Skip to main content
  • Research article
  • Open access
  • Published:

Changes in rural older adults’ sedentary and physically-active behaviors between a non-snowfall and a snowfall season: compositional analysis from the NEIGE study



Levels of physical activity change throughout the year. However, little is known to what extent activity levels can vary, based on accelerometer determined sedentary and physically-active time. The aim of this longitudinal study was to examine older adults’ activity changes from a non-snowfall season to a subsequent snowfall season, with consideration of the co-dependence of domains of time use.


Participants were 355 older Japanese adults (53.1% women, aged 65–84 years) living in a rural area of heavy snowfall who had valid accelerometer (Active style Pro HJA-750C) data during non-snowfall and snowfall seasons. Activity was classified as sedentary behavior (SB), light-intensity PA (LPA), and moderate-to-vigorous PA (MVPA). Compositional changes from the non-snowfall to the snowfall season were analyzed using Aitchison’s perturbation method. The ratios of each component in the composition, such as [SBsnow/SBnon-snow, LPAsnow/LPAnon-snow, MVPAsnow/MVPAnon-snow] for seasonal changes, were calculated and were then divided by the sum of these ratios.


In men, the percentages of time spent in each activity during the non-snowfall/snowfall seasons were 53.9/64.6 for SB; 40.8/31.6 for LPA; and 5.3/3.8 for MVPA; these corresponded to mean seasonal compositional changes (∆SB, ∆LPA, ∆MVPA) of 0.445, 0.287, and 0.268 respectively. In women, the percentages of time spent in each activity during the non-snowfall/snowfall seasons were 47.9/55.5 for SB; 47.9/41.0 for LPA; and 4.2/3.5 for MVPA; these corresponded to mean seasonal compositional changes (∆SB, ∆LPA, ∆MVPA) of 0.409, 0.302, and 0.289 respectively. The degree of seasonal change was greatest in men.


In older adults, activity behaviors were changed unfavorably during snowfall season, particularly so for men. The degree of seasonal change was greatest for SB. Development of strategies to keep rural older adults active during the snowfall season may be needed for maintaining a consistently-active lifestyle for their health.

Peer Review reports


Global physical activity guidelines state that older adults should do at least 150 min per week of moderate-intensity physical activity, at least 75 min vigorous-intensity physical activity, or an equivalent combination of both, in bouts lasting 10 min or more [1]. Physical inactivity increases the risk of many adverse health outcomes including mortality, cardiovascular disease, type 2 diabetes, several types of cancer, and cognitive decline [2,3,4,5,6]. In recent years, there has been significant growth in research investigating the detrimental health effects of sedentary behavior (SB; put simply, too much sitting) [7, 8]. Reducing SB and increasing physical activity are key ingredients of initiatives to reduce the global burden of non-communicable diseases [2, 9].

There is a growing body of evidence identifying the importance of natural environments (the attributes of which can vary greatly by season) as well as built environments as influences on physical activity [10, 11]. One systematic review of 37 studies from eight different countries showed that levels of physical activity tended to be lowest during winter [10]. To date, however, most of these studies have relied on self-report physical activity assessments and have not focused on older adults [10]. Objective assessment (via accelerometers) can now overcome problems of recall and reporting bias and allow more accurate and finer-grained assessments of activity behavior patterns: SB, light-intensity physical activity (LPA), and moderate-to-vigorous physical activity (MVPA) [12]. There is limited evidence on how season affects device-based activity behaviors [13,14,15]. Compositional data analysis (CoDa) allows consideration of the co-dependence of time spent in all behaviors arising within a day or other fixed period [16, 17]. To date, no previous study has investigated seasonal changes of activity behaviors, taking time spent in each activity behavior into account.

We compared times spent in accelerometer-measured activity behaviors during a non-snowfall and a snowfall season in a sample of community-dwelling older Japanese adults, using the CoDa approach. We also explored which activity behaviors were most affected by season.


Study sample and data collection

We used longitudinal data from the Neuron to Environmental Impact across Generations study (NEIGE study). The methods of this study are described in previous study [18]. Participants were older adults without long-term care living in Tokamachi city, Japan. Tokamachi is a rural city, officially registered as a heavy snowfall region during winter, located in the southernmost region of Niigata Prefecture. Participants were from two areas: Matsunoyama (mountain) area or Tokamachi area (downtown). The mountain area (2.89 m maximum snow depth) had more snow than the downtown area (1.97 maximum snow depth) during winter [18]. The average temperature at Tokamachi city in February 2018 was − 0.9 °C (lowest − 8.3 °C, highest 9.1 °C) [19].

Briefly, in 2017, a total of 1346 residents were recruited from a resident registry, using stratified random sampling. In the non-snowfall season (autumn) of 2017, we conducted a questionnaire survey and health examination of 527 older adults who agreed to enrollment in the NEIGE study; at the same time, they were asked to wear an accelerometer. Of these participants, 381 agreed to also wear an accelerometer during the snowfall season (winter) of 2018.

Accelerometer-measured activity behaviors

Habitual time spent in activity behaviors were evaluated by Active style Pro HJA-750C (Omron Healthcare, Kyoto, Japan). Active style Pro is a validated accelerometer [20,21,22,23] and comparable to the devices most commonly used in studies conducted in Western countries [24, 25]. Its measurement algorithm has been explained in detail elsewhere [20, 21]. Participants were instructed to wear an accelerometer over the waist on an elasticated belt for seven consecutive days (except during sleep and water-based activities) during snowfall and non-snowfall season, respectively. In the survey during snowfall season, participants were mailed an accelerometer. No acceleration signal detected for longer than 60 consecutive minutes was defined as “non-wear”. Participants with a wear time corresponding to at least 10 h during waking time per day [26], collected over four or more valid days were included in the analysis [27]. The data were collected in 60-s epochs. Activity behaviors were classified into three intensity categories based on metabolic equivalents (METs): SB (≤1.5 METs), LPA (1.6–2.9 METs), and MVPA (≥3.0 METs) [28, 29].

Sociodemographic, biological, and psychological factors

Participants reported their age, gender, living arrangement (with others/ alone), and self-rated health (very good/ good/ fair/ poor) in autumn. We classified participants between the ages of 65 and 74 years as young-old, and those between ages 75 and 84 years as old-old. Body mass index (BMI) was calculated from height and weight (kg/m2) measured by body composition analyzer MC-780A (TANITA corporation, Tokyo, Japan).

Statistical analyses

All analyses were performed using R version 3.5.2 (R Foundation for Statistical Computing, Vienna, Austria). We used R package “compositions” for CoDa approach. For all analyses, p-values <.05 were considered statistically significant. Analyses were applied stratified by gender since activity behavior patterns were significantly different between men and women.

The Chi-Square test or t test was performed to compare participant characteristics. McNemar’s test was used to compare non-snowfall and snowfall season’s proportions of those adhering to physical activity guidelines (i.e. ≥150 min/week of MVPA in bouts of at least 10 min) [1]. A 10 min-bout MVPA was defined as 10 or more consecutive minutes above the moderate intensity threshold, with the allowance of 1–2 min interruption intervals [27].

We described activity behaviors during the non-snowfall and snowfall season using CoDa approach. As all participants spent some time in every behavior there was no need for a method to deal with zeros. Compositional changes [∆SB, ∆LPA, ∆MVPA] were then determined by Aitchison’s perturbation method [30,31,32]. The ratios of each component in the composition, such as [SBsnow/SBnon-snow, LPAsnow/LPAnon-snow, MVPAsnow/MVPAnon-snow] for seasonal changes, were calculated and were then divided by the sum of these ratios. An equal composition of these three activities in the non-snowfall and snowfall seasons would result in a compositional change of [1/3, 1/3, 1/3]. Compositional changes were plotted as ternary diagrams, with some significant guide values illustrated.


Participant enrollment and characteristics

Of the 381 older adults who agreed to wear an accelerometer during both the non-snowfall and snowfall seasons (response rate: 28.3%), 26 were excluded for; not meeting wearing time criteria (n = 23), bone fracture (n = 1), unreturned accelerometer (n = 1) and malfunction (n = 1). The analytic sample was 355 who had valid accelerometer data.

When compared to participant characteristics between those who had valid accelerometer data in snowfall season and those who did not, a significant difference was found in age groups (young-old: 73.7%, old-old: 59.8%, Chi-Square test). Participants who participated in the survey in the snowfall season were significantly more physically active (approx. 10 more minutes of MVPA per day) than those who did not (t-test).

Table 1 presents the characteristics of the participants. The mean (SD) age was 72.9 (5.4) years (52.4% women) and most of the participants were living with others, < 25.0 kg/m2 BMI, and good perceived health. Compared to women, men were more likely to be from the mountain area, be living with others and to have attained ≥150 min/week of MVPA. There were no significant seasonal differences in proportion of those adhering to global physical activity guidelines (non-snowfall season: overall 24.2%, men 33.1%, women 16.1%, snowfall season: overall 21.7%, men 27.2%, women: 16.7%).

Table 1 Participant’s characteristics at baseline (non-snowfall season)

Activity behaviors in snowfall and non-snowfall season

Mean accelerometer wear time was 891.7 min/day in non-snowfall season and 886.8 min/day in snowfall season. Older adults spent most of their time on SB and LPA. Table 2 presents the descriptive statistics of time spent in each activity behavior. In men, time spent in each activity (SB, LPA, and MVPA) was 53.9, 40.8, and 5.3%, respectively during the non-snowfall season, and 64.6, 31.6, and 3.8%, respectively during the snowfall season, corresponding to mean seasonal change of 0.445 for SB, 0.287 for LPA, and 0.268 for MVPA (Fig. 1). Since larger distance from 1/3 indicates greater change in time spent in activity, the largest seasonal change was observed in SB, compared to LPA and MVPA. In women, time spent in each activity was 47.9, 47.9, and 4.2%, respectively during non-snowfall season while that was 55.5, 41.0, and 3.5%, respectively during snowfall season, corresponding to mean seasonal change of 0.409 for SB, 0.302 for LPA, and 0.289 for MVPA. Significant unfavorable seasonal changes in activity behaviors were observed in both men and women, but the degree of change was larger in men in every behavior if we contrast the changes (the ratios) between men and women. Similar findings were observed after stratification by residential area, but the degree of change in activity behaviors was larger in mountain area than in downtown, regardless of gender.

Table 2 Compositional (geometric) means of time spent in activity behaviors during the non-snowfall and snowfall season
Fig. 1
figure 1

Changes in sedentary behavior (SB), light intensity physical activity (LPA) and in moderate-to-vigorous physical activity (MVPA) from the non-snowfall to snowfall season in rural-dwelling older Japanese adults. a Stratified by gender (blue: men, red: women), b Stratified by gender and residential area (blue: mountain area, green: downtown). Abbreviation: SB; sedentary behavior, LPA; light-intensity physical activity, MVPA; moderate-to-vigorous physical activity


We identified seasonal differences of accelerometer determined activity behaviors in community-dwelling older adults in a rural snowfall area, using the CoDa approach. Older adults had more unfavorable activity behavior patterns during the snowfall season compared to non-snowfall season, with the magnitude of these differences greater in men. No significant seasonal differences were found in the proportions adhering to global physical activity guidelines.

Our findings are consistent with previous studies using self-report [33, 34] and pedometer/ accelerometer [13,14,15] data, where levels of adults’ physical activity during winter was less than the other seasons. A study in Iceland found that both older men and women were more active during summer than winter, with less time spent in accelerometer determined SB (4.4% in men, 2.5% in women) [14]. A study in the UK found that only time spent in LPA was significantly higher during spring than that during winter among older adults (winter: 2.7 ± 1.0 h/day, spring: 3.4 ± 1.3 h/day, summer: 3.1 ± 1.3 h/day, and autumn: 3.1 ± 1.2 h/day) [13]. We found that the magnitude of decline during winter seems to be large, compared to these Icelandic and UK studies. This may result from winter conditions, including amount of snowfall. Furthermore, our study participants were relatively more active and less sedentary during autumn, the non-snowfall season [35]. More highly active participants may experience activity decline to a greater degree, compared to low activity participants. In this sample, there were no significant seasonal changes of adherence to global physical activity guidelines. This may be due to very little time spent in MVPA in bouts of more than 10 min.

Gender differences of activity patterns were in line with our previous findings [35, 36] that Japanese older women were more physically active than older men, provided that all activity behaviors were assessed. In this sample, men experienced greater seasonal activity declines than did women. In Japan, women are more responsible for household chores and thus engage in a certain amount of activities throughout the year. There may be needed to develop a strategy to combat this seasonal decrease in physical activity for men.

In the current study, activity behavior patterns were unfavorably changed, with approximately 20% increase in SB and decrease in MVPA. The degree of the change in activity behaviors may be significant for health. A previous study found decline of physical activity during winter unfavorably affected the physical performance level after one year in community-dwelling older women, particularly its effect on maximum walking speed [37]. Given that the rate of muscle mass decline is higher in older adults compared to middle-aged adults [38], maintaining physical activity may be required to keep physical ability and prevent a negative cycle of frailty [39]. Another study showed short-term decreased physical activity with increased SB caused impairment of metabolism [40] which increases risk of cardiovascular disease [41]. Therefore, promoting physical activity during winter may be one way to tackle age-related diseases and loss of physical functioning.

Strengths and limitations

Strengths of this study included objective assessment of activity behaviors and a novel statistical approach. Even though there has been increasing research using objective methods [13,14,15, 42], no study has treated with consideration to the co-dependence of time spent in activity behaviors. Moreover, we provided results from a seldom-studied elderly population in a rural snowfall area. However, several limitations should be considered to interpret the findings. We may under/overestimate of SB and LPA since Active style Pro cannot provide postural information. Also, some activities (e.g. snow removal and skiing) may be not captured accurately. Second, we may underestimate the decline of activity level since most of the days that participants wore an accelerometer were relatively good weather in spite of heavy snowfall area. It has previously been observed that longer day length is associated with increased device-based physical activity in the older population [42, 43]. Third, as Tokamachi city is a rural area where heavy snowfall is common during winter, it is not necessarily representative of Japanese rural areas with different climates. People in areas with less snowfall may experience a smaller decline of physical activity during snowfall season. More research is needed in the different climate zones from different geographic areas. Finally, selection bias may have occurred. In general, accelerometry respondents have been more active and healthier than non-participating older adults [44]. In this study, those had valid accelerometer data in snowfall season were more physically active than those who did not.

Implications for future research, policy and practice

Our findings suggest several implications in terms of both development of interventions to protect against seasonal physical activity decline and physical activity surveillance/ monitoring. Further research regarding how to stay active during winter may be required for health promotion, particularly in regions that experience long winters and with severe weather (e.g. heavy snow). Given that approximately 60% of the land in Japan has snow and a cold winter and that a quarter of the population lives in those areas [45], leading an active lifestyle during winter potentially has a significant public health impact.

Since SB is the most affected by season, it may be better to focus on developing strategies to reduce time spent in sitting. Older adults might be particularly influenced by seasonal outside conditions due to reduced physical functioning and mobility, and spend much of their time indoors. It thus may be effective to provide supplementary resources for indoor activities (e.g. gymnastic exercise programs), sharing of household chores, and making educational/ instrumental supports for safe snow removal [46]. Further approach includes replacing mentally-passive with mentally-active SB may be effective for health, particularly for preventing cognitive decline [47, 48] and depression [49, 50].

As for physical activity surveillance/ monitoring, investigators should be aware of the potential for under- or overestimation of levels of activity especially when the interest is in its between-individual variation, including community-level and country-level comparisons. Seasonality also should be considered when intervention studies are performed.


Accelerometer determined activity behaviors were greatly affected by season in community-dwelling older adults in a rural snowfall area, resulting in unfavorable changes, particularly in SB time, during snowfall season. Development of strategies to keep rural older adults active during the snowfall season may be needed for maintaining a consistently-active lifestyle for their health.

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.



Sedentary behavior


Light-intensity physical activity


Moderate-to-vigorous physical activity


Compositional data analysis


Metabolic equivalents


  1. World Health Organization. Global recommendations on physical activity for health. 2010. Accessed 15 Jan 2020.

  2. Lee IM, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012;380:219–29.

    PubMed  PubMed Central  Google Scholar 

  3. Kyu HH, Bachman VF, Alexander LT, Mumford JE, Afshin A, Estep K, Veerman JL, Delwiche K, Iannarone ML, Moyer ML, et al. Physical activity and risk of breast cancer, colon cancer, diabetes, ischemic heart disease, and ischemic stroke events: systematic review and dose-response meta-analysis for the global burden of disease study 2013. BMJ. 2016;354:i3857.

    PubMed  PubMed Central  Google Scholar 

  4. Aune D, Norat T, Leitzmann M, Tonstad S, Vatten LJ. Physical activity and the risk of type 2 diabetes: a systematic review and dose-response meta-analysis. Eur J Epidemiol. 2015;30:529–42.

    PubMed  Google Scholar 

  5. Arem H, Moore SC, Patel A, Hartge P, Berrington de Gonzalez A, Visvanathan K, Campbell PT, Freedman M, Weiderpass E, Adami HO, et al. Leisure time physical activity and mortality: a detailed pooled analysis of the dose-response relationship. JAMA Intern Med. 2015;175:959–67.

    PubMed  PubMed Central  Google Scholar 

  6. Sofi F, Valecchi D, Bacci D, Abbate R, Gensini GF, Casini A, Macchi C. Physical activity and risk of cognitive decline: a meta-analysis of prospective studies. J Intern Med. 2011;269:107–17.

    CAS  PubMed  Google Scholar 

  7. Young DR, Hivert MF, Alhassan S, Camhi SM, Ferguson JF, Katzmarzyk PT, Lewis CE, Owen N, Perry CK, Siddique J, Yong CM. Sedentary behavior and cardiovascular morbidity and mortality: a science advisory from the American Heart Association. Circulation. 2016;134:13.

    Google Scholar 

  8. Biswas A, Oh PI, Faulkner GE, Bajaj RR, Silver MA, Mitchell MS, Alter DA. Sedentary time and its association with risk for disease incidence, mortality, and hospitalization in adults: a systematic review and meta-analysis. Ann Intern Med. 2015;162:123–32.

    Google Scholar 

  9. Ding D, Lawson KD, Kolbe-Alexander TL, Finkelstein EA, Katzmarzyk PT, van Mechelen W, Pratt M. The economic burden of physical inactivity: a global analysis of major non-communicable diseases. Lancet. 2016;388:1311–24.

    PubMed  Google Scholar 

  10. Tucker P, Gilliland J. The effect of season and weather on physical activity: a systematic review. Public Health. 2007;121:909–22.

    CAS  PubMed  Google Scholar 

  11. Barnett DW, Barnett A, Nathan A, Van Cauwenberg J, Cerin E. Built environmental correlates of older adults' total physical activity and walking: a systematic review and meta-analysis. Int J Behav Nutr Phys Act. 2017;14:103.

    PubMed  PubMed Central  Google Scholar 

  12. Shephard RJ, Tudor-Locke C. The Objective Monitoring of Physical Activity: Contributions of Accelerometry to Epidemiology, Exercise Science and Rehabilitation. New York: Springer International Publishing; 2016.

  13. Davis MG, Fox KR, Hillsdon M, Sharp DJ, Coulson JC, Thompson JL. Objectively measured physical activity in a diverse sample of older urban UK adults. Med Sci Sports Exerc. 2011;43:647–54.

    PubMed  Google Scholar 

  14. Arnardottir NY, Oskarsdottir ND, Brychta RJ, Koster A, van Domelen DR, Caserotti P, Eiriksdottir G, Sverrisdottir JE, Johannsson E, Launer LJ, et al. Comparison of summer and winter objectively measured physical activity and sedentary behavior in older adults: age, gene/environment susceptibility Reykjavik study. Int J Environ Res Public Health. 2017;14:1268.

  15. Yasunaga A, Togo F, Watanabe E, Park H, Park S, Shephard RJ, Aoyagi Y. Sex, age, season, and habitual physical activity of older Japanese: the Nakanojo study. J Aging Phys Act. 2008;16:3–13.

    PubMed  Google Scholar 

  16. Chastin SF, Palarea-Albaladejo J, Dontje ML, Skelton DA. Combined effects of time spent in physical activity, Sedentary Behaviors and Sleep on Obesity and Cardio-Metabolic Health Markers: A Novel Compositional Data Analysis Approach. PLoS One. 2015;10:e0139984.

    PubMed  PubMed Central  Google Scholar 

  17. Dumuid D, Stanford TE, Martin-Fernandez JA, Pedisic Z, Maher CA, Lewis LK, Hron K, Katzmarzyk PT, Chaput JP, Fogelholm M, et al. Compositional data analysis for physical activity, sedentary time and sleep research. Stat Methods Med Res. 2018;27:3726–38.

    PubMed  Google Scholar 

  18. Shobugawa Y, Murayama H, Fujiwara T, Inoue S. Cohort profile of the NEIGE study in Tokamachi city, Japan. J Epidemiol. 2020;30(7):281–7.

    PubMed  PubMed Central  Google Scholar 

  19. Japan Meteorological Agency. Historical weather data search. Accessed 20 Jul 2020.

  20. Ohkawara K, Oshima Y, Hikihara Y, Ishikawa-Takata K, Tabata I, Tanaka S. Real-time estimation of daily physical activity intensity by a triaxial accelerometer and a gravity-removal classification algorithm. Br J Nutr. 2011;105:1681–91.

    CAS  PubMed  Google Scholar 

  21. Oshima Y, Kawaguchi K, Tanaka S, Ohkawara K, Hikihara Y, Ishikawa-Takata K, Tabata I. Classifying household and locomotive activities using a triaxial accelerometer. Gait Posture. 2010;31:370–4.

    PubMed  Google Scholar 

  22. Park J, Ishikawa-Takata K, Tanaka S, Bessyo K, Tanaka S, Kimura T. Accuracy of estimating step counts and intensity using accelerometers in older people with or without assistive devices. J Aging Phys Act. 2017;25:41–50.

    PubMed  Google Scholar 

  23. Nagayoshi S, Oshima Y, Ando T, Aoyama T, Nakae S, Usui C, Kumagai S, Tanaka S. Validity of estimating physical activity intensity using a triaxial accelerometer in healthy adults and older adults. BMJ Open Sport Exerc Med. 2019;5:e000592.

    PubMed  PubMed Central  Google Scholar 

  24. Kurita S, Yano S, Ishii K, Shibata A, Sasai H, Nakata Y, Fukushima N, Inoue S, Tanaka S, Sugiyama T, et al. Comparability of activity monitors used in Asian and Western-country studies for assessing free-living sedentary behaviour. PLoS One. 2017;12:e0186523.

    PubMed  PubMed Central  Google Scholar 

  25. Murakami H, Kawakami R, Nakae S, Nakata Y, Ishikawa-Takata K, Tanaka S, Miyachi M. Accuracy of wearable devices for estimating Total energy expenditure: comparison with metabolic chamber and doubly labeled water method. JAMA Intern Med. 2016;176:702–3.

    PubMed  Google Scholar 

  26. Tudor-Locke C, Camhi SM, Troiano RP. A catalog of rules, variables, and definitions applied to accelerometer data in the National Health and nutrition examination survey, 2003-2006. Prev Chronic Dis. 2012;9:E113.

    PubMed  PubMed Central  Google Scholar 

  27. Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40:181–8.

    PubMed  Google Scholar 

  28. Haskell WL, Lee IM, Pate RR, Powell KE, Blair SN, Franklin BA, Macera CA, Heath GW, Thompson PD, Bauman A. Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Circulation. 2007;116:1081–93.

    PubMed  Google Scholar 

  29. Pate RR, O'Neill JR, Lobelo F. The evolving definition of “sedentary”. Exerc Sport Sci Rev. 2008;36:173–8.

    PubMed  Google Scholar 

  30. Aitchison J, Ng KW. The role of perturbation in compositional data analysis. Stat Model. 2005;5:173–85.

    Google Scholar 

  31. Boogaart KG, Tolosana-Delgado R. Analyzing Compositional Data with R. Berlin: Springer-Verlag Berlin Heidelberg; 2013.

  32. Winkler EAH, Chastin S, Eakin EG, Owen N, Lamontagne AD, Moodie M, Dempsey PC, Kingwell BA, Dunstan DW, Healy GN. Cardiometabolic impact of changing sitting, standing, and stepping in the workplace. Med Sci Sports Exerc. 2018;50:516–24.

    PubMed  Google Scholar 

  33. Uitenbroek DG. Seasonal variation in leisure time physical activity. Med Sci Sports Exerc. 1993;25:755–60.

    CAS  PubMed  Google Scholar 

  34. Matthews CE, Freedson PS, Hebert JR, Stanek EJ 3rd, Merriam PA, Rosal MC, Ebbeling CB, Ockene IS. Seasonal variation in household, occupational, and leisure time physical activity: longitudinal analyses from the seasonal variation of blood cholesterol study. Am J Epidemiol. 2001;153:172–83.

    CAS  PubMed  Google Scholar 

  35. Amagasa S, Fukushima N, Kikuchi H, Takamiya T, Oka K, Inoue S. Light and sporadic physical activity overlooked by current guidelines makes older women more active than older men. Int J Behav Nutr Phys Act. 2017;14:59.

    PubMed  PubMed Central  Google Scholar 

  36. Amagasa S, Inoue S, Ukawa S, Sasaki S, Nakamura K, Yoshimura A, Tanaka A, Kimura T, Nakagawa T, Imae A, Ding D, Kikuchi H, Tamakoshi A. Are Japanese women less physically active than men? Findings from the DOSANCO health study. J Epidemiol. 2020.

  37. Mizumoto A, Ihira H, Makino K, Saitoh S, Ohnishi H, Furuna T. Physical activity during winter in old-old women associated with physical performance after one year: a prospective study. Curr Gerontol Geriatr Res. 2015;253451:5.

  38. Volpi E, Nazemi R, Fujita S. Muscle tissue changes with aging. Curr Opin Clin Nutr Metab Care. 2004;7:405–10.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, Seeman T, Tracy R, Kop WJ, Burke G, McBurnie MA. Frailty in older adults: evidence for a phenotype. J Gerontol: Series A. 2001;56:M146–57.

    CAS  Google Scholar 

  40. Bowden Davies KA, Sprung VS, Norman JA, Thompson A, Mitchell KL, Halford JCG, Harrold JA, Wilding JPH, Kemp GJ, Cuthbertson DJ. Short-term decreased physical activity with increased sedentary behaviour causes metabolic derangements and altered body composition: effects in individuals with and without a first-degree relative with type 2 diabetes. Diabetologia. 2018;61:1282–94.

    PubMed  Google Scholar 

  41. Ormazabal V, Nair S, Elfeky O, Aguayo C, Salomon C, Zuñiga FA. Association between insulin resistance and the development of cardiovascular disease. Cardiovasc Diabetol. 2018;17:122.

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Wu YT, Luben R, Wareham N, Griffin S, Jones AP. Weather, day length and physical activity in older adults: cross-sectional results from the European prospective investigation into Cancer and nutrition (EPIC) Norfolk cohort. PLoS One. 2017;12:e0177767.

    PubMed  PubMed Central  Google Scholar 

  43. Schepps MA, Shiroma EJ, Kamada M, Harris TB, Lee IM. Day length is associated with physical activity and sedentary behavior among older women. Sci Rep. 2018;8:6602.

    PubMed  PubMed Central  Google Scholar 

  44. Inoue S, Ohya Y, Odagiri Y, Takamiya T, Kamada M, Okada S, Tudor-Locke C, Shimomitsu T. Characteristics of accelerometry respondents to a mail-based surveillance study. J Epidemiol. 2010;20:446–52.

    PubMed  PubMed Central  Google Scholar 

  45. Ministry of Land, Infrastructure, Transport and Tourism. Snow disaster prevention Accessed 20 Jan 2020.

  46. Chastin SF, De Craemer M, Lien N, Bernaards C, Buck C, Oppert JM, Nazare JA, Lakerveld J, O'Donoghue G, Holdsworth M, et al. The SOS-framework (Systems of Sedentary behaviours): an international transdisciplinary consensus framework for the study of determinants, research priorities and policy on sedentary behaviour across the life course: a DEDIPAC-study. Int J Behav Nutr Phys Act. 2016;13:83.

    PubMed  PubMed Central  Google Scholar 

  47. Kesse-Guyot E, Charreire H, Andreeva VA, Touvier M, Hercberg S, Galan P, Oppert JM. Cross-sectional and longitudinal associations of different sedentary behaviors with cognitive performance in older adults. PLoS One. 2012;7:e47831.

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Bakrania K, Edwardson CL, Khunti K, Bandelow S, Davies MJ, Yates T. Associations between sedentary behaviors and cognitive function: cross-sectional and prospective findings from the UK biobank. Am J Epidemiol. 2018;187:441–54.

    PubMed  Google Scholar 

  49. Hallgren M, Owen N, Stubbs B, Zeebari Z, Vancampfort D, Schuch F, Bellocco R, Dunstan D, Trolle Lagerros Y. Passive and mentally-active sedentary behaviors and incident major depressive disorder: a 13-year cohort study. J Affect Disord. 2018;241:579–85.

    PubMed  Google Scholar 

  50. Hallgren M, Nguyen TT, Owen N, Stubbs B, Vancampfort D, Lundin A, Dunstan D, Bellocco R, Lagerros YT. Cross-sectional and prospective relationships of passive and mentally active sedentary behaviours and physical activity with depression. Br J Psychiatry. 2019;21:1–7.

    Google Scholar 

Download references


We thank the city workers and the participants for their time and effort in the NEIGE study.


This study was funded by grant from ‘the Policy Research Institute, Ministry of Agriculture, Forestry and Fisheries in Japan’ and ‘the Pfizer Health Research Foundation’, and by JSPS KAKENHI Grant Number 16H03249, 17 K19794, 18 K10829, and 19H03910. The funding bodies were not involved in any portion of the study design, data collection and analysis, interpretation of the results, and preparation of the manuscript.

Shiho Amagasa is supported by JSPS Research Fellowships for Young Scientists. Neville Owen is supported by a National Health and Medical Research Council of Australia (NHMRC) Centre of Research Excellence Grant (#1057608), NHMRC Senior Principal Research Fellowship (#1118225), and the Victorian Government’s Operational Infrastructure Support Program.

Author information

Authors and Affiliations



YS, HM, SI, and TF developed the NEIGE study. YS, HM, SI, TF, HK, NF, MM, and SA collected the data. SA performed the analysis and prepared the manuscript. SC and NO gave critical feedback and revised the manuscript. All authors advised on the data analysis and interpretation and reviewed the manuscript.

Corresponding author

Correspondence to Shigeru Inoue.

Ethics declarations

Ethics approval and consent to participate

The University Ethics Committees (Niigata University and Tokyo Medical University) granted ethical approval. Written informed consent was obtained from all participants.

Consent for publication


Competing interests

The authors declare no conflict of interest.

Additional information

Publisher’s Note

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

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Amagasa, S., Inoue, S., Murayama, H. et al. Changes in rural older adults’ sedentary and physically-active behaviors between a non-snowfall and a snowfall season: compositional analysis from the NEIGE study. BMC Public Health 20, 1248 (2020).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: