Abstract
[Purpose] This study aimed (1) to describe the actual status of step-count monitoring among older adults who are members of Silver Human Resources Centers, one of the major organizations supporting older adults’ work in Japan, and (2) to examine its relationships with physical activity and sedentary time (ST). [Participants and Methods] A total of 622 participants were included in this study. Logistic regression models were used to evaluate associations between step-count monitoring frequency and exercise habits, while multiple linear regression models examined associations with ST. [Results] Of the participants, 33.6% reported monitoring their steps almost every day. “Mobile phone/smartphone” was the most used device for step-count monitoring. A higher frequency of step-count monitoring was positively associated with exercise habits, with the multivariable-adjusted odds ratio (95% CI) of 3.329 (2.266–4.891) for the high-frequency monitoring group compared to the non-monitoring group. No significant association was observed between step-count monitoring frequency and ST. [Conclusion] This study highlights the widespread practice of step-count monitoring among older adults and its positive association with exercise habits. These findings provide foundational insights for developing dissemination strategies to promote step-count monitoring in this population.
Key words: Step-count monitoring, Physical activity, Smartphone
INTRODUCTION
Promoting participation in social activities, such as employment and volunteer work, among older adults is one of the critical challenges facing Japan as it enters a super-aged society1). However, maintaining consistent involvement in these activities requires a relatively high level of physical fitness2,3,4). Therefore, it is considered important for organizations and groups providing such opportunities to implement initiatives that support the maintenance and improvement of physical fitness among older adult members by promoting physical activity (PA). Daily step count is an indicator that reflects PA levels in daily life and has been reported to be significantly associated with numerous health outcomes, including physical fitness5, 6). Additionally, randomized controlled trials have demonstrated that step-count monitoring increases PA and reduces sedentary time (ST) among older adults7,8,9). Furthermore, the widespread use of wearable devices and smartphones has made self-monitoring of step counts more accessible than ever10). Based on these findings, encouraging step-count monitoring among older adults affiliated with organizations or groups supporting social activities could serve as a highly practical approach to promoting PA in this population.
The first step in developing strategies for promoting health behaviors in a target population is conducting a situational analysis11, 12). Understanding the status of step-count monitoring requires foundational data on how many people engage in it, what devices they use, and how frequently they monitor their steps. However, to the best of the authors’ knowledge, no study has explored step-count monitoring among older adults. Furthermore, it remains unclear what types of PA are associated with current step-count monitoring practices. This information is essential for understanding the effectiveness and limitations of step-count monitoring and promoting evidence-based practices aimed at disseminating step-count monitoring. Therefore, in this study, we aimed (1) to describe the status of step-count monitoring among older adults who are members of Silver Human Resources Centers, one of the major organizations supporting older adults’ work in Japan13), and (2) to examine its relationships with PA and ST.
PARTICIPANTS AND METHODS
This study was conducted using a cross-sectional design. This study targeted all 2,104 members of the M City Silver Human Resources Center who received a self-administered questionnaire via mail. A total of 686 participants returned the questionnaire (response rate: 32.6%). Of these, 25 participants were excluded because of missing data in step-count monitoring measures, and an additional 39 participants were excluded due to missing data in covariates required for the analyses. Consequently, 622 participants were included in the analyses for both Objective 1 and Objective 2. For the device-stratified analyses, the analytic sample comprised 613 participants. A diagram of this study is shown in Fig. 1. The survey was conducted between September to October 2024. The Silver Human Resources Center is a public corporation established by law that provides work opportunities for individuals aged ≥60 years within their local communities13). Currently, there are 1,341 centers across 1,714 municipalities, with a total membership of 676,75613). Membership numbers vary depending on the population size of the municipality, ranging from several dozen to several thousand members per center12). M City, with a population of 497,887, is a core city with an aging rate (proportion of individuals aged ≥65 years) of 27.0%. Spanning an area of 429.4 km2 (including island regions), the city extends 40.3 km east-west and 42.9 km north-south. Members of the M City Silver Human Resources Center are distributed throughout the city.
Fig. 1.
Flow diagram of this study.
In this survey, participants were provided with written information regarding the objectives of the study, the voluntary nature of participation, the anonymity of responses, and the researchers’ commitment to confidentiality. The return of the completed questionnaire was interpreted as consent to participate in the study. This study was approved by the Research Ethics Committee of the Faculty of Collaborative Regional Innovation at Ehime University (approval number: 2021-02).
To assess the methods of step-count monitoring, the frequency of step count checks and the devices used over the past year were surveyed. Participants were first asked about the frequency of checking their daily step count, with six response options available: “almost every day”, “about three times per week”, “about once per week”, “about one or two times per month”, “a few times per year”, and “never”. Those who selected any option other than “never” were further asked to identify the primary device they used to check their step count from among the four options: “pedometer/activity monitor”, “smartwatch”, “mobile phone/smartphone”, or “others”.
Regarding PA, whether participants engaged in a total of 60 minutes or more of exercise per week over the past six months—a threshold corresponding to the definition of exercise habits by the Ministry of Health, Labour and Welfare in Japan14)—was evaluated using the stages of exercise behavior change scale15). In this study, participants classified in the “action” and “maintenance” stages were defined as having exercise habits, while the others were defined as having no exercise habit. Additionally, ST during waking hours on a typical weekday was measured using the ST item from the International Physical Activity Questionnaire short form16, 17).
Other surveyed items included age, sex, height, weight, presence of cohabitants, car or motorcycle ownership, employment status, life satisfaction, subjective health status, and the presence of physical pain. Body mass index (BMI) was calculated from the self-reported height and weight data. Work status was determined based on the average number of hours spent on income-generating work per week, with those working for at least one hour per week classified as workers. For life satisfaction, participants who answered “satisfied” or “somewhat satisfied” were categorized as satisfied, whereas those who answered, “somewhat dissatisfied” or “dissatisfied” to the question, “How satisfied are you with your current life?” were categorized as dissatisfied18). Subjective health status was classified as good for those who answered “good”, “fairly good”, or “average”, to the question, “How is your current health?” and as poor for those who answered, “not very good” or “poor”19).
For continuous and categorical variables, the data were presented as mean ± standard deviation and frequency (%), respectively. The frequency of step-count monitoring was categorized into three groups: “high frequency” (almost every day or about three times per week), “low frequency” (about once per week, about one or two times per month, or a few times per year), and “non-implementation” (never). A logistic regression model was used to examine the association between the frequency of step-count monitoring (high, low, and no implementation) and exercise habits (presence/absence). A multiple linear regression model was used to examine the association between the frequency of step-count monitoring and ST (a continuous variable). Both models included exercise habits or ST as the dependent variable and frequency of step-count monitoring as the independent variable, and included the following confounding factors: age (continuous), sex (male/female), BMI (continuous), presence of cohabitants (presence/absence), ownership of a car or motorcycle (presence/absence), life satisfaction (satisfied/dissatisfied), work status (worker/non-worker), subjective health status (good/poor), and physical pain (presence/absence). Additionally, a model including exercise habits (presence/absence) as a covariate was implemented to analyze the relationship between the frequency of step-count monitoring and ST. In all regression analyses, the non-monitoring group was used as the reference category. Accordingly, p-values represent comparisons with the non-monitoring group, and no direct comparisons were made between the low-frequency and high-frequency monitoring groups. Given that the time spent in sedentary behavior during waking hours inversely corresponds to PA time20), the results of the model adjusting for exercise habits were interpreted as reflecting the non-exercise PA, mainly light-intensity PA20).
The devices used for step-count monitoring were classified into two types based on whether they are worn continuously during waking hours: cellular or smartphones and activity monitors (including pedometers activity monitors, and smartwatches). To examine differences in the associations between step-monitoring and exercise habits or ST, we applied logistic and linear regression models stratified by device type, using non-monitoring as a reference. Participants who selected “Others” for the type of step-count monitoring device (n=9) were excluded from the stratified analyses because their device type could not be classified into the two device categories used in this study.
All statistical analyses were conducted using SPSS (version 29.0; IBM Corp., Armonk, NY, USA) and statistical significance was set at a two-tailed p-value <0.05.
RESULTS
The characteristics of the participants are shown in Table 1. Among the participants, 41.8% were women, and the mean age was 73.3 ± 5.3 years. The proportion of participants with exercise habits was 55.8%, and the mean ST was 464.7 ± 269.5 minutes per day.
Table 1. Characteristics of the study population.
| All (n=622) | ||
| Age, years | 73.3 | (5.3) |
| Female, % | 41.8 | |
| BMI, kg/m2 | 22.7 | (3.2) |
| Living alone, % | 23.2 | |
| Car or motorcycle owners, % | 82.8 | |
| Workers, % | 75.1 | |
| Individuals satisfied with life, % | 81.7 | |
| Individuals with high self-rated health, % | 89.4 | |
| Individuals with physical pain, % | 51.1 | |
| Individuals with exercise habits, % | 55.8 | |
| Sedentary time, min/per day | 464.7 | (269.5) |
Showing mean (standard deviation) for continuous variables: age, BMI, sedentary time. BMI: body mass index.
The step-count monitoring status is listed in Table 2. Among the participants, 33.6% monitored their steps almost daily, and the number of participants tended to decrease with lower monitoring frequency. The proportion of participants who did not monitored their steps was 39.4%. Regarding devices used for step-count monitoring, “mobile phones/smartphones” were the most common (71.4%), followed by “pedometers/activity monitors” and “smartwatches”, both used in similar proportions. Stratified tabulations by sex and age revealed no notable differences from overall trends.
Table 2. Description of status of step-count monitoring.
| All | Men | Women | Age under 75 | Age over 75 | ||
| Frequency of step-count monitoring (n=622) | ||||||
| Number of participants | 622 | 362 | 260 | 365 | 257 | |
| Almost every day, % | 33.6 | 34.5 | 32.3 | 33.7 | 33.5 | |
| Three times per week, % | 9.6 | 10.2 | 8.8 | 9.9 | 9.3 | |
| Once per week, % | 8.0 | 9.1 | 6.5 | 7.4 | 8.9 | |
| One to two times per month, % | 5.3 | 4.1 | 6.9 | 5.8 | 4.7 | |
| A few times per year, % | 4.0 | 2.2 | 6.5 | 5.5 | 1.9 | |
| Almost never, % | 39.4 | 39.8 | 38.8 | 37.8 | 41.6 | |
| Device for step-count monitoring (n=377) | ||||||
| Number of participants | 377 | 218 | 159 | 227 | 150 | |
| Pedometer or activity monitor, % | 13.0 | 11.0 | 15.7 | 12.3 | 14.0 | |
| Smartwatch, % | 13.3 | 13.3 | 13.2 | 13.7 | 12.7 | |
| Cellular or smartphone, % | 71.4 | 72.0 | 70.4 | 72.2 | 70.0 | |
| Other devices, % | 2.4 | 3.7 | 0.6 | 1.8 | 3.3 | |
The characteristics of the participants by frequency of step monitoring are shown in Table 3. The non-monitoring and low-frequency groups had a lower proportion of individuals with exercise habits compared to the high-frequency group. The other variables did not show any noticeable patterns.
Table 3. Characteristics of participants in the different frequency groups.
| High frequency (n=269) | Low frequency (n=108) | Non monitoring (n=245) | |||||
| Age, years | 73.3 | (5.2) | 73.0 | (5.3) | 73.5 | (5.5) | |
| Female, % | 39.8 | 48.1 | 41.2 | ||||
| BMI, kg/m2 | 22.6 | (3.1) | 23.2 | (3.5) | 22.6 | (3.1) | |
| Living alone, % | 22.7 | 25.9 | 22.4 | ||||
| Car or motorcycle owners, % | 83.6 | 82.4 | 82.0 | ||||
| Workers, % | 76.6 | 74.1 | 73.9 | ||||
| Individuals satisfied with life, % | 84.4 | 81.5 | 78.8 | ||||
| Individuals with high self-rated health, % | 91.1 | 92.6 | 86.1 | ||||
| Individuals with physical pain, % | 50.9 | 56.5 | 49.0 | ||||
| Device for step-count monitoring, % | |||||||
| Pedometer or activity monitor | 11.2 | 17.6 | ― | ||||
| Smartwatch | 14.1 | 11.1 | ― | ||||
| Cellular or smartphone | 72.5 | 68.5 | ― | ||||
| Other devices | 2.2 | 2.8 | ― | ||||
| Individuals with exercise habits, % | 70.6 | 46.3 | 43.7 | ||||
| Sedentary time, min/per day | 461.5 | (269.1) | 491.9 | (276.7) | 456.2 | (266.9) | |
Showing mean (standard deviation) for continuous variables: age, BMI, sedentary time. BMI: body mass index.
The crude odds ratios and multivariable-adjusted odds ratios for exercise habits according to step-count monitoring frequency are presented in Table 4. The high-frequency group had a multivariable-adjusted odds ratio (95% confidence interval [CI]; p-value) of 3.329 (2.266–4.891; <0.001), which was significantly higher than those of the non-monitoring group. In the analyses stratified by device type, the odds ratios for the high-frequency group were significantly higher than those for the non-monitoring group across both strata. The odds ratios tended to be higher among participants using activity monitors.
Table 4. Odds ratio (OR) and 95% CI for exercise habit in the different frequency groups.
| n | Case | Crude OR (95% CI) | p-value | Multivariate adjusted ORa (95% CI) | p-value | ||||
| All participants | |||||||||
| Low frequency | 108 | 51 | 1.112 | (0.706–1.752) | 0.648 | 1.080 | (0.673–1.738) | 0.753 | |
| High frequency | 269 | 193 | 3.102 | (2.155–4.464) | <0.001 | 3.329 | (2.266–4.891) | <0.001 | |
| p for trend | p<0.001 | p<0.001 | |||||||
| Monitoring using activity monitors | |||||||||
| Low frequency | 31 | 15 | 1.209 | (0.572–2.555) | 0.619 | 1.241 | (0.560–2.754) | 0.595 | |
| High frequency | 68 | 55 | 5.457 | (2.834–10.505) | <0.001 | 5.768 | (2.857–11.647) | <0.001 | |
| p for trend | p<0.001 | p<0.001 | |||||||
| Monitoring using cellular or smartphone | |||||||||
| Low frequency | 74 | 34 | 1.096 | (0.650–1.848) | 0.710 | 1.043 | (0.603–1.806) | 0.880 | |
| High frequency | 195 | 130 | 2.579 | (1.746–3.811) | <0.001 | 2.752 | (1.828–4.142) | <0.001 | |
| p for trend | p<0.001 | p<0.001 | |||||||
aAdjusted for age (continuous), sex (men/women), BMI (continuous), presence of cohabitants (presence/absence), ownership of a car or motorcycle (presence/absence), life satisfaction (satisfied/dissatisfied), work status (worker/nonworker), subjective health status (good/poor), and presence of physical pain (presence/absence). OR: odds ratio; CI: confidence interval. Boldface indicates statistical significance (p<0.05). n the analyses of all participants, all eligible participants were included (n=622). In the device-specific analyses, participants who selected “Others” as the monitoring device (n=9) were excluded; therefore, the sample size for these analyses was n=613. The non-monitoring group (n=245) was used as the reference category in all analyses. P-values represent comparisons with the non-monitoring (reference) group. Direct comparisons between the low-frequency and high-frequency groups were not performed.
The association between step-count monitoring frequency and ST is shown in Table 5. In both models, no significant association was observed between the step-count monitoring frequency and ST. Similar results were obtained in the stratified analyses by device type.
Table 5. Regression coefficient and 95% CI for sedentary time in the different frequency groups.
| n | Model 1 |
Model 2 |
||||||
| β | 95% CI | p-value | β | 95% CI | p-value | |||
| All participants | ||||||||
| Low frequency | 108 | 36.1 | (−25.2 to 97.5) | 0.248 | 36.1 | (−25.3 to 97.5) | 0.249 | |
| High frequency | 269 | 10.0 | (−36.7 to 56.7) | 0.674 | 9.6 | (−38.6 to 57.9) | 0.695 | |
| Monitoring using activity monitors | ||||||||
| Low frequency | 31 | 17.2 | (−83.3 to 117.8) | 0.736 | 16.1 | (−84.5 to 116.8) | 0.753 | |
| High frequency | 68 | −2.6 | (−74.6 to 69.5) | 0.944 | −10.5 | (−85.6 to 65.0) | 0.783 | |
| Monitoring using cellular or smartphone | ||||||||
| Low frequency | 74 | 37.8 | (−31.7 to 107.3) | 0.285 | 37.8 | (−31.7 to 107.4) | 0.286 | |
| High frequency | 195 | 8.6 | (−41.1 to 58.8) | 0.728 | 9.3 | (−42.0 to 60.5) | 0.723 | |
Model 1: Adjusted for age (continuous), sex (men/women), BMI (continuous), presence of cohabitants (presence/absence), ownership of a car or motorcycle (presence/absence), life satisfaction (satisfied/dissatisfied), work status (worker/nonworker), subjective health status (good/poor), and presence of physical pain (presence/absence). Model 2: Adjusted for Model 1 variables + exercise habit (with/without). β: partial regression coefficient; CI: confidence interval. n the analyses of all participants, all eligible participants were included (n=622).
In the device-specific analyses, participants who selected “Others” as the monitoring device (n=9) were excluded; therefore, the sample size for these analyses was n=613.
The non-monitoring group (n=245) was used as the reference category in all analyses. P-values represent comparisons with the non-monitoring (reference) group. Direct comparisons between the low-frequency and high-frequency groups were not performed.
To perform a sensitivity analysis, the step-count monitoring frequency categories were redefined as high frequency (“almost every day”; n=209), medium frequency (“about three times per week” and “about once per week”; n=110), low frequency (“about one or two times per month” and “a few times per year”; n=58), and non-monitoring (n=245). Compared with that of the non-monitoring group, the odds ratio (95% CI; p-value) for exercise habits was 3.577 (2.361‒5.418;<0.001), 1.953 (1.205‒3.165; 0.007), and 0.853 (0.459–1.583; 0.613) for the high-, medium-, and low-frequency groups, respectively (p for trend <0.001). Odds ratios for the high-frequency and medium-frequency groups were significantly higher than for the non-monitoring group. Compared with the non-monitoring group, the regression coefficients (95% CI; p-value) for ST in Model 1 were 4.9 (−44.6 to 54.5; 0.846) for the high-frequency group, 42.9 (−17.9 to 103.6; 0.166) for the medium-frequency group, and 9.5 (−68.0 to 87.1; 0.809) for the low-frequency group. The corresponding values in Model 2 were 5.0 (−46.1 to 56.2; 0.846), 43.0 (−18.2 to 104.2; 0.168), and 9.5 (−68.1 to 87.2; 0.810), respectively, and no significant association was identified.
DISCUSSION
In this study, approximately 50% of the participants reported checking their step count at least once a week. The most widely employed device for step-count monitoring was “mobile phones/smartphones”. A survey conducted in 1996 reported that one in three to four middle-aged and older adults in Japan owned a pedometer21), suggesting that step-count monitoring had already been widely adopted in Japan at that time. Currently, mobile devices (mobile phones/smartphones) are widely used even among older adults, with an ownership rate of 93.7% in households where the head of the household is aged 65 or older22). Many of these devices are equipped with step-counting functions as a default setting. Although the devices used for step-count monitoring have evolved over time, the findings of this study suggest that step-count monitoring is widely practiced among older adults. To the best of our knowledge, no detailed reports has been published on the status of step-count monitoring among older adults. Therefore, the findings of this study are considered useful as basic information for developing further strategies to promote step-count monitoring.
Additionally, we observed that a higher frequency of step-count monitoring was associated with a higher odds ratio for exercise habit compared with the non-monitoring group (Table 4). In stratified analyses by device type, step-count monitoring using activity monitors was more strongly associated with exercise habits compared to monitoring with cellular or smartphones. However, in both strata, individuals who monitored their steps frequently had significantly higher odds ratios for exercise habits. Self-monitoring of PA reportedly promotes the behavior being monitored23). Walking is the most frequency performed activity among older adults24), and it is reasonable to assume that step-count monitoring is related to its practice. Compared to “mobile phones/smartphones”, activity monitors and smartwatches generally allow monitoring of not only step counts but also other exercise-related metrics such as heart rate and activity time by intensity. Therefore, the use of these devices is likely to be associated with engagement in other exercises. Accordingly, the stronger association between step-count monitoring frequency and exercise habits observed among users of “pedometers/activity monitors” and “smartwatches” is attributable to these factors.
On the other hand, no significant association was observed between the frequency of step-count monitoring and ST in the present study. To reduce ST during waking time, it is necessary to replace sedentary behavior with light-intensity PA, such as non-exercise-based PA20, 24). However, these activities typically involve fewer steps per unit time. Moreover, approximately 70% of participants in this study used “mobile phones/smartphones” for step-count monitoring, which are less likely to be carried during light-intensity PA25). Consequently, step-count monitoring may not contribute to the promotion of light-intensity PA. This could explain the lack of a notable association between step-count monitoring and ST in this study. Based on the findings of reviews of randomized controlled trials using wearable devices, including pedometers, step-count monitoring can contribute to a reduction in ST9). However, in that review the effect size for reducing ST is smaller than its impact on step counts or moderate-to-vigorous PA9). Furthermore, many of these trials involved additional interventions such as counseling, in conjunction with step-count monitoring9). A study involving interventions limited only to step-count monitoring did not detect substantial reductions in ST26). Based on these findings, the effect of step-count monitoring on ST reducing appears limited.
This study revealed that a relatively large number of individuals in this population were engaged in step-count monitoring and that the frequency of step-count monitoring is positively associated with exercise habits. Based on these findings, it is crucial to encourage individuals who already engage in step-count monitoring to continue and increase their monitoring frequency. Conversely, dissemination strategies that leverage the social norms of individuals engaged in step-count monitoring may be effective for those who do not engage in step-count monitoring27). Furthermore, given that step-count monitoring is not associated with PAs other than exercise, it is important to recommend step-count monitoring in association with exercise. Thus, this study provides fundamental information for strategies to promote step-count monitoring. Step-count monitoring is a simple and practical approach to supporting self-management of physical activity in daily life and can be applied to physical therapy practices aimed at promoting physical activity. The findings of this study provide fundamental information that may contribute to the future development of clinical physical therapy from a preventive perspective. Nevertheless, this study has several limitations. First, the survey was conducted at a single center. As the built environment influences PA levels among older adults28, 29), future studies should include members from multiple centers. Older adults affiliated with social organizations, such as those in this study, reportedly have higher levels of health literacy and PA4, 30). Additionally, it should be noted that the response rate for the questionnaire in this study was not necessarily high, which may have introduced selection bias, potentially leading to an overestimation of trends in the target population. Therefore, caution is required when generalizing the results to the broader population of older adults, and future validation should be conducted with older adults who are not members of social organizations. Second, PA and ST were assessed using a self-report questionnaire, which has lower measurement accuracy than objective methods such as accelerometry31). Third, although we assume that checking one’s step count may contribute to the promotion of PA, the cross-sectional design of this study does not allow for causal inference. Future longitudinal studies are needed to evaluate causal relationships and the causal effects, using objective methods to assess both exposures and outcomes. Despite these limitations, to the best of the authors’ knowledge, this is the first study to elucidate how older adults affiliated with a social organization related to employment engage in step-count monitoring in real-world settings and how this is associated with PA. Promoting step-count monitoring dissemination activities in large organizations with numerous members, such as Silver Human Resources Centers, is crucial from the perspective of social impact. We believe that step-count monitoring using devices that are widely owned by the public—such as cellular or smartphones—appears to be associated with higher physical activity levels, and may represent a practical and scalable strategy for promoting physical activity. This implication represents one of the key contributions of the present study.
In conclusion, this study examined the status of step-count monitoring and its association with PA and ST among members of Silver Human Resources Centers. Approximately 33.1% of the participants reported checking their step counts almost every day. Mobile phones and smartphones are the most commonly used devices for step-count monitoring. Furthermore, the frequency of step-count monitoring was positively associated with exercise habits.
Funding
This research was funded by the JSPS KAKENHI (grant number 23K10762).
Conflict of interest
The authors declare no conflicts of interest. The funders had no role in the study design; collection, analyses, or interpretation of data; writing of the manuscript; or decision to publish the results.
Supplementary
Acknowledgments
We thank the staff of the Silver Human Resources Center in City M for their assistance with data collection.
Funding Statement
This research was funded by the JSPS KAKENHI (grant number 23K10762).
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