Association of Lifestyle Activities with Daily Physical Activity Timing in Community-Dwelling Older Adults: A longitudinal observational study

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It is plausible that the benefits of afternoon activity may partly reflect increased social activity among lifestyle activities. We tested the hypothesis that social activity specifically is associated with greater physical activity in the afternoon among lifestyle activities. Methods In this longitudinal observational study, 2,578 community-dwelling adults aged 65 years and older from the National Center for Geriatrics and Gerontology—Study of Geriatric Syndromes cohort completed a lifestyle activities questionnaire at baseline, which yielded scores in cognitive, physical, and social domains. Participants wore accelerometers for at least seven valid days (≥ 10 h/day), and mean steps were calculated for eight three‐hour spans over 24 hours. Correlation analyses were also conducted to explore relationships among the three lifestyle-activity domains. We applied function‐on‐scalar regression models to examine the association between each activity score and the timing of daily steps, adjusting for demographic, health, and behavioral covariates. Stratified analyses by age group, sex, and total daily step counts were conducted, along with a sensitivity analysis restricted to daytime hours. Results Among the 2,578 participants (mean age 70.7 years, 57% women), weak but positive correlations among cognitive, physical, and social activity scores were observed. Higher cognitive activity scores were associated with fewer steps in the early morning; physical activity scores were positively associated with steps from mid-morning to early afternoon; and social activity scores showed modest positive associations with steps throughout the day, especially in the afternoon. These patterns were consistent across age, sex, and daily-step-count groups, and they were also supported by the sensitivity analysis; however, associations between social activity and step counts were not statistically significant among men nor among adults aged over 75 years. Conclusions Associations between lifestyle activities and timing of daily steps vary by activity type and time of day: cognitive activities relate to fewer morning steps, physical activities relate to a morning peak, and social activities relate to sustainably more afternoon steps. These findings suggest that the previously reported association between afternoon physical activity and favorable health outcomes may partly stem from increased social activity. Circadian rhythm Diurnal steps Older adults Objectively measured physical activity Functional data analysis Social activity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Physical activity is fundamental to maintaining health in older adults. Regular physical activity contributes significantly to the prevention of chronic diseases, supports physical and cognitive function, and enhances overall well-being and quality of life [ 1 – 3 ]. Accordingly, promoting and sustaining physical activity is considered a cornerstone of healthy aging. Recently, the timing of physical activity has emerged as an important factor influencing health outcomes among older adults. Specifically, evidence indicates that engaging in afternoon physical activities is associated with lower incidence rates of physical frailty among older populations [ 6 ]. Similarly, physical activities conducted in the afternoon are particularly beneficial, with associations observed between afternoon activity and reduced risks of all-cause and cardiovascular mortality [ 4 , 5 ]. Understanding optimal timing is highly relevant from a public health perspective, so as to maximize the benefits of physical activity interventions in older populations. However, the mechanisms underlying the benefits of afternoon physical activity remain unclear. Lifestyle activities—including physical (sports and exercise), cognitive, and social activities—are known to influence health trajectories in older adults. Stephan et al. reported that these three types of activities each uniquely mediate various health outcomes [ 11 ]. However, it remains unclear which types of lifestyle activities specifically correlate with afternoon physical activity among older adults. Empirical findings from Weber et al. highlighted greater diversity in social activities during the afternoon among older adults [ 7 ]. Given that social participation is both fundamental to health maintenance [ 8 ] and is positively associated with physical activity levels [ 9 , 10 ], it is plausible that the observed benefits of afternoon physical activity might partially stem from increased social activity. Nonetheless, the potential confounding effect of social activity has not been thoroughly considered in previous studies. Given the above, this study aimed to investigate associations of lifestyle activities—physical, cognitive, and social—with the timing of daily physical activity in community-dwelling older adults. We hypothesized that social activities significantly associate with engagement in afternoon physical activity. Methods Design Figure 1 presents an overview of the study design. This longitudinal observational study assessed the timing of objectively-measured physical activity as an outcome. The exposure comprised physical, cognitive, and social activity scores derived from a lifestyle activities questionnaire [ 12 ], which was measured at the baseline assessment. This study followed the STROBE guidelines for reporting observational studies (Supplementary Table S1 ) [ 13 ]. Participants The study size was determined by the availability of sample participants rather than by a power calculation. In detail, participants from the National Center for Geriatrics and Gerontology—Study of Geriatric Syndromes (NCGG-SGS) cohort were included in this study. The NCGG-SGS cohort study sought to establish a screening system for geriatric syndromes and validate evidence-based interventions for their prevention in Japan [ 14 ]. Our study enrolled participants from Takahama City from September 10, 2015, to September 11, 2017 (n = 4,167). The exclusion criteria were participants who: (1) had not completed the responses on the lifestyle activities questionnaire at the baseline (n = 87), or (2) had no valid records of physical activity (n = 1,502). In total, 2,578 participants were included in this study. Exposure Lifestyle activities were assessed at baseline using the Lifestyle Activities Questionnaire [ 12 ], a self-administered instrument designed to evaluate the frequency of engagement in 36 daily activities across the physical, cognitive, and social domains over the previous 12 months. The activities in the physical domain included walking, bicycling, jogging, swimming, strength training, yoga, gymnastics, dancing, hiking, playing golf, playing ground golf, and participating in ball sports. The activities in the cognitive domain included writing letters or keeping a diary, reading books, reading magazines or newspapers, learning activities, using a computer (including Internet use), doing crossword puzzles, playing board games (e.g., card games, Go, or Japanese chess), playing musical instruments, doing handicrafts, listening to music, appreciating art, and engaging in financial investment such as stock trading. The activities in the social domain included serving as an officer of a senior club or neighborhood association, attending regional events, participating in environmental beautification activities, teaching activities, supporting others (including older adults or children), working (including paid employment), going to karaoke, eating out or having tea with friends, shopping with friends, talking with friends or neighbors (including phone conversations), attending events or concerts, and traveling. For each item, participants reported how often they engaged in the activity using a 6-point scale: 0 = not at all, 1 = less than once a month, 2 = several times a month, 3 = 1–2 times per week, 4 = 3–6 times per week, and 5 = every day. Scores for each domain were calculated by summing the item responses; this yielded physical, cognitive, and social activity scores ranging from 0 to 60, with higher scores indicating greater engagement. Outcome The outcome was defined as the mean number of steps measured in three-hour span over a 24-hour period (specifically, 0–3, 3–6, 6–9, 9–12, 12–15, 15–18, 18–21, and 21–24 hours) during the month following the baseline assessment, assessed using objective physical activity data. A triaxial accelerometer (HW-100, Kao Corp., Tokyo, Japan) was used to monitor participants’ physical activity duration. These devices share the same sensor and measure physical activity in 4-second spans using an identical algorithm. At the end of the baseline assessment, participants who wished to receive a physical activity tracker were given one and were instructed to wear the accelerometer on their waist, removing it only for bathing or sleeping. In line with prior studies [ 15 ], accelerometer data were considered valid if participants wore the device for a minimum of 10 hours per day for at least 7 days per month. Potential confounders The covariates included the continuous variables of age (years), years of education, body mass index (kg/m²), number of medications taken per day, gait speed (m/s), Mini-Mental State Examination score [ 16 ], Geriatric Depression Scale score [ 17 ], sleep duration (hours), and awake time in bed. The categorical variables included sex (men or women), hearing impairment (yes or no), current alcohol consumption (yes or no), current smoking status (current or non-current), living arrangement (living alone: yes or no), and meal frequency (three times per day vs. other). Missing value imputation A total of 166 missing values (0.11%) were imputed using the missRanger package (2.4.0) [ 18 ]. This package employs a random forest algorithm that is trained on the observed data to predict continuous and categorical missing values. All variables—i.e., the outcome, exposure, and covariates—were included in the imputation, with the model using 1,000 trees. Statistical analysis Continuous variables are presented as mean ± standard deviation (SD), and categorical variables are presented as counts (percentages). All analyses were conducted in R (version 4.4.2). Statistical significance was set at a two-sided p -value < 0.05. Correlation Analysis To explore pairwise relationships among the three lifestyle activity domains, we created a scatterplot matrix using physical, cognitive, and social activity scores. The matrix included scatterplots, histograms, and correlation coefficients to visualize and quantify these associations. Spearman’s rank correlation coefficients were computed to assess monotonic relationships among the three scores. Function-on-Scalar Regression Analysis To examine the associations between lifestyle activity domains and objectively-measured physical activity, we employed function-on-scalar regression using generalized additive models [ 19 ]. The dependent variable, treated as a functional outcome, was the number of steps taken within each three-hour span across a 24-hour day. Explanatory variables included physical activity, cognitive activity, and social activity scores assessed at baseline, along with the above-mentioned covariates. Smoothing splines were applied to model the interaction between each explanatory variable and time span. Coefficient functions and the corresponding 95% confidence intervals were estimated to visualize the dynamic associations throughout the day. Subgroup and Sensitivity Analyses To assess potential effect modification, stratified function-on-scalar regression models were conducted according to age group (< 75 vs. ≥75 years), sex (men vs. women), and baseline physical activity level (< 6,000 vs. ≥6,000 steps/day) [ 20 ]. The age stratification (< 75 vs. ≥75 years) corresponds to the classification of “early-stage elderly” and “late-stage elderly” commonly used in Japan’s medical care system. Each subgroup analysis maintained the same model structure and covariates as the primary analysis. A sensitivity analysis was also performed to mitigate potential data reliability concerns for early-morning and late-night periods; specifically, this analysis restricted the outcome to the six time spans between 6:00 and 21:00, and the same function-on-scalar regression model was applied with identical covariate adjustments. Results Recruitment and baseline characteristics Table 1 summarizes the baseline characteristics. Continuous variables are presented as mean ± standard deviation (SD), and categorical variables are shown as frequency and percentage (n [%]). Among the 2,578 participants, the mean age was 70.7 ± 6.5 years, and 1,474 (57.0%) were women. Participants had an average of 11.4 ± 2.4 years of education, slept 6.9 ± 1.2 hours per night, and woke up at an average time corresponding to 5.8 ± 1.2 hours (i.e., approximately 5:48 AM). A total of 2,464 participants (96.0%) reported eating three meals a day. The mean scores for physical activity, cognitive activity, and social activity were 7.9 ± 6.0, 15.0 ± 7.0, and 11.0 ± 6.0, respectively. The cohort also recorded an average of 6,457 ± 3,096 steps per day. Table 1 Baseline Characteristics of the Participants Characteristic N = 2,578 1 Age, years 70.7 (6.5) Women, n(%) 1,474 (57%) Physical activity score 7.9 (6.0) Cognitive activity score 15 (7) Social activity score 11 (6) Daily steps, steps 6,457 (3,096) Educational years, years 11.4 (2.4) Sleep duration, hours 6.9 (1.2) Awake time, hour 5.8 (1.2) N of meals, n(%) Three times 2,464 (96%) Other 114 (4.4%) Body mass index, kg/m² 23.5 (3.2) Daily medication use, n 2.7 (2.6) Gait speed, m/s 1.1 (0.2) MMSE score 27.5 (2.4) GDS, score 2.8 (2.6) Hearing problem, n(%) 765 (30%) Current drinking, n(%) 913 (35%) Current smoking, n(%) 218 (8.5%) Living alone, n(%) 244 (9.5%) 1 Data are presented as mean (standard deviation) for continuous variables, or number (%) for categorical variables. Abbreviations: GDS = Geriatric Depression Scale; MMSE = Mini-Mental State Examination. Correlations among lifestyle activity scores Figure 2 displays a scatterplot matrix illustrating the joint distribution and correlations among physical activity, cognitive activity, and social activity scores. The lower triangle of the matrix includes bivariate scatterplots, which show positive gradients. The diagonal panels present histograms indicating that all three activity scores exhibit approximately unimodal, right-skewed distributions. The upper triangle provides Spearman’s rank correlation coefficients, which were all positive and statistically significant: physical activity and cognitive activity (r = 0.33), physical activity and social activity (r = 0.24), and cognitive activity and social activity (r = 0.20), all p -values < 0.001. Main analysis Figure 3 shows that all three activity domains were significantly associated with step count: cognitive activity score [effective degrees of freedom (edf) = 4.3, F = 18.2, p < 0.001], physical activity score (edf = 6.5, F = 73.4, p < 0.001), and social activity score (edf = 2.0, F = 4.9, p = 0.007). The association of step count with cognitive activity score was negative during early morning hours, particularly between 6:00 and 9:00, and gradually became neutral later in the day. The physical activity score showed positive associations with step count from mid-morning to early afternoon, peaking at 9:00–12:00. The linear association of step count with social activity score was weakly positive throughout the day. Subgroup analysis Subgroup analyses (Fig. 4) revealed age and sex differences in the associations between activity domain scores and step counts. Among participants aged < 75 years, all three activity domains were significantly associated with step counts: cognitive (edf = 4.8, F = 10.5, p < 0.001), physical (edf = 6.5, F = 45.6, p < 0.001), and social (edf = 2.0, F = 3.8, p = 0.022). In contrast, among those aged ≥ 75 years, only cognitive activity (edf = 3.4, F = 10.1, p < 0.001) and physical activity (edf = 4.3, F = 27.0, p < 0.001) scores showed significant associations with step counts, while the association with the social activity score did not reach statistical significance (edf = 2.0, F = 2.7, p = 0.066). Among women, all three activity domains were significantly associated with step counts: cognitive (edf = 5.1, F = 10.7, p < 0.001), physical (edf = 7.4, F = 30.22, p < 0.001), and social (edf = 2.6, F = 5.0, p = 0.002). Among men, cognitive activity (edf = 6.9, F = 4.8, p < 0.001) and physical activity (edf = 4.7, F = 53.3, p < 0.001) scores showed significant associations with step count, while the association with the social activity score did not reach the threshold for statistical significance (edf = 7.3, F = 1.8, p = 0.054). Among participants with ≥ 6000 steps/day, cognitive (edf = 4.57, F = 16.1, p < 0.001), physical (edf = 3.82, F = 1.93, p = 0.087), and social (edf = 7.11, F = 2.03, p = 0.041) activity scores showed differing strengths of association across the 24-hour cycle. In contrast, among participants with < 6000 steps/day, all three domains showed significant time-varying associations: cognitive (edf = 6.62, F = 10.8, p < 0.001), physical (edf = 6.80, F = 3.91, p < 0.001), and social (edf = 6.28, F = 10.6, p < 0.001) activity scores. Across subgroups demonstrating significant associations, the temporal patterns were generally consistent: cognitive activity scores tended to show lower coefficients with step counts in the early morning, associations with physical activity scores appeared to peak from mid-morning to early afternoon, and social activity scores showed positive and linear associations with step counts. Sensitivity analysis In the sensitivity analysis using the six 3-hour spans between 6:00 and 21:00, all three activity scores showed statistically significant associations with step counts: cognitive activity score (edf = 2.0, F = 32.4, p < 0.001), physical activity score (edf = 4.5, F = 73.4, p < 0.001), and social activity score (edf = 3.5, F = 4.1, p = 0.003). As shown in Fig. 5, the association with the cognitive activity score was lowest during the early morning, although it increased over the course of the day. The physical activity score showed higher coefficients in the morning and early afternoon, peaking around 9:00–12:00. The association with social activity score fluctuated throughout the day, with more positive coefficients observed in the afternoon, particularly after 15:00. Discussion In this longitudinal cohort study of 2,578 older adults, we found that engagement in cognitive, physical, and social lifestyle activities was significantly associated with patterns of objectively-measured physical activity throughout the day. Cognitive activity scores were negatively associated with step counts during the early-morning hours but became neutral later in the day; physical activity scores showed positive associations with step counts, particularly from mid-morning to early afternoon; and social activity scores showed a modest but positive linear association with step counts across the day. These temporal patterns were generally consistent across analyses by age, sex, and baseline activity level and in sensitivity analyses focused on the daytime period. The observed association between a higher frequency of cognitive activities and fewer morning steps may be partly explained by the age-related shift toward morningness [ 21 ] and the synchrony effect [ 22 ]. Previous research has shown that aging is accompanied by a shift from eveningness to morningness [ 21 ], with older adults exhibiting better cognitive performance during morning hours [ 22 ]. Neuroimaging studies have further indicated that older adults tested in the morning employ the prefrontal and superior parietal control regions more efficiently than in the afternoon, at this time displaying neural activation patterns similar to those observed in younger adults [ 23 ]. Given these findings, it is plausible that older adults, whose cognitive performance peaks in the morning, may prioritize cognitively demanding (i.e., seated) activities during this time. Higher physical activity scores in our study were associated with increased step counts in the morning. With aging, circadian rhythms tend to shift to running earlier [ 24 ]. Older adults with earlier activity peaks have demonstrated higher overall physical activity [ 25 ]. Similarly, an observational study showed that older adults reached their peak step counts during the morning hours—in contrast to younger adults, who tended to peak later in the day [ 26 ]. Additionally, older adults tend to engage in solitary activities (such as walking or exercising alone) during the morning hours [ 27 ]. Collectively, these findings suggest that age-related changes in the circadian rhythm of physical activity may explain the observed association between higher physical activity scores and greater step counts in the morning. We hypothesized that social activities would be significantly associated with afternoon physical activity engagement, and our findings support this hypothesis. This aligns with an ecological study showing that the likelihood of engaging in physical activity with others is reportedly lower in the morning than in the afternoon [ 28 ]. Our social activity score captured a broad range of activities, including both group participation and engagement in paid work or other structured activities. Relatedly, previous studies have shown that adults aged 55–65 years who actively participate in neighborhood and group activities tend to exhibit less sedentary behavior and to be more active on weekend afternoons [ 29 ]; additionally, among aging workers, physical activity peaks during the morning and afternoon hours on working days [ 30 ]. These findings suggest that social activity may help sustain physical activity levels during periods of the day when movement typically declines among older adults. Limitations This study has several limitations. First, this observational study cannot establish causality, although adjusting for a number of demographic, health, and behavioral factors reduces confounding. Second, waist-worn accelerometers miss activity during bathing, sleep, and other non-wear times; however, a daytime-only (06:00–21:00) analysis produced similar results, lessening this concern. Third, lifestyle activity scores were self-reported rather than objectively measured, which may have introduced measurement error. Fourth, the analytic sample comprised participants who volunteered to wear an activity tracker, meaning that health-conscious individuals were likely over-represented; therefore, generalizability to other populations is limited. Fifth, physical-activity data were collected in a single month for each participant but the month varied across the sample, leaving seasonal and weather effects insufficiently controlled for. Conclusions This study examined the association between lifestyle activity scores and timing of daily steps in community-dwelling older adults using function-on-scalar regression. The findings revealed that: (1) higher cognitive activity scores were associated with lower steps throughout the day, with the strongest negative association in the morning; (2) higher physical activity scores were associated with increased steps across the day, peaking in the morning; and (3) higher social activity scores were associated with greater steps throughout the day. These results suggest that the relationship between lifestyle activities and physical activity volume varies by activity domain, and they raise the suggestion that the previously-reported association between afternoon physical activity and favorable health outcomes may, in part, be attributable to engagement in social activities. Abbreviations NCGG National Center for Geriatrics and Gerontology NCGG-SGS National Center for Geriatrics and Gerontology Study of Geriatric Syndromes SD standard deviation MMSE Mini-Mental State Examination GDS Geriatric Depression Scale CI confidence interval edf effective degrees of freedom Declarations Ethics approval and consent to participate: Our study’s protocol was in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the NCGG (1440-7). Written informed consent was obtained from participants at study entry. Availability of data and materials: The datasets used/analyzed during the current study are available from the corresponding author on reasonable request. Competing interests: The authors declare that they have no competing interests. Funding: This work was supported by the Japan Agency for Medical Research and Development (grant numbers: 15dk0107003h0003 and 15dk0207004h0203) and Japan Society for the Promotion of Science (grant: 24KJ2233, and 24K20693). This work was financially supported as joint research with Kao Corporation. Authors’ contributions: MM designed the study, analyzed and interpreted the data, and drafted the manuscript. YY, NT, and MS contributed to the acquisition of data. HS, KH, KF, CN, DK, TO, YY, NT, and MS critically revised the manuscript for important intellectual content. HS conceived the study and contributed for funding acquisition and supervision. Acknowledgements: We express our gratitude to the Takahama City Offices for their support in participant recruitment. We also thank the healthcare staff for their invaluable assistance with the assessments. References Di Lorito C, Long A, Byrne A, Harwood RH, Gladman JRF, Schneider S, et al. Exercise interventions for older adults: A systematic review of meta-analyses. J Sport Health Sci. 2021;10(1):29-47. doi:10.1016/j.jshs.2020.06.003. Iso-Markku P, Aaltonen S, Kujala UM, Halme H-L, Phipps D, Knittle K, et al. Physical Activity and Cognitive Decline Among Older Adults: A Systematic Review and Meta-Analysis. JAMA Network Open. 2024;7(2):e2354285-e85. doi:10.1001/jamanetworkopen.2023.54285. Sánchez-Sánchez JL, He L, Morales JS, de Souto Barreto P, Jiménez-Pavón D, Carbonell-Baeza A, et al. Association of physical behaviours with sarcopenia in older adults: a systematic review and meta-analysis of observational studies. Lancet Healthy Longev. 2024;5(2):e108-e19. doi:10.1016/s2666-7568(23)00241-6. Cho SE, Saha E, Matabuena M, Wei J, Ghosal R. Exploring the association between daily distributional patterns of physical activity and cardiovascular mortality risk among older adults in NHANES 2003-2006. Annals of Epidemiology. 2024;99:24-31. doi:https://doi.org/10.1016/j.annepidem.2024.10.001. 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. Nature Communications. 2023;14(1):930. doi:10.1038/s41467-023-36546-5. Morikawa M, Harada K, Kurita S, Nishijima C, Fujii K, Kakita D, et al. Association of Timing of Physical Activity with Physical Frailty Incidence in Older Adults. Gerontology. 2025;71(3):165-72. doi:10.1159/000543283. Weber C, Quintus M, Egloff B, Luong G, Riediger M, Wrzus C. Same old, same old? Age differences in the diversity of daily life. Psychol Aging. 2020;35(3):434-48. doi:10.1037/pag0000407. Fain RS, Hayat SA, Luben R, Abdul Pari AA, Yip JLY. Effects of social participation and physical activity on all-cause mortality among older adults in Norfolk, England: an investigation of the EPIC-Norfolk study. Public Health. 2022;202:58-64. doi:10.1016/j.puhe.2021.10.017. Ihara S, Ide K, Kanamori S, Tsuji T, Kondo K, Iizuka G. Social participation and change in walking time among older adults: a 3-year longitudinal study from the JAGES. BMC Geriatrics. 2022;22(1):238. doi:10.1186/s12877-022-02874-2. Lindsay Smith G, Banting L, Eime R, O’Sullivan G, van Uffelen JGZ. The association between social support and physical activity in older adults: a systematic review. International Journal of Behavioral Nutrition and Physical Activity. 2017;14(1):56. doi:10.1186/s12966-017-0509-8. Stephan Y, Sutin AR, Luchetti M, Aschwanden D, Terracciano A. Physical, cognitive, and social activities as mediators between personality and cognition: evidence from four prospective samples. Aging Ment Health. 2024;28(9):1294-303. doi:10.1080/13607863.2024.2320135. Bae S, Lee S, Harada K, Makino K, Chiba I, Katayama O, et al. Engagement in Lifestyle Activities is Associated with Increased Alzheimer’s Disease-Associated Cortical Thickness and Cognitive Performance in Older Adults. Journal of Clinical Medicine. 2020;9(5):1424. Cuschieri S. The STROBE guidelines. Saudi J Anaesth. 2019;13(Suppl 1):S31-s34. doi:10.4103/sja.SJA_543_18. Shimada H, Makizako H, Lee S, Doi T, Lee S, Tsutsumimoto K, et al. Impact of Cognitive Frailty on Daily Activities in Older Persons. J Nutr Health Aging. 2016;20(7):729-35. doi:10.1007/s12603-016-0685-2. Gorman E, Hanson HM, Yang PH, Khan KM, Liu-Ambrose T, Ashe MC. Accelerometry analysis of physical activity and sedentary behavior in older adults: a systematic review and data analysis. Eur Rev Aging Phys Act. 2014;11(1):35-49. doi:10.1007/s11556-013-0132-x. Tombaugh TN, McIntyre NJ. The mini‐mental state examination: a comprehensive review. Journal of the American Geriatrics Society. 1992;40(9):922-35. De Craen AJ, Heeren T, Gussekloo J. Accuracy of the 15‐item geriatric depression scale (GDS‐15) in a community sample of the oldest old. International journal of geriatric psychiatry. 2003;18(1):63-66. Mayer M, Mayer MM. Package ‘missRanger’. R package. 2019. Chen L-P. Functional data analysis with R by Ciprian M. Crainiceanu, Jeff Goldsmith, Andrew Leroux, and Erjia Cui, Chapman and Hall/CRC, 2024, ISBN: 9781032244716 https://www.routledge.com/Functional-data-analysis-with-R/Crainiceanu-Goldsmith-Leroux-Cui/p/book/9781032244716. Biometrics. 2025. doi:10.1093/biomtc/ujaf030. Ministry of Health LaW. The 2023 Physical Activity and Exercise Guide for Health Promotion. Available from: https://www.mhlw.go.jp/content/10904750/001171393.pdf. Accessed 11/19 2024. Wilks H, Aschenbrenner AJ, Gordon BA, Balota DA, Fagan AM, Musiek E, et al. Sharper in the morning: Cognitive time of day effects revealed with high-frequency smartphone testing. J Clin Exp Neuropsychol. 2021;43(8):825-37. doi:10.1080/13803395.2021.2009447. Wiłkość-Dębczyńska M, Liberacka-Dwojak M. Time of day and chronotype in the assessment of cognitive functions. Postep Psychiatr Neurol. 2023;32(3):162-66. doi:10.5114/ppn.2023.132032. Anderson JAE, Campbell KL, Amer T, Grady CL, Hasher L. Timing is everything: Age differences in the cognitive control network are modulated by time of day. Psychol Aging. 2014;29(3):648-57. doi:10.1037/a0037243. Stenholm S, Pulakka A, Leskinen T, Pentti J, Heinonen OJ, Koster A, et al. Daily Physical Activity Patterns and Their Association With Health-Related Physical Fitness Among Aging Workers—The Finnish Retirement and Aging Study. The Journals of Gerontology: Series A. 2020;76(7):1242-50. doi:10.1093/gerona/glaa193. Erickson ML, Blackwell TL, Mau T, Cawthon PM, Glynn NW, Qiao YS, et al. Age Is Associated With Dampened Circadian Patterns of Rest and Activity: The Study of Muscle, Mobility, and Aging (SOMMA). J Gerontol A Biol Sci Med Sci. 2024;79(4). doi:10.1093/gerona/glae049. Ang G, Tan CS, Lim N, Tan J, Müller-Riemenschneider F, Cook AR, et al. Hourly step recommendations to achieve daily goals for working and older adults. Communications Medicine. 2024;4(1):132. doi:10.1038/s43856-024-00537-4. Copeland JL, Esliger DW. Accelerometer Assessment of Physical Activity in Active, Healthy Older Adults. Journal of Aging and Physical Activity. 2009;17(1):17-30. doi:10.1123/japa.17.1.17. Delobelle J, Compernolle S, Vetrovsky T, Van Cauwenberg J, Van Dyck D. Contexts, affective and physical states and their variations during physical activity in older adults: an intensive longitudinal study with sensor-triggered event-based ecological momentary assessments. International Journal of Behavioral Nutrition and Physical Activity. 2025;22(1):30. doi:10.1186/s12966-025-01724-9. Van Holle V, McNaughton SA, Teychenne M, Timperio A, Van Dyck D, De Bourdeaudhuij I, et al. Social and physical environmental correlates of adults' weekend sitting time and moderating effects of retirement status and physical health. Int J Environ Res Public Health. 2014;11(9):9790-810. doi:10.3390/ijerph110909790. Pulakka A, Leskinen T, Koster A, Pentti J, Vahtera J, Stenholm S. Daily physical activity patterns among aging workers: the Finnish Retirement and Aging Study (FIREA). Occup Environ Med. 2019;76(1):33-39. doi:10.1136/oemed-2018-105266. Additional Declarations No competing interests reported. Supplementary Files Supplementarytables1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6769060","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":467686140,"identity":"ad27bc58-74ee-4a13-99ff-a208492489a5","order_by":0,"name":"Masanori Morikawa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYJCCgw1Agh/BP0CkFskGUrQwglQbEKEQAnTbex8enFFzWN74dvPjl18YauUYGM/i12125rjBwQ3HDhtuu3PMzFqG4bgxA8O5BPxabqQxHHzAdptx240cNmMJhmOJDQxnDIjQ8u+2/eYZJGnZ2HY7cYNEDvPDDww1RGg5c4zh4My+/8kzbqSZMQPDzZiNoF+OtzF/7PmWZts/I/nxxx8VdXL8EgRCDBmwSfMYHGZgkzhDtA4G5o8/GOqASaeHeC2jYBSMglEwIgAAFHNSp57aSCcAAAAASUVORK5CYII=","orcid":"","institution":"National Center for Geriatrics and Gerontology","correspondingAuthor":true,"prefix":"","firstName":"Masanori","middleName":"","lastName":"Morikawa","suffix":""},{"id":467686142,"identity":"7373a5ef-8856-4e4c-b551-862dbbb4b158","order_by":1,"name":"Kenji Harada","email":"","orcid":"","institution":"National Center for Geriatrics and Gerontology","correspondingAuthor":false,"prefix":"","firstName":"Kenji","middleName":"","lastName":"Harada","suffix":""},{"id":467686143,"identity":"48ca5f16-7ea4-4bf8-a9d0-52953ebba49e","order_by":2,"name":"Chiharu Nishijima","email":"","orcid":"","institution":"National Center for Geriatrics and Gerontology","correspondingAuthor":false,"prefix":"","firstName":"Chiharu","middleName":"","lastName":"Nishijima","suffix":""},{"id":467686144,"identity":"f6294d8f-427a-40ee-8faf-475373f3242e","order_by":3,"name":"Kazuya Fujii","email":"","orcid":"","institution":"National Center for Geriatrics and Gerontology","correspondingAuthor":false,"prefix":"","firstName":"Kazuya","middleName":"","lastName":"Fujii","suffix":""},{"id":467686145,"identity":"dc22bd22-8232-4c80-8a4f-d3aab6390cde","order_by":4,"name":"Daisuke Kakita","email":"","orcid":"","institution":"National Center for Geriatrics and Gerontology","correspondingAuthor":false,"prefix":"","firstName":"Daisuke","middleName":"","lastName":"Kakita","suffix":""},{"id":467686146,"identity":"6e4f50fa-1dba-4b55-8f0c-f6dd01e6ca7a","order_by":5,"name":"Takuto Okuya","email":"","orcid":"","institution":"National Center for Geriatrics and Gerontology","correspondingAuthor":false,"prefix":"","firstName":"Takuto","middleName":"","lastName":"Okuya","suffix":""},{"id":467686147,"identity":"8a6aa864-7814-4ffc-b2e7-ad6de64a404b","order_by":6,"name":"Kazuki Soma","email":"","orcid":"","institution":"National Center for Geriatrics and Gerontology","correspondingAuthor":false,"prefix":"","firstName":"Kazuki","middleName":"","lastName":"Soma","suffix":""},{"id":467686148,"identity":"4bb38ba1-1915-4a10-9e3e-9f8206d7a275","order_by":7,"name":"Yukari Yamashiro","email":"","orcid":"","institution":"Tokyo Research Laboratories, Kao Corporation","correspondingAuthor":false,"prefix":"","firstName":"Yukari","middleName":"","lastName":"Yamashiro","suffix":""},{"id":467686149,"identity":"49e3d3f9-777a-43d0-b92c-9eec73e22ebf","order_by":8,"name":"Naoto Takayanagi","email":"","orcid":"","institution":"Tokyo Research Laboratories, Kao Corporation","correspondingAuthor":false,"prefix":"","firstName":"Naoto","middleName":"","lastName":"Takayanagi","suffix":""},{"id":467686151,"identity":"a20d891a-bcf9-4844-86ec-5a19217e1c8b","order_by":9,"name":"Motoki Sudo","email":"","orcid":"","institution":"Tokyo Research Laboratories, Kao Corporation","correspondingAuthor":false,"prefix":"","firstName":"Motoki","middleName":"","lastName":"Sudo","suffix":""},{"id":467686154,"identity":"d8440de8-d11d-48d1-9bec-8ddf1cdc5eb0","order_by":10,"name":"Hiroyuki Shimada","email":"","orcid":"","institution":"National Center for Geriatrics and Gerontology","correspondingAuthor":false,"prefix":"","firstName":"Hiroyuki","middleName":"","lastName":"Shimada","suffix":""}],"badges":[],"createdAt":"2025-05-28 14:23:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6769060/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6769060/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84339073,"identity":"28e85f0d-e760-4f48-8dd3-936e54f84047","added_by":"auto","created_at":"2025-06-10 18:12:46","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":236280,"visible":true,"origin":"","legend":"\u003cp\u003eDesign diagram in this study.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6769060/v1/8ed6fc9dc5ff9faac592f914.jpg"},{"id":84339080,"identity":"fe0ccd7e-df1b-4342-bf6f-e9357da31f8f","added_by":"auto","created_at":"2025-06-10 18:12:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":428966,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplot matrix illustrating the relationships among physical (PA), cognitive (CA), and social (SA) activity scores. The lower triangle displays bivariate scatter plots, indicating positive associations between variables. The diagonal panels show histograms of each score, all of which exhibit approximately unimodal and right-skewed distributions. The upper triangle presents Spearman’s rank correlation coefficients: PA and CA (r = 0.33), PA and SA (r = 0.24), and CA and SA (r = 0.20), all statistically significant at p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6769060/v1/1c008407191aabcc1dc0d08d.jpg"},{"id":84339076,"identity":"c7ab0aee-0a7f-487b-a5ed-251a088a78c6","added_by":"auto","created_at":"2025-06-10 18:12:46","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":267168,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between activity domain scores and hourly steps over 24 hours. Each panel shows the estimated coefficient curves for cognitive, physical, and social activity scores. Solid lines represent the estimated coefficients, and shaded areas indicate 95% confidence intervals. Cognitive activity scores were negatively associated with steps during the early morning, while physical activity scores were positively associated from mid-morning to early afternoon. Social activity scores showed more positive and linear associations.\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6769060/v1/70866ee1a03074ba865564d0.jpg"},{"id":84339924,"identity":"f962f517-89d6-494a-90db-24f163bd5d94","added_by":"auto","created_at":"2025-06-10 18:20:46","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1286491,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between lifestyle activity scores and steps stratified by age group (\u0026lt;75 and ≥75 years), sex (women and men), and daily steps (≥6,000 steps and \u0026lt;6,000 steps). Each panel displays the estimated coefficients (solid lines) and 95% confidence intervals (shaded areas) for cognitive, physical, and social activity scores.\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6769060/v1/4a51f6b6fd8b81e21ecd4ff6.jpg"},{"id":84341160,"identity":"01c6bcb7-b750-41fc-b9ab-3940d9a7ff5c","added_by":"auto","created_at":"2025-06-10 18:28:47","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":239087,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between activity domain scores and steps across six 3-hour intervals from 6:00 to 21:00 in the sensitivity analysis. Each panel displays the estimated coefficients (solid lines) and 95% confidence intervals (shaded areas) for cognitive, physical, and social activity scores.\u003c/p\u003e","description":"","filename":"fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6769060/v1/700f2e653aa8c9d6b0f0916d.jpg"},{"id":93933052,"identity":"cd500cae-579a-46ff-81dd-af973304621c","added_by":"auto","created_at":"2025-10-20 12:17:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3332426,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6769060/v1/536b16c3-ed63-46af-b91b-546750e7427f.pdf"},{"id":84339072,"identity":"8e491e4e-4838-4504-a0f2-455280230ca3","added_by":"auto","created_at":"2025-06-10 18:12:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":34486,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytables1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6769060/v1/dc4b910d179e3634b142357a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of Lifestyle Activities with Daily Physical Activity Timing in Community-Dwelling Older Adults: A longitudinal observational study","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePhysical activity is fundamental to maintaining health in older adults. Regular physical activity contributes significantly to the prevention of chronic diseases, supports physical and cognitive function, and enhances overall well-being and quality of life [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Accordingly, promoting and sustaining physical activity is considered a cornerstone of healthy aging.\u003c/p\u003e \u003cp\u003eRecently, the timing of physical activity has emerged as an important factor influencing health outcomes among older adults. Specifically, evidence indicates that engaging in afternoon physical activities is associated with lower incidence rates of physical frailty among older populations [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Similarly, physical activities conducted in the afternoon are particularly beneficial, with associations observed between afternoon activity and reduced risks of all-cause and cardiovascular mortality [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Understanding optimal timing is highly relevant from a public health perspective, so as to maximize the benefits of physical activity interventions in older populations.\u003c/p\u003e \u003cp\u003eHowever, the mechanisms underlying the benefits of afternoon physical activity remain unclear. Lifestyle activities\u0026mdash;including physical (sports and exercise), cognitive, and social activities\u0026mdash;are known to influence health trajectories in older adults. Stephan et al. reported that these three types of activities each uniquely mediate various health outcomes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, it remains unclear which types of lifestyle activities specifically correlate with afternoon physical activity among older adults.\u003c/p\u003e \u003cp\u003eEmpirical findings from Weber et al. highlighted greater diversity in social activities during the afternoon among older adults [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Given that social participation is both fundamental to health maintenance [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and is positively associated with physical activity levels [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], it is plausible that the observed benefits of afternoon physical activity might partially stem from increased social activity. Nonetheless, the potential confounding effect of social activity has not been thoroughly considered in previous studies.\u003c/p\u003e \u003cp\u003eGiven the above, this study aimed to investigate associations of lifestyle activities\u0026mdash;physical, cognitive, and social\u0026mdash;with the timing of daily physical activity in community-dwelling older adults. We hypothesized that social activities significantly associate with engagement in afternoon physical activity.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDesign\u003c/h2\u003e \u003cp\u003e\u003cb\u003eFigure 1\u003c/b\u003e presents an overview of the study design. This longitudinal observational study assessed the timing of objectively-measured physical activity as an outcome. The exposure comprised physical, cognitive, and social activity scores derived from a lifestyle activities questionnaire [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], which was measured at the baseline assessment. This study followed the STROBE guidelines for reporting observational studies (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eThe study size was determined by the availability of sample participants rather than by a power calculation. In detail, participants from the National Center for Geriatrics and Gerontology\u0026mdash;Study of Geriatric Syndromes (NCGG-SGS) cohort were included in this study. The NCGG-SGS cohort study sought to establish a screening system for geriatric syndromes and validate evidence-based interventions for their prevention in Japan [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Our study enrolled participants from Takahama City from September 10, 2015, to September 11, 2017 (n\u0026thinsp;=\u0026thinsp;4,167). The exclusion criteria were participants who: (1) had not completed the responses on the lifestyle activities questionnaire at the baseline (n\u0026thinsp;=\u0026thinsp;87), or (2) had no valid records of physical activity (n\u0026thinsp;=\u0026thinsp;1,502). In total, 2,578 participants were included in this study.\u003c/p\u003e\n\u003ch3\u003eExposure\u003c/h3\u003e\n\u003cp\u003eLifestyle activities were assessed at baseline using the Lifestyle Activities Questionnaire [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], a self-administered instrument designed to evaluate the frequency of engagement in 36 daily activities across the physical, cognitive, and social domains over the previous 12 months. The activities in the physical domain included walking, bicycling, jogging, swimming, strength training, yoga, gymnastics, dancing, hiking, playing golf, playing ground golf, and participating in ball sports. The activities in the cognitive domain included writing letters or keeping a diary, reading books, reading magazines or newspapers, learning activities, using a computer (including Internet use), doing crossword puzzles, playing board games (e.g., card games, Go, or Japanese chess), playing musical instruments, doing handicrafts, listening to music, appreciating art, and engaging in financial investment such as stock trading. The activities in the social domain included serving as an officer of a senior club or neighborhood association, attending regional events, participating in environmental beautification activities, teaching activities, supporting others (including older adults or children), working (including paid employment), going to karaoke, eating out or having tea with friends, shopping with friends, talking with friends or neighbors (including phone conversations), attending events or concerts, and traveling.\u003c/p\u003e \u003cp\u003eFor each item, participants reported how often they engaged in the activity using a 6-point scale: 0\u0026thinsp;=\u0026thinsp;not at all, 1\u0026thinsp;=\u0026thinsp;less than once a month, 2\u0026thinsp;=\u0026thinsp;several times a month, 3\u0026thinsp;=\u0026thinsp;1\u0026ndash;2 times per week, 4\u0026thinsp;=\u0026thinsp;3\u0026ndash;6 times per week, and 5\u0026thinsp;=\u0026thinsp;every day. Scores for each domain were calculated by summing the item responses; this yielded physical, cognitive, and social activity scores ranging from 0 to 60, with higher scores indicating greater engagement.\u003c/p\u003e\n\u003ch3\u003eOutcome\u003c/h3\u003e\n\u003cp\u003eThe outcome was defined as the mean number of steps measured in three-hour span over a 24-hour period (specifically, 0\u0026ndash;3, 3\u0026ndash;6, 6\u0026ndash;9, 9\u0026ndash;12, 12\u0026ndash;15, 15\u0026ndash;18, 18\u0026ndash;21, and 21\u0026ndash;24 hours) during the month following the baseline assessment, assessed using objective physical activity data. A triaxial accelerometer (HW-100, Kao Corp., Tokyo, Japan) was used to monitor participants\u0026rsquo; physical activity duration. These devices share the same sensor and measure physical activity in 4-second spans using an identical algorithm. At the end of the baseline assessment, participants who wished to receive a physical activity tracker were given one and were instructed to wear the accelerometer on their waist, removing it only for bathing or sleeping. In line with prior studies [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], accelerometer data were considered valid if participants wore the device for a minimum of 10 hours per day for at least 7 days per month.\u003c/p\u003e\n\u003ch3\u003ePotential confounders\u003c/h3\u003e\n\u003cp\u003eThe covariates included the continuous variables of age (years), years of education, body mass index (kg/m\u0026sup2;), number of medications taken per day, gait speed (m/s), Mini-Mental State Examination score [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], Geriatric Depression Scale score [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], sleep duration (hours), and awake time in bed. The categorical variables included sex (men or women), hearing impairment (yes or no), current alcohol consumption (yes or no), current smoking status (current or non-current), living arrangement (living alone: yes or no), and meal frequency (three times per day vs. other).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMissing value imputation\u003c/h2\u003e \u003cp\u003eA total of 166 missing values (0.11%) were imputed using the missRanger package (2.4.0) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This package employs a random forest algorithm that is trained on the observed data to predict continuous and categorical missing values. All variables\u0026mdash;i.e., the outcome, exposure, and covariates\u0026mdash;were included in the imputation, with the model using 1,000 trees.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and categorical variables are presented as counts (percentages). All analyses were conducted in R (version 4.4.2). Statistical significance was set at a two-sided \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCorrelation Analysis\u003c/h3\u003e\n\u003cp\u003eTo explore pairwise relationships among the three lifestyle activity domains, we created a scatterplot matrix using physical, cognitive, and social activity scores. The matrix included scatterplots, histograms, and correlation coefficients to visualize and quantify these associations. Spearman\u0026rsquo;s rank correlation coefficients were computed to assess monotonic relationships among the three scores.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFunction-on-Scalar Regression Analysis\u003c/h2\u003e \u003cp\u003eTo examine the associations between lifestyle activity domains and objectively-measured physical activity, we employed function-on-scalar regression using generalized additive models [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The dependent variable, treated as a functional outcome, was the number of steps taken within each three-hour span across a 24-hour day. Explanatory variables included physical activity, cognitive activity, and social activity scores assessed at baseline, along with the above-mentioned covariates. Smoothing splines were applied to model the interaction between each explanatory variable and time span. Coefficient functions and the corresponding 95% confidence intervals were estimated to visualize the dynamic associations throughout the day.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup and Sensitivity Analyses\u003c/h2\u003e \u003cp\u003eTo assess potential effect modification, stratified function-on-scalar regression models were conducted according to age group (\u0026lt;\u0026thinsp;75 vs. \u0026ge;75 years), sex (men vs. women), and baseline physical activity level (\u0026lt;\u0026thinsp;6,000 vs. \u0026ge;6,000 steps/day) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The age stratification (\u0026lt;\u0026thinsp;75 vs. \u0026ge;75 years) corresponds to the classification of \u0026ldquo;early-stage elderly\u0026rdquo; and \u0026ldquo;late-stage elderly\u0026rdquo; commonly used in Japan\u0026rsquo;s medical care system. Each subgroup analysis maintained the same model structure and covariates as the primary analysis. A sensitivity analysis was also performed to mitigate potential data reliability concerns for early-morning and late-night periods; specifically, this analysis restricted the outcome to the six time spans between 6:00 and 21:00, and the same function-on-scalar regression model was applied with identical covariate adjustments.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRecruitment and baseline characteristics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the baseline characteristics. Continuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and categorical variables are shown as frequency and percentage (n [%]). Among the 2,578 participants, the mean age was 70.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5 years, and 1,474 (57.0%) were women. Participants had an average of 11.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4 years of education, slept 6.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 hours per night, and woke up at an average time corresponding to 5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 hours (i.e., approximately 5:48 AM). A total of 2,464 participants (96.0%) reported eating three meals a day. The mean scores for physical activity, cognitive activity, and social activity were 7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0, 15.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0, and 11.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0, respectively. The cohort also recorded an average of 6,457\u0026thinsp;\u0026plusmn;\u0026thinsp;3,096 steps per day.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics of the Participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2,578\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, years\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.7 (6.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWomen, n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,474 (57%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysical activity score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.9 (6.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCognitive activity score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocial activity score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDaily steps, steps\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,457 (3,096)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational years, years\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.4 (2.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSleep duration, hours\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.9 (1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAwake time, hour\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.8 (1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN of meals, n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThree times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,464 (96%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBody mass index, kg/m\u0026sup2;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.5 (3.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDaily medication use, n\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.7 (2.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGait speed, m/s\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1 (0.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMMSE score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.5 (2.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGDS, score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.8 (2.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHearing problem, n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e765 (30%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent drinking, n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e913 (35%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent smoking, n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e218 (8.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiving alone, n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e244 (9.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eData are presented as mean (standard deviation) for continuous variables, or number (%) for categorical variables. Abbreviations: GDS\u0026thinsp;=\u0026thinsp;Geriatric Depression Scale; MMSE\u0026thinsp;=\u0026thinsp;Mini-Mental State Examination.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCorrelations among lifestyle activity scores\u003c/h2\u003e \u003cp\u003e \u003cb\u003eFigure 2\u003c/b\u003e displays a scatterplot matrix illustrating the joint distribution and correlations among physical activity, cognitive activity, and social activity scores. The lower triangle of the matrix includes bivariate scatterplots, which show positive gradients. The diagonal panels present histograms indicating that all three activity scores exhibit approximately unimodal, right-skewed distributions. The upper triangle provides Spearman\u0026rsquo;s rank correlation coefficients, which were all positive and statistically significant: physical activity and cognitive activity (r\u0026thinsp;=\u0026thinsp;0.33), physical activity and social activity (r\u0026thinsp;=\u0026thinsp;0.24), and cognitive activity and social activity (r\u0026thinsp;=\u0026thinsp;0.20), all \u003cem\u003ep\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eMain analysis\u003c/h2\u003e \u003cp\u003e \u003cb\u003eFigure 3\u003c/b\u003e shows that all three activity domains were significantly associated with step count: cognitive activity score [effective degrees of freedom (edf)\u0026thinsp;=\u0026thinsp;4.3, F\u0026thinsp;=\u0026thinsp;18.2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001], physical activity score (edf\u0026thinsp;=\u0026thinsp;6.5, F\u0026thinsp;=\u0026thinsp;73.4, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and social activity score (edf\u0026thinsp;=\u0026thinsp;2.0, F\u0026thinsp;=\u0026thinsp;4.9, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007). The association of step count with cognitive activity score was negative during early morning hours, particularly between 6:00 and 9:00, and gradually became neutral later in the day. The physical activity score showed positive associations with step count from mid-morning to early afternoon, peaking at 9:00\u0026ndash;12:00. The linear association of step count with social activity score was weakly positive throughout the day.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analysis\u003c/h2\u003e \u003cp\u003eSubgroup analyses (Fig.\u0026nbsp;4) revealed age and sex differences in the associations between activity domain scores and step counts. Among participants aged\u0026thinsp;\u0026lt;\u0026thinsp;75 years, all three activity domains were significantly associated with step counts: cognitive (edf\u0026thinsp;=\u0026thinsp;4.8, F\u0026thinsp;=\u0026thinsp;10.5, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), physical (edf\u0026thinsp;=\u0026thinsp;6.5, F\u0026thinsp;=\u0026thinsp;45.6, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and social (edf\u0026thinsp;=\u0026thinsp;2.0, F\u0026thinsp;=\u0026thinsp;3.8, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022). In contrast, among those aged\u0026thinsp;\u0026ge;\u0026thinsp;75 years, only cognitive activity (edf\u0026thinsp;=\u0026thinsp;3.4, F\u0026thinsp;=\u0026thinsp;10.1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and physical activity (edf\u0026thinsp;=\u0026thinsp;4.3, F\u0026thinsp;=\u0026thinsp;27.0, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) scores showed significant associations with step counts, while the association with the social activity score did not reach statistical significance (edf\u0026thinsp;=\u0026thinsp;2.0, F\u0026thinsp;=\u0026thinsp;2.7, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.066). Among women, all three activity domains were significantly associated with step counts: cognitive (edf\u0026thinsp;=\u0026thinsp;5.1, F\u0026thinsp;=\u0026thinsp;10.7, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), physical (edf\u0026thinsp;=\u0026thinsp;7.4, F\u0026thinsp;=\u0026thinsp;30.22, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and social (edf\u0026thinsp;=\u0026thinsp;2.6, F\u0026thinsp;=\u0026thinsp;5.0, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). Among men, cognitive activity (edf\u0026thinsp;=\u0026thinsp;6.9, F\u0026thinsp;=\u0026thinsp;4.8, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and physical activity (edf\u0026thinsp;=\u0026thinsp;4.7, F\u0026thinsp;=\u0026thinsp;53.3, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) scores showed significant associations with step count, while the association with the social activity score did not reach the threshold for statistical significance (edf\u0026thinsp;=\u0026thinsp;7.3, F\u0026thinsp;=\u0026thinsp;1.8, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.054). Among participants with \u0026ge;\u0026thinsp;6000 steps/day, cognitive (edf\u0026thinsp;=\u0026thinsp;4.57, F\u0026thinsp;=\u0026thinsp;16.1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), physical (edf\u0026thinsp;=\u0026thinsp;3.82, F\u0026thinsp;=\u0026thinsp;1.93, p\u0026thinsp;=\u0026thinsp;0.087), and social (edf\u0026thinsp;=\u0026thinsp;7.11, F\u0026thinsp;=\u0026thinsp;2.03, p\u0026thinsp;=\u0026thinsp;0.041) activity scores showed differing strengths of association across the 24-hour cycle. In contrast, among participants with \u0026lt;\u0026thinsp;6000 steps/day, all three domains showed significant time-varying associations: cognitive (edf\u0026thinsp;=\u0026thinsp;6.62, F\u0026thinsp;=\u0026thinsp;10.8, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), physical (edf\u0026thinsp;=\u0026thinsp;6.80, F\u0026thinsp;=\u0026thinsp;3.91, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and social (edf\u0026thinsp;=\u0026thinsp;6.28, F\u0026thinsp;=\u0026thinsp;10.6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) activity scores. Across subgroups demonstrating significant associations, the temporal patterns were generally consistent: cognitive activity scores tended to show lower coefficients with step counts in the early morning, associations with physical activity scores appeared to peak from mid-morning to early afternoon, and social activity scores showed positive and linear associations with step counts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eIn the sensitivity analysis using the six 3-hour spans between 6:00 and 21:00, all three activity scores showed statistically significant associations with step counts: cognitive activity score (edf\u0026thinsp;=\u0026thinsp;2.0, F\u0026thinsp;=\u0026thinsp;32.4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), physical activity score (edf\u0026thinsp;=\u0026thinsp;4.5, F\u0026thinsp;=\u0026thinsp;73.4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and social activity score (edf\u0026thinsp;=\u0026thinsp;3.5, F\u0026thinsp;=\u0026thinsp;4.1, p\u0026thinsp;=\u0026thinsp;0.003). As shown in Fig.\u0026nbsp;5, the association with the cognitive activity score was lowest during the early morning, although it increased over the course of the day. The physical activity score showed higher coefficients in the morning and early afternoon, peaking around 9:00\u0026ndash;12:00. The association with social activity score fluctuated throughout the day, with more positive coefficients observed in the afternoon, particularly after 15:00.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this longitudinal cohort study of 2,578 older adults, we found that engagement in cognitive, physical, and social lifestyle activities was significantly associated with patterns of objectively-measured physical activity throughout the day. Cognitive activity scores were negatively associated with step counts during the early-morning hours but became neutral later in the day; physical activity scores showed positive associations with step counts, particularly from mid-morning to early afternoon; and social activity scores showed a modest but positive linear association with step counts across the day. These temporal patterns were generally consistent across analyses by age, sex, and baseline activity level and in sensitivity analyses focused on the daytime period.\u003c/p\u003e \u003cp\u003eThe observed association between a higher frequency of cognitive activities and fewer morning steps may be partly explained by the age-related shift toward morningness [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and the synchrony effect [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Previous research has shown that aging is accompanied by a shift from eveningness to morningness [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], with older adults exhibiting better cognitive performance during morning hours [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Neuroimaging studies have further indicated that older adults tested in the morning employ the prefrontal and superior parietal control regions more efficiently than in the afternoon, at this time displaying neural activation patterns similar to those observed in younger adults [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Given these findings, it is plausible that older adults, whose cognitive performance peaks in the morning, may prioritize cognitively demanding (i.e., seated) activities during this time.\u003c/p\u003e \u003cp\u003eHigher physical activity scores in our study were associated with increased step counts in the morning. With aging, circadian rhythms tend to shift to running earlier [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Older adults with earlier activity peaks have demonstrated higher overall physical activity [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Similarly, an observational study showed that older adults reached their peak step counts during the morning hours\u0026mdash;in contrast to younger adults, who tended to peak later in the day [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Additionally, older adults tend to engage in solitary activities (such as walking or exercising alone) during the morning hours [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Collectively, these findings suggest that age-related changes in the circadian rhythm of physical activity may explain the observed association between higher physical activity scores and greater step counts in the morning.\u003c/p\u003e \u003cp\u003eWe hypothesized that social activities would be significantly associated with afternoon physical activity engagement, and our findings support this hypothesis. This aligns with an ecological study showing that the likelihood of engaging in physical activity with others is reportedly lower in the morning than in the afternoon [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Our social activity score captured a broad range of activities, including both group participation and engagement in paid work or other structured activities. Relatedly, previous studies have shown that adults aged 55\u0026ndash;65 years who actively participate in neighborhood and group activities tend to exhibit less sedentary behavior and to be more active on weekend afternoons [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]; additionally, among aging workers, physical activity peaks during the morning and afternoon hours on working days [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. These findings suggest that social activity may help sustain physical activity levels during periods of the day when movement typically declines among older adults.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, this observational study cannot establish causality, although adjusting for a number of demographic, health, and behavioral factors reduces confounding. Second, waist-worn accelerometers miss activity during bathing, sleep, and other non-wear times; however, a daytime-only (06:00\u0026ndash;21:00) analysis produced similar results, lessening this concern. Third, lifestyle activity scores were self-reported rather than objectively measured, which may have introduced measurement error. Fourth, the analytic sample comprised participants who volunteered to wear an activity tracker, meaning that health-conscious individuals were likely over-represented; therefore, generalizability to other populations is limited. Fifth, physical-activity data were collected in a single month for each participant but the month varied across the sample, leaving seasonal and weather effects insufficiently controlled for.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study examined the association between lifestyle activity scores and timing of daily steps in community-dwelling older adults using function-on-scalar regression. The findings revealed that: (1) higher cognitive activity scores were associated with lower steps throughout the day, with the strongest negative association in the morning; (2) higher physical activity scores were associated with increased steps across the day, peaking in the morning; and (3) higher social activity scores were associated with greater steps throughout the day. These results suggest that the relationship between lifestyle activities and physical activity volume varies by activity domain, and they raise the suggestion that the previously-reported association between afternoon physical activity and favorable health outcomes may, in part, be attributable to engagement in social activities.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Center for Geriatrics and Gerontology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCGG-SGS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Center for Geriatrics and Gerontology Study of Geriatric Syndromes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMMSE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMini-Mental State Examination\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGDS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeriatric Depression Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eedf\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eeffective degrees of freedom\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eOur study\u0026rsquo;s protocol was in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the NCGG (1440-7). Written informed consent was obtained from participants at study entry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe datasets used/analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by the Japan Agency for Medical Research and Development (grant numbers: 15dk0107003h0003 and 15dk0207004h0203) and Japan Society for the Promotion of Science (grant: 24KJ2233, and 24K20693). This work was financially supported as joint research with Kao Corporation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u0026nbsp;\u003c/strong\u003eMM designed the study, analyzed and interpreted the data, and drafted the manuscript. YY, NT, and MS contributed to the acquisition of data. HS, KH, KF, CN, DK, TO, YY, NT, and MS critically revised the manuscript for important intellectual content. HS conceived the study and contributed for funding acquisition and supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe express our gratitude to the Takahama City Offices for their support in participant recruitment. We also thank the healthcare staff for their invaluable assistance with the assessments.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDi Lorito C, Long A, Byrne A, Harwood RH, Gladman JRF, Schneider S, et al. Exercise interventions for older adults: A systematic review of meta-analyses. J Sport Health Sci. 2021;10(1):29-47. doi:10.1016/j.jshs.2020.06.003.\u003c/li\u003e\n\u003cli\u003eIso-Markku P, Aaltonen S, Kujala UM, Halme H-L, Phipps D, Knittle K, et al. Physical Activity and Cognitive Decline Among Older Adults: A Systematic Review and Meta-Analysis. JAMA Network Open. 2024;7(2):e2354285-e85. doi:10.1001/jamanetworkopen.2023.54285.\u003c/li\u003e\n\u003cli\u003eS\u0026aacute;nchez-S\u0026aacute;nchez JL, He L, Morales JS, de Souto Barreto P, Jim\u0026eacute;nez-Pav\u0026oacute;n D, Carbonell-Baeza A, et al. Association of physical behaviours with sarcopenia in older adults: a systematic review and meta-analysis of observational studies. Lancet Healthy Longev. 2024;5(2):e108-e19. doi:10.1016/s2666-7568(23)00241-6.\u003c/li\u003e\n\u003cli\u003eCho SE, Saha E, Matabuena M, Wei J, Ghosal R. Exploring the association between daily distributional patterns of physical activity and cardiovascular mortality risk among older adults in NHANES 2003-2006. Annals of Epidemiology. 2024;99:24-31. doi:https://doi.org/10.1016/j.annepidem.2024.10.001.\u003c/li\u003e\n\u003cli\u003eFeng 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. Nature Communications. 2023;14(1):930. doi:10.1038/s41467-023-36546-5.\u003c/li\u003e\n\u003cli\u003eMorikawa M, Harada K, Kurita S, Nishijima C, Fujii K, Kakita D, et al. Association of Timing of Physical Activity with Physical Frailty Incidence in Older Adults. Gerontology. 2025;71(3):165-72. doi:10.1159/000543283.\u003c/li\u003e\n\u003cli\u003eWeber C, Quintus M, Egloff B, Luong G, Riediger M, Wrzus C. Same old, same old? Age differences in the diversity of daily life. Psychol Aging. 2020;35(3):434-48. doi:10.1037/pag0000407.\u003c/li\u003e\n\u003cli\u003eFain RS, Hayat SA, Luben R, Abdul Pari AA, Yip JLY. Effects of social participation and physical activity on all-cause mortality among older adults in Norfolk, England: an investigation of the EPIC-Norfolk study. Public Health. 2022;202:58-64. doi:10.1016/j.puhe.2021.10.017.\u003c/li\u003e\n\u003cli\u003eIhara S, Ide K, Kanamori S, Tsuji T, Kondo K, Iizuka G. Social participation and change in walking time among older adults: a 3-year longitudinal study from the JAGES. BMC Geriatrics. 2022;22(1):238. doi:10.1186/s12877-022-02874-2.\u003c/li\u003e\n\u003cli\u003eLindsay Smith G, Banting L, Eime R, O\u0026rsquo;Sullivan G, van Uffelen JGZ. The association between social support and physical activity in older adults: a systematic review. International Journal of Behavioral Nutrition and Physical Activity. 2017;14(1):56. doi:10.1186/s12966-017-0509-8.\u003c/li\u003e\n\u003cli\u003eStephan Y, Sutin AR, Luchetti M, Aschwanden D, Terracciano A. Physical, cognitive, and social activities as mediators between personality and cognition: evidence from four prospective samples. Aging Ment Health. 2024;28(9):1294-303. doi:10.1080/13607863.2024.2320135.\u003c/li\u003e\n\u003cli\u003eBae S, Lee S, Harada K, Makino K, Chiba I, Katayama O, et al. Engagement in Lifestyle Activities is Associated with Increased Alzheimer\u0026rsquo;s Disease-Associated Cortical Thickness and Cognitive Performance in Older Adults. Journal of Clinical Medicine. 2020;9(5):1424.\u003c/li\u003e\n\u003cli\u003eCuschieri S. The STROBE guidelines. Saudi J Anaesth. 2019;13(Suppl 1):S31-s34. doi:10.4103/sja.SJA_543_18.\u003c/li\u003e\n\u003cli\u003eShimada H, Makizako H, Lee S, Doi T, Lee S, Tsutsumimoto K, et al. Impact of Cognitive Frailty on Daily Activities in Older Persons. J Nutr Health Aging. 2016;20(7):729-35. doi:10.1007/s12603-016-0685-2.\u003c/li\u003e\n\u003cli\u003eGorman E, Hanson HM, Yang PH, Khan KM, Liu-Ambrose T, Ashe MC. Accelerometry analysis of physical activity and sedentary behavior in older adults: a systematic review and data analysis. Eur Rev Aging Phys Act. 2014;11(1):35-49. doi:10.1007/s11556-013-0132-x.\u003c/li\u003e\n\u003cli\u003eTombaugh TN, McIntyre NJ. The mini‐mental state examination: a comprehensive review. Journal of the American Geriatrics Society. 1992;40(9):922-35.\u003c/li\u003e\n\u003cli\u003eDe Craen AJ, Heeren T, Gussekloo J. Accuracy of the 15‐item geriatric depression scale (GDS‐15) in a community sample of the oldest old. International journal of geriatric psychiatry. 2003;18(1):63-66.\u003c/li\u003e\n\u003cli\u003eMayer M, Mayer MM. Package \u0026lsquo;missRanger\u0026rsquo;. R package. 2019.\u003c/li\u003e\n\u003cli\u003eChen L-P. Functional data analysis with R by Ciprian M. Crainiceanu, Jeff Goldsmith, Andrew Leroux, and Erjia Cui, Chapman and Hall/CRC, 2024, ISBN: 9781032244716 https://www.routledge.com/Functional-data-analysis-with-R/Crainiceanu-Goldsmith-Leroux-Cui/p/book/9781032244716. Biometrics. 2025. doi:10.1093/biomtc/ujaf030.\u003c/li\u003e\n\u003cli\u003eMinistry of Health LaW. The 2023 Physical Activity and Exercise Guide for Health Promotion. Available from: https://www.mhlw.go.jp/content/10904750/001171393.pdf. Accessed 11/19 2024.\u003c/li\u003e\n\u003cli\u003eWilks H, Aschenbrenner AJ, Gordon BA, Balota DA, Fagan AM, Musiek E, et al. Sharper in the morning: Cognitive time of day effects revealed with high-frequency smartphone testing. J Clin Exp Neuropsychol. 2021;43(8):825-37. doi:10.1080/13803395.2021.2009447.\u003c/li\u003e\n\u003cli\u003eWiłkość-Dębczyńska M, Liberacka-Dwojak M. Time of day and chronotype in the assessment of cognitive functions. Postep Psychiatr Neurol. 2023;32(3):162-66. doi:10.5114/ppn.2023.132032.\u003c/li\u003e\n\u003cli\u003eAnderson JAE, Campbell KL, Amer T, Grady CL, Hasher L. Timing is everything: Age differences in the cognitive control network are modulated by time of day. Psychol Aging. 2014;29(3):648-57. doi:10.1037/a0037243.\u003c/li\u003e\n\u003cli\u003eStenholm S, Pulakka A, Leskinen T, Pentti J, Heinonen OJ, Koster A, et al. Daily Physical Activity Patterns and Their Association With Health-Related Physical Fitness Among Aging Workers\u0026mdash;The Finnish Retirement and Aging Study. The Journals of Gerontology: Series A. 2020;76(7):1242-50. doi:10.1093/gerona/glaa193.\u003c/li\u003e\n\u003cli\u003eErickson ML, Blackwell TL, Mau T, Cawthon PM, Glynn NW, Qiao YS, et al. Age Is Associated With Dampened Circadian Patterns of Rest and Activity: The Study of Muscle, Mobility, and Aging (SOMMA). J Gerontol A Biol Sci Med Sci. 2024;79(4). doi:10.1093/gerona/glae049.\u003c/li\u003e\n\u003cli\u003eAng G, Tan CS, Lim N, Tan J, M\u0026uuml;ller-Riemenschneider F, Cook AR, et al. Hourly step recommendations to achieve daily goals for working and older adults. Communications Medicine. 2024;4(1):132. doi:10.1038/s43856-024-00537-4.\u003c/li\u003e\n\u003cli\u003eCopeland JL, Esliger DW. Accelerometer Assessment of Physical Activity in Active, Healthy Older Adults. Journal of Aging and Physical Activity. 2009;17(1):17-30. doi:10.1123/japa.17.1.17.\u003c/li\u003e\n\u003cli\u003eDelobelle J, Compernolle S, Vetrovsky T, Van Cauwenberg J, Van Dyck D. Contexts, affective and physical states and their variations during physical activity in older adults: an intensive longitudinal study with sensor-triggered event-based ecological momentary assessments. International Journal of Behavioral Nutrition and Physical Activity. 2025;22(1):30. doi:10.1186/s12966-025-01724-9.\u003c/li\u003e\n\u003cli\u003eVan Holle V, McNaughton SA, Teychenne M, Timperio A, Van Dyck D, De Bourdeaudhuij I, et al. Social and physical environmental correlates of adults\u0026apos; weekend sitting time and moderating effects of retirement status and physical health. Int J Environ Res Public Health. 2014;11(9):9790-810. doi:10.3390/ijerph110909790.\u003c/li\u003e\n\u003cli\u003ePulakka A, Leskinen T, Koster A, Pentti J, Vahtera J, Stenholm S. Daily physical activity patterns among aging workers: the Finnish Retirement and Aging Study (FIREA). Occup Environ Med. 2019;76(1):33-39. doi:10.1136/oemed-2018-105266.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Circadian rhythm, Diurnal steps, Older adults, Objectively measured physical activity, Functional data analysis, Social activity","lastPublishedDoi":"10.21203/rs.3.rs-6769060/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6769060/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe timing of physical activity, particularly afternoon activity, is associated with positive health outcomes in older adults. It is plausible that the benefits of afternoon activity may partly reflect increased social activity among lifestyle activities. We tested the hypothesis that social activity specifically is associated with greater physical activity in the afternoon among lifestyle activities.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this longitudinal observational study, 2,578 community-dwelling adults aged 65 years and older from the National Center for Geriatrics and Gerontology\u0026mdash;Study of Geriatric Syndromes cohort completed a lifestyle activities questionnaire at baseline, which yielded scores in cognitive, physical, and social domains. Participants wore accelerometers for at least seven valid days (\u0026ge;\u0026thinsp;10 h/day), and mean steps were calculated for eight three‐hour spans over 24 hours. Correlation analyses were also conducted to explore relationships among the three lifestyle-activity domains. We applied function‐on‐scalar regression models to examine the association between each activity score and the timing of daily steps, adjusting for demographic, health, and behavioral covariates. Stratified analyses by age group, sex, and total daily step counts were conducted, along with a sensitivity analysis restricted to daytime hours.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the 2,578 participants (mean age 70.7 years, 57% women), weak but positive correlations among cognitive, physical, and social activity scores were observed. Higher cognitive activity scores were associated with fewer steps in the early morning; physical activity scores were positively associated with steps from mid-morning to early afternoon; and social activity scores showed modest positive associations with steps throughout the day, especially in the afternoon. These patterns were consistent across age, sex, and daily-step-count groups, and they were also supported by the sensitivity analysis; however, associations between social activity and step counts were not statistically significant among men nor among adults aged over 75 years.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAssociations between lifestyle activities and timing of daily steps vary by activity type and time of day: cognitive activities relate to fewer morning steps, physical activities relate to a morning peak, and social activities relate to sustainably more afternoon steps. These findings suggest that the previously reported association between afternoon physical activity and favorable health outcomes may partly stem from increased social activity.\u003c/p\u003e","manuscriptTitle":"Association of Lifestyle Activities with Daily Physical Activity Timing in Community-Dwelling Older Adults: A longitudinal observational study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-10 18:12:41","doi":"10.21203/rs.3.rs-6769060/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ed298b6d-5c0d-4f83-a5bf-3a6a8f56a340","owner":[],"postedDate":"June 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T12:08:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-10 18:12:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6769060","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6769060","identity":"rs-6769060","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}