifestyle was associated with a longer TL and a higher mtDNAc. Our results showed a stronger association of lifestyle with mtDNAc compared with TL. TL is strongly determined by TL at birth [ 61 ]. On the other hand, mtDNAc might be more variable in shorter time periods. Although mtDNAc and TL were strongly correlated, this could explain why lifestyle is more strongly associated with mtDNAc. However, we can only speculate about this, and further research is necessary to confirm our results.

Similar as for the association with mental health, in previous studies, the biomarkers have been associated with health behaviours separately rather than combined [ 62 , 63 , 64 , 65 ]. To our knowledge, we are the first to evaluate the associations between a healthy lifestyle score and mtDNAc. Our results are in line with our expectations. As TL and mtDNAc are known to be correlated [ 60 ], we would expect similar trends for both biomarkers. In the case of TL, few studies included a combined lifestyle score in association with this biomarker. Consistent with our results, in a study population of 1661 men, the sum score of a healthier lifestyle was correlated with a longer TL [ 66 ]. Similar results were found by Sun et al. where a combination of healthy lifestyles in a female study population was associated with a longer TL compared with the least healthy group [ 67 ]. Also, improvement in lifestyle has been associated with TL maintenance in the elderly at risk for dementia [ 68 ], and a lifestyle intervention programme was positively associated with leucocyte telomere length in children and adolescents [ 69 ]. These results suggest that on a biological level, a healthy lifestyle is associated with healthy ageing. Within this context, a study on adults aged 60 and older showed that maintaining a normal weight, not smoking and performing regular physical activity were associated with slower development of disability and a reduction in mortality [ 70 ]. Similarly, midlife lifestyle factors like non-smoking, higher levels of physical activity, non-obesity and good social support have been associated with successful ageing, 22 years later [ 71 ].

Mental health and well-being and biomarkers of ageing

Finally, we evaluated the association between the biomarkers of ageing and the mental health and well-being outcomes. The hypothesis that biological ageing is associated with mental health has been supported by observations showing that chronically stressed or psychiatrically ill persons have a higher risk for age-related diseases like dementia, diabetes and hypertension [ 23 , 72 , 73 ]. Important to notice is that, like our research, the majority of studies on this topic have a cross-sectional design and therefore are unable to identify causality. Therefore, it is currently unknown whether psychological diseases accelerate biological ageing or whether biological ageing precedes the onset of these diseases [ 74 ].

Our results showed a lower mtDNAc for individuals with suicidal ideation or severe psychological distress but not for any of the other mental health outcomes. Evidence on the association between mtDNAc and mental health is inconsistent. Women above 60 years old with depression had a significantly lower mtDNAc compared with the control group [ 75 ]. Furthermore, individuals with a low mtDNAc had poorer outcomes in terms of self-rated health [ 76 ]. In contrast, Otsuka et al. showed a higher peripheral blood mtDNAc in suicide completers [ 77 ], and studies on major depressive syndrome [ 78 ] and self-rated health [ 79 ] showed the same trend. Finally, Vyas et al. showed no significant association between mtDNAc and depression status in mid-life and older adults [ 80 ]. These differences might be due to the differences in study population and methods. For example, the two studies indicating lower mtDNAc in association with poor mental health both had an elderly study population, and one study population consisted of psychiatrically ill patients. Next to that, differences were found in the type of samples, mtDNAc assays and questionnaires or diagnostics. The inconsistency of these studies and our results calls for further research on this association and for standardisation of methods within studies to enable clear comparisons.

As for TL, we did not find an association with any of the mental health and well-being outcomes. Previous studies in adults showed a lower TL in association with current but not remitted anxiety disorder [ 81 ], depressive [ 82 ] and major depressive disorder [ 73 , 83 ], childhood trauma [ 84 ] suicide [ 77 , 85 ], depressive symptoms in younger adults [ 86 ] and acculturative stress and postpartum depression in Latinx women [ 87 ]. Also, in a meta-analysis, psychiatric disorders overall were associated with a shorter leucocyte TL [ 88 ]. However, other studies failed to demonstrate an association between TL and mental health outcomes like major depressive disorder [ 89 ], late-life depression [ 90 ] and anxiety disorder [ 91 ]. Again, this could be due to a different method to assess the mental health outcomes, a different study design, uncontrolled confounding factors and the type of telomere assay. For example, a meta-analysis showed stronger associations with depression when using southern blot or FISH assay compared with qPCR to measure telomere length [ 92 ].

Strengths and limitations

An important strength of this study is the use of a validated lifestyle score that can easily be reproduced and used for other research on lifestyle. Secondly, we were able to use a large sample size for our analyses in the BHIS subset. Thirdly, by assessing multiple dimensions of mental health and well-being, we were able to give a comprehensive overview of the mental health status. To our knowledge, we are the first to evaluate the associations between a healthy lifestyle score and mtDNAc.

Our results should however be interpreted with consideration for some limitations. As mentioned before, the study has a cross-sectional design, and therefore, we cannot assume causality. Secondly, for the lifestyle score, we used self-reported data, which might not always represent the actual situation. For example, BMI values tend to be underestimated due to the overestimation of height and underestimation of weight [ 93 ], and also, smoking behaviour is often underestimated [ 94 ]. Also, equal weights were used for each of the health behaviours as no objective information was available on which weight should be given to a specific health behaviour. Thirdly, there is a distinct time lag between the completion of the BHIS questionnaire and the collection of the BELHES samples. The mean (SD) number of days is 52 (35). This is less than the period for suicidal ideation, assessed over the 12 previous months, but there might be a more limited overlap with the period for assessment of the other mental health variables, such as vitality and psychological distress, assessed over the last few weeks, and depressive and generalised anxiety disorders, assessed over the last 2 weeks. Fourthly, due to a non-response bias, the lowest socio-economic classes are less represented in our study population. This will not affect our dose–response associations but might affect the generalisability of our findings to the overall population. Finally, we do not have data on blood cell counts, which has been associated with mtDNAc [ 95 ].

In this large-scale study, we showed that living a healthy lifestyle was positively associated with mental health and well-being and, on a biological level, with a higher TL and mtDNAc, indicating healthy ageing. Furthermore, individuals with suicidal ideation or suffering from severe psychological distress had a lower mtDNAc. Our findings suggest that implementing strategies to incorporate healthy lifestyle changes in the public’s daily life could be beneficial for public health, and might offset the negative impact of environmental stressors. However, further studies are necessary to confirm our results and especially prospective and longitudinal studies are essential to determine causality of the associations.

Availability of data and materials

The dataset used for this study is available through a request to the Health Committee of the Data Protection Authority.

Abbreviations

Area under the curve

Body mass index

Confidence intervals

Generalised Anxiety Disorder Questionnaire

General Health Questionnaire

Inter-run calibrator

Mitochondrial DNA content

Patient Health Questionnaire

Relative operating characteristic curve

Short Form Health Survey

Telomere length

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Acknowledgements

We are grateful to all BHIS and BELHES participants for contributing to this study.

The HuBiHIS project is financed by Sciensano (PJ) N°: 1179–101. Dries Martens is a postdoctoral fellow of the Research Foundation—Flanders (FWO 12X9620N).

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PH drafted the paper. PH, NS, MD and ED set up the design of the study. NS, DM, JvdH, TN and ED reviewed and commented on the manuscript. All authors read and approved the final manuscript.

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Additional file 1: text s1..

TL, mtDNAc and single copy-gene reaction mixture and PCR cycling conditions. Table S1. The mental health indicators with their scores and uses. Table S2. Comparison of the characteristics of the 6,054 eligible BHIS participants that were included in the BHIS subset compared to the 1,838 eligible participants that were excluded from the BHIS subset. Table S3. Comparison of the characteristics of the 739 participants from the BHIS subset that were included in the BELHES subset compared to the 5,315 participants that were excluded from the BELHES subset. Table S4. Bivariate associations between the characteristics and telomere length (TL), mitochondrial DNA content (mtDNAc), the lifestyle score or psychological distress. Table S5. Results of the sensitivity analysis of the association between lifestyle and mental health. Table S6. Results of the sensitivity analysis of the association between lifestyle and the biomarkers of ageing. Table S7. Results of the sensitivity analysis of the association between mental health and the biomarkers of ageing. Fig. S1. Exclusion criteria. The BHIS subset consisted of 6,055 BHIS participants and the BELHES subset consisted of 739 BELHES participants. Fig. S2. Histogram of the lifestyle score. Fig. S3. Validation of the lifestyle score. ROC curve for the lifestyle score as a predictor for good to very good self-perceived health. The model was adjusted for age, sex, region, highest educational level in the household, household composition and country of birth.

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Hautekiet, P., Saenen, N.D., Martens, D.S. et al. A healthy lifestyle is positively associated with mental health and well-being and core markers in ageing. BMC Med 20 , 328 (2022). https://doi.org/10.1186/s12916-022-02524-9

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ORIGINAL RESEARCH article

Healthy lifestyle over the life course: population trends and individual changes over 30 years of the doetinchem cohort study.

$\nEdith E. Schermer$

1 Center for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment, Bilthoven, Netherlands
2 Julius Centre for Health Sciences and Primary Care, University Medical Centre, Utrecht, Netherlands

For five health-related lifestyle factors (physical activity, weight, smoking, sleep, and alcohol consumption) we describe both population trends and individual changes over a period of 30 years in the same adult population. Dichotomous indicators (healthy/unhealthy) of lifestyle were analyzed for 3,139 participants measured every 5 years in the Doetinchem Cohort Study (1987–2017). Population trends over 30 years in physical inactivity and “unhealthy” alcohol consumption were flat (i.e., stable); overweight and unhealthy sleep prevalence increased; smoking prevalence decreased. The proportion of the population being healthy on all five lifestyle factors declined from 17% in the round 1 to 10.8% in round 6. Underlying these trends a dynamic pattern of changes at the individual level was seen: sleep duration and physical activity level changed in almost half of the individuals; Body Mass Index (BMI) and alcohol consumption in one-third; smoking in one-fourth. Population trends don't give insight into change at the individual level. In order to be able to gauge the potential for change of health-related lifestyle, it is important to take changes at the individual level into account.

Introduction

For public health policy it is important to monitor health and health determinants in the population. This allows discerning worrisome trends in health determinants that require attention, such as an increasing prevalence of obesity, or evaluating the effects of interventions such as anti-smoking campaigns. In addition to these population trends it is relevant to know what the dynamics are that drive these trends. Trends over time in a population are the “net sum” of all changes in individuals who make up that population; visualizing the latter shows a much more nuanced picture than tracing the population average. To take an extreme example: a flat (unchanging) trend in average BMI could mean that most individuals maintain a stable weight, or in contrast that half of the population loses weight while the other half gains weight. This might lead to very different conclusions regarding the need (and potential) for policy interventions. To bring to light the spectrum of changes at the individual level, it is necessary to follow the individuals making up the population over time and summarize these changes in an insightful manner.

Health-related lifestyle factors that are often studied are: physical activity, overweight, smoking, sleep, and alcohol consumption, for which there is substantial knowledge about the population trends ( 1 – 5 ). In general, in high-income countries, levels of physical activity are relatively stable over time ( 1 ), the prevalence of being overweight is increasing ( 2 ), while smoking prevalence is decreasing ( 3 ). There is inconclusive evidence on trends in sleep duration; whereas some countries show an increase in average sleep duration of its inhabitants, others in contrast show a decrease ( 4 ). Similarly, per capita average daily alcohol consumption decreased in some high-income countries and remained stable in others ( 5 ).

There is increasing interest in tracking individual changes in lifestyles using longitudinal cohort data ( 6 , 7 ). These individual changes, often referred to as trajectories, can be described using predefined categories, or evaluated using data-driven methods. For example, trajectories identified for physical activity can be described as moderately stable or highly stable, or in contrast as showing steep or steady in- or decreases over time ( 8 , 9 ). Trajectories of weight are often identified for specific age groups, like old age ( 10 ), or over transition periods, like from adolescence into adulthood ( 11 ). An example of an often observed trajectory of alcohol consumption throughout adult life is one that shows alcohol consumption first rising, then reaching a plateau, and finally declining in older age ( 12 , 13 ). As another example, a 46-year follow-up study found that smoking behavior remained stable in over 60% of its adult population ( 14 ). We have previously shown that half of the adults aged 20 years and above remain stable in their physical activity level and sleep duration over a period of 20 years ( 15 , 16 ).

In addition to studying single lifestyle factors, it is relevant to study combinations of lifestyle factors in order to be able to anticipate the increase in morbidity and mortality in the population due to the accumulation of unhealthy habits ( 17 ). Papers studying several health-related lifestyle factors simultaneously over time are scarce.

Using 30 years of longitudinal data from the Doetinchem Cohort Study, we aim to analyze both population and individual level changes within the same study population. For the population level the time trends refer to circa 30 years and for the individual level the data spans over circa 25 years. In particular, we focus on physical activity, Body Mass Index (BMI), smoking, alcohol consumption, and sleep. The leading question is: what are the population trends and individual changes for five health-related lifestyle factors (physical activity, weight, smoking, sleep, and alcohol consumption)—separately and in combination—in the same adult population?

Design, study population, and data collection

Data was used from the Doetinchem Cohort Study (DCS), a prospective population-based cohort study in a midsize (provincial) town in the Eastern part of the Netherlands. Starting in 1987 as a monitoring study on risk factors for cardiovascular disease, the DCS continues to this day. The study population consists of an age-sex-stratified random sample of the Doetinchem mainly white (Caucasian) population, measured every 5 years in measurement rounds that take 5 years to complete. In the first round, 12,404 individuals participated, more than half of whom were subsequently invited to participate in the following examination rounds ( 18 ). The flowchart ( Figure 1 ) shows the participants in each round. By using questionnaires and a physical examination, data on lifestyle and health is collected. In this study, data were included from round one (1987–1991) through round six (2013–2017), covering a 30-years period of data collection that allow tracking individual changes over a time span of 25 years. Participants were included in the analyses if they took part in the first and sixth rounds and at least three intermediate rounds.

Figure 1 . Flowchart of the participants of the Doetinchem Cohort Study from round 1 till round 6.

Lifestyle factors

We included physical activity, overweight, smoking, sleep, and alcohol consumption because we had comparable data on indicators of these factors over the complete 30 year-period in the same cohort. Each lifestyle factor was dichotomized as “healthy” or “unhealthy”, based on guidelines and literature, so we could use the same analyses for every indicator. Moreover, in monitoring of population trends, variables of interest such as BMI are also often dichotomized (yes/no overweight), in order to define a proportion of the population at increased risk based on well-motivated cut-off values.

Physical activity was assessed with a questionnaire from the European Prospective Investigation in Cancer and Nutrition study (EPIC) ( 19 ). Adherence to the Dutch physical activity guidelines ( 20 )—being being at least moderately active for 30 min per day, for at least 5 days per week—was used as criterion to define participants as active or inactive, which corresponds to 150 min per week. Due to the extensive nature of the EPIC questionnaire with overlapping categories, overreporting of physical activity was taken into account and corrected for, similarly to previous studies using the same cohort ( 15 , 21 ), by using 210 min of at least moderate activity per week as cut-off point. Body Mass Index (BMI), derived from individuals' measured height (m) and weight (kg), was used to define overweight (BMI of ≥ 25 kg/m 2 , i.e., unhealthy). For smoking, non-smokers and former smokers were classified as healthy, current smokers as unhealthy. For sleep, sleep duration was assessed with one self-reported question. The recommended sleep duration for adults is 7–8 h ( 22 ). Participants were defined as unhealthy if they slept <7 h or more than 9 h. For alcohol consumption, the number of glasses per day was used. Based on guidelines by the Dutch health Council in 2015, consuming more than one (>1) glass per day was classified as unhealthy. The numbers of missings were low and varied per lifestyle factor. For the analyses per lifestyle factor those with missings were excluded, so the actual numbers slightly differed per analysis. Those with missings on e.g., smoking were not excluded from the analyses of physical activity.

Sociodemographic characteristics

We were also interested in differences between groups, defined according to sex, age (as generation-definition), and level of education. Sex assigned at birth was used (as registered in the population registers). Four age groups were created based on age at the baseline measurement and labeled as “10-year generations”: those in their 20s- 30s-, 40s- or 50s at baseline. Over the course of the study, all participants became 25 years older, e.g., those who were in their 40s- at baseline were between 65 and 74 years old in the sixth measurement round. Educational level was based on questionnaire data on highest attained level until the fourth examination round, and categorized into three levels: low, intermediate, and high. Low level meant intermediate secondary education or less, intermediate level was intermediate vocational and higher secondary education, and high level meant higher vocational education and university.

Statistical analyses

For the, mainly descriptive, statistical analyses, SAS version 9.4 was used. Population trends were determined by estimating the prevalence of the unhealthy lifestyle factor in each of the consecutive rounds. Individual changes were determined by utilizing the classification of the lifestyle factors into healthy ( h ) or unhealthy (u) Participants had a maximum of six measurements at different time points, resulting in 64 (2 6 ) possible patterns of healthy (h) and unhealthy (u) (not counting missing values). These were grouped into five different patterns. Respondents could remain “stable healthy” or “stable unhealthy” or change in a healthy (“improve”), or unhealthy direction (“worsen”), or “vary” over time. If there was only one intermediate deviating value, or a missing value, the participants were categorized based on the other measurements. For instance, participants with the pattern hhhuhh or uhuuuu would be categorized as respectively “stable healthy” or “stable unhealthy” on that factor. To be classified as “varying” over time, participants had to vary between healthy and unhealthy more than once (e.g., uhuhuh ): those were the participants that could not be classified into one of the other patterns.

For each lifestyle variable the distribution of the population over these five patterns was determined and expressed as proportions of the population adhering to these patterns.

Besides the trends and individual changes of the single lifestyles we present the proportion of participants that were healthy or unhealthy on all of the studied lifestyle factors and for how many of the five lifestyle factors individuals changed during the study. Participants that either “improved”, “worsened”, or “varied” in a lifestyle factor were classified as having changed that factor, resulting in the possibilities to have changed zero to five factors. Remaining stable in all five factors was labeled as being “stable healthy” or “stable unhealthy”.

A total of 3,139 participants were included in the study, 53% of whom were women ( Table 1 ). The average age at baseline was 37.6 years and 63.6 years in the sixth round. Most participants had a low educational level (42.2%), followed by intermediate (31.2%) and high educational level (26.5%).

Table 1 . Characteristic of the study population ( n = 3,139).

Population trends of single health-related lifestyle factors over 30 years

The proportion of adults not meeting the norm for physical activity remained relatively stable during the 30 years of the study: ~43% ( Figure 2A ). No notable differences were found between the 10-year generations or the different educational levels. The proportion of overweight participants (BMI ≥25 kg/m 2 ) increased from 41.9 to 69.9% for men and from 26.6 to 58.9% for women ( Figure 2B ). All 10-year generations showed an increase, and every younger 10-year generation had a bigger proportion of overweight participants than their predecessors: e.g., those in their 20s at baseline were more often overweight by the age of 40 (52.5%) than those in their 40s at baseline (42.3%) (indicated by the dotted black line). During the entire study period, low educated participants were more often overweight (from 40.9 to 69.1%) than high educated participants of the same age (26.1 to 56.4%). In both men and women, smoking prevalence declined, on average, from 28.2 to 11.4% ( Figure 2C ). Participants in their 20s at baseline smoked less once they were in their forties (20.3%) than those in their 40s at baseline (26.2%). Participants with a high education level smoked less on every round of the entire study. There was an increase in participants deviating from 7 or 8 h of sleep a night during the study, from 17.1 to 29% ( Figure 2D ). Those in their 20s at baseline had a higher prevalence of unhealthy sleep by the time they were in their 40s (22.4%) than those in their 40s at baseline (16.3%). Low level educated participants systematically slept more often unhealthily. The proportion of individuals consuming more than 1 glass of alcohol daily increased during the study, followed by a small decrease (overall from 33.3 to 32.3%). This percentage was much higher for men (52.3 to 44.2%) than for women (16.4 to 21.8%). All 10-year generations showed this same pattern—those in their 20s at baseline continued to drink less during the entire study; of the higher-educated subjects, 38.7% drank more than 1 glass per day in the first round and 41% in the last round, for the low educated this was 29.8 and 26.9%, respectively ( Figure 2E ).

Figure 2 . Population trends over 30 years—six measurement rounds with a duration of 5 years (1987–2017)—for (A) physical activity, (B) overweight, (C) smoking, (D) sleep, and (E) alcohol consumption, by sex, 10-year generation and educational level.

Individual changes in single health-related lifestyle factors over 25 years

Stable physical activity was found among 51% of the population (33.7% stable healthy, 18.3% stable unhealthy), and 49% changed (20.4% varied over time, 15.9% improved, 12.7% worsened) ( Figure 3A ). This was more or less comparable for both sexes, all 10-year generations, and all educational levels.

Figure 3 . Individual changes over 25 years in (A) physical activity, (B) overweight, (C) smoking, (D) sleep, and (E) alcohol consumption: those who remain stable (stable healthy or stable unhealthy) and those who change (improve, worsen or vary). By sex, 10-year generation, and educational level.

Stable weight was found among 61% of the participants: 30.7% stable healthy and 30.3% stable unhealthy ( Figure 3B ). Most of those who changed (39%) became overweight (27.5%), some varied over time (9.7%) and a small proportion improved (1.8%). Predominantly men, older generations, and lower educated were stable unhealthy.

The majority of the population showed stable smoking behavior: 76.4% (68% stable healthy non-smoker, 8.4% stable unhealthy smoker) ( Figure 3C ). During the study, 23.6% changed: 16.2% quitted smoking, some varied (6%), only some started (1.4%). Bigger stable healthy proportions were found among the older generations and among higher educated participants.

Sleep was found to remain stable in 56.9% of the participants: predominantly stable healthy (53.6%), a few stable unhealthy (3.3%) ( Figure 3D ). Those that changed their sleep duration (43.1%) were made up of 19.3% varying sleepers, 16.4% that worsened, and 7.4% that improved. Most change (any) was found in women (45.3%), low educated (47.1%), and 50s at baseline (46.6%).

Alcohol consumption did not change in 70.4:50.2% maintained a healthy consumption (stable healthy); 20.2% was stable unhealthy ( Figure 3E ). Those who changed their alcohol consumption (29.6%) mostly varied over time (12.8%), and some improved (9%) or worsened (7.8%). The most stable healthy were women (65.4%), those in their 20s at baseline (60.6%), and lower educated (56.9%).

Population trends of the health-related lifestyle factors combined

The proportion of the population being healthy on all five lifestyle factors declined from 17% in the first round to 10.8% in the last round ( Figure 4A ). The decline was predominantly between round one (17%) and round three (10.9%). Both sexes, all 10-year generations, and educational levels showed a similar trend.

Figure 4 . Five lifestyle factors combined. Population trends over 30 years of (A) a healthy lifestyle—healthy on all five factors, (B) an unhealthy lifestyle—at least four out of five unhealthy, and (C) individual changes of lifestyle over 25 years—number of components that changed.

The proportion of the population unhealthy on at least four of the five factors was circa 4%, which remained more or less similar during the study ( Figure 4B ). For men, this was 5.7% in the first round, 10.7% in the third round, and 6.6% in the last round; for women, respectively, 1.9, 4, and 3.4%. No differences are worth noting between age or educational groups.

Individual changes of the health-related lifestyle factors combined

In 25 years, 11% of the population did not change any of the five lifestyle factors; 29.2% changed one, 32.6% changed two, 19.8% changed three, 6.5% changed four, and 0.9% changed five ( Figure 4C ). The differences between the sexes and educational levels were minor. For those in their 20s at baseline, 9.4% changed none, 24.6% changed one, 36.9% changed two, 20.9% changed three, 7.1% changed four, and 1.1% changed five lifestyle factors. For those in their 50s at baseline this was 14.1, 33.2, 28.3, 19.1, 4.5, and 0.8%, respectively.

This study described both population trends and individual changes in the same population, emphasizing that the picture visualizing individual changes is very different from the image that emerges when populations trends are shown. In 2015, the WHO declared “Surveillance of population health and wellbeing” the first Essential Public Health Operation ( 23 ). As many population-based longitudinal studies monitor lifestyle from an individual perspective, the window they offer on such changes deserves full attention and will strengthen the insights into evolution of health and wellbeing in the population.

Our study illustrates clearly the much greater dynamics of changes in lifestyle at the individual level than can be seen in population trends. This shows better the potential for change of health-related lifestyle, either for the better or for the worse, than appears when only taking national or regional aggregate figures into account. Over a period of 25 years, the majority (60%) of the participants changed at least two of the five lifestyle factors—physical activity, overweight, smoking, sleep, and alcohol consumption—while population trends showed only a small change.

In general, our findings on population trends are in agreement with current trends in high-income countries: physical activity level remained stable, prevalence of overweight increased, smoking declined, unhealthy sleep increased, and alcohol consumption remained stable ( 1 – 5 , 24 ). This may suggest that what we found on the individual patterns may also be similar for most high-income countries.

The findings on individual lifestyle changes are also in accordance with existing literature, although only limited comparison with literature on individual changes was possible due to differences in study populations, lifestyle measurements, and study durations. We found physical activity levels to be stable in half of our population, which is in line with findings from a recent systematic literature review on 27 longitudinal studies (follow-up time from two to 34 years) on physical activity trajectories ( 9 ). Malhotra and colleagues, who followed Americans aged 25 and above for 18 years, found that 66% of the men and 41% of the women became overweight ( 25 ), which is a greater increase than in our study (43 and 37%, respectively). A study that followed Finns for 46 years from late adolescence found smoking behavior to remain relatively stable, with especially non-smokers persisting in abstaining from smoking, in line with our data ( 14 ). A study in (female) (ex-)nurses aged 55 and above followed for 14 years found a comparable proportion of healthy sleepers (49%) as in our study ( 26 ). Nine prospective cohorts (up to 28 years follow-up) from the United Kingdom, studying adults of all ages, found alcohol consumption after 30 to have remained relatively stable, similar to our findings.

For every lifestyle factor, a considerable proportion of our participants showed multiple changes between healthy and unhealthy. Most existing studies pay little or no attention to this varying category ( 9 , 14 , 25 , 26 ), but as it seems highly relevant for the possibilities of prevention, this category should receive more attention in studies of individual changes over time.

The sex differences presented here are minor for physical activity, smoking and sleep, but large for overweight and alcohol consumption. For both, the percentages in men are higher, and especially the categories of “stable unhealthy” for these lifestyle factors are much larger than in women. This is globally the same as reported in other studies ( 1 – 5 ). These sex differences are quite consistent over time—as can be seen in the population trends. Differences by generation are also seen, with as most concerning finding that those in their 40s at measurement round 4 show a higher percentage of overweight and unhealthy sleep compared to those in their 40s at measurement round 1, 20 years before. These findings extend data presented before ( 27 ), and the message is the same. There are well-known differences in the prevalence of unhealthy lifestyles between those representing different socio-economic groups in the population, often based on educational level ( 28 ). These differences are also found here: overweight, smoking and unhealthy sleep were more common in the lower educated. We did not find differences for physical activity, but unhealthy alcohol consumption was more common among the higher educated. The differences by educational level seem quit consistent over time.

The findings of this study have at least two mayor implications for areas of public health: in prevention policy and research. When considering prevention, it is important to realize that population figures do not tell the entire story. To illustrate this from our findings: monitoring of individual physical activity shows that a substantial proportion of individuals changed their activity level, while the population trend was stable. These frequent individual changes emphasize that preventive initiatives should not only target change but should also include the stimulation and support to maintain an healthy lifestyle.

Second, the findings on lifestyle (in)stability should be taken into account in research. Most studies on the role of lifestyle in the development of disease are based on a one-time measurement of lifestyle of individuals. However, lifestyles are subject to change: for most lifestyle factors, at least one-fourth of the individuals did not remain stable during our study. The one-time-measured-life-style-studies do not give a good picture of the disease risks linked to them and it is likely that these risks are underestimations of the real disease risks. Our findings emphasize that more attention should be paid to lifestyle dynamics in the population. In addition, future research may also focus more on what determines lifestyle change.

The main strength of this study is the use of 30 years of longitudinal cohort data, which allowed us to study individual changes parallel with population trends. It should be noted that our approach was entirely descriptive. We did not aim to model, for instance by developing a Markov model, the probability that an individual's lifestyle behavior would improve or worsen over the life course.

When interpreting our results, there are some limitations to be taken into account. First, we chose to use simple dichotomous indicators of the health-related lifestyle factors in order to make the analyses between the indicators comparable and to analyze multiple factors simultaneously. This simplification removes details; only the transitions around the cut-off points are described. Changes within health categories, or the degree of change, are not shown using this approach. However, we used cut-off points that are commonly used and considered meaningful in health research.

Second, the basis for evaluating most of the lifestyle factors was self-report by study participants. The validity of such self-reports can be questioned. For example, physical activity is often over-reported ( 9 ), alcohol consumption is under-reported ( 29 ), and self-reported sleep only moderately correlates with measured sleep ( 30 ). For physical activity, we took overreporting into account, and for alcohol consumption, different kinds of beverages (wine, beer, liquor) were separately assessed, which according to Freunekes et al., leads to a more realistic estimation of the level of intake ( 29 ). For some of these indicators, better (more objective) measurements are currently available, particularly measurement of sleep and physical activity by wearable devices. However, these are not available yet for a large population, and certainly not retrospectively over the last 30 years. Also, one important lifestyle factor we did not include in our analyses is food consumption. We do have (extensive) data on food consumption by means of a food frequency questionnaire in measurement rounds 2–4, but not from 1 to 6 like the other indicators.

Finally, individuals that participate in (longitudinal) population-based cohorts tend to be healthier than the general population ( 31 ). This “healthy cohort effect” might result in findings that are not one-on-one translatable to the general population. The “real” picture is expected to be more unhealthy with lower population figures and larger unhealthy patterns.

Monitoring five health-related lifestyle factors over a period of 25 years in the same population from both a population and an individual perspective shows the degree to which lifestyle at the individual level is more subject to change than can be seen from in population trends. This suggests that the potential for change of health-related lifestyle, either for the better or for the worse, is greater than appears when only taking national or regional aggregate figures into account.

Data availability statement

The datasets presented in this article are not readily available because the participants' informed consent did not include consent to public availability of the data. However, the data are available upon reasonable request, by contacting the scientific committee of the Doetinchem Cohort Study by email: Doetinchemstudie@rivm.nl . Requests to access the datasets should be directed to Doetinchemstudie@rivm.nl .

Ethics statement

The studies involving human participants were reviewed and approved by METC of University of Utrecht. The patients/participants provided their written informed consent to participate in this study.

Author contributions

HP, PE, and ES developed the idea for the analyses. ES and AB participated in the data analyses. HP, AB, and WV participated in the data collection. All authors participated in writing the manuscript and approved the final version.

Acknowledgments

We thank the respondents, epidemiologists, and fieldworkers of the Municipal Health Service in Doetinchem for their contribution to the data collection for this study.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: lifestyle and behavior, cohort, trends, overweight and obesity, physical activity, sleep, alcohol consumption, smoking-epidemiology

Citation: Schermer EE, Engelfriet PM, Blokstra A, Verschuren WMM and Picavet HSJ (2022) Healthy lifestyle over the life course: Population trends and individual changes over 30 years of the Doetinchem Cohort Study. Front. Public Health 10:966155. doi: 10.3389/fpubh.2022.966155

Received: 10 June 2022; Accepted: 22 August 2022; Published: 09 September 2022.

Reviewed by:

Copyright © 2022 Schermer, Engelfriet, Blokstra, Verschuren and Picavet. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: H. Susan J. Picavet, susan.picavet@rivm.nl

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Published: 10 July 2023

Healthy lifestyle and life expectancy free of major chronic diseases at age 40 in China

The china kadoorie biobank collaborative group.

Nature Human Behaviour volume 7 , pages 1542–1550 ( 2023 ) Cite this article

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Lifestyle modification
Risk factors

Whether a healthy lifestyle helps achieve gains in life expectancy (LE) free of major non-communicable diseases and its share of total LE in Chinese adults remains unknown. We considered five low-risk lifestyle factors: never smoking or quitting for reasons other than illness, no excessive alcohol use, being physically active, healthy eating habits and healthy body fat levels. Here we show that after a median follow-up of 11.1 years for 451,233 Chinese adults, the LE free of cardiovascular diseases, cancer and chronic respiratory diseases (95% confidence interval) at age 40 years for individuals with all five low-risk factors was on average 6.3 (5.1–7.5) years (men) and 4.2 (3.6–5.4) years (women) longer than those with 0–1 low-risk factors. Correspondingly, the proportion of disease-free LE to total LE increased from 73.1% to 76.3% for men and from 67.6% to 68.4% for women. Our findings suggest that promoting healthy lifestyles could be associated with gains in disease-free LE in the Chinese population.

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Lifestyle risk score and mortality in Korean adults: a population-based cohort study

Dong Hoon Lee, Jin Young Nam, … Hannah Oh

Combined healthy lifestyle factors are more beneficial in reducing cardiovascular disease in younger adults: a meta-analysis of prospective cohort studies

Ming-Chieh Tsai, Chun-Chuan Lee, … Kuo-Liong Chien

Association of body mass index with life expectancy with and without cardiovascular disease

Nazanin Fekri, Pegah Khaloo, … Farzad Hadaegh

Data availability

CKB data are available to all bona fide researchers. Details of how to access and details of the data release schedule are available from www.ckbiobank.org/site/Data+Access . As stated in the access policy, the CKB study group must maintain the integrity of the database for future use and regulate data access to comply with prior conditions agreed with the Chinese government. Data security is an integral part of CKB study protocols. Data can be released outside the CKB research group only with appropriate security safeguards.

Code availability

Analysis code for this study is available at https://github.com/qiufen-code/Lifestyle-and-disease-free-LE .

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The most important acknowledgement is to the participants in the study and the members of the survey teams in each of the ten regional centres, as well as to the project development and management teams based at Beijing, Oxford and the ten regional centres. This work was supported by National Natural Science Foundation of China (82192904 (J.L.), 82192901 (L.L.), 82192900 (L.L.) and 81941018 (J.L.)). The CKB baseline survey and the first resurvey were supported by a grant from the Kadoorie Charitable Foundation in Hong Kong. The long-term follow-up is supported by grants from the UK Wellcome Trust (212946/Z/18/Z, 202922/Z/16/Z, 104085/Z/14/Z and 088158/Z/09/Z) (Z.C.), grants (2016YFC0900500 (Y.G.)) from the National Key R&D Program of China, National Natural Science Foundation of China (81390540 and 91846303) (L.L.) and Chinese Ministry of Science and Technology (2011BAI09B01) (L.L.). The funders had no role in the study design, data collection, data analysis, interpretation, writing of the report or the decision to submit the article for publication.

A full list of members and their affiliations appears in the Supplementary Information.

Authors and Affiliations

Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China

Qiufen Sun, Yizhen Hu, Canqing Yu, Dianjianyi Sun, Yuanjie Pang, Liming Li & Jun Lv

Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China

Canqing Yu, Pei Pei, Dianjianyi Sun, Liming Li & Jun Lv

Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China

Canqing Yu, Dianjianyi Sun, Yuanjie Pang, Liming Li & Jun Lv

Fuwai Hospital Chinese Academy of Medical Sciences, Beijing, China

Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK

Ling Yang, Yiping Chen & Huaidong Du

Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK

Ling Yang, Yiping Chen, Huaidong Du, Sushila Burgess, Sam Sansome & Zhengming Chen

NCDs Prevention and Control Department, Qingdao CDC, Qingdao, China

China National Center for Food Safety Risk Assessment, Beijing, China

Junshi Chen

, Yizhen Hu
, Canqing Yu
, Ling Yang
, Yiping Chen
, Huaidong Du
, Dianjianyi Sun
, Yuanjie Pang
, Sushila Burgess
, Sam Sansome
, Feng Ning
, Junshi Chen
, Zhengming Chen
, Liming Li
& Jun Lv

J.L. and L.L. conceived and designed the study and contributed to the interpretation of the results and critical revision of the paper for valuable intellectual content. L.L., Z.C. and J.C., as the members of the CKB steering committee, designed and supervised the conduct of the whole study, obtained funding and together with C.Y., Y.G., P.P., L.Y., Y.C., H.D., S.B., S.S. and F.N. acquired the CKB data. Q.S. and Y.H. accessed, verified and analysed the data. Q.S. drafted the paper. All authors had access to the data and have read and approved the final paper. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. J.L. and L.L. are the guarantors.

Corresponding authors

Correspondence to Liming Li or Jun Lv .

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature Human Behaviour thanks Josje Schoufour and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Charlotte Payne, in collaboration with the Nature Human Behaviour team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information.

Supplementary Text (including a list of CKB members and their affiliations), Methods, Tables 1–4 and Figs. 1–12.

Reporting Summary

Peer review file, rights and permissions.

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

The China Kadoorie Biobank Collaborative Group. Healthy lifestyle and life expectancy free of major chronic diseases at age 40 in China. Nat Hum Behav 7 , 1542–1550 (2023). https://doi.org/10.1038/s41562-023-01624-7

Received : 04 October 2022

Accepted : 27 April 2023

Published : 10 July 2023

Issue Date : September 2023

DOI : https://doi.org/10.1038/s41562-023-01624-7

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Published: 29 April 2021

Association between healthy lifestyle practices and life purpose among a highly health-literate cohort: a cross-sectional study

Nobutaka Hirooka 1 ,
Takeru Kusano 1 ,
Shunsuke Kinoshita 1 ,
Ryutaro Aoyagi 1 &
Nakamoto Hidetomo 1

BMC Public Health volume 21 , Article number: 820 ( 2021 ) Cite this article

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The national health promotion program in the twenty-first century Japan (HJ21) correlates life purpose with disease prevention, facilitating the adoption of healthy lifestyles. However, the influence of clustered healthy lifestyle practices on life purpose, within the context of this national health campaign remains uninvestigated. This study assessed the association between such practices and life purpose, in line with the HJ21.

We performed a nationwide cross-sectional survey on certified specialists in health management. Participants’ demographic information, lifestyle, and purpose in life were measured using a validated tool. The cohort was median-split into two groups based on their clustered health-related lifestyle score. The values for health-related lifestyle and purpose were compared between the two groups and the correlation between health-related lifestyle and purpose in life was measured.

Data from 4820 participants were analyzed. The higher-scoring health-related lifestyle group showed a significantly higher life purpose than the lower group (35.3 vs 31.4; t = 23.6, p < 0.001). There was a significant association between the scores of clustered healthy lifestyle practices and life purpose ( r = 0.401, p < 0.001). The higher-scoring health-related lifestyle group achieved a higher life purpose than the lower-scoring group. This association between healthy lifestyle practices and life purpose denotes a positive and linear relationship.

Our results suggest that individuals who have a better health-related lifestyle gain a higher sense of life purpose. In other words, a healthy lifestyle predicts a purpose in life. Our findings posit that examining the causal relationship between healthy lifestyle and purpose in life may be a more efficient approach toward health promotion.

Several studies have investigated the implications of life purpose, and literature has shown that a strong sense of purpose in life was positively associated with positive health outcomes [ 1 , 2 , 3 , 4 , 5 , 6 ]. Thus, having a sense of purpose in life is a vital component of human life. Due to a rapidly aging society in Japan, a national health promotion program in the twenty-first century—Health Japan twenty-first century (HJ21)—considers purpose in life as one of the major target goals of health promotion [ 7 ].

Purpose in life is defined as “a self-organizing life aim that stimulates goals” [ 1 ] and is known to promote healthy behaviors and give life meaning [ 8 , 9 ]. Ikigai is a Japanese word for what is considered an important factor for achieving better health and a fulfilling life [ 10 ]. Ikigai is defined as something to live for, exemplifying the joy and the goal of living [ 11 ]. Although Ikigai may not be fully comparable to purpose in life, it does contain the respective concept and plays a cardinal role in yielding positive health-related outcomes [ 12 ].

Notably, health outcomes associated with life purpose or Ikigai include physical [ 1 , 12 , 13 ] and mental health [ 3 , 13 ], quality of life [ 4 ], disease mortality [ 1 , 12 ], and longevity [ 12 ]. Possessing a strong sense of purpose in life has been associated with a lower risk of mortality and cardiovascular diseases [ 1 ] (relative risk: 0.83 and 0.83, respectively). The study concluded that purpose in life tends to yield health benefits. One of the mechanisms considered in the literature was the benefits associated with a healthy lifestyle. People who have adopted a higher purpose in life tend to follow healthier lifestyle practices, which may decrease the incidence of non-communicable chronic diseases, such as cardiovascular diseases or cancer.

Healthcare personnel are responsible for the health of their patients. Studies have shown that healthcare personnel are more likely to encourage healthy lifestyle behaviors among their patients if they engage in such behaviors themselves. Our study population comprises certified specialists in health management who routinely provide advice on health to individuals in their community. Investigating the relationship between lifestyle and purpose in life among healthcare personnel, our target population, is therefore of great scientific interest.

There is a hierarchy of causality among chronic diseases. Non-communicable diseases, such as diabetes, stroke, cancer, and coronary artery disease, have risk factors. In the case of risk factors, such as hypertension, smoking, dyslipidemia, hyperglycemia, studies typically signified proximal causes [ 14 , 15 ]. A healthy lifestyle is a central causality for these risk factors and thus basic lifestyle should be considered a fundamental and proximal risk factor for the aforementioned non-communicable diseases. Studies also highlight that healthy lifestyle practices prevent many similar chronic diseases [ 16 , 17 ], and that intervening to promote healthier lifestyle decreases mortality due to non-communicable diseases [ 18 , 19 ]. Hence, the notion that health benefits are brought through a healthy lifestyle may be supported if the lifestyle strongly correlates with purpose in life.

In this context, however, research exploring the association between purpose in life and healthy lifestyle practices remain scarce. Moreover, existing literature usually considers a single health behavior in relation to purpose in life. To determine the relationship between purpose in life and clustered health-related lifestyle—the fundamental and proximal cause of many health outcomes—the potential benefits of purpose in life towards disease prevention and health must be deciphered.

This study aimed to investigate the association between health-related lifestyles, in line with the HJ21, and purpose in life, measured with a validated tool to better understand the relational mechanisms.

Study design

The design was a cross-sectional study on a cohort of nationwide certified specialists in health management. We surveyed health-related lifestyles similar to those in the questionnaire used for the HJ21. Our questionnaire is based on the one of the oldest national health surveys around the world, the National Health and Nutrition Survey conducted by Japanese Government [ 20 ]. This survey is the oldest of all national health examination surveys currently conducted worldwide and serves as a comprehensive database for risk factors related to non-communicable diseases in Japan. The survey includes questions on demographic data and health-related habits, such as physical activity and exercise, nutrition and diet, smoking, stress, and alcohol intake. Purpose in life was measured with a validated tool in Japanese using the purposeful life scale (Ikigai-9) [ 21 ]. The ethics committee of the Saitama Medical University approved the study (ID: 896, 2018).

Participants

Study participants were certified specialists in health management who actively pursued professional growth provided by the Japanese Association of Preventive Medicine for Adult Disease [ 22 ]. This certification is sponsored by the Ministry of Education, Culture, Sports, Science and Technology, Japan. We excluded specialists who did not actively engage in continuing education or health promotion activities. These specialists are expected to engage the community and the society they live in to promote health and wellbeing. Specialists in health management are certified in multiple processes of study. Candidates study various aspects within the course, including health promotion, lifestyle-related diseases, mental health, nutrition, environment and health, physical activity and exercise, emergency medicine, life support, and health care system. To register, candidates must pass the final written examination. The Japanese Association of Preventive Medicine for Adult Disease encourages specialists to participate in numerous activities by facilitating health promotion workshops, speeches, and activities after registration. Among these individuals who met our inclusion criteria ( N = 9149), 4820 agreed to participate in the survey.

Variables and measurements

Variables measured in this study were demographic characteristics; health-related habits, including physical activity and exercise, nutrition and diet, smoking, stress, and alcohol intake; and purpose in life. There were eleven health-related lifestyle questions, of which five were two-scaled (“Intention to maintain ideal weight,” “Exercise,” “Alcohol intake,” “Manage lifestyle to prevent disease,” and “Smoking”). For these items, a score of “1” was assigned for an unhealthy lifestyle and a score of “4” was assigned for a healthy lifestyle. The rest of the six health-related habits (“Reading nutritional information labels,” “Maintaining a balanced diet in daily life,” “Intention for exercise,” “Stress,” “Rest,” and “Sleep”) were to be answered on a four-point scale, from “4” (most favorable) to “1” (least favorable). Finally, we added the values of each answer to the questions on the health-related lifestyle of the participants as their clustered health-related lifestyle scores. To measure purpose in life, we used the Ikigai-9 scale, a validated tool to quantify purpose in life. The Ikigai-9 is a psychometric tool that measures across the dimensions of (1) optimistic and positive emotions toward life, (2) active and positive attitudes towards one’s life, and (3) acknowledgement of the meaning of one’s existence [ 23 ]. The Ikigai-9 scale consists of nine questions on various aspects of life purpose and each question must be answered on a five-point scale, from “1” (Strongly disagree) to “5” (Strongly agree). These variables and measurements were previously described elsewhere [ 24 ]. Considering the variables, age, weight, height, BMI, volume of alcohol intake, and purpose in life scores were numeric. Sex, healthy lifestyle, smoking, alcohol intake, and stress comprised either binary or ordinal data.

Descriptive statistics (i.e., mean, standard deviation, range) were used to describe participants’ characteristics. The cohort was divided into two groups (i.e., a higher and lower group, with a cut-off using the median score) based on the clustered health-related lifestyle scores. The correlations between age and lifestyle score and between age and purpose in life score were analyzed. The difference in the Ikigai-9 score between the two clustered health-related lifestyle score groups was investigated. Further, the effect size of the difference in Ikigai-9 score between the two groups was calculated with using Cohen’s d . The association between the clustered health-related lifestyle score and the Ikigai-9 score was also analyzed as a bivariate correlation and a correlation coefficient was calculated to see whether the health-related lifestyles accounted for life purpose. A multiple regression analysis was performed to determine the association between the clustered health-related lifestyle score and the purpose in life score, after controlling for age. All statistical tests were two-tailed and the software IBM SPSS Statistics (Version 26.0. Armonk, NY) was used for the analysis.

The demographic and health-related lifestyle characteristics of the study participants are shown in Table 1 . In total, 4820 certified specialists in health management were included in the analysis. There were 3190 women (66.2%) and 1630 men (33.8%). The mean ( SD ) age of all study participants was 55.4 (±12.2) years. The majority of the participants (85.0%) were non-obese and “intended to keep ideal weight” and “maintain a healthy lifestyle (82.6% and 89.2%, respectively) to prevent lifestyle-related disease,” such as obesity, metabolic syndrome, and cardiovascular disease. We also found that more than 80% of the study participants “read nutritional information labels” and more than 90% “maintained a balanced diet in daily life.” Regarding exercise and physical activity, more than 80% of the study participants “intended to exercise” and approximately 64% of them achieved the recommended levels. These findings reflected a low rate of obesity among the participants, which was 15.0% in the study. While most of the participants reported resting and sleeping adequately, the rate of taking on stress was high (74.4%). The descriptive analysis of the Ikigai-9 scores confirmed that it was normally distributed, based on the histogram and P-P plot.

Table 2 shows the demographics and healthy lifestyle practices for both the higher and lower clustered health-related lifestyle score groups. We found consistent favorable results in all measured health-related habits in the higher clustered health-related lifestyle score group. There was a significant difference in the scores of purpose in life between the higher group and the lower clustered health-related lifestyle score group ( t = 23.6, p < .0001). In the higher group, the average score of purpose in life was 35.3 (95% CI; [35.1–35.5]), while for the lower group, the average score for purpose in life was 31.4 (95% CI; [31.2–31.7]). The differences in the Ikigai-9 purpose in life scores of the two groups and its effect sizes (Cohen’s d) were 3.8 (95% CI; [3.5–4.2]) and 0.68, respectively. Moreover, there was a significant association between the clustered health-related lifestyle score and purpose in life score, r = .401, p < .001. The significance remained after controlling for age. Correlation between age and both lifestyle and purpose in life were significant (Pearson r = 0.29 and 0.15, respectively; both p < .05).

We found that the higher-scoring clustered health-related lifestyle group showed a statistically significant higher purpose in life than the lower-scoring clustered health-related lifestyle group. The study also highlighted a significant positive association between the clustered health-related lifestyle score and the Ikigai-9 score. To the best of our knowledge, this study was the first to show that a strong sense of purpose in life correlates with clustered health-related lifestyles in the context of a national health campaign. Several studies indicate a positive relationship between purpose in life and health-related lifestyles [ 1 , 25 , 26 , 27 ]. Furthermore, many publications reveal a correlation between a single healthy habit and purpose in life. Therefore, our findings—that affirm a positive relationship between purpose in life and clustered health-related lifestyle—are consistent with previously reported results and help broaden the evidence of this association.

Exploring the mechanistic link of purpose in life with a healthy lifestyle may help us understand this relationship. While studies highlight the positive relationship between purpose in life and health-related lifestyle, a few studies’ results are inconsistent with our findings. For example, an existing prospective study did not observe a positive association between purpose in life and healthy sleep patterns [ 28 ]. In other studies, the purpose of life was not associated with smoking [ 29 , 30 ]. Notably, the mechanistic link between health-related lifestyle and purpose in life has not been well examined. Hooker et al. proposed a hypothesized model linking purpose in life with health [ 31 ]. They summarized the relationship between life purpose and health outcomes by utilizing the concept of self-regulation. In the model, they proposed that life purpose influenced health through three self-regulatory processes and skills: stress-buffering, adaptive coping, and health behaviors. Health-related lifestyle, one of the self-regulatory processes, is the result of individuals setting goals, monitoring their progress, and using feedback to modify their lifestyle [ 31 ]. Thus, a purpose provides the foundation and motivation for engaging in a healthy lifestyle. Kim et al. also suggested that sense of purpose in life enhances the likelihood for engagement in restorative health-related lifestyle practices (e.g., physical activity, healthy sleep quality, use of preventive health care services) from cardiovascular disease to the indirect effect of behavior [ 32 ].

There is an alternative explanation for the mechanistic link between purpose in life and health-related lifestyle. A reverse causality model suggested that engaging in healthy lifestyle practices could predict a greater purpose in life [ 31 , 33 ]. Our results denoted that the group with a higher score in purpose in life performed healthier lifestyle practices and behaviors (Table 2 ), which can be supported by either of the hypothesized models. Age statistically significantly influenced both lifestyle and purpose in life in this study, while gender did not. However, age did not change overall relation between lifestyle and purpose in life. This infers that age may act as a moderator on the association. Further research is needed to clarify the mechanism and the directionality of the association, including any modifying factors. The mechanism to explain the causal relationship between life purpose and healthy lifestyle practices helped prepare for healthy aging by preventing diseases, increasing health longevity, and imbuing a health-oriented drive, which are the major goals of the HJ21.

Additionally, the difference in life purpose scores between the two groups (35.3 vs 31.4), shown in Table 2 , should be further explored, whilst we found a statistically significant difference and a correlation between healthy lifestyle practices and purpose in life. Rather than being a single concept, purpose in life has several elements and a more comprehensive construct. The majority of measurement tools concerned with meaning in life assess two distinct concepts: coherence and purpose [ 34 ]. Coherence is a sense of comprehensibility, or one’s life “making sense,” which is descriptive and value-neutral. Purpose means a sense of core goals, aims, and direction in one’s life, which is more evaluative and value-laden in concept. Ikigai is the Japanese concept meaning a sense of life worth living. The Ikigai-9 scale used in this study has three constructs for measuring the purpose in life; (1) optimistic and positive emotions toward life, (2) active and positive attitudes towards one’s life, and (3) acknowledgement of the meaning of one’s existence. The scale seems to measure more similarly to the purpose; however, the total score does not distinguish between the association of specific constructs and healthy lifestyle practices. Thus, further methodological sophistication regarding the evaluation of a specific concept encompassed within life purpose needs to be reached. This aspect broadens our understanding of purpose in life and its relation to health. This particular cohort of certified specialists shared many features of high health literacy through the process of professional development and certification, combined with life-long learning and activities related to their role as health management specialists. Further, health-related lifestyle practices mean that the certified specialists were far healthier than the national average. These characteristics represent an individual’s health literacy. Health literacy is considered to be an individuals’ capacity to obtain and understand basic health information and services and to make appropriate health-related decisions based on this information [ 35 ]. Therefore, health literacy is directly associated with disease mortality [ 36 ], overall health status [ 37 ], disease prevention [ 38 , 39 ], and health behaviors. These can be attributed to purpose in life [ 2 ].

Thus, health literacy and health-related lifestyle appear to have a similar relationship with disease prevention and better health outcomes. The mediating effect of health literacy on the relationship between healthy lifestyle and life purpose should be investigated. Such inquiries in a prospective cohort study can better explain the mechanism of the causal link between purpose in life, health-related lifestyle, and health literacy.

Limitations

There are several limitations to this study. First, all the measurements were self-reported, which can be a source of bias. Second, while the survey questionnaires are widely used in national health promotion, they have not been fully validated. Third, the real-life meaning of purpose in life has not been determined yet. The Ikigai-9 score, one of the tools used to measure the life purpose score, was validated in a small and limited population; however, the instrument may not capture it holistically. This limitation was implicated by the previously reported systematic review. Furthermore, Zheng et al. found variability in the strength of correlation among the questionnaire for quality of life, part of which included questions regarding a purposeful life [ 40 ]. Lastly, the correlational analysis did not include an adjustment for confounding factors other than age. Hence, little is known about factors influencing the relationship between a healthy lifestyle and purpose in life. We need to establish other potential influencing factors and determine which variables have mediating, moderating, and confounding effects on purpose in life to understand the causal relationship between healthy lifestyle practices and life purpose [ 41 ]. This exploration proposes a promising model for future intervention programs.

Despite these limitations, this study has several strengths. First, the study sample size, N = 4820, was large and distributed throughout Japan. This aspect of the study increases generalizability. According to the previous review, numerous studies on purpose in life focused on older adults [ 42 ], whereas only a few were concerned with younger or middle-aged adults. In the present study, the majority of the participants were younger and middle-aged adults. Second, previous studies used relatively simple questions or did not employ validated tools to measure purpose in life. However, we used a validated tool, Ikigai-9, in this study. This aspect allows the study results to increase the reliability and validity of the measurement of purpose in life and also hold applicability in other studies. Lastly, study participants were certified specialists in health management who have shown high health literacy. This inclusion criterion provides guidance on improving healthy lifestyle practices through health literacy as an approach to health promotion.

In conclusion, a healthy lifestyle was found to be positively associated with purpose in life among a cohort of highly health-literate professionals. Healthcare personnel who receive specific training for health management may play important roles in promoting a population’s health and wellbeing. However, the mechanism to explain the relationship between purpose in life and health-related lifestyle remains unknown. Therefore, causal relations between improving healthier lifestyles and increasing purpose in life should be tested.

The datasets used in the current study are available from the corresponding author upon reasonable request.

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Ryff CD, Singer B, Love GD. Positive health: connecting well-being with biology. Philos Trans R Soc Lond Ser B Biol Sci. 2004;359(1449):1383–94. https://doi.org/10.1098/rstb.2004.1521 .

Steptoe A, Fancourt D. Leading a meaningful life at older ages and its relation with social engagement, prosperity, health, biology, and time use. Proc Natl Acad Sci U S A. 2019;116(4):1207–12. https://doi.org/10.1073/pnas.1814723116 .

Chen Y, Kim ES, Koh HK, Fraizer AL, Van der Weele TJ. Sense of mission and subsequent health and well-being among young adults: an outcome-wide analysis. Am J Epidemiol. 2019;188(4):664–73. https://doi.org/10.1093/aje/kwz009 .

Hooker SA, Master KS, Park CL. A meaningful life is a healthy life: a conceptual model liking meaning and meaning salience to health. Rev Gen Psychol. 2018;22(1):11–24. https://doi.org/10.1037/gpr0000115 .

Kim ES, Delaney SW, Kubzansky LD. Sense of purpose in life and cardiovascular disease: underlying mechanisms and future directions. Cur Cardiol Rep. 2019;21(11):135. https://doi.org/10.1007/s11886-019-1222-9 .

Kahana E, Lawrence RH, Kahana B, Kercher K, Wisniewski A, Stoller E, et al. Long-term impact of preventive proactivity on the quality of life of the old-old. Psychosom Med. 2002;64(3):382–94. https://doi.org/10.1097/00006842-200205000-00003 .

Martela F, Sterger MF. The three meanings of meaning in life: distinguishing coherence, purpose, and significance. J Posit Psychol. 2016;11(5):531–45. https://doi.org/10.1080/17439760.2015.1137623 .

Nutbeam D. Health literacy as a public goal: a challenge for contemporary health education and communication strategies into the 21 st century. Health Prom Int. 2000;15(3):259–67. https://doi.org/10.1093/heapro/15.3.259 .

Baker DW, Wolf MS, Feinglass J, Thompson JA, Gazmararian JA, Huang J. Health literacy and mortality among elderly persons. Arch Intern Med. 2007;167(14):1503–9. https://doi.org/10.1001/archinte.167.14.1503 .

Berkman ND, Sheridan SL, Donahue KE, Halpern DJ, Crotty K. Low health literacy and health outcomes: an updated systematic review. Ann Intern Med. 2011;155(2):97–107. https://doi.org/10.7326/0003-4819-155-2-201107190-00005 .

Fernandez DM, Larson JL, Zikmund-Fisher BJ. Associations between health literacy and preventive health behaviors among older adults: findings from the health and retirement study. BMC Public Health. 2016;16(1):596. https://doi.org/10.1186/s12889-016-3267-7 .

Santos P, Sá L, Couto L, Hespanhol A. Health literacy as a key for effective preventive medicine. Cogent Soc Sci. 2017;3(1):1407522. https://doi.org/10.1080/23311886.2017.1407522 .

Zheng M, Jin H, Shi N, Duan C, Wang D, Yu X, et al. The relationship between health literacy and quality of life: a systematic review and meta-analysis. Health Qual Life Outcomes. 2018;16(1):1–10.

Alimujiang A, Wiensch A, Boss J, Fleischer NL, Mondul AM, McLean K, et al. Association between life purpose and mortality among US adults older than 50 years. JAMA Netw Open. 2019;2(5):e194270. https://doi.org/10.1001/jamanetworkopen.2019.4270 .

Hill PL, Turiano NA. Purpose in life as a predictor of mortality across adulthood. Psychol Sci. 2014;25(7):1482–6. https://doi.org/10.1177/0956797614531799 .

We would like to thank Editage ( www.editage.com ) for English language editing.

This research was not supported by any grants or other funding support.

Department of General Internal Medicine, Saitama Medical University, Morohongo 38, Moroyama-machi, Iruma-gun, Saitama, 350-0495, Japan

Nobutaka Hirooka, Takeru Kusano, Shunsuke Kinoshita, Ryutaro Aoyagi & Nakamoto Hidetomo

All authors contributed to the study conception and design. Material preparation and data analysis were performed by Nobutaka Hirooka, Takeru Kusano, and Shunsuke Kinoshita. Nobutaka Hirooka, Shunsuke Kinoshita, and Ryutaro Aoyagi collected the data. Nobutaka Hirooka, Takeru Kusano, and Hidetomo Nakamoto interpreted the analysis. The first draft of the manuscript was written by Nobutaka Hirooka and all authors commented on drafted versions of the manuscript. All authors read and approved the final version of the manuscript.

Correspondence to Nobutaka Hirooka .

Ethics approval and consent to participate.

This study compiled with all the principles of the Declaration of Helsinki and obtained approval from the university ethics board. Informed consent was obtained from all individual participants included in this study. The ethics committee of the Saitama Medical University approved the study (ID: 896, 2018).

Consent for publication

All authors declare that they have no conflict of interest, no financial interest, nor benefit from the direct application of this research.

Hirooka, N., Kusano, T., Kinoshita, S. et al. Association between healthy lifestyle practices and life purpose among a highly health-literate cohort: a cross-sectional study. BMC Public Health 21 , 820 (2021). https://doi.org/10.1186/s12889-021-10905-7

Received : 17 February 2021

Accepted : 20 April 2021

Published : 29 April 2021

DOI : https://doi.org/10.1186/s12889-021-10905-7

Health-related lifestyle
Purpose in life
Health literacy

BMC Public Health

ISSN: 1471-2458

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Healthy lifestyle can help prevent depression – and new research may explain why

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A healthy lifestyle that involves moderate alcohol consumption, a healthy diet, regular physical activity, healthy sleep and frequent social connection, while avoiding smoking and too much sedentary behaviour, reduces the risk of depression, new research has found.

Although our DNA – the genetic hand we’ve been dealt – can increase our risk of depression, we’ve shown that a healthy lifestyle is potentially more important. Barbara Sahakian

In research published today in Nature Mental Health , an international team of researchers, including from the University of Cambridge and Fudan University, looked at a combination of factors including lifestyle factors, genetics, brain structure and our immune and metabolic systems to identify the underlying mechanisms that might explain this link.

According to the World Health Organization, around one in 20 adults experiences depression, and the condition poses a significant burden on public health worldwide. The factors that influence the onset of depression are complicated and include a mixture of biological and lifestyle factors.

To better understand the relationship between these factors and depression, the researchers turned to UK Biobank, a biomedical database and research resource containing anonymised genetic, lifestyle and health information about its participants.

By examining data from almost 290,000 people – of whom 13,000 had depression – followed over a nine-year period, the team was able to identify seven healthy lifestyle factors linked with a lower risk of depression. These were:

moderate alcohol consumption
healthy diet
regular physical activity
healthy sleep
never smoking
low-to-moderate sedentary behaviour
frequent social connection

Of all of these factors, having a good night’s sleep – between seven and nine hours a night – made the biggest difference, reducing the risk of depression, including single depressive episodes and treatment-resistant depression, by 22%.

Frequent social connection, which in general reduced the risk of depression by 18%, was the most protective against recurrent depressive disorder.

Moderate alcohol consumption decreased the risk of depression by 11%, healthy diet by 6%, regular physical activity by 14%, never smoking by 20%, and low-to-moderate sedentary behaviour by 13%.

Based on the number of healthy lifestyle factors an individual adhered to, they were assigned to one of three groups: unfavourable, intermediate, and favourable lifestyle. Individuals in the intermediate group were around 41% less likely to develop depression compared to those in the unfavourable lifestyle, while those in the favourable lifestyle group were 57% less likely.

The team then examined the DNA of the participants, assigning each a genetic risk score. This score was based on the number of genetic variants an individual carried that have a known link to risk of depression. Those with the lowest genetic risk score were 25% less likely to develop depression when compared to those with the highest score – a much smaller impact than lifestyle.

In people at high, medium, and low genetic risk for depression, the team further found that a healthy lifestyle can cut the risk of depression. This research underlines the importance of living a healthy lifestyle for preventing depression, regardless of a person's genetic risk.

Professor Barbara Sahakian, from the Department of Psychiatry at the University of Cambridge, said: “Although our DNA – the genetic hand we’ve been dealt – can increase our risk of depression, we’ve shown that a healthy lifestyle is potentially more important.

“Some of these lifestyle factors are things we have a degree control over, so trying to find ways to improve them – making sure we have a good night’s sleep and getting out to see friends, for example – could make a real difference to people’s lives.”

To understand why a healthy lifestyle might reduce the risk of depression, the team studied a number of other factors.

First off, they examined MRI brain scans from just under 33,000 participants and found a number of regions of the brain where a larger volume – more neurons and connections – was linked to a healthy lifestyle. These included the pallidum, thalamus, amygdala and hippocampus.

Next, the team looked for markers in the blood that indicated problems with the immune system or metabolism (how we process food and produce energy). Among those markers found to be linked to lifestyle were the C-reactive protein, a molecule produced in the body in response to stress, and triglycerides, one of the primary forms of fat that the body uses to store energy for later.

These links are supported by a number of previous studies. For example, exposure to stress in life can affect how well we are able to regulate blood sugar, which may lead to a deterioration of immune function and accelerate age-related damage to cells and molecules in the body. Poor physical activity and lack of sleep can damage the body’s ability to respond to stress. Loneliness and lack of social support have been found to increase the risk of infection and increase markers of immune deficiency.

The team found that the pathway from lifestyle to immune and metabolic functions was the most significant. In other words, a poorer lifestyle impacts on our immune system and metabolism, which in turn increases our risk of depression.

Dr Christelle Langley, also from the Department of Psychiatry at the University of Cambridge, said: “We’re used to thinking of a healthy lifestyle as being important to our physical health, but it’s just as important for our mental health. It’s good for our brain health and cognition, but also indirectly by promoting a healthier immune system and better metabolism.”

Professor Jianfeng Feng, from Fudan University and Warwick University, added: “We know that depression can start as early as in adolescence or young adulthood, so educating young people on the importance of a healthy lifestyle and its impact on mental health should begin in schools.”

This study was supported by grants from organisations including the National Natural Science Foundation of China and the Ministry of Science, China*.

Reference Zhao, Y & Yang, L et al. The brain structure, immunometabolic and genetic mechanisms underlying the association between lifestyle and depression. Nature Mental Health; 11 Sept 2023; DOI: 10.1038/s44220-023-00120-1

*A full list of funders can be found in the paper.

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WHAT IS LIFESTYLE MEDICINE ?

Lifestyle medicine strives to optimize physical and mental health through seven lifestyle pillars: nutrition, sleep, fitness, stress management, social relationships, passion and purpose, and cognitive enhancement. Rather than just treating symptoms, lifestyle medicine uses evidence-based principles to develop preventive measures and address the underlying cause of disease. The ultimate goal is to increase longevity and improve quality of life for people of all ages and backgrounds.

WHY WE NEED LIFESTYLE MEDICINE

In 2020, a report from the Centers for Disease Control and Prevention (CDC) revealed a considerable decline in human longevity in the United States. Life expectancy decreased from 78.8 to 77.0 between 2019 and 2020 – exceeding the average change in life expectancy in other high-income countries. This is likely a result of a the ongoing COVID-19 pandemic and the continued increases in chronic disease. US life expectancy is predicted to have declined to 76.6 in the year 2021 , which would indicate a net loss of 2.2 years from the year 2019. The growing decline in longevity in the US highlights the importance of preventing chronic disease and maintaining a healthy immune system.

According to the World Health Organization , 80% of all heart disease, stroke, and type 2 diabetes as well as 40% of all cancer diagnoses are preventable with proper risk-factor management via lifestyle interventions. Additionally, the underlying cause of mental health conditions such as depression and anxiety may be rooted in lifestyle patterns. Research studies have shown that participants completing a multimodal lifestyle intervention can experience 20% improvement in overall mental health and 30% reduction in depressive symptoms, anxiety, and stress. A recent study in the Journal of the American Medical Association has also revealed a lower risk of dementia in participants with a favorable lifestyle, emphasizing the beneficial impact of healthy lifestyle habits on cognitive function.

Every decade, the US Department of Health and Human Services launches a federal initiative that establishes key guidelines to improve national health outcomes. Their most recent program, Healthy People 2020 strives to promote healthy behaviors and minimize preventable diseases in hopes of increasing longevity. Lifestyle Medicine practices can serve as the foundational approach to achieving these goals in individuals across all stages of life. New Initiatives such as the Blue Zones Project® have already begun to improve the health of various communities through lifestyle modifications. As new literature continues to bring to light the numerous benefits of living a healthy lifestyle, there is a clear necessity for Lifestyle Medicine. Implementation of Lifestyle Medicine principles can encourage sustainable daily habits, optimize physical and mental health, and improve overall quality of life for all individuals.

HOW WE IMPLEMENT LIFESTYLE MEDICINE

EMPOWERMENT THROUGH EVIDENCE-BASED RECOMMENDATIONS

The current healthcare system is experiencing a monumental shift by empowering patients to take control of their health. Lifestyle medicine advocates for a patient-centered healthcare system by providing individuals with the information they need to make healthy decisions. Stanford’s Lifestyle Medicine initiative provides evidence-based recommendations to inform best practices regarding key lifestyle pillars: nutrition, sleep, fitness, stress management, social relationships, passion and purpose, and cognitive enhancement.

Health practitioners including physicians, nurses, registered dieticians, and health coaches can be certified in lifestyle medicine through the American College of Lifestyle Medicine (ACLM).

Healthy Longevity

Longevity is the achievement of a long life. We may hope for longevity so that we can experience many years of quality time with loved ones or have time to explore the world. But living to a ripe old age doesn’t necessarily mean healthy or happy longevity if it is burdened by disability or disease. The population of people over age 65 has grown more quickly than other age groups due to longer life spans and declining birth rates, and yet people are living more years in poor health. [1] Therefore, we will explore not just one’s lifespan but healthspan , which promotes more healthy years of life.

What you do today can transform your healthspan or how you age in the future. Although starting early is ideal, it’s never too late to reap benefits.

Five Key Lifestyle Factors

Researchers from Harvard University looked at factors that might increase the chances of a longer life. [2] Using data collected from men and women from the Nurses’ Health Study and Health Professionals Follow-up Study who were followed for up to 34 years, researchers identified five low-risk lifestyle factors: healthy diet, regular exercise (at least 30 minutes daily of moderate to vigorous activity), healthy weight (as defined by a body mass index of 18.5-24.9), no smoking, and moderate alcohol intake (up to 1 drink daily for women, and up to 2 daily for men). Compared with those who did not incorporate any of these lifestyle factors, those with all five factors lived up to 14 years longer.

In a follow-up study, the researchers found that those factors might contribute to not just a longer but also a healthier life. [2] They saw that women at age 50 who practiced four or five of the healthy habits listed above lived about 34 more years free of diabetes , cardiovascular diseases , and cancer , compared with 24 more disease-free years in women who practiced none of these healthy habits. Men practicing four or five healthy habits at age 50 lived about 31 years free of chronic disease, compared with 24 years among men who practiced none. Men who were current heavy smokers, and men and women with obesity, had the lowest disease-free life expectancy.

Five factors for a longer and healthier lifespan

Healthy diet – The prevalence of hypertension (high blood pressure) and dementia increases with age. Eating patterns such as those from the DASH , MIND , and Mediterranean diets can lower the risk of these and other chronic conditions that accompany older ages. A multivitamin-mineral supplement may also help to improve cognitive function and memory in some people, according to large randomized controlled trials.
Regular exercise – Regular physical activity lowers the risk of several chronic conditions that increase with age including heart disease, hypertension, diabetes, osteoporosis, certain cancers, and cognitive decline. Exercise also helps to lower anxiety and blood pressure, and improve sleep quality. The Physical Activity Guidelines for Americans from the U.S. Department of Health and Human Services first recommends to move more and sit less, with some activity better than none. For additional health benefits, they advise a minimum of 150-300 minutes weekly of moderate to vigorous activity, like brisk walking or fast dancing, as well as two days a week of muscle-strengthening exercises. Older adults who are at risk for falls may also wish to include balance training such as tai chi or yoga . See additional physical activity considerations for older adults .
Healthy weight – Determining one’s healthy weight range is unique for each person. Factors to consider include reviewing current health conditions, family history, weight history, and genetically inherited body type. Rather than focusing on scale weight alone, monitoring an increase in harmful visceral “belly fat” and weight change since age 20 may be useful.
Not smoking – Smoking is a strong risk factor for cancer, diabetes, cardiovascular disease, lung diseases, and earlier death as it promotes chronic inflammation and oxidative stress (a condition that can damage cells and tissues). [2] Smoking harms nearly every organ of the body. Quitting greatly reduces the risk of these smoking-related diseases. [4]
Moderate alcohol – Research finds that moderate drinking, defined as 1 drink daily for women and 2 drinks daily for men, is associated with lower risk of type 2 diabetes, heart attacks, and early death from cardiovascular disease. Low to moderate amounts of alcohol raises levels of “good” cholesterol or high-density lipoprotein (HDL) and prevent small blood clots that can block arteries. However, because alcohol intake—especially heavier drinking—is also associated with risks of addiction, liver disease, and several types of cancer, it is a complex issue that is best discussed with your physician to weigh your personal risk versus benefit.

Additional Factors for Healthy Longevity

Beyond the five core lifestyle habits mentioned above, a growing body of research is identifying additional factors that may be key to increasing our healthspans:

Having life purpose/meaning. Research shows that having a sense of meaning or purpose in daily life is associated with better sleep , healthier weight , higher physical activity levels , and lower inflammation in some people. [5,6] It also promotes optimism. If people are healthier at older ages, they can potentially contribute more to their family, community, and society as a whole. [1] This translates to being stronger and more mobile to assist younger generations with childcare or other family activities, working beyond retirement age, volunteering for local causes, pursuing pleasurable hobbies, and engaging in community groups. In reciprocation of these activities, people reap a sense of meaning and purpose.
Social connections. Studies of adults 50 years and older show that loneliness and social isolation are associated with a higher risk of disease, disability, and mortality. [7-10] The U.S. Health and Retirement study comprised of 11,302 older participants found that almost 20% met criteria for loneliness. [7] Those who experienced persistent loneliness had a 57% increased risk of early death compared with those who never experienced loneliness; those who were socially isolated had a 28% increased risk. Participants who experienced both loneliness and social isolation showed signs of advanced biological aging (e.g., chronic inflammation that can increase the risk of morbidities). Conversely, people experiencing cognitive decline may have less social contact due to greater difficulty initiating and maintaining social interactions. [11]
Brain stimulation. Stressing the brain or doing activities that entail strenuous mental effort, such as learning a new skill, language, or exercise format during leisure time may reduce the risk of cognitive decline. Research has shown a strong association of attaining higher education and engaging in work that is intellectually demanding with a lower risk of dementia, Alzheimer’s disease, and cognitive impairment. [10,12]
Improving sleep quality . Research is still inconclusive, but some reports suggest that insomnia is associated with higher rates of Alzheimer’s disease (AD) and other forms of cognitive decline. Chronic disrupted sleep may lead to systemic (throughout the body) inflammation, which is a precursor to the development of beta-amyloid plaques in the brain as found with AD. [13] The reverse can also occur with advanced stages of AD causing disturbed circadian rhythms that regulate sleep. However, a cohort study of 1,629 adults aged 48 to 91 years from the Alzheimer’s Disease Neuroimaging Initiative did not find that sleep disturbance affected cognitive decline in later years. [14]
Intermittent fasting. Animal research shows that caloric restriction over a lifetime, such as with intermittent fasting , increases lifespan. [15] The body responds to fasting with improved regulation of blood glucose, greater stress resistance, and decreased inflammation and production of damaging free radicals. During fasting, cells remove or repair damaged molecules. [15] These effects may prevent the development of chronic disorders including obesity, diabetes, cardiovascular disease, cancer, and neurological decline including Alzheimer’s disease. [16] Other effects of intermittent fasting in animals include better balance and coordination, and improved cognition, specifically with memory. Human studies have found improved insulin sensitivity, lower blood pressure, decreased LDL cholesterol, and weight loss. [15,17] However, human studies and randomized controlled trials on the effects of fasting on aging and longevity are still needed.

How sensory changes with aging affect how we eat

These senses can decline over time for various reasons: normal aging, which causes a gradual decrease in taste and smell; prescription drugs that reduce taste sensitivity and promote dry mouth or lack of saliva; deficiencies in micronutrients such as zinc that reduce taste; and poor dentition with tooth loss or dentures leading to chewing problems . [19] Up to 60% of adults 70 years and older may lose their sense of taste. [20] With this loss may come heavier seasoning of food with sugar and salt . [21] They may prefer softer lower-fiber foods that don’t require much chewing. Poor taste and smell in the elderly is associated with lower dietary quality and poorer appetite. [22]

Food aromas are important as they trigger the release of saliva, stomach acid, and enzymes in preparation for digestion. [23] The scent of food can trigger the release of dopamine and serotonin, causing a feeling of wellbeing to encourage eating. An impaired sense of smell in older adults is also associated with less variety in food choices and poorer nutrition, but can also lead to increased food intake and weight gain in some individuals. [23]

sautéing tomatoes in a pot next to simmering broth

Eating and food preparation are also important activities offering socialization and mental stimulation such as when learning new cooking skills. Preparing meals helps to reduce sedentariness as there are several action steps involved: selecting and purchasing, washing and chopping, and cooking the ingredients.

Spotlight on longevity in Japan

Looking ahead.

Identifying additional factors that improve and extend our healthspans is an active area of scientific inquiry. In the meantime, current research findings are encouraging, and underscore the importance of following healthy lifestyle habits throughout one’s life course. That said, sticking to these behaviors is easier said than done, and public policies must support and promote these habits by improving the food and physical environments that surround us.

National Academy of Medicine. 2022. Global Roadmap for Healthy Longevity . Washington, DC: The National Academies Press.
Li Y, Pan A, Wang DD, Liu X, Dhana K, Franco OH, Kaptoge S, Di Angelantonio E, Stampfer M, Willett WC, Hu FB. Impact of healthy lifestyle factors on life expectancies in the US population. Circulation . 2018 Jul 24;138(4):345-55.
Li Y, Schoufour J, Wang DD, Dhana K, Pan A, Liu X, Song M, Liu G, Shin HJ, Sun Q, Al-Shaar L. Healthy lifestyle and life expectancy free of cancer, cardiovascular disease, and type 2 diabetes: prospective cohort study. BMJ . 2020 Jan 8;368.
Centers for Disease Control and Prevention. Smoking and Tobacco Use: Fast Facts . Accessed 7/28/2022.
Kim ES, Shiba K, Boehm JK, Kubzansky LD. Sense of purpose in life and five health behaviors in older adults. Preventive Medicine . 2020 Oct 1;139:106172.
Guimond AJ, Shiba K, Kim ES, Kubzansky LD. Sense of purpose in life and inflammation in healthy older adults: A longitudinal study. Psychoneuroendocrinology . 2022 Jul 1;141:105746.
Crowe CL, Domingue BW, Graf GH, Keyes KM, Kwon D, Belsky DW. Associations of loneliness and social isolation with health span and life span in the US health and retirement study. The Journals of Gerontology: Series A . 2021 Nov;76(11):1997-2006.
Yu B, Steptoe A, Chen Y. Social isolation, loneliness, and all-cause mortality: A cohort study of 35,254 Chinese older adults. Journal of the American Geriatrics Society . 2022 Mar 1.
Joyce J, Ryan J, Owen A, Hu J, McHugh Power J, Shah R, Woods R, Storey E, Britt C, Freak-Poli R, ASPREE Investigator Group. Social isolation, social support, and loneliness and their relationship with cognitive health and dementia. International Journal of Geriatric Psychiatry . 2022 Jan;37(1).
Lisko I, Kulmala J, Annetorp M, Ngandu T, Mangialasche F, Kivipelto M. How can dementia and disability be prevented in older adults: where are we today and where are we going?. Journal of internal medicine . 2021 Jun;289(6):807-30.
Harling G, Kobayashi LC, Farrell MT, Wagner RG, Tollman S, Berkman L. Social contact, social support, and cognitive health in a population-based study of middle-aged and older men and women in rural South Africa. Social Science & Medicine . 2020 Sep 1;260:113167.
Kivipelto M, Mangialasche F, Ngandu T. Lifestyle interventions to prevent cognitive impairment, dementia and Alzheimer disease. Nature Reviews Neurology . 2018 Nov;14(11):653-66.
Irwin MR, Vitiello MV. Implications of sleep disturbance and inflammation for Alzheimer’s disease dementia. The Lancet Neurology . 2019 Mar 1;18(3):296-306.
Mecca AP, Michalak HR, McDonald JW, Kemp EC, Pugh EA, Becker ML, Mecca MC, van Dyck CH, Alzheimer’s Disease Neuroimaging Initiative (ADNI. Sleep disturbance and the risk of cognitive decline or clinical conversion in the ADNI cohort. Dementia and geriatric cognitive disorders . 2018;45(3-4):232-42.
de Cabo R, Mattson MP. Effects of intermittent fasting on health, aging, and disease. New England Journal of Medicine . 2019 Dec 26;381(26):2541-51.
Mattson MP, Longo VD, Harvie M. Impact of intermittent fasting on health and disease processes. Ageing research reviews . 2017 Oct 1;39:46-58.
Dong TA, Sandesara PB, Dhindsa DS, Mehta A, Arneson LC, Dollar AL, Taub PR, Sperling LS. Intermittent fasting: a heart healthy dietary pattern?. The American journal of medicine . 2020 Aug 1;133(8):901-7.
Gervis JE, Fernández-Carrión R, Chui KK, Ma J, Coltell O, Sorli JV, Asensio EM, Ortega-Azorín C, Pérez-Fidalgo JA, Portolés O, Lichtenstein AH. Associations between taste perception profiles and empirically derived dietary patterns: an exploratory analysis among older adults with metabolic syndrome. Nutrients . 2021 Dec 29;14(1):142.
Pisano M, Hilas O. Zinc and taste disturbances in older adults: a review of the literature. The Consultant Pharmacist . 2016 May 1;31(5):267-70.
Correia C, Lopez KJ, Wroblewski KE, Huisingh-Scheetz M, Kern DW, Chen RC, Schumm LP, Dale W, McClintock MK, Pinto JM. Global sensory impairment in older adults in the United States. Journal of the American Geriatrics Society . 2016 Feb;64(2):306-13.
Rolls BJ. Do chemosensory changes influence food intake in the elderly?. Physiology & behavior . 1999 Apr 1;66(2):193-7.
Fluitman KS, Hesp AC, Kaihatu RF, Nieuwdorp M, Keijser BJ, IJzerman RG, Visser M. Poor taste and smell are associated with poor appetite, macronutrient intake, and dietary quality but not with undernutrition in older adults. The Journal of nutrition . 2021 Mar;151(3):605-14.
Olofsson JK, Ekström I, Larsson M, Nordin S. Olfaction and aging: A review of the current state of research and future directions. i-Perception . 2021 Jun;12(3):20416695211020331.
Tsugane S. Why has Japan become the world’s most long-lived country: Insights from a food and nutrition perspective. European Journal of Clinical Nutrition . 2021 Jun;75(6):921-8.

Last reviewed December 2022

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Calorie restriction study reveals complexities in how diet impacts aging

Penn State researchers may have uncovered another layer of complexity in the mystery of how diet impacts aging. A new study led by researchers in the Penn State College of Health and Human Development examined how a person's telomeres -- sections of genetic bases that function like protective caps at the ends of chromosomes -- were affected by caloric restriction.

The team published their results in Aging Cell . Analyzing data from a two-year study of caloric restriction in humans, the researchers found that people who restricted their calories lost telomeres at different rates than the control group -- even though both groups ended the study with telomeres of roughly the same length. Restricting calories by 20% to 60% has been shown to promote longer life in many animals, according to previous research.

Over the course of human life, every time a person's cells replicate, some telomeres are lost when chromosomes are copied to the new cell. When this happens, the overall length of the cell's telomeres becomes shorter. After cells replicate enough times, the protective cap of telomeres completely dissipates. Then, the genetic information in the chromosome can become damaged, preventing future reproduction or proper function of the cell. A cell with longer telomeres is functionally younger than a cell with short telomeres, meaning that two people with the same chronological age could have different biological ages depending on the length of their telomeres.

Typical aging, stress, illness, genetics, diet and more can all influence how often cells replicate and how much length the telomeres retain, according to Idan Shalev, associate professor of biobehavioral health at Penn State. Shalev led the researchers who analyzed genetic samples from the national CALERIE study -- the first randomized clinical trial of calorie restriction in humans. Shalev and his team sought to understand the effect of caloric restriction on telomere length in people. Because telomere length reflects how quickly or slowly a person's cells are aging, examining telomere length could allow scientists to identify one way in which caloric restriction may slow aging in humans.

"There are many reasons why caloric restriction may extend human lifespans, and the topic is still being studied," said Waylon Hastings, who earned his doctorate in biobehavioral health at Penn State in 2020 and was lead author of this study. "One primary mechanism through which life is extended relates to metabolism in a cell. When energy is consumed within a cell, waste products from that process cause oxidative stress that can damage DNA and otherwise break down the cell. When a person's cells consume less energy due to caloric restriction, however, there are fewer waste products, and the cell does not break down as quickly."

The researchers tested the telomere length of 175 research participants using data from the start of the CALERIE study, one year into the study and the end of the study after 24 months of caloric restriction. Approximately two-thirds of study participants participated in caloric restriction, while one-third served as a control group.

During the study, results showed that telomere loss changed trajectories. Over the first year, participants who were restricting caloric intake lost weight, and they lost telomeres more rapidly than the control group. After a year, the weight of participants on caloric restriction was stabilized, and caloric restriction continued for another year. During the second year of the study, participants on caloric restriction lost telomeres more slowly than the control group. At the end of two years, the two groups had converged, and the telomere lengths of the two groups was not statistically different.

"This research shows the complexity of how caloric restriction affects telomere loss," Shalev said. "We hypothesized that telomere loss would be slower among people on caloric restriction. Instead, we found that people on caloric restriction lost telomeres more rapidly at first and then more slowly after their weight stabilized."

Shalev said the results raised a lot of important questions. For example, what would have happened to telomere length if data had been collected for another year? Study participants are scheduled for data collection at a 10-year follow-up, and Shalev said that he was eager to analyze those data when they become available.

Despite the ambiguity of the results, Shalev said there is promise for the potential health benefits of caloric restriction in humans. Previous research on the CALERIE data has demonstrated that caloric restriction may help reduce harmful cholesterol and lower blood pressure. For telomeres, the two-year timeline was not sufficient to show benefits, but those may still be revealed, according to Shalev and Hastings.

Three of Shalev's trainees, Hastings, current graduate student Qiaofeng Ye and former postdoctoral scholar Sarah Wolf, led the research under Shalev's guidance.

Hastings said the opportunity to lead this study was critical to his career.

"I was recently hired as an assistant professor in the Department of Nutrition at Texas A&M University, and I will begin that work in the fall semester," Hastings said. "Prior to this project, I had limited experience in nutrition. This project literally set the course of my career, and I am grateful to Dr. Shalev for trusting me with that responsibility."

Calen Ryan and Daniel Belsky of Columbia University Mailman School of Public Health, Sai Krupa Das of Tufts University, Kim Huffman and William Kraus of Duke University School of Medicine, Michael Kobor and Julia MacIsaac of University of British Columbia, Corby Martin and Leanne Redman of Pennington Biomedical Research Center and Susan Racette of Arizona State University College of Health Solutions all contributed to this research.

The National Institute on Aging funded this research.

Healthy Aging
Diet and Weight Loss
Human Biology
Diseases and Conditions
Epigenetics Research
Cell Biology
Chemical synapse
Baldness treatments
Insulin-like growth factor
Double blind
Immune system
Biological tissue
Endangered species
Domestication

Story Source:

Materials provided by Penn State . Original written by Aaron Wagner. Note: Content may be edited for style and length.

Journal Reference :

Waylon J. Hastings, Qiaofeng Ye, Sarah E. Wolf, Calen P. Ryan, Sai Krupa Das, Kim M. Huffman, Michael S. Kobor, William E. Kraus, Julia L. MacIsaac, Corby K. Martin, Susan B. Racette, Leanne M. Redman, Daniel W. Belsky, Idan Shalev. Effect of long‐term caloric restriction on telomere length in healthy adults: CALERIE™ 2 trial analysis . Aging Cell , 2024; DOI: 10.1111/acel.14149

Cite This Page :

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Try These 12 Daily Habits and Become a Healthier You

Take control of your wellness by adding these healthy habits to your lifestyle. Here are the top ones to try.

Woman makes a fruit smoothie with an immersion blender.

We all want to be healthy, but sometimes life gets in the way. Starting over with your wellness journey can feel too big to take on. The good news is that small changes to your daily habits can make a surprisingly big difference to your overall health, especially as the effects accumulate over time.

We've got a dozen healthy habits that can help you enjoy better physical and mental health, all backed by science.

It doesn't have to stop here. See which foods you should eat for a happiness boost , hacks to handle stress and six tips to reboot your sleep habits .

12 daily habits to improve your health

Here, we're talking about small adjustments that benefit every human. With these minor modifications to your daily routine, you can start working toward better health without having to give up a ton of time, money or enjoyment.

1. Prioritize sleep

Going without sleep is a lot like expecting your phone to run all day on a 12% battery. Your body needs time to not just rest and recharge, but also to do important work like learning new things and solidifying memories.

Adults should get at least seven hours of shut-eye each night. If this is a challenge for you, turn to your circadian rhythm . This is your body's natural process that should help you fall asleep, stay asleep and wake up feeling refreshed.

How do you use your circadian rhythm for better sleep ? Go to bed and get up at the same time every day.

2. Walk more

Heading out for a stroll boosts your physical and mental health, so it's well worth adding to your list of healthy daily habits.

On the physical front, regular walking supports your immune system, reduces joint pain and makes it easier to maintain a healthy weight.

Any exercise helps your mental health, and that includes walking . If you want to shift your daily habits to combat symptoms of depression or anxiety or to boost your mental wellness in general, make it a point to lace up your walking shoes each day.

3. Read for 30 minutes

Feeling stressed? Crack open a book. One study found that a half hour of reading can have the same stress-busting effect as known sources of calm, like yoga and humor.

Reading also does a lot for your brain, strengthening connections there. That study showed that diving into a book has both short and long-term benefits for your brain health. So to maintain the boost, make reading one of your daily habits When you do, you'll also be actively working to fight cognitive decline as you age.

4. Meditate

Another stress reducer and mental health booster , meditation gives you a way to tune into the present moment. In our busy, hyperconnected world, this can go a long way toward not just keeping yourself healthy, but also protecting your happiness.

Starting meditation could be as simple as doing a little reading on it and setting a timer for, say, 5 minutes each day. There are also plenty of good apps to guide you. You can even incorporate a meditative mindset into your regular activities, such as mindful eating .

Black man in a red shirt meditates by an open door.

Meditation gives you a way to tune into the present moment, so you can reduce stress and improve your mental health.

5. Spend time in nature

Getting into nature can help us soothe ourselves. It offers an effective counterbalance to all the screentime built into most of our days. In fact, an expanding body of research shows that time in nature can:

Improve our cognition
Increase attention span
Lower risk of mental illness
Increase empathy and social connectedness

You can combine this with other healthy habits, like your daily walk. Ideally, aim for green (like a forest) or blue (like bodies of water) spaces during your time outdoors.

6. Eat more plant-based foods

You probably already know that eating nutritious food makes you feel better. As an overarching concept, healthy eating habits can feel a little vague.

So let's be specific: work to get more plants onto your plate. A plant-based diet helps you maintain healthy cholesterol and blood pressure levels and reduces your risk for some chronic conditions. Plants are full of the vitamins, minerals and other nutrients we need to keep our bodies working optimally.

Try to incorporate more fruit, vegetables, whole grains, nuts and legumes into your daily meals. It might be helpful to keep a produce bowl on your kitchen counter so you can grab things as a quick snack, too.

A spread of plant-based meals, including curry, burger and tofu salad.

A plant-based diet helps maintain healthy cholesterol and blood pressure levels and reduces your risk for some chronic conditions.

7. Drink more water

This is one of those areas where it's easy to see how healthy habits help. Since we're mostly water , it makes sense that we would need to continually replenish our body's supply. Getting enough water helps your body flush waste and keeps your joints lubricated, while acting as a shock absorber for your spine and helping your digestive processes.

To build healthy habits around water, start carrying a reusable water bottle with you. Whenever you're bored, take a sip. Your body will thank you.

8. Reduce alcohol intake

Reducing the alcohol you consume does a lot for you , especially if you used to binge drink.:

Lowers risk of high blood pressure, depression and other conditions
Decreases symptoms of those conditions
Helps your body better absorb nutrients
Improves sleep and minimizes fatigue
Supports liver health

The Centers for Disease Control and Prevention recommend that men have two drinks or fewer each day, while women stick to a max of one drink per day. To help yourself out here, figure out a nonalcoholic beverage you like a lot. Soda water, bitters and a lime can scratch the cocktail itch without adding another alcoholic drink to your daily total.

9. Quit smoking

Does this come as any surprise? Smoking is bad for your heart and lungs, and it's also bad for your longevity . Long story short, if you want to live a longer, healthier life, kick the habit.

As you're figuring out how to be healthier, don't turn to vaping. It might be less harmful, but it's just as addictive and still comes with health risks .

Smoking is one of the hardest daily habits to ditch. The CDC and the American Lung Association have resources to help.

10. Spend time with those you love

If you're pursuing healthy habits to feel happier in 2023, hang with your people. Social connection goes a long way toward boosting our moods.

If you already have a group of friends or family, let this be a reminder to hit them up. Call someone you haven't talked to in a while or invite a few people over for a game or movie night. Check how you feel afterward. Better? We thought so.

If you don't have a social circle, make 2023 the year you intentionally work on making connections. That could mean striking up a conversation with a coworker or getting to know your neighbors .

Two friends smiling at each other while studying in a grassy park.

Social connection goes a long way toward boosting our overall mood.

11. Take a break from electronics

Screen time takes its toll. In fact, studies directly link it with lower psychological well-being .

Fortunately, the reverse is true. A digital detox can:

Improve your sleep
Boost your focus and productivity
Reduce symptoms of depression and anxiety
Support real-life social connections (see the point above)

You could try going off social media apps for a while and see how you feel. If you want to incorporate this into your healthy daily habits, carve out time each day when you're screen-free. For better sleep, maybe make that the last hour before bed.

12. Take on a new hobby

Your healthy habits can also be fun and rewarding. What have you always wanted to do? Your answer to that question might point you toward a new hobby to explore in 2024. And getting into it can help you reduce stress and boost mental well-being.

Plus, some hobbies can get you moving, supporting both your physical and mental health. Maybe you get into playing pick-up soccer at the park, or you could explore yoga .

Ultimately, you've got a lot of options for healthy daily habits you could incorporate into your lifestyle. You can pick one or two, or go big and go for the full dozen. Either way, you'll be moving toward a healthier, happier you.

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How can I plan what to eat or drink when I have diabetes?

How can physical activity help manage my diabetes, what can i do to reach or maintain a healthy weight, should i quit smoking, how can i take care of my mental health, clinical trials for healthy living with diabetes.

Healthy living may help keep your body’s blood pressure , cholesterol , and blood glucose level, also called blood sugar level, in the range your primary health care professional recommends. Your primary health care professional may be a doctor, a physician assistant, or a nurse practitioner. Healthy living may also help prevent or delay health problems from diabetes that can affect your heart, kidneys, eyes, brain, and other parts of your body.

Making lifestyle changes can be hard, but starting with small changes and building from there may benefit your health. You may want to get help from family, loved ones, friends, and other trusted people in your community. You can also get information from your health care professionals.

What you choose to eat, how much you eat, and when you eat are parts of a meal plan. Having healthy foods and drinks can help keep your blood glucose, blood pressure, and cholesterol levels in the ranges your health care professional recommends. If you have overweight or obesity, a healthy meal plan—along with regular physical activity, getting enough sleep, and other healthy behaviors—may help you reach and maintain a healthy weight. In some cases, health care professionals may also recommend diabetes medicines that may help you lose weight, or weight-loss surgery, also called metabolic and bariatric surgery.

Choose healthy foods and drinks

There is no right or wrong way to choose healthy foods and drinks that may help manage your diabetes. Healthy meal plans for people who have diabetes may include

dairy or plant-based dairy products
nonstarchy vegetables
protein foods
whole grains

Try to choose foods that include nutrients such as vitamins, calcium , fiber , and healthy fats . Also try to choose drinks with little or no added sugar , such as tap or bottled water, low-fat or non-fat milk, and unsweetened tea, coffee, or sparkling water.

Try to plan meals and snacks that have fewer

foods high in saturated fat
foods high in sodium, a mineral found in salt
sugary foods , such as cookies and cakes, and sweet drinks, such as soda, juice, flavored coffee, and sports drinks

Your body turns carbohydrates , or carbs, from food into glucose, which can raise your blood glucose level. Some fruits, beans, and starchy vegetables—such as potatoes and corn—have more carbs than other foods. Keep carbs in mind when planning your meals.

You should also limit how much alcohol you drink. If you take insulin or certain diabetes medicines , drinking alcohol can make your blood glucose level drop too low, which is called hypoglycemia . If you do drink alcohol, be sure to eat food when you drink and remember to check your blood glucose level after drinking. Talk with your health care team about your alcohol-drinking habits.

A woman in a wheelchair, chopping vegetables at a kitchen table.

Find the best times to eat or drink

Talk with your health care professional or health care team about when you should eat or drink. The best time to have meals and snacks may depend on

what medicines you take for diabetes
what your level of physical activity or your work schedule is
whether you have other health conditions or diseases

Ask your health care team if you should eat before, during, or after physical activity. Some diabetes medicines, such as sulfonylureas or insulin, may make your blood glucose level drop too low during exercise or if you skip or delay a meal.

Plan how much to eat or drink

You may worry that having diabetes means giving up foods and drinks you enjoy. The good news is you can still have your favorite foods and drinks, but you might need to have them in smaller portions or enjoy them less often.

For people who have diabetes, carb counting and the plate method are two common ways to plan how much to eat or drink. Talk with your health care professional or health care team to find a method that works for you.

Carb counting

Carbohydrate counting , or carb counting, means planning and keeping track of the amount of carbs you eat and drink in each meal or snack. Not all people with diabetes need to count carbs. However, if you take insulin, counting carbs can help you know how much insulin to take.

Plate method

The plate method helps you control portion sizes without counting and measuring. This method divides a 9-inch plate into the following three sections to help you choose the types and amounts of foods to eat for each meal.

Nonstarchy vegetables—such as leafy greens, peppers, carrots, or green beans—should make up half of your plate.
Carb foods that are high in fiber—such as brown rice, whole grains, beans, or fruits—should make up one-quarter of your plate.
Protein foods—such as lean meats, fish, dairy, or tofu or other soy products—should make up one quarter of your plate.

If you are not taking insulin, you may not need to count carbs when using the plate method.

Plate method, with half of the circular plate filled with nonstarchy vegetables; one fourth of the plate showing carbohydrate foods, including fruits; and one fourth of the plate showing protein foods. A glass filled with water, or another zero-calorie drink, is on the side.

Work with your health care team to create a meal plan that works for you. You may want to have a diabetes educator or a registered dietitian on your team. A registered dietitian can provide medical nutrition therapy , which includes counseling to help you create and follow a meal plan. Your health care team may be able to recommend other resources, such as a healthy lifestyle coach, to help you with making changes. Ask your health care team or your insurance company if your benefits include medical nutrition therapy or other diabetes care resources.

Talk with your health care professional before taking dietary supplements

There is no clear proof that specific foods, herbs, spices, or dietary supplements —such as vitamins or minerals—can help manage diabetes. Your health care professional may ask you to take vitamins or minerals if you can’t get enough from foods. Talk with your health care professional before you take any supplements, because some may cause side effects or affect how well your diabetes medicines work.

Research shows that regular physical activity helps people manage their diabetes and stay healthy. Benefits of physical activity may include

lower blood glucose, blood pressure, and cholesterol levels
better heart health
healthier weight
better mood and sleep
better balance and memory

Talk with your health care professional before starting a new physical activity or changing how much physical activity you do. They may suggest types of activities based on your ability, schedule, meal plan, interests, and diabetes medicines. Your health care professional may also tell you the best times of day to be active or what to do if your blood glucose level goes out of the range recommended for you.

Do different types of physical activity

People with diabetes can be active, even if they take insulin or use technology such as insulin pumps .

Try to do different kinds of activities . While being more active may have more health benefits, any physical activity is better than none. Start slowly with activities you enjoy. You may be able to change your level of effort and try other activities over time. Having a friend or family member join you may help you stick to your routine.

The physical activities you do may need to be different if you are age 65 or older , are pregnant , or have a disability or health condition . Physical activities may also need to be different for children and teens . Ask your health care professional or health care team about activities that are safe for you.

Aerobic activities

Aerobic activities make you breathe harder and make your heart beat faster. You can try walking, dancing, wheelchair rolling, or swimming. Most adults should try to get at least 150 minutes of moderate-intensity physical activity each week. Aim to do 30 minutes a day on most days of the week. You don’t have to do all 30 minutes at one time. You can break up physical activity into small amounts during your day and still get the benefit. 1

Strength training or resistance training

Strength training or resistance training may make your muscles and bones stronger. You can try lifting weights or doing other exercises such as wall pushups or arm raises. Try to do this kind of training two times a week. 1

Balance and stretching activities

Balance and stretching activities may help you move better and have stronger muscles and bones. You may want to try standing on one leg or stretching your legs when sitting on the floor. Try to do these kinds of activities two or three times a week. 1

Some activities that need balance may be unsafe for people with nerve damage or vision problems caused by diabetes. Ask your health care professional or health care team about activities that are safe for you.

Group of people doing stretching exercises outdoors.

Stay safe during physical activity

Staying safe during physical activity is important. Here are some tips to keep in mind.

Drink liquids

Drinking liquids helps prevent dehydration , or the loss of too much water in your body. Drinking water is a way to stay hydrated. Sports drinks often have a lot of sugar and calories , and you don’t need them for most moderate physical activities.

Avoid low blood glucose

Check your blood glucose level before, during, and right after physical activity. Physical activity often lowers the level of glucose in your blood. Low blood glucose levels may last for hours or days after physical activity. You are most likely to have low blood glucose if you take insulin or some other diabetes medicines, such as sulfonylureas.

Ask your health care professional if you should take less insulin or eat carbs before, during, or after physical activity. Low blood glucose can be a serious medical emergency that must be treated right away. Take steps to protect yourself. You can learn how to treat low blood glucose , let other people know what to do if you need help, and use a medical alert bracelet.

Avoid high blood glucose and ketoacidosis

Taking less insulin before physical activity may help prevent low blood glucose, but it may also make you more likely to have high blood glucose. If your body does not have enough insulin, it can’t use glucose as a source of energy and will use fat instead. When your body uses fat for energy, your body makes chemicals called ketones .

High levels of ketones in your blood can lead to a condition called diabetic ketoacidosis (DKA) . DKA is a medical emergency that should be treated right away. DKA is most common in people with type 1 diabetes . Occasionally, DKA may affect people with type 2 diabetes who have lost their ability to produce insulin. Ask your health care professional how much insulin you should take before physical activity, whether you need to test your urine for ketones, and what level of ketones is dangerous for you.

Take care of your feet

People with diabetes may have problems with their feet because high blood glucose levels can damage blood vessels and nerves. To help prevent foot problems, wear comfortable and supportive shoes and take care of your feet before, during, and after physical activity.

A man checks his foot while a woman watches over his shoulder.

If you have diabetes, managing your weight may bring you several health benefits. Ask your health care professional or health care team if you are at a healthy weight or if you should try to lose weight.

If you are an adult with overweight or obesity, work with your health care team to create a weight-loss plan. Losing 5% to 7% of your current weight may help you prevent or improve some health problems and manage your blood glucose, cholesterol, and blood pressure levels. 2 If you are worried about your child’s weight and they have diabetes, talk with their health care professional before your child starts a new weight-loss plan.

You may be able to reach and maintain a healthy weight by

following a healthy meal plan
consuming fewer calories
being physically active
getting 7 to 8 hours of sleep each night 3

If you have type 2 diabetes, your health care professional may recommend diabetes medicines that may help you lose weight.

Online tools such as the Body Weight Planner may help you create eating and physical activity plans. You may want to talk with your health care professional about other options for managing your weight, including joining a weight-loss program that can provide helpful information, support, and behavioral or lifestyle counseling. These options may have a cost, so make sure to check the details of the programs.

Your health care professional may recommend weight-loss surgery if you aren’t able to reach a healthy weight with meal planning, physical activity, and taking diabetes medicines that help with weight loss.

If you are pregnant , trying to lose weight may not be healthy. However, you should ask your health care professional whether it makes sense to monitor or limit your weight gain during pregnancy.

Both diabetes and smoking —including using tobacco products and e-cigarettes—cause your blood vessels to narrow. Both diabetes and smoking increase your risk of having a heart attack or stroke , nerve damage , kidney disease , eye disease , or amputation . Secondhand smoke can also affect the health of your family or others who live with you.

If you smoke or use other tobacco products, stop. Ask for help . You don’t have to do it alone.

Feeling stressed, sad, or angry can be common for people with diabetes. Managing diabetes or learning to cope with new information about your health can be hard. People with chronic illnesses such as diabetes may develop anxiety or other mental health conditions .

Learn healthy ways to lower your stress , and ask for help from your health care team or a mental health professional. While it may be uncomfortable to talk about your feelings, finding a health care professional whom you trust and want to talk with may help you

lower your feelings of stress, depression, or anxiety
manage problems sleeping or remembering things
see how diabetes affects your family, school, work, or financial situation

Ask your health care team for mental health resources for people with diabetes.

Sleeping too much or too little may raise your blood glucose levels. Your sleep habits may also affect your mental health and vice versa. People with diabetes and overweight or obesity can also have other health conditions that affect sleep, such as sleep apnea , which can raise your blood pressure and risk of heart disease.

Man with obesity looking distressed talking with a health care professional.

NIDDK conducts and supports clinical trials in many diseases and conditions, including diabetes. The trials look to find new ways to prevent, detect, or treat disease and improve quality of life.

What are clinical trials for healthy living with diabetes?

Clinical trials—and other types of clinical studies —are part of medical research and involve people like you. When you volunteer to take part in a clinical study, you help health care professionals and researchers learn more about disease and improve health care for people in the future.

Researchers are studying many aspects of healthy living for people with diabetes, such as

how changing when you eat may affect body weight and metabolism
how less access to healthy foods may affect diabetes management, other health problems, and risk of dying
whether low-carbohydrate meal plans can help lower blood glucose levels
which diabetes medicines are more likely to help people lose weight

Find out if clinical trials are right for you .

Watch a video of NIDDK Director Dr. Griffin P. Rodgers explaining the importance of participating in clinical trials.

What clinical trials for healthy living with diabetes are looking for participants?

You can view a filtered list of clinical studies on healthy living with diabetes that are federally funded, open, and recruiting at www.ClinicalTrials.gov . You can expand or narrow the list to include clinical studies from industry, universities, and individuals; however, the National Institutes of Health does not review these studies and cannot ensure they are safe for you. Always talk with your primary health care professional before you participate in a clinical study.

This content is provided as a service of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), part of the National Institutes of Health. NIDDK translates and disseminates research findings to increase knowledge and understanding about health and disease among patients, health professionals, and the public. Content produced by NIDDK is carefully reviewed by NIDDK scientists and other experts.

NIDDK would like to thank: Elizabeth M. Venditti, Ph.D., University of Pittsburgh School of Medicine.

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Iran J Public Health
v.44(11); 2015 Nov

Impact of Lifestyle on Health

Dariush d. farhud.

1. School of Public Health, Tehran University of Medical Sciences, Tehran, Iran

2. Dept. of Basic Sciences, Iranian Academy of Medical Sciences,Tehran, Iran

Lifestyle is a way used by people, groups and nations and is formed in specific geographical, economic, political, cultural and religious text. Lifestyle is referred to the characteristics of inhabitants of a region in special time and place. It includes day to day behaviors and functions of individuals in job, activities, fun and diet.

In recent decades, life style as an important factor of health is more interested by researchers. According to WHO, 60% of related factors to individual health and quality of life are correlated to lifestyle ( 1 ). Millions of people follow an unhealthy lifestyle. Hence, they encounter illness, disability and even death. Problems like metabolic diseases, joint and skeletal problems, cardio-vascular diseases, hypertension, overweight, violence and so on, can be caused by an unhealthy lifestyle. The relationship of lifestyle and health should be highly considered.

Today, wide changes have occurred in life of all people. Malnutrition, unhealthy diet, smoking, alcohol consuming, drug abuse, stress and so on, are the presentations of unhealthy life style that they are used as dominant form of lifestyle. Besides, the lives of citizens face with new challenges. For instance, emerging new technologies within IT such as the internet and virtual communication networks, lead our world to a major challenge that threatens the physical and mental health of individuals. The challenge is the overuse and misuse of the technology.

Therefore, according to the existing studies, it can be said that: lifestyle has a significant influence on physical and mental health of human being. There are different forms of such influences. Consanguinity in some ethnicity is a dominant form of life style that it leads to the genetic disorders. Reformation of this unhealthy life style is a preventing factor for decreasing the rate of genetic diseases ( 2 ). In some countries, the overuse of drugs is a major unhealthy life style. Iran is one of the 20 countries using the most medications. They prefer medication to other intervention. Furthermore, in 15–40% of cases they use medications about without prescription ( 3 ). Pain relievers, eye drops and antibiotics have the most usage in Iran. While self-medications such as antibiotics have a negative effect on the immune system, if the individual would be affected by infection, antibiotics will not be effective in treatment. Overall, 10 percent of those who are self-medicated will experience severe complications such as drug resistance. Sometimes drug allergy is so severe that it can cause death ( 4 ).

Finally, variables of lifestyle that influence on health can be categorized in some items:

Diet and Body Mass Index (BMI) : Diet is the greatest factor in lifestyle and has a direct and positive relation with health. Poor diet and its consequences like obesity is the common healthy problem in urban societies. Unhealthy lifestyle can be measured by BMI. Urban lifestyle leads to the nutrition problems like using fast foods and poor foods, increasing problems like cardiovascular ( 5 ).
Exercise: For treating general health problems, the exercise is included in life style ( 6 ). The continuous exercise along with a healthy diet increases the health. Some studies stress on the relation of active life style with happiness ( 7 , 8 ).
Sleep: One of the bases of healthy life is the sleep. Sleep cannot be apart from life. Sleep disorders have several social, psychological, economical and healthy consequences. Lifestyle may effect on sleep and sleep has a clear influence on mental and physical health ( 9 ).
Sexual behavior: Normal sex relation is necessary in healthy life. Dysfunction of sex relation is the problem of most of societies and it has a significant effect on mental and physical health. It can be said that dysfunctional sex relation may result in various family problems or sex related illnesses like; AIDS
Substance abuse: Addiction is considered as an unhealthy life style. Smoking and using other substance may result in various problems; cardiovascular disease, asthma, cancer, brain injury. According to the resent studies in Iran, 43% of females and 64% of males experience the use of hubble-bubble ( 10 ). A longitudinal study shows that 30% of people between 18–65 years old smoke cigarette permanently ( 11 ).
Medication abuse: It is a common form of using medication in Iran and it is considered as an unhealthy life style. Unhealthy behaviors in using medication are as followed: self-treatment, sharing medication, using medications without prescription, prescribing too many drugs, prescribing the large number of each drug, unnecessary drugs, bad handwriting in prescription, disregard to the contradictory drugs, disregard to harmful effects of drugs, not explaining the effects of drugs.
Application of modern technologies: Advanced technology facilitates the life of human beings. Misuse of technology may result in unpleasant consequences. For example, using of computer and other devices up to midnight, may effect on the pattern of sleep and it may disturb sleep. Addiction to use mobile phone is related to depression symptoms ( 12 ).
Recreation: Leisure pass time is a sub factor of life style. Neglecting leisure can bring negative consequences. With disorganized planning and unhealthy leisure, people endanger their health.
Study: Study is the exercise of soul. Placing study as a factor in lifestyle may lead to more physical and mental health. For example, prevalence of dementia, such as Alzheimer's disease is lowerin educated people. Study could slow process of dementia.

With a look at existing studies in health domain, 9 key factors can be suggested for healthy life style ( Fig. 1 ). In regard to each factor, the systematic planning in micro and macro level can be established. It can provide a social and individual healthy lifestyle.

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The authors declare that there is no conflict of interests.

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7 Surprising Health Benefits of Matcha Tea

Research has found this ancient green powder may help memory and heart health, as well as promote healthy aging.

Angela Myers,

Once considered an exotic drink central to Japanese tea ceremonies, matcha is now found in coffee shops and cafes around the world. But matcha’s vibrant green hue isn’t just good fodder for Instagram feeds. This green powder may also boost the health of older Americans. Research has found benefits for memory, heart health , immune system and healthy aging.

Many other types of teas and foods offer these benefits as well. Learn more about what sets matcha apart and whether it’s worth adding to your routine.

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What is matcha?

Known for its distinct green color, matcha is a highly concentrated, powdered form of green tea made from the Camellia sinensis plant. Tea leaves used for matcha are grown in shade, creating a richer, sweeter flavor than other teas.

“Matcha is most readily consumed as tea and people in Asia, especially in Japan and China, have drunk matcha tea for centuries,” says Frank Hu, the chair of the Department of Nutrition at Harvard T.H. Chan School of Public Health. Recently, matcha powders have also been added to smoothies, ice cream, desserts and infusions.

Benefits of matcha

Researchers believe matcha’s benefits primarily originate from the tea’s high concentration of polyphenols, an antioxidant-rich nutrient found in plants. Many other foods also contain polyphenols, but the concentration in matcha sets this powder apart.

“I expected matcha to have a high antioxidant potential, but the result surprised me,” Karolina Jakubczyk, a professor in the Department of Human Nutrition and Metabolomics at Pomeranian Medical University in Poland said in an email. “It is by far the strongest antioxidant I have tested in the lab.”

Jakubcyzk adds that the polyphenols are 10 times higher in matcha than in green tea.

Polyphenols are known for anti-inflammatory and antioxidant properties, says Ron Hills, a professor of pharmaceutical sciences who specializes in integrative nutrition at the University of New England. He says many of the potential health benefits of matcha likely come from polyphenols

1. May benefit brain health

Matcha is known for its properties that promote healthy aging, especially for brain health . Research published in 2020 study investigated the effect of matcha on older adults’ cognitive functioning in Japan. In the study, 61 participants received a daily drink with either matcha or a placebo for two weeks.

Female participants who received matcha saw improvements for two markers of Alzheimer’s disease: overall functioning and episodic memory, the ability to remember details about everyday events. The male participants didn’t see the same results.

Other studies support matcha’s impact on memory, cognitive function and enhanced focus, though the research has found greater cognitive benefits in women than men and scientists say more large studies in humans need to be done to confirm matcha's benefits to brain health.

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2. Improves gut health

The polyphenols in matcha affect gut health . Hills credits this to the EGCG catechins, a type of polyphenol highly concentrated in matcha.

The gut microbiome plays an important role in diseases like diabetes, obesity and liver disorders.

The EGCG catechins also add healthy bacteria to the gut and may improve metabolism. After two weeks of one matcha tea a day, changes often begin in the microbiome, Hills says.

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3. Lowers heart disease risks

Some research has found the antioxidant and anti-inflammatory properties in matcha may strengthen heart health.

A landmark study from 2001 found green tea, including matcha, prevented atherosclerosis, the buildup of plaques in the arteries, but the study was done in animals, not humans.

Recent studies on certain vitamins in matcha, including vitamin C , support the tea’s preventative properties against atherosclerosis, but more research is needed to confirm this effect.

4. Lowers stress

A 2023 analysis of studies in Current Research in Food Science found matcha may help decrease stress and anxiety . This may be due to matcha’s high levels of L-theanine, an amino acid linked to better mood and lower stress levels.

Swapping coffee or energy drinks for matcha may be a great way to get the benefits of caffeine without the stress because matcha’s caffeine properties differ from coffee.

“Coffee can cause rapid action, but also a sudden drop in energy and drowsiness, which is known as the ‘roller-coaster’ effect,” says Jakubczyk. “The caffeine in matcha has a different effect due to the presence of L-theanine, which makes the effects of matcha longer and milder.”

5. Supports a stronger immune system

A matcha a day keeps the doctor away. At least, that’s what evidence linking matcha to a stronger immune system suggests. Matcha’s anti-inflammatory properties support a healthy immune system, while its marked improvements in gut health also strengthen immune response.

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6. May slow premature aging

The catechins found in matcha help protect cells from oxidative stress, potentially slowing the aging process. This benefit was mainly found in the brain, but matcha may slow aging in other cells too.

7. May lower risk of certain cancers

One of the newest, and most exciting, possible benefits of matcha is its potential to lower cancer risk . Early studies on animals suggest it may interrupt cancer cells’ cycle regulation. Most of the studies look at matcha’s impact on breast cancer cells. Much more research on humans needs to be done, however, to prove these benefits. Studies on animals don’t always translate to human benefits.

“Overall evidence suggests potential for preventing cancer, but I don't think we have strong enough evidence to make recommendations for cancer prevention at this point,” Hu says.

Potential negative effects of matcha

Part of what makes matcha a superfood is its high concentration of micronutrients . However, Hu notes, it also has a higher concentration of caffeine than other teas, although it doesn’t have as much caffeine as coffee.

Hu doesn’t recommend matcha for those who are sensitive to caffeine. Drinking too much matcha — especially for those sensitive to caffeine — can cause anxiety, higher blood pressure, difficulty sleeping and a faster heartbeat.

Jakubczyk’s team also found high concentrations of fluoride in matcha. Large amounts of fluoride have been linked to joint pain,brittle bones and diarrhea. However, significant amounts of fluoride must be consumed to experience these negative effects. One to two cups of matcha a day shouldn’t cause them.

Are some forms of matcha better than others?

“The important thing seems to be the introduction of matcha into the daily diet itself, the form is already secondary,” Jakubczyk says.

But not all forms of matcha are created equal. Adding sugar, cream or other processed ingredients won’t take away from matcha’s benefits, but it can negatively impact the body independent of matcha, Hu states. Matcha products without added sugars are better to consume than highly processed matcha desserts and beverages.

Ready to Try Matcha?

Here are some things to consider:

The most common way to consume matcha is as a tea. Experts recommend purchasing ceremonial-grade matcha for maximum benefits.
For a healthy take on a matcha latte, Hills suggests combining matcha, hot milk and honey.
If lattes and tea aren’t for you, a matcha smoothie may do the trick. Hills adds almond milk, frozen spinach, frozen bananas, honey and matcha to his smoothie.
Due to the high caffeine concentration, don’t consume matcha before bedtime, recommends Jakubczyk. Avoiding any caffeine four to six hours before bedtime is a good idea since that’s how long it takes to metabolize half of consumed caffeine.
Some forms of matcha may be contaminated by pesticides or heavy metals, Hu says. To avoid contamination, purchase reputable, quality products.
Matcha is an addition to a healthy diet, not a substitute. The health benefits from matcha may be more prevalent in individuals who eat a whole foods diet alongside matcha, suggests Hills.
If already consuming coffee, be mindful when adding matcha to your diet. Matcha on top of two to three coffees a day may be too much caffeine, Hu says.

Angela Myers is a contributing writer who covers health and medical technology. Her work has appeared in Forbes and Healthline , among other publications.

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Growing public concern about the role of artificial intelligence in daily life

A growing share of Americans express concern about the role artificial intelligence (AI) is playing in daily life, according to a new Pew Research Center survey.

Pew Research Center conducted this study to understand attitudes about artificial intelligence and its uses. For this analysis, we surveyed 11,201 U.S. adults from July 31 to Aug. 6, 2023.

Everyone who took part in the survey is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way, nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

Here are the questions used for this analysis , along with responses, and its methodology .

A bar chart showing that concern about artificial intelligence in daily life far outweighs excitement.

Overall, 52% of Americans say they feel more concerned than excited about the increased use of artificial intelligence. Just 10% say they are more excited than concerned, while 36% say they feel an equal mix of these emotions.

The share of Americans who are mostly concerned about AI in daily life is up 14 percentage points since December 2022, when 38% expressed this view.

Concern about AI outweighs excitement across all major demographic groups. Still, there are some notable differences, particularly by age. About six-in-ten adults ages 65 and older (61%) are mostly concerned about the growing use of AI in daily life, while 4% are mostly excited. That gap is much smaller among those ages 18 to 29: 42% are more concerned and 17% are more excited.

Rising awareness, and concern, about AI

A bar chart that shows those who are familiar with artificial intelligence have grown more concerned about its role in daily life.

The rise in concern about AI has taken place alongside growing public awareness. Nine-in-ten adults have heard either a lot (33%) or a little (56%) about artificial intelligence. The share who have heard a lot about AI is up 7 points since December 2022.

Those who have heard a lot about AI are 16 points more likely now than they were in December 2022 to express greater concern than excitement about it. Among this most aware group, concern now outweighs excitement by 47% to 15%. In December, this margin was 31% to 23%.

Similarly, people who have heard a little about AI are 19 points more likely to express concern today than they were in December. A majority now express greater concern than excitement (58%) about AI’s growing role in daily life, while just 8% report the opposite feeling.

Our previous analyses have found that Americans’ concerns about AI include a desire to maintain human control over these technologies , doubts that AI will improve the way things are now, and caution over the pace of AI adoption in fields like health and medicine .

Opinions of whether AI helps or hurts in specific settings

A bar chart that shows Americans have a negative view of AI’s impact on privacy, more positive toward impact in other areas.

Despite growing public concern over the use of artificial intelligence in daily life, opinions about its impact in specific areas are more mixed. There are several uses of AI where the public sees a more positive than negative impact.

For instance, 49% say AI helps more than hurts when people want to find products and services they are interested in online. Just 15% say it mostly hurts when used for this purpose, and 35% aren’t sure.

Other uses of AI where opinions tilt more positive than negative include helping companies make safe cars and trucks and helping people take care of their health.

In contrast, public views of AI’s impact on privacy are much more negative. Overall, 53% of Americans say AI is doing more to hurt than help people keep their personal information private. Only 10% say AI helps more than it hurts, and 37% aren’t sure. Our past research has found majorities of Americans express concern about online privacy generally and a lack of control over their own personal information.

Public views on AI’s impact are still developing, though. Across the eight use cases in the survey, 35% to 49% of Americans say they’re not sure what impact AI is having.

Demographic differences in views of AI’s impact

A bar chart showing that Americans with higher levels of education tend to be more positive about AI’s impact in many areas.

There are significant demographic differences in the perceived impact of AI in specific use cases.

Americans with higher levels of education are more likely than others to say AI is having a positive impact across most uses included in the survey. For example, 46% of college graduates say AI is doing more to help than hurt doctors in providing quality care to patients. Among adults with less education, 32% take this view.

A similar pattern exists with household income, where Americans with higher incomes tend to view AI as more helpful for completing certain tasks.

A big exception to this pattern is views of AI’s impact on privacy. About six-in-ten college graduates (59%) say that AI hurts more than it helps at keeping people’s personal information private. Half of adults with lower levels of education also hold this view.

Men also tend to view AI’s impact in specific areas more positively than women. These differences by education, income and gender are generally consistent with our previous work on artificial intelligence .

Note: Here are the questions used for this analysis , along with responses, and its methodology .

Artificial Intelligence

Many Americans think generative AI programs should credit the sources they rely on

Americans’ use of chatgpt is ticking up, but few trust its election information, q&a: how we used large language models to identify guests on popular podcasts, striking findings from 2023, what the data says about americans’ views of artificial intelligence, most popular.

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ABOUT PEW RESEARCH CENTER Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

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Research Expo Highlights Innovation Across CONHI

Friday, Apr 19, 2024 • Samuel Galindo : Connect

Attendees listening at CONHI Research Expo

More than 30 presentations and posters were shared across various research topics, including musculoskeletal disease, rural health, AI, cardiovascular research and much more.

Zui Pan explaining her research poster at at CONHI Research Expo

“We have not met as a college in some time and over the past year we also welcomed a number of new faculty so [I] felt like the timing was right,” Paul Fadel, associate dean for research, said. “Also, our college centers are growing and [it’s] good for them to provide updates on progress and resources available through the centers.”

With the recent launch of President Cowley’s UTA 2030 strategic plan including an emphasis on research and innovation, Fadel believes research expos help contribute to that overall goal.

“The number of excellent oral and poster presentations speaks directly to how CONHI is contributing to the Universities’ focus on research and innovation,” Fadel added. “What was also apparent is the diversity of research in our college from basic science to clinical application focused on advancing the strategic research area of health and the human condition.”

Kristine Gigli, a UTA assistant professor in the Department of Graduate Nursing and one of the Expo’s presenters, shared her enthusiasm for the event.

“For the UTA research community, expos bring life to new ideas and support and strengthen future research efforts, raising the UTA research profile,” said Gigli.

Yue Liao, assistant professor in the UTA Department of Kinesiology, echoed this sentiment.

Attendees observing poster at CONHI Research Expo

Fadel indicated that the next research expo is slated for the Fall and will provide opportunities for different faculty members to share their research.

But Fadel’s commitment to showcasing UTA’s research doesn’t stop at internal event only. CONHI will be well-represented at UTA’s upcoming Research and Innovation Expo, as Presidential Distinguished Professor Florence Haseltine and Associate Chair of Graduate Programs in Exercise Science Matthew Brothers, will both be speaking.

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According to the World Health Organization (WHO), a healthy lifestyle is defined as "a way of living that lowers the risk of being seriously ill or dying early" [].Public health authorities emphasise the importance of a healthy lifestyle, but despite this, many individuals worldwide still live an unhealthy lifestyle [].In Europe, 26% of adults smoke [], nearly half (46%) never exercise ...

At age 50, women who didn't adopt any of the five healthy habits were estimated to live on average until they were 79 years old and men until they were 75.5 years. In contrast, women who adopted all five healthy lifestyle habits lived 93.1 years and men lived 87.6 years. Independently, each of the five healthy lifestyle factors significantly ...

A Digest on Healthy Eating and Healthy Living. Download the printable Healthy Living Guide (PDF) As we transition from 2020 into 2021, the COVID-19 pandemic continues to affect nearly every aspect of our lives. For many, this health crisis has created a range of unique and individual impacts—including food access issues, income disruptions ...

Multiple daily practices have a profound impact on both long-term and short-term health and quality of life. This review will focus on 5 key aspects of lifestyle habits and practices: regular physical activity, proper nutrition, weight management, avoiding tobacco products, and stress reduction/mental health. This initial section will focus on ...

Thus, healthy lifestyles may result in improvements in the emotional, metabolic, physical, or social state depending on the individual. Based on this, a previous research was carried out in Scopus using the terms "healthy," "lifestyle," and "wellbeing.".

Health lifestyles research puts advantages and disadvantages on equal footing, considering structure, agency, resiliency, and constraints across multiple dimensions of (dis)advantage. The cultural perspective articulates how allegedly "healthy" lifestyles perpetuate inequalities, which is at odds with the goal of reducing health disparities ...

The health lifestyles framework contributes to understandings of health, health disparities, and social inequalities by integrating individual- and group-level influences and synthesizing constellations of health behaviors with underlying social psychological phenomena including norms and identities. While health lifestyles research is ...

A Digest on Healthy Eating and Healthy Living. Download the printable Healthy Living Guide (PDF) Over the course of 2021, many of us continued to adapt to a "new normal," characterized by a return to some pre-pandemic activities mixed with hobbies or habits that have emerged since 2020's lockdowns. On the topic of food and eating ...

Most research on lifestyle change stems from studies assessing the effectiveness of intervention programs which encourage healthy eating and/or exercise to produce weight loss or manage chronic disease. 13,14 These studies generally report low adherence 15,16 and high rates of attrition. 14 Successful, long-term behaviour change, particularly ...

A healthy lifestyle is associated with longer total life expectancy and a larger proportion of remaining years lived without a major noncommunicable disease in the Chinese population. Public ...

1 Center for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment, Bilthoven, Netherlands; 2 Julius Centre for Health Sciences and Primary Care, University Medical Centre, Utrecht, Netherlands; For five health-related lifestyle factors (physical activity, weight, smoking, sleep, and alcohol consumption) we describe both population trends and ...

Whether a healthy lifestyle helps achieve gains in life expectancy (LE) free of major non-communicable diseases and its share of total LE in Chinese adults remains unknown. We considered five low ...

Research for Healthy Living. Scientific and technological breakthroughs generated by NIH research have helped more people in the United States and all over the world live longer, healthier lives. These advancements were achieved by making disease less deadly through effective interventions to prevent and treat illness and disability.

A healthy lifestyle that involves moderate alcohol consumption, a healthy diet, regular physical activity, healthy sleep and frequent social connection, while avoiding smoking and too much sedentary behaviour, reduces the risk of depression, new research has found.

Having life purpose/meaning. Research shows that having a sense of meaning or purpose in daily life is associated with better ... Dhana K, Pan A, Liu X, Song M, Liu G, Shin HJ, Sun Q, Al-Shaar L. Healthy lifestyle and life expectancy free of cancer, cardiovascular disease, and type 2 diabetes: prospective cohort study. BMJ. 2020 Jan 8;368 ...

Research regularly shows the significant influence of lifestyle selections on mental well-being. Embracing a healthy lifestyle can result in significant advantages such as less stress, improved mood, and general well-being improvements. An integrated strategy involving consistent physical exercise, a well-rounded diet, sufficient rest, and successful stress control is very impactful ...

Life's Essential 8. The American Heart Association announced a checklist to measure cardiovascular health, which now includes healthy sleep - a response to the latest research showing that sleep impacts total health, and that people who get the recommended 7-9 hours of sleep per night tend to manage other health factors more effectively.

Restricting calories by 20% to 60% has been shown to promote longer life in many animals, according to previous research. Over the course of human life, every time a person's cells replicate, some ...

Improve our cognition. Increase attention span. Lower risk of mental illness. Increase empathy and social connectedness. You can combine this with other healthy habits, like your daily walk ...

Research has shown that most individuals can help preserve their health and mobility as they age by adopting or continuing healthy habits and lifestyle choices. Read on to learn about 10 common misconceptions related to aging and older adults. 1. Are depression and loneliness normal in older adults?

Intervention research on lifestyles in health psychology appears to have been dominated in history by a predominantly cognitive approach, for which it is assumed that a healthy lifestyle choice depends mainly on the subject and is influenced by a series of factors all rigorously individuals, such as self-efficacy, motivation, control and ...

Healthy living is a way to manage diabetes. To have a healthy lifestyle, take steps now to plan healthy meals and snacks, do physical activities, get enough sleep, and quit smoking or using tobacco products. Healthy living may help keep your body's blood pressure, cholesterol, and blood glucose level, also called blood sugar level, in the ...

Poorer people are living shorter, less healthy lives. Many very rich people are pouring huge amounts of money into research, hoping to develop sophisticated technologies to prevent aging.

April 13, 2024 9:00 pm ET. Text. Active religious practice, such as going to churches, synagogues and mosques, is linked to mental well-being, according to a growing body of research. One possible ...

Lifestyle may effect on sleep and sleep has a clear influence on mental and physical health ( 9 ). Sexual behavior: Normal sex relation is necessary in healthy life. Dysfunction of sex relation is the problem of most of societies and it has a significant effect on mental and physical health.

Matcha is known for its properties that promote healthy aging, especially for brain health. Research published in 2020 study investigated the effect of matcha on older adults' cognitive functioning in Japan. In the study, 61 participants received a daily drink with either matcha or a placebo for two weeks.

Still, there are some notable differences, particularly by age. About six-in-ten adults ages 65 and older (61%) are mostly concerned about the growing use of AI in daily life, while 4% are mostly excited. That gap is much smaller among those ages 18 to 29: 42% are more concerned and 17% are more excited.

The UTA College of Nursing and Health Innovation (CONHI) held its first Research Expo, where several faculty members met on the sixth floor of the Central Library to discuss innovative research being done across the college. ... "For the UTA research community, expos bring life to new ideas and support and strengthen future research efforts ...

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Description. Lot poster created for Phish's October 2023 three night run in Chicago, IL. Inspired by Phish's song, Ocelot, and a classic Chicago Bulls poster. Design by Taylor Swope. Digitally signed and individually numbered out of 100 limited edition prints. • 12" x 18" print on 13" x 19" with 1/2" margin.
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Surrender To The Flow Fall 2023 25th Anniversary Issue
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Phish Outline Fall 2023 Tour Dates
Today, Phish have announced their fall 2023 tour dates. The impending run is slated to begin in early October. It will consist of three multi-night stands, the first of which will occur in ...
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research on healthy lifestyle

A healthy lifestyle is positively associated with mental health and well-being and core markers in ageing

Conclusions

Study population

Health interview survey

Healthy lifestyle score

Telomere length and mitochondrial DNA content assay

Statistical analysis

Population characteristics

Mental health prevalence and lifestyle characteristics

Healthy lifestyle and mental health and well-being

The biomarkers of ageing

Sensitivity analysis

Healthy lifestyle and biomarkers of ageing

Mental health and well-being and biomarkers of ageing

Strengths and limitations

Availability of data and materials

Abbreviations

Acknowledgements

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BMC Medicine

ORIGINAL RESEARCH article

Introduction

Design, study population, and data collection

Lifestyle factors

Sociodemographic characteristics

Statistical analyses

Population trends of single health-related lifestyle factors over 30 years

Individual changes in single health-related lifestyle factors over 25 years

Population trends of the health-related lifestyle factors combined

Individual changes of the health-related lifestyle factors combined

Data availability statement

Ethics statement

Author contributions

Acknowledgments

Conflict of interest

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Healthy lifestyle and life expectancy free of major chronic diseases at age 40 in China

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Lifestyle risk score and mortality in Korean adults: a population-based cohort study

Combined healthy lifestyle factors are more beneficial in reducing cardiovascular disease in younger adults: a meta-analysis of prospective cohort studies

Association of body mass index with life expectancy with and without cardiovascular disease

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