Clustering of Domain-Specific Sedentary Behaviors and Their Association With Physical Function Among Community-Dwelling Older Adults

in Journal of Physical Activity and Health

Background: The present study examined the cluster of domain-specific sedentary behaviors (SBs) and their associations with physical function among community-dwelling older adults to identify the target groups that require intervention for SBs. Methods: A total of 314 older adults who participated in a population-based cross-sectional survey and an on-site functional assessment in Matsudo City in Chiba participated in this study. Participants were asked to report the daily average of 6 domain-specific SBs. To identify the cluster of domain-specific SBs, hierarchical cluster analysis was performed using the Ward method. Analysis of covariance adjusted for sociodemographic factors, exercise habit, chronic disease, and total SB time was performed to examine the associations between each cluster and physical functional status. Results: The average age of the participants was 74.5 (5.2) years. The 4 clusters identified were leisure cluster, low cluster, work and personal computer use cluster, and television viewing cluster. The analysis of covariance adjusted for covariates showed that grip strength (P = .01), maximum walking speed (P = .03), and 1-leg standing time (P = .03) were significantly poorer in the television viewing cluster than other clusters. Conclusions: It has been concluded that the television viewing group identified as a high-risk group of physical functional decline; therefore, interventions targeting this group are needed to prevent physical functional decline.

With the rapid growth of the aging population, functional status decline and the increasing burden of long-term care for older adults have become major issues in public health. A previous study reported that behavioral factors such as physical inactivity have a negative impact on functional status among older adults.1 Therefore, it is important to examine effective strategies aimed at reducing physical inactivity among older adults to help prevent functional decline and improve quality of life.

Previous studies have shown an association between greater time spent in total sedentary behavior (SB) and poorer health outcomes, such as high mortality, among older adults.2,3 SBs are defined as any waking behavior characterized by an energy expenditure of ≤1.5 metabolic equivalents while in a sitting, reclining, or recumbent posture that is distinct from physical inactivity.4,5 Moreover, greater time spent in total SBs results in poorer cognitive and physical function performance, which is independent of engaging in physical activity of moderate to vigorous intensity.69

Sedentary behaviors occur on a daily basis within different domains, including work, transport use, and leisure time (eg, watching television [TV], computer use, or other leisure activities). Each domain-specific SB does not occur in isolation. It is, therefore, important to identify the patterns of domain-specific behaviors and their associations within population groups as well as health outcomes to identify the needs for interventions aimed at reducing SBs and the population groups requiring intervention. With the rapid aging of our society, it is necessary to identify the target populations where interventions are needed and target SBs that need to be addressed to help prevent decline in physical function. These interventions will, in turn, help to decrease the burden of long-term care for community-dwelling older adults. However, most previous studies have examined the associations of only one specific domain of SBs with cognitive and physical functions among older adults.1016 Moreover, although Shibata et al17 showed the objectively assessed patterns and domain-specific of SBs and their interrelationships among community-dwelling older adults, little is known about the clusters of domain-specific SBs and their association with physical functional status. This study aimed to identify the clusters of domain-specific SBs and their associations with physical function among community-dwelling older adults to develop an effective strategy for intervention to prevent functional status decline.

Methods

Participants

This cross-sectional study recruited participants from community-dwelling residents aged 65–85 years. Of the 107,928 adults aged 65 years and older in April 2013, 3000 residents living in Matsudo City, a Japanese local core city in Chiba, and a suburban area of central Tokyo were randomly selected from the registry of residential addresses and stratified by sex and age (65–74 or 75–85 y). Only one resident was selected from every household.

The survey activities occurred over a 10-month period between March and December 2013 and were divided into 2 phases: the questionnaire-based postal survey and the on-site physical and functional examinations. For the postal survey, invitation letters describing the content of the study were first mailed to all 3000 potential subjects 2 weeks before the survey. Nonrespondents were sent an additional request to join the survey. At the completion of the postal survey, all potential subjects were asked whether or not they could travel to a local testing site for the physical and functional examinations and participate in a future follow-up survey (on the last page of the questionnaire). Formal invitation letters for on-site physical and functional examination were sent via postal mail to respondents of the postal survey (n = 1250/3000; response rate: 41.7%), who agreed to participate in future additional study surveys (n = 951/1250; participant rate: 76.1%).

The study sample was comprised of participants who responded to the postal survey and attended the local testing site for physical and functional examinations (n = 349/951; 36.7%). Of these, participants with missing data for the relevant variables (n = 35) were excluded. Finally, 314 participants were included in the study.

Measurements

Self-reported SB

A 6-item survey was administered with a 1-week recall period using a self-reported questionnaire provided at the local testing site. Participants were asked to report their daily average SB time in hours and minutes over the preceding 7 days, across the following 6 domains: (1) being transported to and from a place by car; (2) using public transport; (3) working at a job; (4) watching TV, videos, and DVDs; (5) using a computer, cell phone, or tablet outside of working hours, and (6) leisure time (eg, talking, reading, listening to music, and engaging in a hobby while sitting). Participants were asked to provide separate responses for workdays (weekdays) and nonworkdays (weekends). A scale was developed for use in this study with reference to a previous study.18 For unemployed participants, weekdays instead of workdays and weekends instead of nonworkdays were used in the scale. Each domain-specific SB time (in hours per week) was also calculated by weighting for 5 workdays and 2 nonworkdays ([daily average for workdays × 5] + [daily average for nonworkdays × 2]). The total SB time was calculated by adding together each domain’s sedentary time.

Physical Function Examinations (Outcome Variable)

Physical function examinations included the index values of grip strength (in kilograms), maximum walking speed (in meter per minute), 1-leg standing time (in seconds), and timed up and go test (in seconds), which have been used for assessment of physical function among community-dwelling older adults.1922 At the local testing site, all values were collected by trained investigators based on standard procedures.23 The maximum walking speed is an established indicator of overall gait performance and was measured over a 5-m distance between markers placed at 3 and 8 m from the start of the walkway, on flat ground, along an 11-m line. The maximum walking speed (in meter per minute) was defined as the highest value measured in 2 trials. Grip strength is a valid indicator of overall muscle strength and is a particularly useful indicator of upper extremity strength. Grip strength of the dominant hand was measured using a Smedley-type hand dynamometer (Takei Scientific Instruments Co, Ltd, Niigata, Japan). The highest value in the 2 trials was used in the analysis. One-leg standing time is an indicator of static balance. Standing time duration was measured for up to 60 seconds, and the highest value measured in 2 trials was used in the analysis. The timed up and go test of static and dynamic balance assesses the time taken for a person to rise from a chair, walk 3 m, turn around, walk back to the chair, and sit down. The time(s) to complete the timed up and go test were measured in 2 trials, and the higher value from the 2 trials was used in the analysis.

Covariates

Sociodemographics

We collected data on the sex and age of the participants from the basic resident registry. The postal survey assessed the following sociodemographic and health-related measures using a self-reported questionnaire: educational attainment (completion of graduate school, college, 2-year college or career college, high school, or junior high school), living arrangements (alone, households consisting of 1 couple only, or different households within the same dwelling), and employment status (working days per week). Educational attainment, living arrangements, and employment status were each divided into 2 categories: educational attainment (university or further education and high school or less), living arrangements (alone and with others), and employment status (none or part-time work [<5 d] and full-time work [≥5 d]).

Exercise Habits

For the postal survey, exercise habits were assessed by asking participants whether they performed regular physical exercise for 2 or more days per week for at least 30 minutes per day using a self-reported questionnaire; participant exercise regimens were also assessed over a 1-year period.24

Body Mass Index

Body mass index (BMI; measured in kilograms per square meter) was measured at the local testing site. Participants were classified as underweight (<18.5 kg/m2), normal weight (18.5 to <25 kg/m2), or overweight/obesity (≥25 kg/m2) based on the definition provided in the Japanese Guidelines for the Management of Obesity Disease and previous studies.12,17,25,26

Chronic Disease

We assessed the chronic diseases using the self-reported questionnaire administered at the local testing site. We identified 13 chronic diseases (hypertension, cerebrovascular accident, heart disease, diabetes mellitus, dyslipidemia, hyperuricemia, arteriosclerosis obliterans, osteoporosis, osteoarthritis, spinal stenosis, rheumatoid arthritis, collagen diseases, and cancer) that were common in older adults in Japan. Prevalence of chronic disease was divided into 2 categories (≥1 or none).

Statistical Analyses

First, to identify the clusters of domain-specific SBs, we applied hierarchical cluster analysis using the Ward method, which is based on squared Euclidian distances. To determine the optimal number of clusters, we identified the points at which each cluster became clinically meaningful and the range of the average Euclidean distance coefficient became wider.27 Based on the 6 domain-specific SBs, 4 clusters appeared to be representative of the participants in this study. The 1-way analysis of variance test was used to investigate the differences between each cluster of the 6 domain-specific SBs.

Second, the chi-squared test was used to identify proportional differences in sex, age, educational attainment, BMI, chronic disease, and exercise habits within each cluster. The Fisher exact test was used to identify proportional differences in employment status, living arrangements, and BMI within each cluster. A 1-way analysis of variance was used to identify the differences in the mean total SB time (SD) within each cluster. Furthermore, a 1-way analysis of variance was conducted to examine the associations between each cluster and each physical function examination result (model A). Analysis of covariance adjusted for covariates (sex, age, educational attainment, employment status, living arrangements, BMI, chronic disease, exercise habits, and total SB time) was conducted to examine the associations between each cluster and the physical function examination results (model B). All statistical analyses were performed using IBM SPSS Statistics (version 23.0; IBM Corp, Armonk, NY), and statistical significance was set at 5%.

Ethics Approval and Consent to Participate

This study was approved by the Waseda University Institutional Committee on human research and the institutional review board of Waseda University and conformed to the Declaration of Helsinki. All participants provided written informed consent.

Results

The average age of the 314 participants was 74.5 (SD = 5.2) years. Of these, 61.5% were men, 191 (60.8%) had completed high school or less, 274 (87.3%) were living with others, 219 (69.7%) had normal weight, and 196 (62.4%) engaged in regular exercise. On average, the participants spent 50.8 (SD = 23.9) hours engaged in SBs per week.

The differences between the means of each cluster solution reported in row values for the 6 domain-specific SB times are presented in Table 1. The 4 clusters were classified as the leisure cluster (cluster 1), the low cluster (cluster 2), the work and personal computer (PC) use cluster (cluster 3), and the TV viewing cluster (cluster 4). Regarding the 4 clusters, the participants in the leisure cluster spent the greatest amount of time engaged in other leisure SBs and sitting in a car, followed by TV viewing participants and those using PCs. The low cluster was characterized by the least amount of time spent in TV viewing SBs and a lower amount of time spent in other domain-specific SBs. Participants in the work and PC use cluster spent the greatest amount of time at work and using a PC. The participants in the TV viewing cluster spent the greatest amount of time in TV viewing SBs.

Table 1

Mean Values of 6 Domain-Specific Sedentary Behaviors by Clusters

Domain-specific SB, h/wkCluster 1

Leisure SB (n = 181, 56.9%)
Cluster 2

Low SB (n = 86, 27.0%)
Cluster 3

Work and PC SB (n = 25, 7.9%)
Cluster 4

TV viewing SB (n = 22, 6.9%)
Pa
MeanSDMeanSDMeanSDMeanSD
Car3.04.47.411.75.25.87.811.3.150
Public transport2.13.72.03.83.13.43.14.2.312
Work0.21.02.45.430.212.00.00.0<.001
TV24.711.010.36.722.211.163.317.1<.001
PC5.88.61.82.79.08.33.16.4.008
Other leisure14.614.49.37.111.86.911.46.6.166

Abbreviations: PC, personal computer; SB, sedentary behavior; TV, television.

aOne-way analysis of variance.

Table 2 presents the differences between sociodemographic factors, exercise habits, chronic diseases, and total SB time among the 4 clusters. The proportion of participants with full-time work among the work and PC use cluster was significantly higher than other clusters (P < .001). The proportion of participants reporting exercise habits was significantly lower than that of participants reporting no exercise habits in the TV viewing cluster (P = .02). Total SB time was significantly lower in the low cluster than in other clusters (P < .001).

Table 2

Characteristics of 4 Clusters in 314 Older Adults

CharacteristicsTotalCluster 1

Leisure SB
Cluster 2

Low SB
Cluster 3

Work and PC SB
Cluster 4 TV viewing SBPa
N = 314n = 181n = 86n = 25n = 22
Gender, %      
 Men61.558.667.468.054.5.42
 Women38.541.432.632.045.5 
Age group, %      
 65–7449.749.748.856.045.5.90
 75–8550.350.351.244.054.5 
Educational attainment, %      
 ≤High school60.864.154.756.063.6.48
 ≥University39.235.945.344.036.4 
Living arrangements, %      
 Alone12.713.38.116.022.7.24
 With others87.386.791.984.077.3 
Employment status      
 Full-time work (≥5 d)10.81.715.172.00.0<.001
 Non or part-time work (<5 d)89.298.384.928.0100.0 
BMI, %      
 <18.54.53.37.00.09.1.04
 18.5 to <2565.370.262.864.036.4 
 ≥2530.326.530.236.054.5 
Exercise habit, %      
 Yes62.465.262.868.031.8.02
 No37.634.837.232.068.2 
Chronic disease, %      
 ≥173.672.470.984.081.8.46
 No26.427.629.116.018.2 
Mean total SB time (SD), h/wk50.8 (23.9)50.4 (14.4)b33.1 (22.6)81.4 (17.4)b,c88.7 (23.9)b,c<.001

Abbreviations: ANOVA, analysis of variance; BMI, body mass index; PC, personal computer; SB, sedentary behavior; TV, television.

aχ2 test, Fisher exact test, or 1-way ANOVA. bSignificantly greater time than cluster 2 by using Bonferroni-adjusted univariate multiple comparison. cSignificantly greater time than cluster 1 by using Bonferroni-adjusted univariate multiple comparison.

Table 3 presents the associations among the 4 clusters and each physical function examination, both unadjusted and adjusted for the covariates. All physical functions were poorer in the TV viewing group than in the other groups. The analysis of covariance (model B) showed that grip strength was significantly poorer in the TV viewing cluster than in the work and PC use cluster (P = .003). The maximum walking speed and the 1-leg standing time were lower in the TV viewing cluster than in the leisure cluster (maximum walking speed: P = .02; 1-leg standing time: P = .03).

Table 3

The Associations of the Clusters With Each Physical Function in 314 Older Adults

ClusterGrip strength, kgMaximum walking speed, m/min1-leg standing time, sTUG, s
Mean95% CIPadMean95% CIPadMean95% CIPadMean95% CIPad
Model Ab
 C126.625.5–27.7.001 106.1103.7–108.5.002>4**43.440.2–46.5.01>4*6.36.1–6.5.16 
 C228.526.7–30.4 >4**106.3102.3–110.3 >4**43.138.6–47.5 >4*6.15.9–6.4  
 C330.527.1–33.8 >4**108.7102.8–114.6 >4**48.840.4–57.2 >4*6.05.6–6.4  
 C422.218.5–25.9  92.084.0–100.0  29.318.7–39.9  6.75.9–7.6  
Model Bc
 C126.825.9–27.6.01 106.7104.4–109.0.03>4*44.141.1–47.1.04>4*6.36.1–6.4.61 
 C227.526.2–28.9  106.0102.3–109.7  44.339.6–49.1  6.15.9–6.4  
 C330.928.0–33.8 >4**103.195.1–111.0  38.828.6–49.0  6.45.8–7.0  
 C424.021.3–26.8  94.887.2–102.4  29.319.6–39.0  6.66.0–7.1  

Abbreviations: d, Bonferroni-adjusted univariate multiple comparison (*<.05, **<.01); ANCOVA, analysis of covariance; ANOVA, analysis of variance; C1, cluster 1 leisure SB; C2, cluster 2 low SB; C3, cluster 3 work and PC use SB; C4, cluster 4 TV viewing PC, personal computer; SB; CI, confidence interval; SB, sedentary behavior; TUG, timed up and go test; TV, television.

aOne-way ANOVA and ANCOVA. bOne-way ANOVA was performed to examine the associations between the 4 clusters and each physical function. cANCOVA adjusted for the covariates (sex, age, educational attainment, employment status, living arrangements, body mass index, exercise habit, and total SB time) was performed to examine the associations between the 4 clusters and each physical function.

Discussion

To our knowledge, this study is the first to examine the cluster of domain-specific SBs and their associations with physical function status among community-dwelling older adults. We identified 4 groups of domain-specific SBs. The total SB time was longer in the work and PC use group and the TV viewing group than in the low and leisure groups. Moreover, participants in the TV viewing group had poorer grip strength, walking speeds, and static balance than did those in the other groups after adjustment for the covariables.

This study showed that participants in the work and PC use and TV viewing groups spent a longer time in total SB than those in the low and leisure groups. Overall, 15% of the study participants represent a target subgroup for interventions for reducing SBs to decrease functional decline. Prior studies have reported that greater time in total SBs caused decline in cognitive and physical functions.69 Because Dunlop et al6 suggested that older adults spending almost 9 hours per day in SB faced more functional disability and older adults spending almost 63 hours per week in SBs would be at high risk of functional decline. Therefore, participants in the work and PC use group and TV viewing group are target groups for interventions, as these groups had greater total SB time than did the leisure group—more than 63 hours per week. This finding showed that SB time spent at work was the longest among participants in the work and PC use group, whereas that spent viewing TV was the longest among participants in the TV viewing group. Therefore, effective preventative intervention could be achieved by reducing the SB time within the work environment among the work and PC use group and by reducing the amount of TV watched among the TV viewing group. For the work and PC use group, it may be useful to introduce sit–stand devices or point-of-choice prompting software on the PC, as these interventions have demonstrated effectiveness in decreasing SB time in the workplace.2830 In addition, mobile health  applications with effective interventions for decreasing SB (eg, goal setting consultations and tailored feedback) have been developed for older adults.31,32 These mobile health applications may be able to reduce SB time in the TV viewing group due to the rising number of older adults using tablets and smartphones in Japan.33 There is an increasing need for the development and trial of these and other innovative interventions to reduce SB time.

Although the total time of SB was somewhat similar between the work and PC use group and TV viewing group, all physical functions were poorer in the TV viewing group than in the other groups. After adjustment for covariates, the results of the analysis showed that the TV viewing group had poorer grip strength than did the work and PC use cluster, and poorer gait performance and static balance than did the leisure cluster. These results may be partially explained by the difference in time spent within the domain-specific SBs between the TV viewing group and work and PC use group. The mental activity context of SBs, such as computer use or work, has been reported to be beneficially associated with physical and cognitive performance.11,12 Because the work and PC use group was more likely to spend time on mental activities context of SBs, it may have had a higher function status than the TV viewing group. Although the mechanism by which TV viewing entails more specific health risks than do the mechanisms behind other domain-specific SBs is unknown, a possible explanation is that TV viewing is more easily recalled and longer prolonged than other domain-specific SBs.11 TV viewing was associated with longer prolonged sedentary time and a lesser number of breaks of SB.17 Liao et al34 indicated the associations of duration of prolonged sedentary time with poorer gait performance. Sardinha et al35 showed that breaking up SB time was associated with better physical function in older adults. Consequently, the TV viewing group may have had poorer physical function, as they have longer prolonged sedentary time and fewer breaks from SB. Yasunaga et al36 showed that replacing small of SB and light-intensity physical activity contributed to improvement in physical function of older adults. Therefore, it may be a useful intervention to replace TV viewing sedentary time with other physical activities in community-dwelling older adults.

The major strength of this study is that it is the first to examine the cluster of domain-specific SBs and their associations with physical function status among community-dwelling older adults. A further strength of this study is the use of objective physical function examinations to assess physical function status of participants. However, several limitations must be considered in interpreting the study results. First, the analysis was cross-sectional; therefore, making determinations of cause and effect was not feasible. Second, the use of self-reported measures for domain-specific sedentary time could be subject to recall error and social desirability bias. In addition, because prior studies show that estimated SB time from self-reports is half of that measured objectively,37 the time of all SBs and domain-specific SBs may be underestimated. Third, selection bias may have occurred because the small number of respondents who visited the local testing site for physical and functional examinations may have been more active and healthier than nonparticipating older adults, as has been previously reported.38 In additional analysis of the postal survey data of this study, the proportion of exercise habits was higher in participants who attended the local testing site than in nonparticipants (participants vs nonparticipants: 62.0% vs 48.5%; P < .001, chi-squared test). This may limit the generalizability of our findings. The rate of the TV viewing group may be higher in the general population of community-dwelling older adults than in this study population because individuals with no exercise likely to be classified as TV viewing group than other groups. Moreover, the participants may have had higher physical function than that of the general population of community-dwelling older adults. This limitation may have led to an underestimation of the associations among the SB clusters and all physical functions. Therefore, the study sample of community-dwelling older adults as well as the sample size should be considered in future studies to decrease possible selection bias.

Conclusion

This study identified 4 clusters of SBs: (1) a leisure cluster, (2) a low cluster, (3) a work and PC use cluster, and (4) a TV viewing cluster. Moreover, because this study showed that the TV viewing group may have had a lower physical status than other SB groups, participants in the TV viewing group were identified as having a high risk of physical functional decline. Therefore, preventive strategies targeting the TV viewing group are needed to decrease the incidence of physical functional decline among community-dwelling older adults.

Acknowledgments

The authors would like to thank all the study participants. This study was supported by a grant-in-aid for scientific research from the Japan Society for the Promotion of Science (15K01647) and MEXT-Supported Program for the Strategic Research Foundation at Private Universities (S1511017).

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    Kawai H, Ihara K, Kera T, et al. Association between statin use and physical function among community-dwelling older Japanese adults. Geriatr Gerontol Int. 2018;18:623630. PubMed ID: 29278297 doi:10.1111/ggi.13228

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    Kera T, Kawai H, Hirano H, et al. Differences in body composition and physical function related to pure sarcopenia and sarcopenic obesity: a study of community-dwelling older adults in Japan. Geriatr Gerontol Int. 2017;17:26022609. PubMed ID: 28657168 doi:10.1111/ggi.13119

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    Ministry of Health, Labour and Welfare. Manual for preventive care [Kaigoyobou Manual]. 2012. http://www.mhlw.go.jp/topics/2009/05/tp0501-1.html (In Japanese). Accessed January 20, 2020.

    • Export Citation
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    National Institute of Health and Nutrition. Outline of the National Health and Nutrition Survey Japan, 2011: section of the National Health and Nutrition Survey. 2011. http://www.nibiohn.go.jp/eiken/english/research/project_nhns.html. Accessed January 20, 2020.

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    Japan Society for the Study of Obesity. Guidelines for the Management of Obesity Disease 2016Tokyo, Japan: Life Science Publishing Co, Ltd; 2016.

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    Cheng FW, Gao X, Mitchell DC, et al. Body mass index and all-cause mortality among older adults. Obesity. 2016;24:22322239. PubMed ID: 27570944 doi:10.1002/oby.21612

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    Alkhajah TA, Reeves MM, Eakin EG, Winkler EA, Owen N, Healy GN. Sit-stand workstations: a pilot intervention to reduce office sitting time. Am J Prev Med. 2012;43:298303. PubMed ID: 22898123 doi:10.1016/j.amepre.2012.05.027

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    Pronk NP, Katz AS, Lowry M, Payfer JR. Reducing occupational sitting time and improving worker health: the Take-a-Stand Project, 2011. Prev Chronic Dis. 2012;9:E154. PubMed ID: 23057991 doi:10.5888/pcd9.110323

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    • PubMed
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    Evans RE, Fawole HO, Sheriff SA, Dall PM, Grant PM, Ryan CG. Point-of-choice prompts to reduce sitting time at work: a randomized trial. Am J Prev Med. 2012;43:293297. PubMed ID: 22898122 doi:10.1016/j.amepre.2012.05.010

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    Gardiner PA, Eakin EG, Healy GN, Owen N. Feasibility of reducing older adults’ sedentary time. Am J Prev Med. 2011;41:174177. PubMed ID: 21767725 doi:10.1016/j.amepre.2011.03.020

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    • PubMed
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  • 32.

    Yerrakalva D, Yerrakalva D, Hajna S, Griffin S. Effects of mobile health app interventions on sedentary time, physical activity, and fitness in older adults: systematic review and meta-analysis. J Med Internet Res. 2019;21:e14343. PubMed ID: 31778121 doi:10.2196/14343

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    • PubMed
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  • 33.

    Ministry of Internal Affairs and Communications. Information & communications statistics database. Communications usage trend survey in 2018. 2019. http://www.soumu.go.jp/johotsusintokei/statistics/statistics05a.html. Accessed January 20, 2020.

    • Export Citation
  • 34.

    Liao Y, Hsu HH, Shibata A, Ishii K, Koohsari MJ, Oka K. Associations of total amount and patterns of objectively measured sedentary behavior with performance-based physical function. Prev Med Rep. 2018;12:128134. PubMed ID: 30234001 doi:10.1016/j.pmedr.2018.09.007

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  • 35.

    Sardinha LB, Santos DA, Silva AM, Baptista F, Owen N. Breaking-up sedentary time is associated with physical function in older adults. J Gerontol A Biol Sci Med Sci. 2015;70:119124. PubMed ID: 25324221 doi:10.1093/gerona/glu193

    • Crossref
    • PubMed
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  • 36.

    Yasunaga A, Shibata A, Ishii K, et al. Associations of sedentary behavior and physical activity with older adults’ physical function: an isotemporal substitution approach. BMC Geriatr. 2017;17:280. PubMed ID: 29212458 doi:10.1186/s12877-017-0675-1

    • Crossref
    • PubMed
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  • 37.

    Harvey JA, Chastin SF, Skelton DA. How sedentary are older people? A systematic review of the amount of sedentary behavior. J Aging Phys Act. 2015;23:471487. PubMed ID: 25387160 doi:10.1123/japa.2014-0164

    • Crossref
    • PubMed
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  • 38.

    Seino S, Taniguchi Y, Yoshida H, et al. A 10-year community intervention for disability prevention and changes in physical, nutritional, psychological and social functions among community-dwelling older adults in Kusatsu, Gunma Prefecture, Japan. Nihon Koshu Eisei Zasshi. 2014;61:286298. PubMed ID: 25098645

    • PubMed
    • Search Google Scholar
    • Export Citation

If the inline PDF is not rendering correctly, you can download the PDF file here.

Mitsutake is with the Human Care Research Team, Tokyo Metropolitan Institute of Gerontology, Itabashi-ku, Tokyo, Japan; and the Waseda Institute for Sport Sciences, Waseda University, Mikajima, Tokorozawa, Saitama, Japan. Shibata is with the Faculty of Health and Sport Sciences, University of Tsukuba, Tennodai Tsukuba, Ibaraki, Japan. Ishii and Oka are with the Faculty of Sport Sciences, Waseda University, Mikajima, Tokorozawa, Saitama, Japan. Amagasa, Kikuchi, Fukushima, and Inoue are with the Department of Preventive Medicine and Public Health, Tokyo Medical University, Shinjuku, Shinjuku-ku, Tokyo, Japan.

Mitsutake (mitsu@tmig.or.jp) is corresponding author.
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    Kawai H, Ihara K, Kera T, et al. Association between statin use and physical function among community-dwelling older Japanese adults. Geriatr Gerontol Int. 2018;18:623630. PubMed ID: 29278297 doi:10.1111/ggi.13228

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    Kera T, Kawai H, Hirano H, et al. Differences in body composition and physical function related to pure sarcopenia and sarcopenic obesity: a study of community-dwelling older adults in Japan. Geriatr Gerontol Int. 2017;17:26022609. PubMed ID: 28657168 doi:10.1111/ggi.13119

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    Ministry of Health, Labour and Welfare. Manual for preventive care [Kaigoyobou Manual]. 2012. http://www.mhlw.go.jp/topics/2009/05/tp0501-1.html (In Japanese). Accessed January 20, 2020.

    • Export Citation
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    National Institute of Health and Nutrition. Outline of the National Health and Nutrition Survey Japan, 2011: section of the National Health and Nutrition Survey. 2011. http://www.nibiohn.go.jp/eiken/english/research/project_nhns.html. Accessed January 20, 2020.

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    Japan Society for the Study of Obesity. Guidelines for the Management of Obesity Disease 2016Tokyo, Japan: Life Science Publishing Co, Ltd; 2016.

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    Cheng FW, Gao X, Mitchell DC, et al. Body mass index and all-cause mortality among older adults. Obesity. 2016;24:22322239. PubMed ID: 27570944 doi:10.1002/oby.21612

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    • PubMed
    • Search Google Scholar
    • Export Citation
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    Romesburg HC. Cluster Analysis for Researchers. Malabar, FL: Robert E. Krieger Publishing Company; 1989.

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    Alkhajah TA, Reeves MM, Eakin EG, Winkler EA, Owen N, Healy GN. Sit-stand workstations: a pilot intervention to reduce office sitting time. Am J Prev Med. 2012;43:298303. PubMed ID: 22898123 doi:10.1016/j.amepre.2012.05.027

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    • PubMed
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    Pronk NP, Katz AS, Lowry M, Payfer JR. Reducing occupational sitting time and improving worker health: the Take-a-Stand Project, 2011. Prev Chronic Dis. 2012;9:E154. PubMed ID: 23057991 doi:10.5888/pcd9.110323

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    • PubMed
    • Search Google Scholar
    • Export Citation
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    Evans RE, Fawole HO, Sheriff SA, Dall PM, Grant PM, Ryan CG. Point-of-choice prompts to reduce sitting time at work: a randomized trial. Am J Prev Med. 2012;43:293297. PubMed ID: 22898122 doi:10.1016/j.amepre.2012.05.010

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    • Export Citation
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    Gardiner PA, Eakin EG, Healy GN, Owen N. Feasibility of reducing older adults’ sedentary time. Am J Prev Med. 2011;41:174177. PubMed ID: 21767725 doi:10.1016/j.amepre.2011.03.020

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
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    Yerrakalva D, Yerrakalva D, Hajna S, Griffin S. Effects of mobile health app interventions on sedentary time, physical activity, and fitness in older adults: systematic review and meta-analysis. J Med Internet Res. 2019;21:e14343. PubMed ID: 31778121 doi:10.2196/14343

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 33.

    Ministry of Internal Affairs and Communications. Information & communications statistics database. Communications usage trend survey in 2018. 2019. http://www.soumu.go.jp/johotsusintokei/statistics/statistics05a.html. Accessed January 20, 2020.

    • Export Citation
  • 34.

    Liao Y, Hsu HH, Shibata A, Ishii K, Koohsari MJ, Oka K. Associations of total amount and patterns of objectively measured sedentary behavior with performance-based physical function. Prev Med Rep. 2018;12:128134. PubMed ID: 30234001 doi:10.1016/j.pmedr.2018.09.007

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 35.

    Sardinha LB, Santos DA, Silva AM, Baptista F, Owen N. Breaking-up sedentary time is associated with physical function in older adults. J Gerontol A Biol Sci Med Sci. 2015;70:119124. PubMed ID: 25324221 doi:10.1093/gerona/glu193

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 36.

    Yasunaga A, Shibata A, Ishii K, et al. Associations of sedentary behavior and physical activity with older adults’ physical function: an isotemporal substitution approach. BMC Geriatr. 2017;17:280. PubMed ID: 29212458 doi:10.1186/s12877-017-0675-1

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 37.

    Harvey JA, Chastin SF, Skelton DA. How sedentary are older people? A systematic review of the amount of sedentary behavior. J Aging Phys Act. 2015;23:471487. PubMed ID: 25387160 doi:10.1123/japa.2014-0164

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 38.

    Seino S, Taniguchi Y, Yoshida H, et al. A 10-year community intervention for disability prevention and changes in physical, nutritional, psychological and social functions among community-dwelling older adults in Kusatsu, Gunma Prefecture, Japan. Nihon Koshu Eisei Zasshi. 2014;61:286298. PubMed ID: 25098645

    • PubMed
    • Search Google Scholar
    • Export Citation
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