Abstract
Although wearable activity trackers (WAT) are considered beneficial for health-management in older adults, their prevalence and impact on health satisfaction are unclear. We limited our study to older Japanese adults who used smartphones, tablets, or personal computers. We categorized the participants into WAT users and non-users. The survey examined the use of WAT which was a wristwatch, other type (glasses, ring, and clip); demographics; health-related measures; Information and Communication Technology (ICT) accessibility; and health-management satisfaction. From 12,869 older Japanese adults, 8,876 adults responded to the survey, and we included 3,467 adults who used digital devices. The prevalence of WAT use was 4.4% (men: 61.4%). The reason for using WAT was health-management in 61.4% of cases, mainly for monitoring blood pressure/heart rate control (64.3%) and exercise (60.6%). Gender-stratified logistic regression analysis showed that men with higher ICT accessibility, exercise habits, and cardiometabolic diseases were more likely to use WAT. The study found no factors of the WAT use in women and no significant difference in health-management satisfaction between WAT users and non-users. The results suggest that simply wearing a WAT does not increase satisfaction with health-management. The study recommends greater opportunities to teach the effective and active use of WAT.
Keywords: wearable activity tracker, cross-sectional study, health-related indices, older adults
Introduction
If a Wearable Activity Tracker (WAT) can contribute to maintaining and improving health among older adults, identifying the factors associated with the WAT use could aid in the widespread dissemination of health promotion measures. The prevalence of global WAT use varies in previous reports, ranging from 6.4% to 38.0% (DataReportal, 2024). This variation could be due to differences in age, health conditions (e.g., disease), and cultural background (Yang Meier et al., 2020). Specifically, WATs are primarily used to manage health conditions such as cardiovascular disease and diabetes (Hughes et al., 2023; Pardamean et al., 2020; Shan et al., 2019), and are less prevalent in countries with national cultures of high power distance, high masculinity, high collectivism, and low uncertainty avoidance (McCoy et al., 2017; Yang Meier et al., 2020). Furthermore, several models have been proposed to explain barriers to the WAT use, with usability identified as the most significant (Li et al., 2019). Although these findings are reasonable and valuable, the characteristics of older adults who are reluctant to use WAT are not yet fully understood. Assessing the associated factors, including demographics and clinical characteristics, for the WAT use among older adults contributes to facilitating its use to possibly improve their health status.
To extend the current knowledge regarding the WAT use among older adults, this study aimed to clarify the characteristics of the WAT use among older Japanese adults. Specifically, we aimed to (1) identify the factors associated with the WAT use after clarifying the relevance of the WAT use among healthy older adults and (2) determine whether the WAT use increases satisfaction with their health management.
Methods
Study Designs
Cross-sectional study among community-dwelling older adults using mailed questionnaires.
Participants
We utilized data from the third wave (Year 2022) of a community-wide intervention study aimed at preventing frailty launched in 2016 in Ota City, Tokyo, Japan (Seino et al., 2019). Ota City has a population of 730,005, and the prevalence of those aged 65 years and older is 22.6% (n = 165,193 as of July 2022). In the first wave (2016), we sampled 15,500 residents aged 65 to 84 who were not certified under long-term care insurance. These individuals were randomly selected by sex and age group (65–74 and 75–84) from 18 districts in Ota City. The third wave of the survey was conducted from July to August 2022 by mailing questionnaires to 12,869 eligible residents from the first wave of the survey (including those who did not respond to the first wave survey). The study excluded those who died or moved out of the 15,500 residents in the first wave. We exclude foreign nationals from our survey when collaborating with local governments. The participants of this study were all ethnically Japanese.
Possession of Digital Devices and Usage of WAT
The frequency of communication-device usage was evaluated. Those who used a smartphone, tablet computer, or personal computer at least once a week were defined as individuals with a device that could be connected to the WAT. These individuals were subsequently assessed for their use of WAT, which included either a wristwatch or any other type of WAT (glasses, ring, or clip). To facilitate the participants’ understanding of the question, the questionnaire included an explanation of the WAT along with an image of a typical WAT (e.g., a smartwatch).
Individuals who did not own a digital device (i.e., smartphone, PC, and Tablet) were excluded from the analysis of this study because of the absence of a digital device, which is considered essential for the WAT use. The exclusion led to significant demographic differences (Shei et al., 2022).
Qualitative Assessments
Participants who reported having a WAT were assessed for their reasons for starting to use it, with the following options: (1) health management, (2) fashion, (3) cashless payment, (4) timekeeping, (5) recommendation by family (for monitoring purposes), and (6) influence from others around them who wore WAT. They were then evaluated for the functions of the WAT they used, with the following choices: (1) pulse rate/blood pressure management, (2) exercise management (distance/steps), (3) sleep management, (4) cashless payment, and (5) others. Based on a previous definition of Information and Communication Technology (ICT) (World Health Organization, 2021), we assessed the ICT accessibility status of participants in terms of their ability to (1) exchange messages (e.g., LINE, Email), (2) search for information (e.g., Google, Yahoo), (3) watch videos (e.g., YouTube), (4) make video calls (e.g., LINE video, Zoom), (5) use social networking services (e.g., Facebook, Twitter, Instagram), and (6) shop online (e.g., Amazon, Rakuten). Responses to each question were categorized as “using,” “not using, but can use,” or “cannot use and not in use,” and were further classified as having high (three or more uses of internet tools) or low (less than two uses of internet tools) ICT accessibility.
Covariates
Living arrangements (living alone or not), educational attainment (<10, 10–12, >12 years), household income (high income greater than ¥3,000,000), presence of cardiometabolic disease, exercise habits (participation in weekly activities, including walking, yoga, cycling, and swimming), depressive mood, subjective memory complaints, instrumental activities of daily living (IADL), and health management satisfaction were also assessed as sociodemographic, clinical, and health-related variables. Depressive mood was assessed using the five-item version of the Geriatric Depression Scale (GDS-5), and scores of two and above were indicative of depressive mood based on previous findings (Hoyl et al., 1999; Rinaldi et al., 2003). Subjective memory complaints were assessed by asking, “Are you anxious about forgetfulness?” Responses were categorized as “very anxious,” “anxious,” “not very anxious,” or “not anxious at all.” We defined “very anxious” and “anxious” as having subjective memory complaints. IADL was assessed using the TMIG Index of Competence (TMIG-IC), where a higher score indicated higher functional capacity (Fujiwara et al., 2003).
Study Outcome
Factors associated with the WAT use among healthy older adults and the association between the WAT use and satisfaction with health management by gender were identified.
Statistical Analysis
After excluding individuals who do not own a digital device, respondents were classified into two groups: (1) WAT users and (2) digital device users (smartphones, tablet computers, and personal computers, but not WAT) who did not use WAT (non-WAT users). Descriptive statistics for differences between WAT and non-WAT users were assessed using the t-test or chi-squared test. Multivariate logistic regression models adjusted for age, living arrangements, education, household income, cardiometabolic disease, exercise habits, depressive mood, IADL, and ICT accessibility were established to identify the factors associated with the WAT usage. These analyses were performed by sex because older women have been reported to lag behind older men in adopting digital devices and using the Internet (Sun et al., 2020). Finally, to test the hypothesis that the WAT use is associated with higher satisfaction with health management, multivariate logistic regression analysis was performed with health management satisfaction as the dependent variable and the WAT use as the independent variable. The covariates in this study were independent variables used to identify factors associated with WAT usage. All multivariate logistic regression models are presented as adjusted odds ratios and 95% confidence intervals. IBM SPSS (version 23.0; SPSS Inc., Chicago, IL, USA) was used for all statistical analyses, and the significance level was set at p < .05.
Results
Prevalence of Digital Devices and WAT and Participants’ Characteristics
Figure 1 shows the flow diagram of the present study. Of 12,869 mailed questionnaires, 8,876 were returned, yielding a response rate of 69.0%. Among them, 3,442 and 1,967 were excluded because of invalid or incomplete data and individuals who do not own a digital device. As a result, 3,467 were included in the final analysis. Two hundred and forty-one respondents (4.4% of all analytical cases) used WAT. Among them, 206 used wristwatch-type devices (85.5%), while the rest used other types of devices (5.8%) or both (8.7%).
Figure 1.
Schematic diagram of the selection of study participants.
Figure 2 shows the respondents’ motives for the WAT use and the functions used. The most reported reason was health management (61.4%), followed by timekeeping (37.7%) and recommendation by family members (23.2%). The most frequently used functions were pulse/blood pressure monitoring (64.3%) and exercise monitoring (60.6%), followed by sleep monitoring (33.6%).
Figure 2.
Response rate for (A) the use of WAT function and (B) reasons for starting to use WAT.
Table 1 shows the characteristics of WAT and non-WAT users. Compared to than non-WAT users, more WAT users were males (61.4%); had cardiometabolic diseases (71.8%); reported regular exercise habits (65.1%) and depressive mood (28.2%); and showed higher IADL (75.1%) and ICT accessibility (70.1%).
Table 1.
Characteristics of WAT Users and Non-WAT Users.
| Variables | WAT users (n = 241) | Non-WAT users (n = 3,226) | p-value |
|---|---|---|---|
| Age, mean (SD) | 78.3 (5.3) | 77.9 (4.9) | .20 |
| Gender, n (%) | |||
| Men | 148 (61.4) | 1643 (50.9) | <.01 |
| Women | 93 (38.6) | 1583 (49.1) | |
| Living situation, n (%) | |||
| Alone | 47 (19.5) | 720 (22.3) | .17 |
| Living together | 194 (80.5) | 2506 (77.7) | |
| Education, n (%) | |||
| ≤9 years | 25 (10.3) | 386 (11.9) | .23 |
| 10–12 years | 77 (32.0) | 1163 (36.1) | |
| ≥13 years | 139 (57.7) | 1677 (52.0) | |
| Household income, n (%) | |||
| <3,000,000 yen | 81 (33.6) | 1206 (37.3) | .44 |
| >3,000,000 yen | 122 (50.6) | 1505 (46.7) | |
| Unknown | 38 (15.8) | 515 (16.0) | |
| BMI, n (%) | |||
| <18.5 | 15 (6.2) | 247 (7.7) | .61 |
| 18.5–24.9 | 179 (74.3) | 2311 (71.6) | |
| ≥25.0 | 47 (19.5) | 668 (20.7) | |
| Cardiometabolic diseases, n (%) | |||
| Yes | 173 (71.8) | 2094 (64.9) | .01 |
| No | 68 (28.2) | 1132 (35.1) | |
| Exercise habits, n (%) | |||
| <1 week | 84 (34.9) | 1520 (47.1) | <.01 |
| ≥1 week | 157 (65.1) | 1706 (52.9) | |
| Depression, n (%) | |||
| Yes | 68 (28.2) | 1103 (34.2) | .03 |
| No | 173 (71.8) | 2123 (65.8) | |
| IADL, n (%) | |||
| Low | 60 (24.9) | 976 (30.3) | .04 |
| High | 181 (75.1) | 2250 (69.7) | |
| ICT accessibility, n (%) | |||
| Low | 72 (29.9) | 1527 (47.3) | <.01 |
| High | 169 (70.1) | 1699 (52.7) | |
| Memory complaints, n (%) | |||
| Yes | 152 (63.1) | 2139 (66.3) | .17 |
| No | 89 (36.9) | 1087 (33.7) | |
| Health-management satisfaction, n (%) | |||
| Low | 59 (24.5) | 858 (26.6) | .26 |
| High | 182 (75.5) | 2368 (73.4) | |
Data are presented as n (%) for categorical variables. Cardiometabolic diseases include cardiovascular and cerebrovascular diseases, hypertension, diabetes, and dyslipidemia. SD = standard deviation; IADL = instrumental activities of daily living; ICT = information and communication technology.
WAT Usage, Correlated Factors, and Association with Health Management Satisfaction
Table 2 shows the factors associated with the WAT use assessed by sex. Higher ICT accessibility (odds ratio [OR]: 2.58, 95% CI [1.71, 3.91], p < .01), exercise habits (OR: 1.60, 95% CI [1.09, 2.35], p = .01), and the presence of cardiometabolic diseases (OR: 0.59, 95% CI [0.39, 0.89], p = .01) were associated with the WAT use. Conversely, no significant factors correlated with the WAT use in women.
Table 2.
Factors Associated with WAT Usage by Sex.
| Independent variables | Women (n = 1,676) |
Men (n = 1,791) |
||
|---|---|---|---|---|
| OR (95% CI) | p-value | OR (95% CI) | p-value | |
| Age, mean (SD) | ||||
| <75 | Reference | Reference | ||
| ≥75 | 0.65 (0.42–1.09) | .11 | 1.05 (0.73–1.51) | .78 |
| Living situation | ||||
| Alone | Reference | Reference | ||
| Living together | 1.07 (0.65–1.76) | .71 | 0.97 (0.57–1.62) | .91 |
| Education | ||||
| ≤9 years | Reference | Reference | ||
| 10–12 years | 1.13 (0.54–2.35) | .73 | 0.79 (0.42–1.50) | .48 |
| ≥13 years | 1.16 (0.55–2.46) | .68 | 0.76 (0.41–1.40) | .38 |
| Household income | ||||
| <3,000,000 yen | Reference | Reference | ||
| >3,000,000 yen | 1.09 (0.64–1.85) | .73 | 0.97 (0.65–1.44) | .90 |
| Unknown | 1.37 (0.78–2.39) | .26 | 1.00 (0.53–1.89) | .97 |
| BMI, n (%) | ||||
| <18.5 | Reference | Reference | ||
| 18.5–24.9 | 0.95 (0.49–1.84) | .88 | 1.41 (0.49–3.99) | .51 |
| ≥25.0 | 0.89 (0.38–2.07) | .80 | 1.23 (0.41–3.64) | .70 |
| Cardiometabolic diseases | ||||
| Yes | Reference | Reference | ||
| No | 0.91 (0.59–1.42) | .70 | 0.59 (0.39–0.89) | .01 |
| Exercise habits | ||||
| <1 week | Reference | Reference | ||
| ≥1 week | 1.40 (0.91–2.16) | .12 | 1.60 (1.09–2.35) | .01 |
| Depression | ||||
| Yes | Reference | Reference | ||
| No | 1.18 (0.74–1.90) | .47 | 1.14 (0.77–1.70) | .50 |
| IADL | ||||
| Low | Reference | Reference | ||
| High | 0.90 (0.52–1.59) | .73 | 1.34 (0.91–1.98) | .13 |
| ICT accessibility | ||||
| Low | Reference | Reference | ||
| High | 1.45 (0.92–2.29) | .10 | 2.58 (1.71–3.91) | <.01 |
OR = odds ratio; CI = confidence intervals.
Table 3 shows the association between WAT use with health-management satisfaction by sex. Health-management satisfaction with the WAT use tended to be higher among women (OR: 1.65, 95% CI [0.90, 3.03], p = .10) and lower among men (OR: 0.69, 95% CI [0.45, 1.04], p = .07); however, no significant associations were found for either sex.
Table 3.
Association Between the WAT Usage and Health-Management Satisfaction by Sex.
| Variables | Women |
Men |
||
|---|---|---|---|---|
| OR (95% CI) | p-value | OR (95% CI) | p-value | |
| Non-WAT users | Reference | Reference | ||
| WAT users | 1.65 (0.90–3.03) | .10 | 0.69 (0.45–1.04) | .07 |
Covariates: age, education, living situation, household income, cardiometabolic disease, depressive mood, IADL, exercise habits, and ICT accessibility. OR = odds ratio; CI = confidence intervals.
Discussion
To assess the characteristics of WAT users and their satisfaction with health management, we conducted a questionnaire study among older Japanese adults who used digital devices. The prevalence of the WAT use was low (4.4%). While no significantly correlated factors for the WAT use were found among older women, several were observed among older men, including high ICT accessibility, regular exercise habits, and the presence of cardiometabolic diseases. These results suggest that the WAT use among older women is independent of demographic background and more complex, whereas men have high digital accessibility and health management awareness. Furthermore, the WAT use was not associated with health management satisfaction in either men or women, indicating that few older adults believe that WAT helps them manage their health or feel capable of using WAT for health management.
WAT Prevalence and Usage
The 4.4% WAT utilization observed in this study aligns with another Japanese study reporting 4.2% utilization (Deguchi et al., 2024). In contrast, the WAT use among older adults is higher in other countries: 14.4% to 17.4% in the U.S. (Onyekwere et al., 2023) and 6.6% in Switzerland (Chandrasekaran et al., 2021). This suggests that the WAT use among older Japanese adults is relatively low. Despite Japan’s high smartphone-penetration rate (Van Dijk, 2020), the digital proficiency of its older population is significantly lower than in other countries (Aung et al., 2022). Using WAT requires advanced digital skills to connect to smartphones and interpret the data, which can be challenging for older individuals (Moore et al., 2021). International surveys indicate that while Japan progressed in technology, the digital device use among older adults remains lower in Japan than in other countries. The limited prevalence of wearable devices among older adults in Japan could be attributed to their unfamiliarity with these complex devices and the lack of a support system to assist them in their use. A previous study showed that very few older adults use chat tools and social networking services although about 60% of older Japanese adults aged 65 and older have smartphones, suggesting their unfamiliarity with these complex devices or internet services (Aung et al., 2022).
Characteristics of WAT Usage
Higher ICT accessibility, exercise habits, and cardiometabolic diseases were associated with the WAT use among men, whereas no correlated factors were found in women. This aligns with previous studies showing a strong association between ICT accessibility and the WAT use (Seifert et al., 2017; Yang Meier et al., 2020). In this study, 61.4% of men used WAT, a higher percentage than that of women. Previous studies confirm that men are more interested in new technologies and more likely to use ICT devices than women (Kim et al., 2017; Sun et al., 2020). Men’s interest and proficiency in advanced devices like WAT may contribute to their higher usage. To bridge the gender gap, a system supporting older women with poor digital skills should be developed. For instance, teaching WAT use in social participation activities, as shown in previous studies (Kim et al., 2017). This will further promote WAT use among the general older population.
Regular exercise habits were also associated with the WAT use among men. This is supported by findings that WAT users are more likely to meet the WHO’s physical activity recommendations (Onyekwere et al., 2023). Sex differences in the WAT use and exercise habits may be due to men’s preference for objective performance tracking and competitive elements in WAT (Kim et al., 2017).
Cardiometabolic diseases were linked to the WAT use in men, consistent with the finding of studies that individuals with cardiovascular diseases and diabetes tend to use WAT (Deguchi et al., 2024; Venn et al., 2023). In this study, 60% of the respondents used WAT for pulse and blood-pressure control, indicating its role in disease management. A previous study found that the WAT use is associated with attendance at cardiology clinics (Venn et al., 2023). The higher prevalence of the WAT use among men with cardiometabolic conditions suggests that it aids disease management. Although the higher prevalence of the WAT use among men with cardiometabolic conditions suggests that it aids disease management, further research on WAT’s effect on disease progression is warranted.
No correlation factors for the WAT use were found in older women. Previous research on smartwatch acceptance (Deguchi et al., 2024) indicated that younger, single women with higher incomes and the ability to use new devices were more likely to accept smartwatches. However, our study, which focused on the actual prevalence of WAT, did not find an association between these factors, such as socioeconomic status, digital skills, and the actual WAT use. This suggests a discrepancy between the factors influencing acceptance and those affecting actual prevalence among older women. While we cannot speculate on the reasons for this discrepancy, complex factors beyond the scope of this study may influence older women’s decisions to use WAT. Further research is needed to understand older women’s willingness to adopt WAT.
Contribution of the WAT Use to Satisfaction with Health Management
Our results indicated that the WAT use was not associated with satisfaction with health management, suggesting that it does not unconditionally increase satisfaction among older adults. Conflicting results exist regarding the WAT use and health conditions (Kyytsonen et al., 2023; Li et al., 2019; Seifert et al., 2017). Some studies showed that the WAT use positively affects health behaviors, improving health-related indicators, such as weight loss, physical activity, and chronic diseases management (Hvalic-Touzery et al., 2022; Jo et al., 2019). However, the benefits of WAT may vary depending on users’ health needs and the duration of the usage. WAT users’ health needs and the duration of the present study varied, which may have contributed to the lack of health care satisfaction with WAT. Further research is needed to determine the long-term health benefits of the WAT use among community-dwelling older adults and the methods that contribute to maintaining their health.
Limitation
Our study has several limitations. First, we included older adults living in urban regions, which limits the generalizability of our findings. Second, owing to the questionnaire-survey design, recall bias may have confounded our results. For instance, if participants inaccurately recall using a WAT when they actually did not, our analysis might erroneously suggest that higher levels of WAT use are associated with lower ICT Accessibility and low health risks, potentially leading to misleading conclusions about the association between these variables. Third, although our classifications regarding the use of WAT and some covariates were defined based on previous studies, the inadequacy of our classification method cannot be denied. Fourth, this was a cross-sectional study, which prevented us from assuming causal associations between the WAT use and healthcare satisfaction. Fifth, since this study may be subject to selection bias due to the exclusion of two-thirds of the initial mailing questionnaire population, caution must be exercised in interpreting our findings. Finally, the number of measured items was limited. In this regard, if we had measured other items, we might have found interesting factors correlated with the WAT use. Therefore, further studies are required to examine the causal relationship between the WAT use and healthcare satisfaction, including an examination of the correlating factors for the WAT use using a wider range of measures.
Conclusion
Our study revealed fewer WAT use among older Japanese adults. Among older men, high ICT accessibility, regular exercise habits, and cardiometabolic diseases were associated with the WAT use. However, these factors did not correlate with the WAT use among older women. The WAT use does not necessarily lead to increased satisfaction with health management. Simply wearing a WAT is insufficient, and users need guidance on its effective and active use. Our study emphasizes that while WATs show promise in the public health domain, caution should be exercised in their application. Clear guidelines for interpreting and applying the data generated by these devices are necessary to maximize their potential impact on health outcomes and digital literacy. Further research is required to identify the specific WAT use that contributes to health maintenance.
Acknowledgments
We would like to thank Editage (www.editage.jp) for English language editing.
Footnotes
Author Contributions: KH: Conceptualization, Methodology, Data curation, Writing – original draft. RS: Conceptualization, Methodology, Resources, Writing – original draft & editing, Project administration. HS: Conceptualization, Methodology, Writing – review & editing. SO: Methodology, and Data curation. SS and TH: Methodology, Investigation, Resources, Data curation, Writing – review & editing. YF and SA: Conceptualization, Methodology, Writing – review & editing, Project administration.
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study received funding from the Tokyo Metropolitan Government Bureau of Social Welfare and Public Health, as part of the Smart Watch Innovation for Next Geriatrics and Gerontology (SWING-Japan) project.
Ethics Approval and Patient Consent: The study was conducted in accordance with the ethical standards of the Declaration of Helsinki. The research protocol was approved by the Tokyo Metropolitan Institute of Gerontology. All participants provided written consent before their participation in the study.
ORCID iD: Keigo Hinakura 
https://orcid.org/0009-0008-6482-0303
Data Availability: Data will be made available on request.
References
- Aung M. N., Koyanagi Y., Nagamine Y., Nam E. W., Mulati N., Kyaw M. Y., Moolphate S., Shirayama Y., Nonaka K., Field M., Cheung P., Yuasa M. (2022). Digitally inclusive, healthy aging communities (DIHAC): A cross-cultural study in Japan, Republic of Korea, Singapore, and Thailand. International Journal of Environmental Research and Public Health, 19(12), 6976. 10.3390/ijerph19126976 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Chandrasekaran R., Katthula V., Moustakas E. (2021). Too old for technology? Use of wearable healthcare devices by older adults and their willingness to share health data with providers. Health Informatics Journal, 27(4), 14604582211058073. 10.1177/14604582211058073 [DOI] [PubMed] [Google Scholar]
- DataReportal. (2024. April). Digital 2024 April global statshot report: Ownership of smartwatches. https://datareportal.com/reports/digital-2024-april-global-statshot
- Deguchi N., Osuka Y., Kojima N., Motokawa K., Iwasaki M., Inagaki H., Miyamae F., Okamura T., Hirano H., Awata S., Sasai H. (2024). Sex-specific factors associated with acceptance of smartwatches among urban older adults: The Itabashi longitudinal study on aging. Frontiers in Public Health, 12, 1261275. 10.3389/fpubh.2024.1261275 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Fujiwara Y., Shinkai S., Kumagai S., Amano H., Yoshida Y., Yoshida H., Kim H., Suzuki T., Ishizaki T., Watanabe S., Haga H., Shibata H. (2003). Changes in TMIG-index of competence by subscale in Japanese urban and rural community older populations: Six years prospective study. Geriatrics & Gerontology International, 3(s1), S63–S68. 10.1111/j.1444-0594.2003.00097.x [DOI] [Google Scholar]
- Hoyl M. T., Alessi C. A., Harker J. O., Josephson K. R., Pietruszka F. M., Koelfgen M., Mervis J. R., Fitten L. J., Rubenstein L. Z. (1999). Development and testing of a five-item version of the geriatric depression scale. Journal of the American Geriatrics Society, 47(7), 873–878. 10.1111/j.1532-5415.1999.tb03848.x [DOI] [PubMed] [Google Scholar]
- Hughes A., Shandhi M. M. H., Master H., Dunn J., Brittain E. (2023). Wearable devices in cardiovascular medicine. Circulation Research, 132(5), 652–670. 10.1161/CIRCRESAHA.122.322389 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hvalic-Touzery S., Setinc M., Dolnicar V. (2022). Benefits of a wearable activity tracker with safety features for older adults: An intervention study. International Journal of Environmental Research and Public Health, 19(23), 15723. 10.3390/ijerph192315723 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Jo A., Coronel B. D., Coakes C. E., Mainous A. G., III. (2019). Is there a benefit to patients using wearable devices such as fitbit or health apps on mobiles? A systematic review. American Journal of Medicine, 132(12), 1394–1400. 10.1016/j.amjmed.2019.06.018 [DOI] [PubMed] [Google Scholar]
- Kim J., Lee H. Y., Christensen M. C., Merighi J. R. (2017). Technology access and use, and their associations with social engagement among older adults: Do women and men differ? Journals of Gerontology—Series B Psychological Sciences and Social Sciences, 72(5), 836–845. 10.1093/geronb/gbw123 [DOI] [PubMed] [Google Scholar]
- Kyytsonen M., Vehko T., Anttila H., Ikonen J. (2023). Factors associated with use of wearable technology to support activity, well-being, or a healthy lifestyle in the adult population and among older adults. PLOS Digital Health, 2(5), e0000245. 10.1371/journal.pdig.0000245 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Li J., Ma Q., Chan A. H., Man S. S. (2019). Health monitoring through wearable technologies for older adults: Smart wearables acceptance model. Applied Ergonomics, 75, 162–169. 10.1016/j.apergo.2018.10.006 [DOI] [PubMed] [Google Scholar]
- McCoy S., Galletta D. F., King W. R. (2017). Applying TAM across cultures: The need for caution. European Journal of Information Systems, 16(1), 81–90. 10.1057/palgrave.ejis.3000659 [DOI] [Google Scholar]
- Moore K., O’Shea E., Kenny L., Barton J., Tedesco S., Sica M., Crowe C., Alamaki A., Condell J., Nordstrom A., Timmons S. (2021). Older adults’ experiences with using wearable devices: Qualitative systematic review and meta-synthesis. JMIR Mhealth Uhealth, 9(6): e23832. 10.2196/23832 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Onyekwere A. O., Okobi O. E., Ifiora F. C., Akinboro M. K., Akueme N. T., Iroro J., Dan-Eleberi A. O., Onyeaka F. C., Ghansah A. A. (2023). Association between wearable device use and levels of physical activity among older adults in the US: Evidence from the 2019–2020 health information national trends survey. Cureus, 15(8), e44289. 10.7759/cureus.44289 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Pardamean B., Soeparno H., Budiarto A., Mahesworo B., Baurley J. (2020). Quantified self-using consumer wearable device: Predicting physical and mental health. Healthcare Informatics Research, 26(2), 83–92. 10.4258/hir.2020.26.2.83 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Rinaldi P., Mecocci P., Benedetti C., Ercolani S., Bregnocchi M., Menculini G., Catani M., Senin U., Cherubini A. (2003). Validation of the five-item geriatric depression scale in elderly subjects in three different settings. Journal of the American Geriatrics Society, 51(5), 694–698. 10.1034/j.1600-0579.2003.00216.x [DOI] [PubMed] [Google Scholar]
- Seifert A., Schlomann A., Rietz C., Schelling H. R. (2017). The use of mobile devices for physical activity tracking in older adults’ everyday life. Digital Health, 3, 2055207617740088. 10.1177/2055207617740088 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Seino S., Kitamura A., Tomine Y., Tanaka I., Nishi M., Nonaka K., Nofuji Y., Narita M., Taniguchi Y., Yokoyama Y., Amano H., Ikeuchi T., Fujiwara Y., Shinkai S. (2019). A community-wide intervention trial for preventing and reducing frailty among older adults living in metropolitan areas: Design and baseline survey for a study integrating participatory action research with a cluster trial. Journal of Epidemiology, 29(2), 73–81. 10.2188/jea.JE20170109 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Shan R., Sarkar S., Martin S. S. (2019). Digital health technology and mobile devices for the management of diabetes mellitus: State of the art. Diabetologia, 62(6), 877–887. 10.1007/s00125-019-4864-7 [DOI] [PubMed] [Google Scholar]
- Shei R. J., Holder I. G., Oumsang A. S., Paris B. A., Paris H. L. (2022). Wearable activity trackers-advanced technology or advanced marketing? European Journal of Applied Physiology, 122(9): 1975–1990. 10.1007/s00421-022-04951-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sun B., Mao H., Yin C. (2020). Male and female users’ differences in online technology community based on text mining. Frontiers in Psychology, 11, 806. 10.3389/fpsyg.2020.00806 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Van Dijk J. A. G. M. (2020). The digital divide. https://www.researchgate.net/publication/336775102_The_Digital_Divide
- Venn R. A., Khurshid S., Grayson M., Ashburner J. M., Al-Alusi M. A., Chang Y., Foulkes A., Ellinor P. T., McManus D. D., Singer D. E., Atlas S. J., Lubitz S. A. (2024). Characteristics and attitudes of wearable device users and non-users in a large healthcare system. Journal of the American Heart Association, 13(1), e032126. [DOI] [PMC free article] [PubMed] [Google Scholar]
- World Health Organization. (2021). Global strategy on digital health 2020–2025. https://iris.who.int/bitstream/handle/10665/344249/9789240020924-eng.pdf?sequence=1
- Yang Meier D., Barthelmess P., Sun W., Liberatore F. (2020). Wearable technology acceptance in health care based on national culture differences: Cross-country analysis between Chinese and Swiss consumers. Journal of Medical Internet Research, 22(10), e18801. 10.2196/18801 [DOI] [PMC free article] [PubMed] [Google Scholar]