Is overall and timing-specific physical activity associated with depression in older adults?

Objective: Regarding the circadian rhythm regulating an individual’s response to external stimulation, it remains unclear whether older adults engaged in physical activity at different times of day may be differently related to depression symptoms. Thus, this study aimed to investigate the association between overall and timing-specific physical activity and depressive symptoms in older Taiwanese adults.

Methods: This cross-sectional study was conducted at a medical center in Taipei City, Taiwan, between September 2020 and December 2021. The participants were community-dwelling older adults aged ≥65 who could walk independently and were not at high risk of cognitive dysfunction. Physical activity was measured using a triaxial accelerometer (GT3X+, ActiGraph) and categorized into timing-specific periods (morning: 06:01–12:00; afternoon: 12:01–18:00; evening: 18:01–24:00) as well as overall physical activity, which included both light physical activity (LPA) and moderate-to-vigorous physical activity (MVPA). A 15-item Geriatric Depression Scale was utilized to assess and measure depressive symptoms. Multivariate linear regression models were estimated for data analysis.

Results: A total of 180 older adults (55.0% female; 80.5 ± 7.1 years old; 18.3% at risk of depression) were included. On average, the participants spent 237.3 (± 85.7) minutes in LPA per day and 12.8 (± 17.0) minutes in MVPA per day. The results showed that overall MVPA engagement was associated with lower depressive-symptom scores [B = −1.357, 95% CI (−2.561, −0.153)] in older adults. However, no significant associations were observed between overall LPA, timing-specific MVPA and LPA, and depression in older adults.

Conclusion: To prevent depression in older adults, it is advisable to accumulate a higher amount of MVPA throughout the entire day rather than engage in LPA. Further studies employing a prospective design are necessary to validate and strengthen our findings.

1. Introduction

Several nations worldwide are entering an aging society as a consequence of demographic changes. By 2050, it is anticipated that one in six individuals globally will be aged 65 years or older. Additionally, it is projected that Taiwan will achieve the status of a super-aged society by 2025 (1). Consequently, there is an increasing number of health issues among the older population, including depression, which is listed by the World Health Organization as among the world’s three most prevalent diseases, along with heart disease and cancer (2). Additionally, depression is more prevalent in old age and could lead not only to higher mortality rates but also to an economic and medical burden on society (3). Therefore, there is an urgent need to develop effective strategies to prevent depression in older adults.

In recent years, a growing number of studies have confirmed the positive benefits of physical activity (PA) in the prevention of depression among older adults (4). PA has been recognized as the most fundamental chronobiological tool and a potential zeitgeber for the human circadian-rhythm system (5). Circadian rhythms play a crucial role in health, exerting significant effects on aging, longevity, and disease pathogenesis, and are dynamic over an individual’s lifespan (6, 7). These rhythms become increasingly disrupted as aging progresses, due to factors such as changes in the endocrine system and sleep patterns, causing a variety of negative health conditions (8). Conversely, different intensities and timing of PA stimuli can regulate a circadian rhythm and help maintain chronobiological homeostasis, regulate metabolism, prevent certain diseases, and ultimately improve physical health, including physical and mental aspects (9). Indeed, engaging in PA at different times of the day may alter metabolism and the circadian rhythm, thus changing different disease conditions in some cohorts (10). In recent studies, the health benefits of PA have been found to differ depending on whether the activity is performed in the morning, afternoon, or evening (11, 12). In addition, it is worth noting the inconsistent results regarding which time periods and PA intensities are optimal for preventing depression in older adults. For example, PA in the morning is beneficial to weight loss (13) and improves metabolism (14), while exercise in the afternoon or evening improves blood glucose (15). Nevertheless, regardless of timing, overall moderate-to-vigorous-intensity physical activity (MVPA) is related to favorable physical function (16) and lower risks of all-cause, cardiovascular-disease, and cancer mortality (17). In summary, most of these previous studies on the association between PA at different times and health focused on physical aspects (13–18); only one study was based on a population of older adults (16), while limited studies have objectively used PA measurements (16, 17). Using objective measurement (i.e., triaxial accelerometers), total and timing-specific PA and its intensity PA can be accurately captured throughout the day to avoid recall and social-desirability biases, especially in the older adult population (19–21).

Given that light and fresh air are possible mediators of the circadian rhythm, there may be synergistic effects. We hypothesized that engagement in MVPA during the morning and afternoon was negatively associated with older adults’ depressive-symptom scores. Therefore, this study focused on investigating the association between total or timing-specific PA and depression among community-dwelling older adults in Taiwan.

2. Methods

2.1. Participants and study design

This cross-sectional study collected data from community-dwelling older adults aged ≥65 years who could walk independently and were not at risk of cognitive dysfunction. Data were collected using convenience sampling from September 2020 to December 2021. Participants with chronic diseases such as cardiovascular disease and neurological issues were not excluded as the recruitment site was the Department of Geriatrics and Gerontology (DGG) at National Taiwan University (NTU) Hospital (NTUH). A doctor from the NTUH’s DGG outpatient department assessed whether the participants met the criteria and explained the study process. The sample participants provided written informed consent and were informed of the detailed implementation measures. The participants completed self-reported questionnaires on sociodemographic status, health status, and lifestyle behaviors. PA data were collected using a tri-accelerometer worn on the hip for 7 consecutive days. Valid data were defined as those collected from participants who wore the acceleration protocol for at least 10 h during waking hours on at least four valid days. Depression scores were calculated using the Chinese version of the 15-item Geriatric Depression Scale (GDS-15) (22). At the end of the study, the participants received a US$7 gift voucher as a token of our appreciation. This research received ethical approval from the Research Ethics Committee of the NTUH (REC number: 202008046RINC). We recruited a total of 300 participants for the first phase of this study. The exclusion criteria were as follows: (1) participants with a high risk of cognitive dysfunction after screening by the Mini-Mental State Examination (MMSE) (n = 51); (2) participants who failed to complete the self-reported questionnaire (n = 36); and (3) the participants’ accelerometer data did not meet the valid-wear criterion, which was at least 4 valid days (including three weekdays and one weekend day) and at least 10 h/day (n = 33). After the data were screened, 180 participants were included in the final sample for the study.

2.2. Measures

2.2.1. Geriatric depressive symptoms

The GDS-15 was used to assess depressive symptoms in the older adults. This scale has been shown to have good validity and reliability, with a Cronbach’s α value of 0.81 (23); its validity and reliability were also demonstrated based on a sample of Taiwanese older adults (22). The GDS-15 comprises 15 questions and is easy for older adults to complete. Total scores range from 0 (lower depressive symptoms) to 15 (higher depressive symptoms) points. A total score of ≥5 points indicates the presence of clinical depressive symptoms, while a score of <5 points indicates no depressive symptoms (24).

2.2.2. PA

A waist-worn triaxial accelerometer (ActiGraph GT3X+, Pensacola, FL, United States) was used to measure the times spent in LPA (100–2019 counts/min) and MVPA (≥ 2020 counts/min) (25). The accelerometer was not worn during water-related activities, such as swimming, and the participants were requested to record that time in their diaries. Following previous studies (26), we identified the different time intervals for PA: (a) morning: 06:01–12:00; (b) afternoon: 12:01–18:00; and (c) evening: 18:01–24:00. The accelerometer data showed that approximately 90% of the participants slept between 0:00 and 06:00 for each night in this study. The ActiLife software version 6.0 was used to calculate the accelerometer data; all the data were processed using 60 s epochs of sampling frequency at the default setting (i.e., 30 Hz).

2.2.3. Covariates

We collected data on sociodemographic characteristics, health status, and lifestyle behaviors using self-rated questionnaires. Sociodemographic characteristics included age, sex (female and male), marital status, employment, living status, and education level (either university-education level or none). Health status included body-mass index (BMI), chronic diseases (< 4 and ≥4), diabetes, hypertension, hyperlipidemia, and depressive symptoms (no and yes). BMI was calculated as weight in kilograms divided by height in meters squared and was divided into four groups based on the official standard of the Taiwan Ministry of Health and Welfare: underweight (< 18.5 kg/m²), normal (18.5–23.9 kg/m²), overweight (24–26.9 kg/m²), and obese (≥ 27 kg/m²). Depressive symptoms were assessed using the GDS-15, with a cut-off point of <5 points indicating no depressive symptoms and that ≥5 points indicating the presence of depressive symptoms. Lifestyle behaviors included whether they had a habit of smoking and alcohol use. Monitor wear time was assessed by the accelerometer.

2.3. Statistical analyses

Descriptive statistical analysis was conducted to obtain the means and standard deviations (SDs) of the sample characteristics (n = 180), such as socio-demographic factors (age, sex, educational level, and BMI), lifestyle behaviors (smoking, alcohol use, high blood pressure, high blood cholesterol, and diabetes), GDS-15 scores, LPA, and MVPA (Table 1). Multiple linear regression models were used to estimate the association between the time spent in timing-specific (in percentage) and overall LPA/MVPA (in minutes) based on the GDS-15 using 95% confidence intervals (CI) and unstandardized coefficients (B); p < 0.05 was considered significant. Binary logistic regression analyses were also used to estimate the association between overall and timing-specific PA with depression risk. Age, sex, education level, BMI, living alone, smoking, drinking, high blood pressure, diabetes, high blood cholesterol, and total accelerometer wear time were controlled for as confounders. All statistical analyses in this study were conducted using IBM SPSS Statistics 23.0, while p < 0.05 indicated the significance level.

TABLE 1

Table 1. Characteristic of the participants (n = 180).

3. Results

Table 1 presents the characteristics of the 180 older adults (55.0% female; 80.5 ± 7.1 years old). Of the participants, 74.4% were over 75 years of age, 55% were women, 43.9% had university or higher education level, 91.7% participants lived with others, 91.1% were nonsmokers, 87.8% did not consume alcohol, 51.1% had a normal BMI, 85.6% had fewer than four chronic diseases, and 18.3% were at risk of depression. On average, the participants spent 237.3 (± 85.7) minutes in LPA per day and 12.8 (± 17.0) minutes in MVPA per day.

Table 2 presents the associations between timing-specific and overall LPA/MVPA and depressive-symptom scores. In the adjusted model, the overall MVPA duration was significantly associated with lower total depressive symptom scores [B = −1.357, 95% CI (−2.561, −0.153), p < 0.05]. However, timing-specific LPA [during the morning: B = −2.321, 95% CI (−7.034, 2.391), p > 0.05; during the afternoon: B = 4.617, 95% CI (–1.760, 10.994), p > 0.05; during the evening: B = −1.711, 95% CI (−6.635, 3.214), p > 0.05], timing-specific MVPA [during the morning: B = −1.080, 95% CI (−2.677, 0.518), p > 0.05; during the afternoon: B = 1.593, 95% CI (−0.069, 3.255), p > 0.05; during the evening: B = −0.285, 95% CI (−1.902, 1.332), p > 0.05], and even overall LPA [B = −0.009, 95% CI (−0.394, 0.376), p > 0.05] were not significantly associated with the total depressive symptom scores. Binary logistic regression analysis results also showed no significant associations between overall and timing-specific PA with depression risk in older individuals (Supplementary Table S1).

TABLE 2

Table 2. Associations of overall and timing-specific light physical activity/moderate-to-vigorous physical activity with depressive scores (n = 180).

4. Discussion

To our knowledge, this is the first study to utilize an accelerometer to objectively examine the relationship between overall and timing-specific PA and depression in Taiwanese older adults. The most important finding of this study is that accumulation in MVPA, but not in LPA, throughout the day, regardless of different timing, was significantly associated with lower depressive symptom scores in older adults. This finding is significant for intervention designers and public health practitioners in designing effective programs for older adults to prevent depression.

This study provided two pieces of evidence. The first evidence is that accumulation in total PA throughout the day rather than the specific timing of PA is more optimal for depression prevention, which contradicts our hypothesis based on the circadian rhythm. Several previous studies have found that engaging in PA at different times of the day may be related to different physical-health outcomes (13–15, 18, 27). Our findings are similar to those obtained in two studies (16, 17) that indicated that accumulating more overall MVPA duration was more important than timing for physical function (16) and mortality risks (17).

A possible reason for the unexpected results is that this study did not investigate the participants’ activity patterns (outdoor or indoor), illumination time, and dietary behavior. Previous studies have reported that depression may be related to other behavior (e.g., a diet that reduces inflammation in the brain by consuming anti-inflammatory substances, indirectly lowering the risk of mental illness (28)) and environmental factors (e.g., light exposure promotes the synthesis of vitamin D, which acts as a hormone to stimulate a positive emotion) (29). Consequently, this may explain why various time periods for PA are not significant, and the fundamental mechanism could be explored in future work.

Another important finding is that engaging in MVPA rather than LPA was more important for depression prevention in older adults. It is well-established that engaging in MVPA is associated with lower risks of depression among older adults (30, 31) because MVPA could contribute to psychological (distraction and self-efficacy), social (social support and social interaction), and physiological (increase in endorphins) benefits (32). A possible reason for LPA not being related to depression is that compared with MVPA, LPA is more likely to come from standing, housekeeping, or slow walking, which is likely to be less stimulating to the brain (33). Therefore, it is possible that older adults who engage in at least a moderate intensity of PA may produce sufficient stimulation to the brain, which then activates the production of endorphins and increases serum endocrine concentrations, with a tendency toward homogeneous pleasure responses (34, 35). Consequently, these findings imply that MVPA may play a crucial protective role against depressive symptoms in older adults.

A strength of the present study is that the measurement of PA was objective and under free-living environments. Additionally, our study is the first attempt to examine the associations of different timings for PA with mental health. Our data aid public policy research on overall PA and depression, which requires gathering and applying solid evidence on PA and depression in the future. However, the results should be interpreted in the context of several limitations. First, the sample of participants recruited clinically was not representative of the entire community-dwelling population, as it included a smaller number of participants, roughly half of whom were female, and 18% displayed risks of depression. Future research will necessitate a more extensive and representative sample to investigate the relationship between time-specific and overall MVPA and depression in older adults. Second, the study excluded the time periods between 0:00 a.m. and 6:00 a.m., which were tacitly recognized as sleep time. This might have reduced the time included in the calculation of physical activities. However, our data indicate that approximately 90% of the participants have overlapping sleep patterns covering the sleep-time period. Third, due to the cross-sectional design of the study, a causal relationship between the percentage and duration of time-specific and total MVPA and depression cannot be inferred. Finally, The GDS-15, used in this study, evaluates depressive symptoms over the past week, limiting its capacity to reflect longer-term psychological changes. To better understand the evolving nature of depression in older adults, future studies should consider methods like ecological momentary assessment.

5. Conclusion

This study demonstrated that engaging in total MVPA was associated with the prevention of depression in adults, regardless of the time of day that PA was performed. It is proposed that future depression-prevention initiatives for older adults focus on a sufficient intensity of PA accumulation throughout the day.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.

Ethics statement

The study commenced following the participants’ signing of an informed-consent contract. The procedure in this study was approved by the Research Ethics Committee of the NTUH (REC number: 202008046RINC). All the methods included in this study are in accordance with the declaration of Helsinki.

Author contributions

JC: conceptualization, writing original draft, and formal analysis. T-FL: conceptualization, writing-review and editing, and formal analysis. L-JL: writing-review, editing, and supervision. J-HP: writing-review, editing, and funding acquisition. YL: conceptualization, writing-review, editing, funding acquisition, and supervision. All authors contributed to the article and approved the submitted version.

Funding

YL received personal grants from the Ministry of Science and Technology, Taiwan (MOST 111-2628-H-003-006-MY3). This work was also supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2022M3E8A1077761). The Ministry of Science and Technology, Taiwan was not involved in the study design, data collection, analysis, and interpretation or writing of the manuscript.

Acknowledgments

The authors would like to thank you to all of the corresponding authors of the studies we included in this study and their co-author teams for providing the data that enabled us to conduct our analysis.

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.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2023.1241170/full#supplementary-material

Abbreviations

BMI, Body-mass index; CI, Confidence interval; DGG, Department of Geriatrics and Gerontology; GDS-15, 15-item Geriatric Depression Scale; LPA, light physical activity; MVPA, moderate-to-vigorous physical activity; NTU, National Taiwan University; NTUH, National Taiwan University Hospital; PA, Physical activity.

References

1. United Nations Department of Economic and Social Affairs , Population division world population ageing 2019: highlights. New York: United Nations department of economic and social affairs, population division (2019).

Google Scholar

2. World Health Organization . Depression. (2021); Available at: https://www.who.int/news-room/fact-sheets/detail/depression

Google Scholar

3. GBD 2016 Disease and Injury Incidence and Prevalence Collaborators . Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet. (2017) 390:1211–59. doi: 10.1016/S0140-6736(17)32154-2

CrossRef Full Text | Google Scholar

4. Cunningham, C , O’Sullivan, R , Caserotti, P , and Tully, MA . Consequences of physical inactivity in older adults: a systematic review of reviews and meta-analyses. Scand J Med Sci Sports. (2020) 30:816–27. doi: 10.1111/sms.13616

PubMed Abstract | CrossRef Full Text | Google Scholar

5. Youngstedt, SD , Elliott, JA , and Kripke, DF . Human circadian phase-response curves for exercise. J Physiol. (2019) 597:2253–68. doi: 10.1113/JP276943

PubMed Abstract | CrossRef Full Text | Google Scholar

6. Kondratov, RV . A role of the circadian system and circadian proteins in aging. Ageing Res Rev. (2007) 6:12–27. doi: 10.1016/j.arr.2007.02.003

CrossRef Full Text | Google Scholar

7. Kondratova, AA , and Kondratov, RV . The circadian clock and pathology of the ageing brain. Nat Rev Neurosci. (2012) 13:325–35. doi: 10.1038/nrn3208

PubMed Abstract | CrossRef Full Text | Google Scholar

8. Hood, S , and Amir, S . The aging clock: circadian rhythms and later life. J Clin Invest. (2017) 127:437–46. doi: 10.1172/JCI90328

PubMed Abstract | CrossRef Full Text | Google Scholar

9. Lewis, P , Korf, HW , Kuffer, L , Groß, JV , and Erren, TC . Exercise time cues (zeitgebers) for human circadian systems can foster health and improve performance: a systematic review. BMJ Open Sport Exerc Med. (2018) 4:e000443. doi: 10.1136/bmjsem-2018-000443

PubMed Abstract | CrossRef Full Text | Google Scholar

10. Gabriel, BM , and Zierath, JR . Zeitgebers of skeletal muscle and implications for metabolic health. J Physiol. (2022) 600:1027–36. doi: 10.1113/JP280884

CrossRef Full Text | Google Scholar

11. Janssen, I , Campbell, JE , Zahran, S , Saunders, TJ , Tomasone, JR , and Chaput, JP . Timing of physical activity within the 24-hour day and its influence on health: a systematic review. Health Promot Chronic Dis Prev Can. (2022) 42:129–38. doi: 10.24095/hpcdp.42.4.02

PubMed Abstract | CrossRef Full Text | Google Scholar

12. Ammar, A , Chtourou, H , and Souissi, N . Effect of time-of-day on biochemical markers in response to physical exercise. J Strength Cond Res. (2017) 31:272–82. doi: 10.1519/JSC.0000000000001481

PubMed Abstract | CrossRef Full Text | Google Scholar

13. Willis, EA , Creasy, SA , Honas, JJ , Melanson, EL , and Donnelly, JE . The effects of exercise session timing on weight loss and components of energy balance: midwest exercise trial 2. Int J Obes. (2020) 44:114–24. doi: 10.1038/s41366-019-0409-x

PubMed Abstract | CrossRef Full Text | Google Scholar

14. Sato, S , Basse, AL , Schönke, M , Chen, S , Samad, M , Altıntaş, A, et al. Time of exercise specifies the impact on muscle metabolic pathways and systemic energy homeostasis. Cell Metab. (2019) 30:92–110.e4. doi: 10.1016/j.cmet.2019.03.013

PubMed Abstract | CrossRef Full Text | Google Scholar

15. Savikj, M , Gabriel, BM , Alm, PS , Smith, J , Caidahl, K , Björnholm, M, et al. Afternoon exercise is more efficacious than morning exercise at improving blood glucose levels in individuals with type 2 diabetes: a randomised crossover trial. Diabetologia. (2019) 62:233–7. doi: 10.1007/s00125-018-4767-z

PubMed Abstract | CrossRef Full Text | Google Scholar

16. Lai, TF , Liao, Y , Lin, CY , Huang, WC , Hsueh, MC , and Chan, DC . Moderate-to-vigorous physical activity duration is more important than timing for physical function in older adults. Sci Rep. (2020) 10:21344. doi: 10.1038/s41598-020-78072-0

PubMed Abstract | CrossRef Full Text | Google Scholar

17. Feng, H , Yang, L , Liang, YY , Ai, S , Liu, Y , Liu, Y, et al. Associations of timing of physical activity with all-cause and cause-specific mortality in a prospective cohort study. Nat Commun. (2023) 14:930. doi: 10.1038/s41467-023-36546-5

PubMed Abstract | CrossRef Full Text | Google Scholar

18. Munan, M , Dyck, RA , Houlder, S , Yardley, JE , Prado, CM , Snydmiller, G, et al. Does exercise timing affect 24-hour glucose concentrations in adults with type 2 diabetes? A follow up to the exercise-physical activity and diabetes glucose monitoring study. Can J Diabetes. (2020) 44:711–718.e1. doi: 10.1016/j.jcjd.2020.05.012

PubMed Abstract | CrossRef Full Text | Google Scholar

19. Dowd, KP , Szeklicki, R , Minetto, MA , Murphy, MH , Polito, A , Ghigo, E, et al. A systematic literature review of reviews on techniques for physical activity measurement in adults: a DEDIPAC study. Int J Behav Nutr Phys Act. (2018) 15:15. doi: 10.1186/s12966-017-0636-2

CrossRef Full Text | Google Scholar

20. Sagelv, EH , Ekelund, U , Pedersen, S , Brage, S , Hansen, BH , Johansson, J, et al. Physical activity levels in adults and elderly from triaxial and uniaxial accelerometry. The Tromsø study. PLoS One. (2019) 14:e0225670. doi: 10.1371/journal.pone.0225670

PubMed Abstract | CrossRef Full Text | Google Scholar

21. Lai, T-F, Huang, W-C, Hsueh, M-C, and Liao, Y eds. Applying the tri-axial accelerometer to measure physical activity and sedentary behavior: a narrative review. J Sports Perform. (2022) 9:059–77. doi: 10.53106/240996512022090902001

CrossRef Full Text | Google Scholar

22. Liao, Y-C , Yang, Y-K , Lu, F-H , Chang, C-J , Ko, H-C , and Lo, C-M . Reliability and validity of the Taiwan geriatric depression scale. Taiwan Soc Psychiatry. (2004) 18:30–41.

Google Scholar

23. Almeida, OP , and Almeida, SA . Short versions of the geriatric depression scale: a study of their validity for the diagnosis of a major depressive episode according to ICD-10 and DSM-IV. Int J Geriatr Psychiatry. (1999) 14:858–65. doi: 10.1002/(SICI)1099-1166(199910)14:10<858::AID-GPS35>3.0.CO;2-8

CrossRef Full Text | Google Scholar

24. Anderson, JK , Zimmerman, L , Caplan, L , and Michaud, K . Measures of rheumatoid arthritis disease activity: Patient (PtGA) and Provider (PrGA) Global Assessment of Disease Activity, Disease Activity Score (DAS) and Disease Activity Score With 28-Joint Counts (DAS28), Simplified Disease Activity Index (SDAI), Clinical Disease Activity Index (CDAI), Patient Activity Score (PAS) and Patient Activity Score-II (PASII), Routine Assessment of Patient Index Data (RAPID), Rheumatoid Arthritis Disease Activity Index (RADAI) and Rheumatoid Arthritis Disease Activity Index-5 (RADAI-5), Chronic Arthritis Systemic Index (CASI), Patient-Based Disease Activity Score With ESR (PDAS1) and Patient-Based Disease Activity Score Without ESR (PDAS2), and Mean Overall Index for Rheumatoid Arthritis (MOI-RA). Arthritis Care Res. (2011) 63:S14–36. doi: 10.1002/acr.20621

CrossRef Full Text | Google Scholar

25. Troiano, RP , Berrigan, D , Dodd, KW , Mâsse, LC , Tilert, T , and Mcdowell, M . Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. (2008) 40:181–8. doi: 10.1249/mss.0b013e31815a51b3

CrossRef Full Text | Google Scholar

26. Klenk, J , Dallmeier, D , Denkinger, MD , Rapp, K , Koenig, W , Rothenbacher, D, et al. Objectively measured walking duration and sedentary behaviour and four-year mortality in older people. PLoS One. (2016) 11:e0153779. doi: 10.1371/journal.pone.0153779

PubMed Abstract | CrossRef Full Text | Google Scholar

27. Chiang, SL , Heitkemper, MML , Hung, YJ , Tzeng, WC , Lee, MS , and Lin, CH . Effects of a 12-week moderate-intensity exercise training on blood glucose response in patients with type 2 diabetes: a prospective longitudinal study. Medicine. (2019) 98:e16860. doi: 10.1097/MD.0000000000016860

CrossRef Full Text | Google Scholar

28. Miki, T , Kochi, T , Eguchi, M , Kuwahara, K , Tsuruoka, H , Kurotani, K, et al. Dietary intake of minerals in relation to depressive symptoms in Japanese employees: the Furukawa nutrition and health study. Nutrition. (2015) 31:686–90. doi: 10.1016/j.nut.2014.11.002

PubMed Abstract | CrossRef Full Text | Google Scholar

29. Gianfredi, V , Buffoli, M , Rebecchi, A , Croci, R , Oradini-Alacreu, A , Stirparo, G, et al. Association between urban greenspace and health: a systematic review of literature. Int J Environ Res Public Health. (2021) 18:5137. doi: 10.3390/ijerph18105137

PubMed Abstract | CrossRef Full Text | Google Scholar

30. Loprinzi, PD . Objectively measured light and moderate-to-vigorous physical activity is associated with lower depression levels among older US adults. Aging Ment Health. (2013) 17:801–5. doi: 10.1080/13607863.2013.801066

PubMed Abstract | CrossRef Full Text | Google Scholar

31. Liu, Y , Ozodiegwu, ID , Yu, Y , Hess, R , and Bie, R . An association of health behaviors with depression and metabolic risks: data from 2007 to 2014 U.S. National Health and Nutrition Examination Survey. J Affect Disord. (2017) 217:190–6. doi: 10.1016/j.jad.2017.04.009

PubMed Abstract | CrossRef Full Text | Google Scholar

32. Teychenne, M , Ball, K , and Salmon, J . Physical activity and likelihood of depression in adults: a review. Prev Med. (2008) 46:397–411. doi: 10.1016/j.ypmed.2008.01.009

CrossRef Full Text | Google Scholar

33. Joshi, S , Mooney, SJ , Kennedy, GJ , Benjamin, EO , Ompad, D , Rundle, AG, et al. Beyond METs: types of physical activity and depression among older adults. Age Ageing. (2016) 45:103–9. doi: 10.1093/ageing/afv164

PubMed Abstract | CrossRef Full Text | Google Scholar

34. Ekkekakis, P , Hall, EE , and Petruzzello, SJ . Variation and homogeneity in affective responses to physical activity of varying intensities: an alternative perspective on dose-response based on evolutionary considerations. J Sports Sci. (2005) 23:477–500. doi: 10.1080/02640410400021492

PubMed Abstract | CrossRef Full Text | Google Scholar

35. Paluska, SA , and Schwenk, TL . Physical activity and mental health: current concepts. Sports Med. (2000) 29:167–80. doi: 10.2165/00007256-200029030-00003

CrossRef Full Text | Google Scholar