With declining fertility and increasing life expectancy, global populations are aging rapidly. In Hong Kong, the proportion of older adults is projected to rise from 17.9% in 2018 to 31.9% by 2038, placing increasing pressure on health care and welfare systems [1,2]. Most older adults prefer aging in place over institutional care, even when frail, making informal caregivers central to community-based elderly care [3].

These caregivers assist with daily tasks, medical appointments, and decision-making, often acting as crucial advocates for their loved ones [4,5]. While their contributions help reduce health care use and improve outcomes for older adults, caregiving is physically and emotionally demanding. Many caregivers face limited access to support, inadequate training, and social isolation, leading to feelings of guilt, fatigue, and mental distress. Studies show that more than 60% of caregivers experience high caregiving burden, 55% report depressive symptoms, and 40% face poor family functioning [6-8].

Despite their vital role, informal caregivers often receive far less support than care recipients. In Hong Kong, the lack of an integrated health and social care system contributes to fragmented services, making it difficult for caregivers to seek timely and coordinated assistance [9]. A local study in 2022 found that many caregivers expressed a strong need for a centralized platform offering information, caregiving resources, and service referrals [10,11].

Advances in digital health offer promising solutions. Telecare, which leverages information and communication technologies, enables remote monitoring and timely support from health care professionals [12,13]. It is especially useful for working caregivers, offering both practical information and emotional support without the constraints of time and location [3,14].

Several systematic reviews have demonstrated that telehealth or telecare interventions—often delivered via phone or videoconferencing—can moderately reduce caregiver stress, burden, and depressive symptoms, particularly among those caring for individuals with chronic conditions or dementia [14,15]. However, most studies have focused on specific clinical populations and lacked integration with broader community-based health and social services [14]. Few programs have incorporated real-time digital case management, asynchronous tailored messaging, and interactive web-based peer support in a comprehensive model. Furthermore, although some telecare interventions have been tested among older adults with multimorbidity and young populations [16], there remains a significant gap in research targeting informal caregivers of community-dwelling older adults with diverse needs. Addressing these gaps, this study evaluates the effectiveness of a multicomponent telecare program, co-developed by a health-social partnership team, in improving caregivers’ psychological well-being and caregiving outcomes. We hypothesized that participants in the intervention group would demonstrate greater reductions in stress and depression and greater improvements in self-efficacy, quality of life, and caregiving burden compared to those receiving usual care.

The design of this program was informed by Lazarus and Folkman’s [17] transactional theory of stress and coping and the COPE (Creativity, Optimism, Planning, and Expert Information) model. In line with the transactional theory, the intervention aimed to reduce caregiver stress by enhancing their appraisal and coping capacity. Caregivers were supported to adopt problem-focused strategies (eg, goal setting and seeking solutions) and emotion-focused strategies (eg, stress management and emotional support) to manage caregiving challenges [18].

The COPE model provided a practical framework for empowering caregivers through the intervention components. For instance, creativity was encouraged through guided problem-solving discussions; optimism was fostered by reframing challenges and sharing success stories; planning was embedded in structured goal-setting and action plans; and expert information was delivered via health-social professionals and curated digital content [19]. Together, these models guided the development of a multicomponent program that aimed to enhance caregivers’ psychological well-being and self-efficacy in managing care responsibilities.

The details can be found in the published protocol [20]. The reporting of the trial adhered to the CONSORT (CONsolidated Standards Of Reporting Trials) 2025 checklist (Checklist 1).

This is a single-blinded, 2-armed randomized controlled trial. The study was conducted in collaboration with one of the largest nongovernmental organizations in the world, which has more than 1.7 million older members.

The criteria for the inclusion of participants were as follows: (1) being aged ≥18 years; (2) having no cognitive impairment; (3) being able to understand and communicate in Cantonese; (4) providing care for older adults aged ≥60 years for a minimum of 4 hours per week over a period of at least 3 months; (5) being a current smartphone user; (6) having knowledge of how to access the internet; (7) committing to attend biweekly online meetings with the program providers, lasting 15 to 30 minutes, over a period of 3 months; and (8) being willing to receive individual-specific video messages covering caregiving skills via WhatsApp (Meta Platforms Inc.). Exclusion criteria included (1) being an alcoholic or using psychiatric drugs, (2) being illiterate (inability to read and write), (3) having a diagnosis of chronic mental problems, and (4) having previously participated in health or social programs offered by other organizations.

Participants were recruited between January 2023 and September 2024. The intervention was delivered from January 2023 to December 2024. In community centers, a trained research assistant assessed and recruited eligible participants by sharing details of the study with them and inviting them to join. Verbal consent was obtained from those who agreed to join the program. Following the collection of baseline data, the research assistant contacted the principal investigator of the research team to proceed with randomization. The principal investigator used a software program called “Research Randomizer” to generate numbers, which were placed in sealed envelopes. Each envelope corresponded to either the intervention group (“1”) or the control group (“2”). The envelopes were opened sequentially upon receiving a call from the research assistant. In this study, the research assistant was blinded to group assignments, whereas the principal investigator and health care providers were not blinded.

In addition to the telecare-based intervention group, the control group also received the usual services from community centers. The participants in the control group attended 6 face-to-face educational talks during the intervention period, which took place at the community center. These talks were conducted by members of the health-social partnership team, including a registered nurse, a Chinese medicine practitioner, a physiotherapist, and a mental health nurse.

Each talk covered 1 or 2 specific topics and lasted approximately 2 hours. The health care professionals provided informal caregivers with knowledge and live demonstrations related to self-care and elderly care. The topics addressed during the talks included the management of chronic diseases, acupressure techniques, eye and oral care for older adults, pain management strategies, lifting and transfer techniques, fall prevention measures, swallowing issues, and the management of insomnia. During the questions and answers session of each talk, participants had the opportunity to ask questions and seek clarification on the topics that had been discussed. Upon completion of the follow-up data collection, participants in the control group were offered access to the online caregiver platform and its educational resources to ensure equitable support and knowledge dissemination.

The data were collected at two time points: at baseline (T1) and at the follow-up after the interventions (T2). Data collection for the study involved the use of Qualtrics, a survey platform, where participants were provided with a link to complete the questionnaires themselves. However, for participants who encountered difficulties in filling out the questionnaires online, alternative methods were used. In such cases, data collection took place face-to-face with the assistance of the research assistant and staff from the nongovernmental organizations. They provided support and guidance to the participants, ensuring that the questionnaires were completed accurately and comprehensively. This approach allowed for flexibility in data collection, accommodating participants who may have experienced challenges with completing the online survey.

The secondary outcomes included self-efficacy, depression, quality of life, and caregiving burden of the caregivers.

A 2-tailed alpha of .05, a power of 80% (a beta error probability of .2), and an effect size of 0.403 were used to determine the size of the sample. This effect size was based on a recent telephone-based mobile health program that assessed the same primary outcome measure as in this study, namely perceived stress level [38]. On the basis of these considerations, a sample size of 34 individuals per group was necessary.

Considering an attrition rate of 10% to 15% reported in a previous program [38], a dropout rate of 15% was assumed for this study. Taking into account this dropout rate, the total sample size required was 80 participants, with 40 participants allocated to each group.

The statistical analysis was conducted using the Statistical Package for the Social Sciences (version 29.0). To compare the demographic data of the intervention and control groups at baseline, the Mann-Whitney U test, t tests, and chi-square tests were used. Normality was tested using frequency distribution histograms, and all data were found to conform to a normal distribution. Therefore, independent t tests (two-tailed) were used to analyze the mean differences in primary and secondary outcomes between the two groups, and paired t tests (two-tailed) were used to compare within-group changes before and after the intervention. A primary intention-to-treat analysis was performed. Missing outcome data for participants lost to follow-up were addressed using multiple imputation based on the assumption of missing completely at random. A significant result was determined when the P value (level of significance) was .04 for a 2-tailed test, indicating a statistically significant difference.

This study was approved by the Human Subjects Ethics Sub-Committee of the Hong Kong Polytechnic University (reference number HSEARS20210907001). All participants were provided with detailed information about the study’s purpose, procedures, risks, and benefits. Written informed consent was obtained from all participants before data collection, and they were informed of their right to withdraw at any time without consequences. All collected data were anonymized, and identifying information was removed before analysis to ensure confidentiality. Secure storage procedures were implemented to safeguard digital and paper records. No personal identifiers were used in reporting results. Participants received a supermarket coupon valued at HK $50 (approximately US $6.50) as a token of appreciation for their time and effort.

A total of 94 participants were assessed for eligibility. Of these, 75 (79.8%) individuals (n=38, 51%, in the intervention group; and n=37, 49%, in the control group) were eventually recruited and randomized for the study. Seven participants from both groups dropped out for various reasons, such as the death of a caretaker, physical illness, mismatched services, or they were unable to be contacted (Figure 1). Among the 75 participants, 14 (18.7%) were male and 61 (81.3%) were female, with a mean age of 65.5 years. Approximately 62.7% (47/75) were retired, and 52.0% (39/75) were caregivers for their parents. A significant proportion (44/75, 58.6%) had been taking care of older adults for 5 years or longer. All participants were experienced in using smartphones. There were no statistically significant differences in participant characteristics or outcome measures between the groups at baseline. Table 1 presents the demographic characteristics of the participants at baseline.

There were no statistically significant differences in the means between the 2 groups at T2. However, a statistically significant within-group difference was found in the intervention group, where the perceived stress level of the participants was lower at postintervention than preintervention (t=−2.21; P=.04). No within-group difference was observed in the control group.

The intervention group had statistically significant higher self-efficacy scores (t=1.98; P=.04) when compared to the control group. There was also a within-group effect on self-efficacy level in the intervention group (t=−2.28; P=.03).

The participants in the intervention group had statistically lower depression scores than those in the control group (t=−2.24; P=.03). The intervention group showed a significant within-group effect (t=2.98; P=.009), whereas no significant within-group effect was observed in the control group.

There were no statistically significant within-group effects in either the intervention group or the control group from T1 to T2 in the physical components of quality of life. A statistically significant within-group difference was observed in the intervention group from T1 to T2 in the mental components of quality of life. There was no between-group difference at T2 in either the physical or mental components of quality of life.

Similar to perceived stress levels, there was no statistically significant between-group difference at T2 for caregiving burden. However, a statistically significant within-group difference was found in the intervention group from T1 to T2 (t=2.89; P=.009), where participants had a lower caregiving burden postintervention than preintervention.

Comparisons within and between groups in the pretest and posttest mean scores for all primary and secondary outcomes can be found in Table 2.

As populations increasingly age, the role of informal caregivers remains critical in promoting aging in place and ensuring continuity of care. This randomized controlled trial evaluated the effectiveness of a theory-informed, telecare-based intervention in improving caregiver outcomes. The findings demonstrate that caregivers who received the intervention reported significantly higher self-efficacy and lower levels of depression compared to those receiving usual care. Additionally, significant within-group improvements were observed in stress levels, mental quality of life, and caregiving burden in the intervention group, although not reflected in between-group comparisons.

The findings of this randomized controlled trial provide evidence that a telecare-based intervention, grounded in psychological and behavioral theories, can significantly improve depression and self-efficacy among informal caregivers of older adults. These findings align with prior studies demonstrating that accessible, technology-enabled interventions can offer caregivers both emotional support and practical guidance, leading to enhanced psychological resilience and self-management skills [39,40]. Notably, the individualized approach in this intervention—through nurse-led case management and tailored video content—may have allowed caregivers to address their unique caregiving challenges, which supports findings from prior personalized eHealth models [41].

While between-group differences were not observed for perceived stress and caregiving burden, significant within-group improvements in these domains suggest that the intervention had a positive psychosocial effect. This reinforces the importance of early and sustained caregiver support, particularly in settings where community resources are fragmented. Given the rising demand for informal care and global efforts to enable aging in place, these results contribute to a growing body of literature supporting the integration of digital care into community-based services.

The observed reduction in depression among participants may also be attributed to the intervention’s multidimensional support system. Caregivers had multiple channels for engagement, including structured video consultations, asynchronous communication via WhatsApp, and peer interaction on a dedicated web platform. This combination not only improved knowledge and problem-solving capabilities but may also have addressed the social isolation frequently reported by caregivers [42]. The inclusion of personalized content likely enhanced relevance and motivation, 2 factors known to influence behavior change in caregiver populations [43].

The lack of significant improvement in the physical components of quality of life may be attributed to the short duration of the intervention, as changes in physical functioning often require longer follow-up or more intensive caregiver support services. Additionally, variations in care recipients’ health status and caregiving intensity could have influenced these outcomes. Future studies should consider stratifying analyses by caregiving complexity and incorporating longer-term follow-up, cost-effectiveness analysis, and implementation evaluation to better understand differential effects and inform wider adoption.

Several limitations should be noted. First, this trial was prospectively powered for the primary outcome and not powered for subgroup analyses or for detecting smaller effects on secondary outcomes; these estimates should therefore be interpreted with caution. Second, the intervention could not be blinded to participants or providers, potentially introducing response bias. Third, some participants encountered technical difficulties with WhatsApp and the online forum, suggesting that digital literacy and access may affect scalability. Fourth, the duration and content of nurse case management sessions were not formally tracked, limiting fidelity assessment. Fifth, only short-term effects were measured; long-term follow-up is needed to assess sustained impact.

Additionally, we did not collect data on care recipients’ functional status, digital literacy, or the caregivers’ relationship to the care recipient and caregiving network size. These contextual factors may influence intervention effectiveness and should be considered in future research to enable subgroup analyses and more tailored support.

This study demonstrates that integrating health-social partnership teams with telecare services can significantly improve caregivers’ psychological outcomes. Such community-based interventions offer accessible, tailored support to informal caregivers, helping to reduce emotional burden and promote mental well-being—both of which are essential to sustaining long-term caregiving capacity and ensuring quality of care.