ABSTRACT
Background
Self‐quantification (SQ) tools, which enable individuals to track and reflect on health‐related data, show promise for improving caregivers' mental health. However, their effectiveness in reducing caregiver burden and depression remains largely underexplored. This study aimed to evaluate whether the Caregiving Visualisation Project (CARE‐VIP), which incorporates SQ tools, reduces caregiver burden and depression.
Methods
A web‐based survey was conducted among family caregivers providing care to individuals aged 65 years or older living at home and certified under the Japanese long‐term care insurance system. Participants were recruited through an Internet research company. After completing the baseline survey, the participants were invited to join the CARE‐VIP program. Among them, those who chose to participate in CARE‐VIP used the SQ tool twice a day for 60 days to complete surveys on caregiving activities, health status and mood and wore an actigraph device to continuously record sleep and physical activity, such as step counts, throughout the day and night. Those who chose not to participate in CARE‐VIP completed only the follow‐up survey. A total of 1628 caregivers who completed both baseline and follow‐up surveys were included in the analysis. We assessed caregiver burden using the Japanese Zarit Caregiver Burden Interview (J‐ZBI‐8) and depression measured using the Center for Epidemiologic Studies Depression Scale (CES‐D‐11). A multivariate regression analysis, adjusted for caregiver and care recipient factors, was conducted utilising inverse probability weighting to compare the outcomes between groups.
Results
A total of 187 (11.5%) CARE‐VIP participants demonstrated significant reductions in caregiver burden (B: −2.63, 95% CI: −3.96 to −1.29) and depression (B: −1.73, 95% CI: −2.79 to −0.68) compared to non‐CARE‐VIP participants.
Conclusions
The CARE‐VIP program effectively reduced caregiver burden and depression. These findings suggest that SQ tools may support caregivers' mental health by facilitating daily recording and monitoring of caregiving experiences.
Keywords: caregiver burden, depression, family caregiver, inverse probability treatment weighting, self‐quantification
1. Introduction
Globally, demographic trends indicate an increasing number of individuals aged 60 years and above [1], with Japan having the highest proportion of individuals aged 65 years or older, accounting for 29.1% of its population in 2023 [2]. In the same year, over seven million individuals in Japan required long‐term care [3], leading to a growing demand for family‐based caregiving. Family caregivers often experience significant burdens, which are associated with declines in health‐related quality of life, particularly in terms of mental health [4]. Compared to noncaregivers, they face a higher risk of stress [5], depression [6] and reduced physical activity [7]. Moreover, the mental health of caregivers can negatively impact the health of care recipients [8]. Therefore, maintaining and improving caregivers' mental health is crucial.
Various interventions, including psychosocial support, respite care, occupational interventions and information and communication technologies (ICT)‐based solutions, have proven effective in reducing caregiver burden, depression and anxiety and enhancing self‐efficacy [9, 10]. However, participation in these interventions is often limited by financial, geographical and time constraints [11]. Conversely, ICT‐based interventions, such as mobile apps and web‐based tools, offer convenient and accessible alternatives, enabling caregivers to engage with these resources at their convenience [12, 13]. In a scoping review of digital mental health tools for informal caregivers, Petrovic et al. [13] synthesised findings from randomised controlled trials, nonrandomised studies, mixed‐methods research and qualitative studies, reporting that such tools may help enhance coping skills, reduce stress and improve caregivers' mental health.
Many studies have recently focused on self‐quantification (SQ) tools [14]. SQ tools refer to tracking tools to collect, manage and reflect on personal health data, facilitating a better understanding of one's body, health behaviours and environmental interactions [15]. SQ enables users to monitor their physiological and emotional states, potentially increasing motivation through feedback [16], promoting self‐awareness and introspection and encouraging healthier behaviour patterns [17, 18].
Feng et al. [14] conducted a systematic review of 67 empirical studies on self‐tracking and the quantified self in the context of health and well‐being. These studies primarily focused on ICT‐based tools, such as mobile apps, wearable devices and digital platforms, and involved various stakeholder groups, including end users, patients and caregivers. Mattison et al. [19], in their review, reported that while wearable devices effectively increase physical activity and improve weight management, they do not significantly affect specific health indicators such as BMI and HbA1c levels. The importance of such feedback in achieving these outcomes has also been emphasized in several studies [18, 19, 20]. In caregiver support, a tool that integrates SQ with a mobile app incorporating cognitive behavioral therapy principles has been linked to reduced depression and improved emotional well‐being and self‐esteem [21].
Additionally, SQ tools function similarly to traditional diaries [22, 23]. Diary‐keeping has proven to be a valuable tool for caregivers, providing a space to reflect on and manage their experiences, with therapeutic benefits [24]. Moreover, emotional disclosure through diary‐keeping has been associated with reduced depression [25] and improved mental health [11]. A notable example is the Co‐Care‐KIT, a reflective toolkit developed to capture caregivers' daily emotional and physical experiences [26]. A study by Bosch et al. [26], involving seven informal caregivers over 7–14 days, utilised multiple tools, including a custom‐designed journal for daily reflection, a photo‐based experience sampling app for recording emotions and contexts in situ, and a heart rate tracker for monitoring stress through heart rate variability. Follow‐up semi‐structured interviews revealed that the participants experienced increased self‐awareness and gained a deeper understanding of their well‐being. However, this research was limited by a small sample size, a short intervention period and a primary focus on improving caregivers' self‐awareness and understanding of their role. Therefore, to address these limitations, this study aimed to examine the effects of SQ tools on caregiver burden and depression among a larger group of caregivers over an extended period. The study utilized the Caregiving Visualisation Project (CARE‐VIP), a recording‐based support program designed to assist family caregivers in monitoring their daily caregiving and health‐related experiences.
2. Materials and Methods
2.1. Study Design and Participants
2.1.1. Study Design
This study was a nonrandomised observational study with a longitudinal design, comparing caregivers who voluntarily participated in the CARE‐VIP with those who did not. Participants chose whether to enrol in CARE‐VIP after completing a baseline survey assessing caregiving experiences, health status and sleep patterns, combined with continuous actigraphy monitoring. CARE‐VIP participants were expected to engage in twice‐daily online surveys and to wear an actigraph device for 60 consecutive days. Participation was voluntary, and to avoid behavioural modifications during the documentation period, participants did not receive direct feedback from the CARE‐VIP system.
2.1.2. Participant Recruitment
This study utilised a sample obtained through an Internet survey. Participants included Japanese family caregivers aged 20 years or older who provided care for individuals aged 65 years or older, certified as requiring care under the Japanese long‐term care insurance system and living at home. We recruited the participants through a web‐based survey from a prominent Internet research company that maintains a panel of registered respondents meeting these criteria. Stratified sampling divided Japan into eight regions. The proportion of care levels under the Japanese long‐term care insurance system in each region was calculated, and the sample size for each region was allocated based on the ratio of care recipients in that region to the total population requiring care across the country.
An initial screening survey identified family caregivers interested in participating in the study. The inclusion criteria were (1) age 20 years or older; (2) self‐reported caregiving for at least one community‐dwelling older adult aged 65 years or older, certified as needing care; (3) not institutionalised nor hospitalised; (4) actively caring for an older adult at least once a week and (5) owning a smartphone on which the actigraph app can be installed.
2.1.3. Baseline Survey
In total, 2743 participants completed the baseline survey, which collected information about their demographics, health status and caregiving activities. The participants were then invited to express their interest in participating in the CARE‐VIP program. Those who expressed interest and provided informed consent were included in the CARE‐VIP group as participants, while those who did not express interest were classified as nonparticipants. An online explanation of the CARE‐VIP project was provided before the participants made their decision.
2.2. Intervention: CARE‐VIP
The CARE‐VIP, as previously introduced in Wakui et al. [27], was developed to investigate the potential effects of caregiving on both negative and positive perceptions among family caregivers of community‐dwelling older adults over daily, medium‐term, long‐term and extended periods. It is an online tool that enables family caregivers to record their daily caregiving activities through a website accessible via a computer or smartphone. Participation in the CARE‐VIP was entirely voluntary, allowing caregivers to decide whether or not to join.
The participants completed surveys twice daily to assess their caregiving experiences, health and sleep. The morning surveys included items such as mood upon waking; bedtime and wake‐up times; sleep quality and sleep disruptions due to factors such as care recipient needs, personal issues or family‐related reasons. The evening surveys included items such as self‐rated health, time spent on caregiving, specific caregiving tasks performed (e.g., bathing, meal preparation, medication management), perceived support, services used, self‐assessed physical and emotional conditions, activities engaged in throughout the day and a caregiving diary featuring an open‐ended response section. Reminder emails were sent every morning and evening to encourage survey completion. Sleep and physical activity, including walking steps, were monitored using actigraphy (MTN‐210; Kissei Comtec, Nagano, Japan) throughout the day and night, excluding bath times. The participants used both CARE‐VIP and Actigraph devices for 60 consecutive days.
At the end of the study, the CARE‐VIP participants were offered a reward of approximately US$120 (¥16 000).
2.3. Follow‐Up Survey
After 60 days of participation in the CARE‐VIP program, both CARE‐VIP and non‐CARE‐VIP participants were invited to complete a follow‐up survey assessing the same variables as the baseline survey.
This study was approved by the ethics review board of the Tokyo Metropolitan Institute for Geriatrics and Gerontology (R21‐076). It was conducted as part of the Smart Watch Innovation for Next Geriatrics and Gerontology (SWING) study, an intensive longitudinal investigation examining the potential of behavioral data, such as physical activity and sleep patterns monitored by wearable devices, to enhance health outcomes among older adults.
2.4. Measurements
2.4.1. Dependent Variable
Caregiver burden was assessed at follow‐up using the short version of the Japanese Zarit Caregiver Burden Interview (J‐ZBI‐8) [28], which consists of eight items. Responses were recorded on a 5‐point Likert scale from 0 (never) to 4 (nearly always), with a total score range of 0–32; higher scores indicate greater caregiver burden. The Cronbach's alpha coefficient was 0.92 for the J‐ZBI‐8 in this study. Depression was measured at follow‐up using 11 items from the Center for Epidemiologic Studies Depression Scale (CES‐D‐11) [29]. Responses were recorded on a scale from 0 (not at all or less than 1 day within a week) to 3 (more than 5 days a week), with a total score range of 0–33; higher scores indicate more severe depressive symptoms. The Cronbach's alpha coefficient was 0.83 for the CES‐D‐11 in this study.
2.4.2. Independent Variable
The independent variable was participation in the CARE‐VIP program (yes or no).
2.4.3. Covariates
Covariates included factors related to both caregivers and care recipients. Caregiver factors included sex, age, marital status (married or unmarried), subjective economic status, education (less than vocational school or above), employment status (unemployed or employed), main caregiver status, social support (instrumental, emotional and informational; categorised as not available or available) and self‐rated health. Care recipient factors included dementia symptoms, levels of dependency in activities of daily living/instrumental activities of daily living (ADL/IADL), relationship to the caregiver (partner, mother, father, mother‐in‐law, father‐in‐law, sibling, grandparents or others), living arrangement (cohabiting with or living separately from the care recipient) and availability of long‐term care services. Subjective economic status was assessed continuously from 1 (very difficult) to 5 (very comfortable). Self‐rated health was assessed continuously from 1 (very poor) to 5 (excellent). ADL/IADL were assessed utilising the total score from 12 items on the Katz ADL scale [30] and the Lawton IADL scale [31], with a score range of 0–12; higher scores indicate greater levels of dependency. Additionally, baseline caregiver burden was included as a covariate when caregiver burden was the outcome, and baseline depression was included as a covariate when depression was the outcome.
2.5. Statistical Analysis
Caregiver burden and depression were compared between the CARE‐VIP and non‐CARE‐VIP participants. Baseline characteristics were summarised as means (standard deviations) for continuous variables and counts (percentages) for categorical variables.
To assess the association between participation in the CARE‐VIP program and caregiver burden and depression outcomes, we conducted multivariate regression analyses. These analyses were adjusted utilising inverse probability of treatment weighting (IPTW), which utilises propensity scores to control for potential confounders and ensure balance in baseline characteristics between the two groups [32]. IPTW estimates the average treatment effect, reflecting the effect of the treatment if it were provided to all individuals in the study population. Propensity scores were calculated utilising a logistic regression model based on baseline characteristics, including caregiver factors (sex, age, marital status, subjective economic status, education, employment status, main caregiver, social support and self‐rated health) and care recipient factors (dementia symptoms, ADL/IADL, relationship, living arrangement and long‐term care services). Additionally, baseline caregiver burden was included as a covariate when caregiver burden was the outcome, and baseline depression was included as a covariate when depression was the outcome. Propensity scores were utilised to weight the participants: individuals in the CARE‐VIP group were weighted by the probability of participation, while the inverse of the likelihood of nonparticipation weighted those in the non‐CARE‐VIP group. This approach facilitates a balanced comparison between the groups by adjusting for differences in baseline characteristics.
We calculated the standardised differences between the two groups to evaluate the balance of covariates after weighting. The standardised difference is obtained by dividing the mean difference between groups by the standard deviation. A standardised difference of less than 0.1 indicates that the covariates are well‐balanced between the groups [33]. Results are reported as unstandardised coefficients with 95% confidence intervals (CIs). Statistical significance was defined as p < 0.05. All analyses were conducted utilising Stata/MP version 18.0 (STATA Corp LLC, College Station, TX, USA).
3. Results
Of the 2743 participants who completed the baseline survey, 201 agreed to participate in CARE‐VIP and 2542 did not. After two CARE‐VIP participants withdrew from the study, 199 participants remained in the CARE‐VIP group and participated for an average of 58.8 days (SD: 3.7) over the 60‐day period. Twelve participants were excluded from the group due to missing data, resulting in a final sample of 187 participants (valid response rate: 94.0%).
In the non‐CARE‐VIP group, 94 participants were excluded for not receiving the follow‐up survey, largely due to withdrawal from the Internet research company during the baseline‐to‐follow‐up period. Of the remaining 2448 eligible non‐CARE‐VIP participants, 1695 completed the follow‐up survey (response rate: 69.2%). Among these respondents, 258 participants were excluded due to having ceased caregiving activities (e.g., care recipient's death or admission to a facility), as these factors could influence mental health outcomes and introduce bias based on prior research [34]. Additionally, 495 nonrespondents were excluded. Finally, 254 participants were excluded for missing or inconsistent data, resulting in 1441 valid non‐CARE‐VIP participants (valid response rate: 58.6%) (Figure 1).
FIGURE 1.
Flowchart of the study population.
Table 1 summarises the participants' characteristics. The CARE‐VIP participants, who represented 11.5% of the total sample, reported higher baseline levels of caregiver burden and depression than the non‐CARE‐VIP participants. At follow‐up, both caregiver burden and depression scores decreased in the CARE‐VIP group, while they increased in the non‐CARE‐VIP group. Before the IPTW adjustment, the standardised differences for some covariates related to caregiver burden and depression exceeded 0.1, indicating a potential imbalance. However, after IPTW adjustment, all covariates showed standardised differences below 0.1, indicating that the groups were well‐balanced (see Figures 1 and 2).
TABLE 1.
Characteristics of the participants by CARE‐VIP program.
| Total | CARE‐VIP participants | Non‐CARE‐VIP participants | ||
|---|---|---|---|---|
| Variable | (n = 1628) | (n = 187) | (n = 1441) | |
| Caregivers | ||||
| Baseline survey J‐ZBI‐8 score, mean (SD) | 13.6 (8.8) | 15.8 (9.2) | 13.3 (8.7) | |
| Follow‐up survey J‐ZBI‐8 score, mean (SD) | 13.7 (8.6) | 12.5 (8.3) | 13.8 (8.6) | |
| Baseline survey CES‐D‐11 score, mean (SD) | 9.4 (6.1) | 10.7 (6.6) | 9.2 (6.0) | |
| Follow‐up survey CES‐D‐11 score, mean (SD) | 9.5 (6.6) | 8.7 (6.3) | 9.6 (6.7) | |
| Sex, n (%) | Female | 819 (50.3%) | 82 (43.9%) | 737 (51.1%) |
| Male | 809 (49.7%) | 105 (56.1%) | 704 (48.9%) | |
| Age, mean (SD) | 56.8 (10.4) | 54.8 (10.1) | 57.0 (10.4) | |
| Marital status, n (%) | Married | 501 (30.8%) | 54 (28.9%) | 447 (31.0%) |
| Unmarried | 1127 (69.2%) | 133 (71.1%) | 994 (69.0%) | |
| Subjective economic status, mean (SD) | 2.9 (1.2) | 2.9 (1.2) | 2.8 (1.3) | |
| Education, n (%) | Less than vocational school | 438 (26.9%) | 45 (24.1%) | 393 (27.3%) |
| Vocational school or above | 1190 (73.1%) | 142 (75.9%) | 1048 (72.7%) | |
| Employment status, n (%) | Unemployed | 587 (36.1%) | 50 (26.7%) | 537 (37.3%) |
| Employed | 1041 (63.9%) | 137 (73.3%) | 904 (62.7%) | |
| Main caregiver, n (%) | No | 605 (37.2%) | 62 (33.2%) | 543 (37.7%) |
| Yes | 1023 (62.8%) | 125 (66.8%) | 898 (62.3%) | |
| Instrumental social support, n (%) | Not available | 243 (14.9%) | 34 (18.2%) | 209 (14.5%) |
| Available | 1385 (85.1%) | 153 (81.8%) | 1232 (85.5%) | |
| Emotional social support, n (%) | Not available | 149 (9.2%) | 18 (9.6%) | 131 (9.1%) |
| Available | 1479 (90.8%) | 169 (90.4%) | 1310 (90.9%) | |
| Informational social support, n (%) | Not available | 197 (12.1%) | 29 (15.5%) | 168 (11.7%) |
| Available | 1431 (87.9%) | 158 (84.5%) | 1273 (88.3%) | |
| Self‐rated health, mean (SD) | 3.2 (1.1) | 3.2 (1.0) | 3.2 (1.2) | |
| Care recipient | ||||
| Dementia symptoms, n (%) | No | 564 (34.6%) | 60 (32.1%) | 504 (35.0%) |
| Yes | 1064 (65.4%) | 127 (67.9%) | 937 (65.0%) | |
| ADL/IADL, mean (SD) | 14.3 (5.7) | 13.9 (5.8) | 14.4 (5.7) | |
| Relationship, n (%) | Partner | 122 (7.5%) | 11 (5.9%) | 111 (7.7%) |
| Mother | 886 (54.4%) | 98 (52.4%) | 788 (54.7%) | |
| Father | 321 (19.7%) | 40 (21.4%) | 281 (19.5%) | |
| Mother‐in‐law | 150 (9.2%) | 16 (8.6%) | 134 (9.3%) | |
| Father‐in‐law | 45 (2.8%) | 8 (4.3%) | 37 (2.6%) | |
| Sibling | 15 (0.9%) | 1 (0.5%) | 14 (1.0%) | |
| Grandparents | 74 (4.5%) | 9 (4.8%) | 65 (4.5%) | |
| Others | 15 (0.9%) | 4 (2.1%) | 11 (0.8%) | |
| Living arrangements, n (%) | Cohabiting with the care recipient | 509 (31.3%) | 63 (33.7%) | 446 (31.0%) |
| Lives separately from the care recipient | 1119 (68.7%) | 124 (66.3%) | 995 (69.0%) | |
| Long‐term care services, n (%) | Not available | 166 (10.2%) | 20 (10.7%) | 146 (10.1%) |
| Available | 1462 (89.8%) | 167 (89.3%) | 1295 (89.9%) | |
Abbreviations: ADLs/IADLs, activities of daily living/instrumental activities of daily living; CES‐D, Center for Epidemiologic Studies Depression Scale; J‐ZBI‐8, the Japanese version of the Zarit Caregiver Burden Interview; SD, standard deviation.
Table 2 presents the unstandardised coefficients and 95% CI from the follow‐up survey for caregiver burden and depression, comparing the CARE‐VIP participants to the non‐CARE‐VIP participants utilising stabilised IPTW methods. Participation in the CARE‐VIP program was significantly associated with lower levels of caregiver burden (B: −2.63; 95% CI: −3.96 to −1.29) and depression (B: −1.73, 95% CI: −2.79 to −0.68) compared to nonparticipation.
TABLE 2.
Estimated effects of the CARE‐VIP program on caregiver burden and depression among family caregivers in Japan.
| B | 95% confidence interval | p | |
|---|---|---|---|
| Caregiver burden | |||
| Participation | −2.63 | (−3.96 to −1.29) | < 0.001 |
| Depression | |||
| Participation | −1.73 | (−2.79 to −0.68) | 0.001 |
Note: B = unstandardized coefficients for caregiver burden and depression outcomes.
4. Discussion
This study demonstrated that the CARE‐VIP program significantly reduced caregiver burden and depression among family caregivers. Participants who engaged in daily recording of caregiving activities, health status and emotions showed marked improvements in mental health outcomes compared to nonparticipants. These findings suggest that systematic self‐monitoring is an effective approach for supporting family caregivers' psychological well‐being.
In the broader context of technology‐assisted healthcare interventions, studies have demonstrated the effectiveness of wearable devices in various health domains. Prior studies have highlighted the positive impact of wearable devices on health outcomes, particularly in areas such as physical activity and weight management [19]. Similarly, interventions utilising wearable technologies combined with self‐monitoring approaches have been shown to improve sleep outcomes [35]. However, these interventions have primarily targeted physiological or observable health behaviours, such as physical activity, sleep patterns and weight control. In contrast, our findings extend to psychological domains, demonstrating improvements in caregiver burden and depression—both of which are closely linked to caregivers' physical health [36, 37]. These results suggest that wearable technologies and systematic self‐monitoring may contribute to health improvements not only in physical but also in psychological aspects. While prior studies have often attributed the benefits of SQ tools to feedback on physiological parameters [19, 38], it remains unclear in our study whether similar mechanisms were involved, as no feedback was provided. Future research should systematically investigate whether and how feedback influences psychological outcomes among family caregivers participating in self‐monitoring interventions.
These effects may be explained by the promotion of self‐awareness, reflection and emotional disclosure through self‐monitoring and recording using SQ tools. Self‐monitoring through the daily use of mood tracking, behavioural tracking and caregiving record applications may have promoted self‐awareness and awareness of caregiving practices. This increased awareness may have led to enhanced attention to their own physical and mental health through reflection on their bodies and emotions, leading to improved self‐care behaviours and stress management. Previous studies have found that encouraging health‐promoting self‐care behaviours can alleviate caregiver stress [39]. Additionally, self‐tracking is useful for motivating and supporting basic activities during periods of low mood, providing a sense of accomplishment and stability [40].
Furthermore, self‐monitoring and writing down one's feelings and caregiving experiences may have promoted self‐reflection, facilitated objective re‐evaluation of caregiving, and fostered new insights and more appropriate approaches to caregiving. This process may have contributed to improved motivation and quality of care, while reducing stress. Self‐reflection through recording may help caregivers find inner strength, explore options and make decisions [41]. Through this process, caregivers may discover meaning in their caregiving roles, which is closely linked to their perseverance in these roles [42]. This meaning‐making process enhances their motivation to cope with the challenges associated with caregiving [43], potentially reducing depression and improving self‐esteem [44].
The act of recording daily caregiving activities and emotions serves as a form of written emotional disclosure, which has been shown to enhance emotional regulation and reduce stress [45]. Through emotional disclosure, the CARE‐VIP participants may have processed caregiving‐related emotions, such as sadness and anger, more effectively. This enhanced emotional processing may explain the observed reductions in caregiver burden and depression.
Despite the promising findings, this study has some limitations. First, recruiting participants through online platforms and offering financial incentives may have introduced both selection bias and self‐selection bias. Caregivers with higher digital literacy, those experiencing more distress, or individuals more motivated to seek support may have been more likely to participate. These factors, along with unmeasured confounders such as personality traits (e.g., resilience, proactiveness, self‐reflection), may have influenced both participation in the CARE‐VIP program and the outcomes. Although baseline differences in caregiver burden and depression between groups were adjusted using IPTW, residual confounding may still exist, and the results should be interpreted with caution. As a result, the generalizability of the findings to the broader population of family caregivers may be limited. Second, the study only evaluated the immediate effects of the intervention and did not assess long‐term outcomes. Consequently, whether the benefits of using wearable devices and SQ tools are sustained over a more extended period remains unclear. Future research should incorporate longer follow‐up periods to determine whether the observed improvements in caregiver burden and depression are maintained over time. Finally, changes in caregiving demands or the health status of care recipients during the intervention period may have influenced the outcomes, but these time‐varying external factors were not fully controlled for, making it difficult to isolate the specific effects of the intervention.
5. Conclusion
These results indicate that engaging in daily recording and monitoring of caregiving experiences, health status and mood may help improve caregivers' mental health.
This suggests that SQ tools may offer a promising and accessible means of supporting caregivers' mental health, warranting further investigation into their broader applicability and long‐term effects.
Ethics Statement
This study received ethical approval from the Tokyo Metropolitan Institute for Gerontology and Geriatrics (approval no. R21‐076) and adhered to the principles of the Declaration of Helsinki, including its amendments and equivalent ethical standards for conducting research.
Consent
This study involved an initial questionnaire, a self‐quantification program and a follow‐up questionnaire. Informed consent was obtained from all participants who participated in the self‐quantification program through online and telephone methods.
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Data S1. Supplementary Information.
Acknowledgements
We would like to express our sincere gratitude to all family caregivers who participated in this study. Additionally, we extend our appreciation to the members of the SWING‐Japan team for their valuable support and contributions to this research. This work was supported by the JSPS Grants‐in‐Aid for Scientific Research (B) [grant number 21H03282, 23K21580] and a grant from the Smart Watch Innovation for Next Geriatrics & Gerontology (SWING‐JAPAN) program, sponsored by the Tokyo Metropolitan Government.
Funding: This work was supported by the JSPS Grants‐in‐Aid for Scientific Research (21H03282, 23K21580) and a grant from the Smart Watch Innovation for Next Geriatrics & Gerontology (SWING‐JAPAN) program, sponsored by the Tokyo Metropolitan Government.
Data Availability Statement
Data sharing is not applicable to this article as no new data were created or analyzed in this study.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data S1. Supplementary Information.
Data Availability Statement
Data sharing is not applicable to this article as no new data were created or analyzed in this study.