Abstract
Background
Older adults are highly sedentary, and too much sedentary behavior (SB) is associated with negative health effects, but little is known about SB patterns and their associations with functional status.
Aims
To examine the association between objectively measured sedentary behavior time (SBT) and sedentary behavior fragmentation (SBF) and functional status in older adults using the National Health Aging Trends Study (NHATS) dataset, a nationally representative sample from 2021.
Methods
Data from NHATS were analyzed using weighted linear regressions to examine the association between objective measures of SBT (mean hours spent in SB/day during waking hours) and SBF, and six functional variables (difficulties with activities of daily living [ADL], short physical performance battery, hand grip strength, immediate word recall, delayed word recall, and mental health), accounting for sociodemographic, body mass index, and the number of chronic conditions.
Results
A total of 738 individuals from the NHATS were included. Higher SBT was associated with greater difficulties with ADL, poorer short physical performance battery and hand grip strength, lower scores in both immediate and delayed word recall, and poorer mental health. Higher SBF was associated with fewer difficulties with ADL, better short physical performance battery and hand grip strength, a higher score in immediate word recall, and better mental health.
Discussionand conclusions
Greater fragmentation of SB was associated with better function, and increasing SBF may be a useful strategy for mitigating the effects of SB in older adults, but prospective research is needed to support this approach.
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Background
High sedentary behavior (SB) in older adults is associated with adverse health effects [1]. Too much SB is associated with poorer cognitive and physical function [1], a higher risk of disability in activities of daily living, instrumental activities of daily living [2], physical frailty [3], and premature mortality [4]. Sedentary behavior is any waking behavior in a sitting or lying position that requires low energy expenditure (\(\le\) 1.5 metabolic equivalents) [5]. Evidence indicates older adults are more sedentary than other age groups, [6] spending 9.4 h/day during their waking hours in SB [7]. Given the rapidly aging population worldwide [8], high SB among older adults poses a growing public health concern in many countries.
Previous studies linking SB with health outcomes in older adults mainly focused on the total sedentary time [7, 9], and there is limited research on patterns of SB accumulation, such as sedentary behavior fragmentation (SBF). Sedentary behavior fragmentation is a relatively new concept that quantifies the likelihood of breaking up bouts of sedentary time by transitioning to any activity, exceeding low energy expenditure (> 1.5 metabolic equivalents) [10]. Sedentary behavior fragmentation is distinct from the total volume of SB. For example, one individual may accumulate sedentary time through a few bouts of prolonged sitting, whereas another may accumulate sedentary time in numerous short sitting bouts throughout the day. Although both individuals spend similar amounts of total sedentary time, the number of transitions from sitting to the active state can differ significantly, potentially influencing mental health, and physical and cognitive function. Currently, studies have suggested that higher SBF is associated with a better cardiometabolic health [11, 12]. However, SBF still remains unexplored in association with health and physical and cognitive functional status in older adults.
Reducing SB could be an important target to promote mental health, and physical and cognitive function in older adults. There are no U.S. guidelines for sedentary behavior for older adults, but Canadian guidelines recommend limiting sedentary time to 8 h or less a day [13]. Achieving the recommended levels of total sedentary time may not be realistic for highly sedentary older adults. However, interventions focused on components of SB, such as breaking up bouts of sedentary time and increasing SBF, may be more feasible and effective for older adults because intervention strategies could include simply standing up for brief intervals. Understanding sedentary patterns and their effects on function in older adults may have implications for the development of interventions to target SB.
The aims of this study were to describe the SB pattern among older adults and examine the association between daily mean SBT and SBF and functional variables using the National Health and Aging Trends Study (NHATS) dataset gathered from a nationally representative sample of US older adults.
Methods
Data sources and study population
This is a cross-sectional secondary analysis using a dataset from the National Health and Aging Trends Study (NHATS). The NHATS researchers collect data annually from a nationally representative panel of older Medicare beneficiaries (aged \(\ge\) 65) living in the community, residential care, and nursing homes in the United States [14]. Data have been collected since 2011. We analyzed the public dataset in Round 11 of the NHATS, which was collected in 2021. In Round 11, the NHATS researchers began collecting physical activity data using wrist accelerometry. The accelerometry sample was selected proportional to the Round 9 analytic weight [15]. Among NHATS participants who were eligible to wear an accelerometer, 872 respondents completed NHATS in Round 11, and 747 returned the accelerometer with usable data [15]. Respondents with at least 3 valid days of accelerometry data were included; nine were excluded, leaving a study sample of 738 participants.
Measurements
Functional Variables Functional variables included difficulty with activities of daily living, lower extremity function, limb muscle strength, cognitive function, and mental health. The difficulty with activities of daily living (ADL) was based on self-reported difficulty with six activities: eating, showering or bathing, using the toilet, getting dressed, getting out of bed, and getting around inside the home [16]. The possible number of difficulties with ADL ranged from 0 to 6, where a higher number indicates a greater disability. Lower extremity function was based on the short physical performance battery (SPPB) tests of gait speed, chair stand, and balance [17]. Performance of each activity scored from 0 to 4, and the total possible score ranged from 0–12, where higher scores indicate a better lower extremity function [17]. Limb muscle strength was based on hand grip strength (in kg) in the self-reported dominant hand. Two measures were taken, and the highest score was used [18]. Cognitive function was based on the immediate word recall score and delayed word recall score. Potential scores for each immediate and delayed word recall ranged from 0 to 10, where a higher score indicates a better cognitive function [19]. Mental health was assessed by summing scores of two items assessing depression from Patient Health Questionnaire-2 and two items assessing anxiety from the Generalized Anxiety Disorder-2 scale [20]. Possible scores ranged from 4 to 16, with a higher score indicating poorer mental health [20]. The reliability and validity of this scale have been supported [21].
Sedentary behavior Sedentary behavior was measured with the Actigraph CentrePoint Insight Watch (“Activity Watch”). The NHATS participants were instructed to wear the Activity Watch 24 h a day on self-reported non-dominant wrist for seven consecutive days, except for swimming or bathing lasting longer than 30 min. The accelerometer records wrist movement in units of gravity (g) at a sampling rate of 64 Hz [15]. After the data collection period, participants returned the monitor to a research center via prepaid padded envelopes. The accelerometry data were processed using minute-level epochs by the Johns Hopkins research team [15]. Nonwear time was detected and removed using a 90 consecutive minutes threshold [22]. A valid day was defined as wearing the device \(>\) 90% of each day (1296 min a day) [15].
We processed the NHATS accelerometry data (accelerometry detailed file) using the R ARCTOOLS package [23] to obtain SB variables for data collected between 5:01 am and 10:59 pm, an approximation of waking hours [24]. Sedentary behavior was defined as the time spent below a threshold of 1853 counts a minute [25]. A sedentary bout was defined as consecutive minutes in the sedentary state lasting at least 1 min [26]. Two SB variables were used from the NHATS accelerometry dataset: daily mean sedentary time (SBT) and sedentary behavior fragmentation (SBF). The SBF reflected the probability of transitioning from a sedentary state to an active state and was calculated as the reciprocal of the average sedentary bout duration for each study participant (= 1/ mean sedentary bout length) [26]. A higher value indicates a more fragmented SB.
Covariates Covariates included body mass index (BMI), the number of comorbidities, and sociodemographic characteristics: age (categorized into 2 age intervals [65–79 and 80 and over]), sex, race/ethnicity (non-Hispanic White and Other), education (less than a college degree and college degree), and marital status (married/partnered and not married), and residence (community and residential care facilities/nursing homes). Body mass index was calculated from participant-reported height and weight. The number of chronic conditions was based on self-reported chronic conditions, including heart attack, heart disease, blood pressure, arthritis, osteoporosis, diabetes, lung, stroke, dementia, and cancer.
Statistical analysis
Descriptive statistics (the count and percentages for categorical variables, mean and standard deviations for continuous variables) were calculated to summarize covariates (socio-demographics, BMI, the number of comorbidities), six functional variables of interest, and SB variables.
We conducted 12 weighted linear regressions to investigate the relationship between each SB variable and each of 6 functional variables of interest, controlling for 6 sociodemographic characteristics (age, sex, race/ethnicity, educational level, marital status, and residence), the number of comorbidities, and BMI. Each functional variable was considered in its own model. Due to multicollinearity between SB variables (SBT and SBF), we did not include both SB variables in the same model. The complex sample design and sampling weights in Round 11 were accounted for in all analyses of this study. The adjusted coefficients, standard errors, and 95% confidence intervals were computed. P-values < 0.05 were considered significant, but we report p-values to 3 decimal places. All statistical analyses were performed using Stata/BE 17.0 software.
Results
Characteristics of the study participants
Table 1 shows the characteristics of the participants. More than half of the participants (60.3%) were younger than 80 years old. Most were community-dwelling (94.3%) with a mean BMI of 28.2 (SD 6.9) kg/m2 and a mean of 2.9 (SD 1.3) chronic conditions. The most prevalent chronic condition was high blood pressure, affecting 74.4% of study participants, followed by arthritis (72.6%) and osteoporosis (36.3%). Participants spent an average of 12.7 h/day (SD 2.0) in SB during waking hours (5:01 am-10:59 pm) and had a sedentary fragmentation of 0.11 (SD 0.04). Total active time/day, mean sedentary bout duration/day, and sedentary behavior fragmentation are shown in Table 1.
Sedentary behavior and functional variables
Table 2 shows the relationships between mean SBT and functional variables. In the weighted adjusted models, higher SBT was significantly associated with greater difficulty with ADL (\(\beta\)= 0.17, 95% CI [0.10, 0.25], p < 0.001), lower SPPB scores (\(\beta\)=-0.43, 95% CI [– 0.57, – 0.28], p < 0.001), lower hand grip scores (\(\beta\) = – 0.98, 95% CI [– 1.48, – 0.48], p < 0.001), lower immediate word recall scores (\(\beta\) = – 0.09, 95% CI [– 0.16, – 0.02], p = 0.013) and delayed word recall score (\(\beta\) = – 0.10, 95% CI [-0.19, – 0.01], p = 0.025), and higher mental health scores (\(\beta\) = 0.15, 95% CI [0.04, 0.26], p = 0.007) (see Table 2).
Table 3 shows the result of the linear regression models that examine the relationships between SBF and functional variables. In the weighted adjusted models, higher SBF was significantly associated with fewer difficulties with ADL (\(\beta\) = – 7.04, 95% CI [-10.54, -3.53], p < 0.001), higher SPPB (\(\beta\) = 16.54, 95% CI [9.12, 23.96], p < 0.001), higher hand grip strength (\(\beta\) = 38.11, 95% CI [12.26, 63.95], p = 0.005), higher immediate word recall (\(\beta\) = 4.17, 95% CI [0.83, 7.51], p = 0.015), and lower mental health scores (\(\beta\) = – 6.90, 95% CI [-12.19, -1.61], p = 0.011) (see Table 3). However, SBF was not associated with delayed word recall (\(\beta\) = 1.77, 95% CI [– 3.00, 6.54], p = 0.467).
Discussion
In this study, lower SBT and higher SBF were associated with fewer difficulties with ADL, better physical and cognitive function, and better mental health. To our knowledge, this is the first study to explore the associations between SBF and functional variables in older adults from a nationally representative US sample.
This study supports the existing evidence that US older adults spend most of their waking hours in SB. The SBT in the present study is consistent with a previous study using the same accelerometer [24] but higher than the average sedentary time reported in a systemic review of community-dwelling older adults (SBT = 9.4 h) [7]. The inconsistent findings may be explained by the heterogeneity of measurement tools. For example, the review was primarily based on studies with waist-worn ActiGraph, whereas the NHATS used wrist-worn ActiGraph, in which processing data reduction is less established [27, 28].
It is well-established that low sedentary time is protective for health, physical function, and depression [29,30,31,32,33,34,35]. We confirmed the association between low SBT and better physical and cognitive function and mental health. The strength of the association between SBT and SPPB in this study indicates a potentially clinically meaningful effect. For example, for every 1-h decrease in SBT, we observed an increase of 0.43 points in SPPB scores. The result is within the ranges of minimally significant changes in 0.3–0.8 points for the SPPB [36].
The observed positive relationship between SBF and function is consistent with prior research focusing on breaks in SB. A greater number of SB breaks was associated with lower cardiometabolic risks [37], better physical performance [38], and a lower likelihood of disability in instrumental activities of daily living [2]. The number of SB breaks captures the number of interruptions in SB but doesn’t reflect the duration of sitting bouts. However, SBF captures the tendency to stay in SB by capturing sedentary breaks and the duration of sitting bouts [26]. This supported the need for tailored interventions addressing SBF to improve the functions of sedentary older adults.
The inconsistent relationship between SBT and SBF and cognitive variables in this study is consistent with prior reports. For instance, a systemic review of 18 studies demonstrated varied and inconclusive evidence for the association between SB and cognitive function in older adults [39]. Similarly, another study reported that sedentary time had a minor association with executive functions, but prolonged sedentary time was not associated with any cognitive test scores [40]. The inconsistent results may be attributed to the effects of different types of SB on cognition in older adults [41]. Mentally active SB (e.g., reading or writing) may be protective for cognition, contrary to the harmful effects of mentally passive SB (e.g., watching TV) on cognition [41].
The above findings highlight the potential benefits of both reducing total sedentary time and increasing SBF. Prolonged sitting hours with frequent SB breaks may be targeted through interventions, especially in highly sedentary older populations or potentially individuals who have mild mobility issues in community or care settings. Clinicians may encourage older adults to reduce sedentary time, especially prolonged sedentary time, and suggest more transitions from sitting to standing. This may provide an opportunity to curb SB for sedentary older adults.
This research advances the science of SB in older adults, and it has several strengths. We used a national dataset collected from a nationally representative sample of US older adults. The use of objectively measured SB reduced the risk of recall bias and is more accurate than self-reported measures. Limitations of this research include a cross-sectional analysis that cannot establish the causality of the relationships between SB patterns and functional variables. Our data did not include information about individual sleep information, affecting the estimates of the SB variables.
Conclusion
This is the first to identify the associations between SBF and functional status in older adults from a nationally representative US sample. Older individuals with higher sedentary time and lower sedentary fragmentation tend to report greater difficulties with ADL, poorer upper and lower extremity function, poorer cognitive function, and worse mental health. Our study suggests that interventions may focus on reducing overall sedentary time and increasing sedentary behavior fragmentation to promote functions in older adults, but prospective research is needed to confirm this conclusion.
Data availability
Publicly available data from the National Health and Aging Trends Study (NHATS) were analyzed for this research. www.nhats.org
References
Saunders TJ, McIsaac T, Douillette K et al (2020) Sedentary behaviour and health in adults: an overview of systematic reviews. Appl Physiol Nutr Metab 45:S197-s217. https://doi.org/10.1139/apnm-2020-0272
Chen T, Narazaki K, Haeuchi Y et al (2016) Associations of sedentary time and breaks in sedentary time with disability in instrumental activities of daily living in community-dwelling older adults. J Phys Act Health 13:303–309. https://doi.org/10.1123/jpah.2015-0090
Song J, Lindquist LA, Chang W et al (2015) Sedentary behavior as a risk factor for physical frailty independent of moderate activity: results from the osteoarthritis initiative. Am J Public Health 105:1439–1445. https://doi.org/10.2105/ajph.2014.302540
Ekelund U, Tarp J, Steene-Johannessen J et al (2019) Dose-response associations between accelerometry measured physical activity and sedentary time and all cause mortality: systematic review and harmonised meta-analysis. BMJ 366:l4570. https://doi.org/10.1136/bmj.l4570
Tremblay MS, Aubert S, Barnes JD et al (2017) Sedentary behavior research network (SBRN)—terminology consensus project process and outcome. Int J Behav Nutr Phys Act 14:75. https://doi.org/10.1186/s12966-017-0525-8
Rezende LFMd, Rey-López JP, Matsudo VKR et al (2014) Sedentary behavior and health outcomes among older adults: a systematic review. BMC Public Health 14:333. https://doi.org/10.1186/1471-2458-14-333
Harvey JA, Chastin SF, Skelton DA (2015) How sedentary are older people? a systematic review of the amount of sedentary behavior. J Aging Phys Act 23:471–487. https://doi.org/10.1123/japa.2014-0164
World Health Organization. Ageing and health 2022 [Available from: https://www.who.int/news-room/fact-sheets/detail/ageing-and-health]. Accessed 11 Mar 2023
Copeland JL, Ashe MC, Biddle SJ et al (2017) Sedentary time in older adults: a critical review of measurement, associations with health, and interventions. Br J Sports Med 51:1539. https://doi.org/10.1136/bjsports-2016-097210
Wanigatunga AA, Cai Y, Urbanek JK et al (2022) Objectively Measured Patterns of Daily Physical Activity and Phenotypic Frailty. J Gerontol A Biol Sci Med Sci 77:1882–1889. https://doi.org/10.1093/gerona/glab278
Chastin SF, Egerton T, Leask C et al (2015) Meta-analysis of the relationship between breaks in sedentary behavior and cardiometabolic health. Obesity (Silver Spring) 23:1800–1810. https://doi.org/10.1002/oby.21180
Diaz KM, Howard VJ, Hutto B et al (2017) Patterns of sedentary behavior and mortality in U.S. middle-aged and older adults: a national cohort study. Ann Intern Med 167:465–475. https://doi.org/10.7326/m17-0212
Ross R, Chaput JP, Giangregorio LM et al (2020) Canadian 24-Hour Movement Guidelines for Adults aged 18–64 years and Adults aged 65 years or older: an integration of physical activity, sedentary behaviour, and sleep. Appl Physiol Nutr Metab 45:S57-s102. https://doi.org/10.1139/apnm-2020-0467
Freedman VA, Schrack J, Skehan M, et al (2022) National Health and Aging Trends Study User Guide: Rounds 1–11 Final Release. Johns Hopkins University School of Public Health, Baltimore. Available at www.NHATS.org. Accessed 21 Dec 2022
Schrack JA, Skehan ME, Zipunnikov V, et al (2022) National Health and Aging Trends Study Accelerometry User Guide: Final Release. Johns Hopkins University Bloomberg School of Public Health, Baltimore. Available at www.NHATS.org. Accessed 21 Dec 2022
Freedman VA, Kasper JD, Cornman JC et al (2011) Validation of new measures of disability and functioning in the national health and aging trends study. J Gerontol Ser A 66A:1013–1021. https://doi.org/10.1093/gerona/glr087
Guralnik JM, Simonsick EM, Ferrucci L et al (1994) A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol 49:M85-94. https://doi.org/10.1093/geronj/49.2.m85
Lim JP, Yew S, Tay L et al (2020) Grip strength criterion matters: impact of average versus maximum handgrip strength on sarcopenia prevalence and predictive validity for low physical performance. J Nutr Health Aging 24:1031–1035. https://doi.org/10.1007/s12603-020-1515-0
Shankle WR, Romney AK, Hara J et al (2005) Methods to improve the detection of mild cognitive impairment. Proc Natl Acad Sci U S A 102:4919–4924. https://doi.org/10.1073/pnas.0501157102
Kroenke K, Spitzer RL, Williams JB (2003) The Patient Health questionnaire-2: validity of a two-item depression screener. Med Care 41:1284–1292. https://doi.org/10.1097/01.Mlr.0000093487.78664.3c
Kroenke K, Spitzer RL, Williams JB et al (2009) An ultra-brief screening scale for anxiety and depression: the PHQ-4. Psychosomatics 50:613–621. https://doi.org/10.1176/appi.psy.50.6.613
Choi L, Ward SC, Schnelle JF et al (2012) Assessment of wear/nonwear time classification algorithms for triaxial accelerometer. Med Sci Sports Exerc 44:2009–2016. https://doi.org/10.1249/MSS.0b013e318258cb36
Karas M, Schrack J, Urbanek J (2021) arctools: Processing and Physical Activity Summaries of Minute Level Activity Data. R package version 11:3
Wanigatunga AA, Di J, Zipunnikov V et al (2019) Association of Total Daily Physical Activity and Fragmented Physical Activity With Mortality in Older Adults. JAMA Netw Open 2:e1912352. https://doi.org/10.1001/jamanetworkopen.2019.12352
Koster A, Shiroma EJ, Caserotti P et al (2016) Comparison of sedentary estimates between activPAL and Hip- and Wrist-Worn ActiGraph. Med Sci Sports Exerc 48:1514–1522. https://doi.org/10.1249/mss.0000000000000924
Schrack JA, Kuo PL, Wanigatunga AA et al (2019) Active-to-Sedentary Behavior Transitions, Fatigability, and Physical Functioning in Older Adults. J Gerontol A Biol Sci Med Sci 74:560–567. https://doi.org/10.1093/gerona/gly243
Loprinzi PD, Smith B (2017) Comparison between wrist-worn and waist-worn accelerometry. J Phys Act Health 14:539–545. https://doi.org/10.1123/jpah.2016-0211
Webster KE, Colabianchi N, Ploutz-Snyder R et al (2021) Comparative assessment of ActiGraph data processing techniques for measuring sedentary behavior in adults with COPD. Physiol Meas. https://doi.org/10.1088/1361-6579/ac18fe
Amaral Gomes ES, Ramsey KA, Rojer AGM et al (2021) The association of objectively measured physical activity and sedentary behavior with (Instrumental) activities of daily living in community-dwelling older adults: a systematic review. Clin Interv Aging 16:1877–1915. https://doi.org/10.2147/cia.S326686
Eriksson M, Nääs S, Berginström N et al (2020) Sedentary behavior as a potential risk factor for depression among 70-year-olds. J Affect Disord 263:605–608. https://doi.org/10.1016/j.jad.2019.11.035
Katzmarzyk PT, Powell KE, Jakicic JM et al (2019) Sedentary behavior and health: update from the 2018 physical activity guidelines advisory committee. Med Sci Sports Exerc 51:1227–1241. https://doi.org/10.1249/mss.0000000000001935
Rojer AGM, Ramsey KA, Amaral Gomes ES et al (2021) Objectively assessed physical activity and sedentary behavior and global cognitive function in older adults: a systematic review. Mech Ageing Dev 198:111524. https://doi.org/10.1016/j.mad.2021.111524
Rosenberg DE, Bellettiere J, Gardiner PA et al (2015) Independent associations between sedentary behaviors and mental, cognitive, physical, and functional health among older adults in retirement communities. J Gerontol Ser A 71:78–83. https://doi.org/10.1093/gerona/glv103
Yerramalla MS, van Hees VT, Chen M et al (2022) Objectively measured total sedentary time and pattern of sedentary accumulation in older adults: associations with incident cardiovascular disease and all-cause mortality. J Gerontol Ser A 77:842–850. https://doi.org/10.1093/gerona/glac023
Cunningham C, O’Sullivan R, Caserotti P et al (2020) Consequences of physical inactivity in older adults: a systematic review of reviews and meta-analyses. Scand J Med Sci Sports 30:816–827. https://doi.org/10.1111/sms.13616
Kwon S, Perera S, Pahor M et al (2009) What is a meaningful change in physical performance? Findings from a clinical trial in older adults (the LIFE-P study). J Nutr Health Aging 13:538–544. https://doi.org/10.1007/s12603-009-0104-z
Healy GN, Matthews CE, Dunstan DW et al (2011) Sedentary time and cardio-metabolic biomarkers in US adults: NHANES 2003–06. Eur Heart J 32:590–597. https://doi.org/10.1093/eurheartj/ehq451
Sardinha LB, Santos DA, Silva AM et al (2015) Breaking-up sedentary time is associated with physical function in older adults. J Gerontol Ser A 70:119–124. https://doi.org/10.1093/gerona/glu193
Olanrewaju O, Stockwell S, Stubbs B et al (2020) Sedentary behaviours, cognitive function, and possible mechanisms in older adults: a systematic review. Aging Clin Exp Res 32:969–984. https://doi.org/10.1007/s40520-019-01457-3
Wanigatunga AA, Manini TM, Cook DR et al (2018) Community-based activity and sedentary patterns are associated with cognitive performance in mobility-limited older adults. Front Aging Neurosci 10:341. https://doi.org/10.3389/fnagi.2018.00341
Zhou W, Webster KE, Veliz PT et al (2022) Profiles of sedentary behaviors in the oldest old: findings from the National Health and Aging Trends Study. Aging Clin Exp Res 34:2071–2079. https://doi.org/10.1007/s40520-022-02157-1
Acknowledgements
We would like to thank Youmin Cho and Yeamin Huh for their assistance with this research.
Funding
J.Y.S. was supported by NIH T32 NR016914. K.W. was supported by NIH T32 NR018407.
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JYS, WZ, and JLL contributed to the study conception and design. JYS, WZ, and DJM contributed to the data analysis. JYS, WZ, KEW-D, DJM, and JLL contributed to the preparation and revision of the manuscript. All the authors approved the final version of the manuscript.
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NHATS was approved by the Johns Hopkins Bloomberg School of Public Health Institutional Review Board.
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Son, J.Y., Zhou, W., Webster-Dekker, K.E. et al. Association between accelerometry measured patterns of sedentary behaviors and functional status in older adults. Aging Clin Exp Res 36, 11 (2024). https://doi.org/10.1007/s40520-023-02644-z
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DOI: https://doi.org/10.1007/s40520-023-02644-z