This is a pilot, single group, pre-post study to evaluate the feasibility and effectiveness of intergenerational, home-based eHealth interventions on eHealth literacy among older adults in Hong Kong. Data collection occurred both in-person (baseline) and via telephone (2-week follow-up) in November 2022. Consolidated Standards of Reporting Trials (CONSORT) reporting guidelines and the extension to randomized pilot and feasibility trials were followed [35] (Checklist 1).

Older adults were recruited through collaborations with local nongovernment organizations covering 18 districts across Hong Kong. Older adults were eligible if they were (1) 65 years or older, (2) spoke Chinese, and (3) were physically, mentally, and cognitively capable of engaging in simple physical tasks and (4) understanding survey questions. Older adult participants were told the availability of home-based eHealth and health behavior coaching sessions, which remove physical barriers commonly seen in centralized interventions.

Recruitment involved partnerships with community centers, senior clubs, and health care facilities. Information sessions introduced the study, followed by screening interviews for interested individuals. Those meeting the inclusion criteria were scheduled for baseline assessments. Home visits were arranged for those unable to attend group sessions.

This study was conducted in accordance with ethical guidelines for research involving human participants. The research protocol was approved by the Institutional Review Board of the University of Hong Kong (UW 22‐693). Informed consent was ensured through comprehensive information sheets and verbal explanations, with consent forms signed before any procedures. The consent form included information about the study’s purpose, procedures, risks, and benefits, as well as the participant’s right to withdraw at any time without penalty.

As a token of appreciation for their time and participation, older adults enrolled in the study received HKD $50 (US $6.44; HKD $1=US $0.13) gift bags as an incentive during the initial student visits.

All data collected during the study were deidentified to protect participants’ privacy. Data were initially recorded on paper and subsequently digitized using Qualtrics, with all identifying information removed. Access to the data was restricted to the research team, and all digital data were stored securely.

Student interventionists were current full-time students enrolled in the Bachelor of Nursing or Master of Science in Nursing programs at the University of Hong Kong, who were fluent in Cantonese. Students were recruited through internal university emails and on-campus promotional materials such as posters. Recruited students were trained by research and clinical faculty members from the School of Nursing before attending home visits. A special-designed curriculum was developed with learning materials covering (1) the aging population and primary health care’s new journey in Hong Kong, (2) effective communication techniques with older adults, (3) assistance with digital devices and mobile-based eHealth services, (4) knowledge on NCD prevention by promoting healthy lifestyles, (5) COVID-19 preventive measures, (6) strategies for building positive mental health and well-being, and (7) home visit protocol and general safety practice guidelines.

Participants’ data were collected by a self-administered survey at baseline and a telephone survey at 2 weeks after the intervention. The baseline questionnaire covered demographics (9 items), physical health (4 items), exercise habits (8 items), dietary practices (5 items), smoking (3 items), alcohol consumption (1 item), psychosocial health (16 items), eHealth literacy and mobile phone and app usage (17 items), and COVID-19 preventive measures (13 items). The primary outcome was eHealth literacy assessed by the 8-item eHealth Literacy Scale (eHEALS) [30], ranging from 8 to 40, with higher scores indicating better eHealth literacy. This scale is widely regarded as reliable and is frequently used in studies involving Chinese older adults, including those in Hong Kong [36-38].

Secondary outcomes included mental well-being, evaluated by the World Health Organization–Five Well-Being Index (WHO-5 [39]; multiplied by 4 to create a convenient scale of 0 to 100; higher scores indicate better well-being), and the 3-item University of California, Los Angeles (UCLA) Loneliness Scale [40] (3 to 9; lower scores indicate lower loneliness). Physical well-being was measured by a single item (1 to 5; higher scores indicate better physical well-being). Physical activities were assessed using a modified version of the International Physical Activity Questionnaire–Short Form [41].

Feasibility outcomes included (1) the recruitment rate (number of participants enrolled/number of eligible participants) and (2) the retention rate (number of participants who completed the follow-up/number of participants enrolled). Two additional feasibility outcomes were assessed: (1) the time required to conduct assessments and deliver the intervention package and (2) student interventionists’ compliance with the intervention. Acceptability was assessed using a 5-point Likert scale measuring satisfaction with the home-based interventions and the 14-day digital follow-up message, ranging from 1 (strongly dissatisfied) to 5 (strongly satisfied).

The 2-week follow-up survey included similar items (demographics excluded) with 4 additional questions on perceived behavioral changes that occurred compared to previsit and 2 more questions on their satisfaction with the home-based interventions and 14-day digital follow-up messages.

Trained student interventionists conducted assessments and delivered interventions during home visits (median 60, IQR 40.8-70 minutes). Written consent was obtained from participants before the assessment began.

The assessment process was flexible, adapting to older adults’ responses. Student interventionists conducted the baseline assessment in a semistructured manner, engaging participants in natural conversations while covering questionnaire items. Questions were read aloud to older adults, and responses were given verbally.

The intervention was similarly semistructured, covering four major themes:

Health education leaflets with QR codes linking to online resources were provided and stored in a small blue folder. Student interventionists explained the leaflet content and tailored discussions to each older adult’s health needs. They demonstrated smartphone use, including scanning QR codes and installing health apps, ensuring participants understood and could perform these tasks independently.

At the end of the intervention, student interventionists created dedicated one-on-one WhatsApp group chats with the older adults, sending daily health-related messages for the next 14 days. Participants were encouraged to engage with the content and share their opinions, using simple responses like emojis or voice messages.

A follow-up survey was conducted via telephone by the same student interventionist 2 weeks after the visit.

Given the exploratory nature of this pilot study, a sample size of 100 older adults was deemed appropriate to provide preliminary insights into the feasibility and potential effectiveness of the intergenerational, home-based eHealth literacy intervention. This sample size allowed for a meaningful assessment of the intervention’s impact on eHealth literacy, health behaviors, and overall well-being, while also informing the design of future, larger-scale studies.

Data were initially recorded on paper and subsequently digitized using Qualtrics. Data were analyzed using IBM SPSS Statistics (version 29.0). Descriptive statistics were calculated to summarize the data, using means, standard deviations, and standard errors or frequencies and percentages as appropriate. Paired t tests assessed pre- to posttest changes, and Wilcoxon tests were used for nonnormal data.

To assess potential biases due to loss to follow-up, baseline characteristics of participants who completed the follow-up (n=71) were compared with those who did not (n=30) using independent sample t tests for continuous variables (eg, age, eHEALS) and chi-square tests for categorical variables (eg, gender, education). Missing data were addressed using multiple imputation (MI) with 50 imputations, as missing completely at random (MCAR) tests did not reject the missing at random assumption (P=.85 for all variables; P=.08 for t test variables) [42,43]. The MI method was chosen for its ability to reduce bias under the assumption that data are missing at random and to provide more accurate standard errors and confidence intervals [44].

The imputation model included demographic variables (eg, age, education) and several auxiliary variables, such as self-rated happiness, weekly fruit and vegetable consumption, received COVID-19 vaccine doses, and frequencies of going outside. All reported pre- and posttest values are based on MI to ensure robust findings and mitigate potential biases associated with missing data. Complete case analyses were included as sensitivity analysis. Statistical significance was set at P<.05.

From the beginning of October to November 2022, 104 participants were screened as eligible; 3 (2.9%) were excluded due to being younger than 65 years, and the remaining 101 (97.1%) participants consented to the study (Figure 1). The baseline demographic characteristics of the 101 older adults are shown in Table 1. The median age was 80 (IQR 77-85) years, with the oldest participant being 93 years old. Nearly two-thirds (64/101, 63.4%) were female. Only 4.0% (4/101) reported receiving tertiary education such as college. Out of the 101 participants, 41 (40.6%) had lost their spouse, whereas 55 (54.5%) were married. Fifty-one (51.0%) participants reported living with their family. Ninety-eight (97.0%) participants reported having less than HKD $25,000 (US $3225.73) per month of household income, 42 (42.0%) reported claiming Comprehensive Social Security Assistance, and all older adults reported living in government-subsidized public housing. Out of all participants, 95 (94.1%) were retired. Physical well-being, mental well-being, and lifestyle habits of older adult participants, along with their medical history, socialization patterns, and habits, are detailed in Tables S1–S3 in (Multimedia Appendix 1). The retention rate at 2 weeks was 70.3% (71/101).

The changes in measures between baseline and the 2-week follow-up among older adults are shown in Table 2. Results from Wilcoxon rank tests (Table S4 in Multimedia Appendix 1) were consistent with the parametric t tests.

In terms of digital literacy and daily smartphone use, the mean eHEALS score was 21.41 (SE 0.79) at baseline and 23.80 (SE 1.41) at follow-up. This increase of 2.39 points was not statistically significant (P=.11). Daily smartphone usage increased by 0.45 hours, from 2.09 (SE 0.16) to 2.54 (SE 0.22) hours, which was also not significant (P=.07).

Among mental health measures, there was a nonsignificant decrease of 1.87 in adjusted WHO-5 scores, from 73.15 (SE 2.17) at baseline to 71.28 (SE 3.72) at follow-up (P=.61). The 3-item UCLA Loneliness Scale scores showed a nonsignificant increase of 0.21, from 3.92 (SE 0.13) to 4.13 (SE 0.22; P=.39).

For physical well-being and activity measures, self-rated physical well-being had a nonsignificant change from 3.51 (SE 0.09) at baseline to 3.67 (SE 0.19) at follow-up (P=.42). Changes in exercise variables were not significant either; strenuous exercise increased from 47.41 (SE 12.66) to 65.88 (SE 17.91; P=.36) minutes per week, moderate exercise decreased from 171.12 (SE 26.77) to 153.94 (SE 29.02; P=.61) minutes per week, and light exercise (walking) increased from 525.92 (SE 58.92) to 773.00 (SE 153.80; P=.13) minutes per week. Sitting, measured in minutes per day, had a nonsignificant increase from 367.32 (SE 26.14) to 398.09 (SE 52.93; P=.57).

All complete case results were similar to those obtained using MI, except for self-rated physical well-being. Complete case analyses showed a significant increase in physical well-being ratings, from 3.61 (SE 0.11) to 3.82 (SE 0.09; P=.04).

At the 2-week follow-up, 70% (50/71) of older adults reported exercising more frequently, 61% (43/71) reported watching health-related videos more often, and 55% (39/71) washed their hands more regularly (Table S5 in Multimedia Appendix 1).

At baseline, 57 university student interventionists delivered material (n=101, 100%) on eHealth literacy, NCD prevention, psychosocial health, COVID-19 prevention, and educational leaflets. They demonstrated QR code scanning, encouraged independent practice, assisted with installing health apps and registering for services, and created one-on-one WhatsApp group chats with older adults after the intervention (n=101, 100%).

At the 2-week follow-up, participants self-reported their mean satisfaction with the intervention and the 14-day WhatsApp messaging period as 4.32 (SD 0.53) and 3.87 (SD 0.72), respectively, out of 5.

No significant differences were found between participants who completed the follow-up (n=71) and those who did not (n=30) in age, gender, income, or living arrangement. A significant difference was observed in education level, but it should be noted that this observation was made among a lower number of participants (n<5) at times (Table S7 in Multimedia Appendix 1).

This pilot study indicates that an intergenerational, home-based eHealth intervention package is a feasible and acceptable method of improving older adults’ engagement with digital health resources while promoting healthy behaviors. These findings address our objective to examine the feasibility and effectiveness of such an intervention in Hong Kong’s older adult population.

The intervention showed promising, though modest, improvements in eHealth literacy and smartphone usage among older adults. The mean eHEALS scores increased by 2.39 (21.41, SE 0.79 to 23.80, SE 1.41) points, and daily smartphone use rose by 0.45 hours (2.09 to 2.54 hours), though these changes were not statistically significant. Despite the lack of statistical significance, the positive trends suggest potential benefits that warrant further investigation with larger sample sizes and longer follow-up periods. The high satisfaction rating (4.32 out of 5) indicates strong acceptability among participants. Furthermore, most participants reported improved health behaviors, including increased physical activity (50/71, 70%), more frequent viewing of health videos (43/71, 61%), and improved handwashing practices (39/71, 55%), indicating the intervention positively impacted their daily routines and health.

Comparisons with existing literature reveal interesting patterns. Our findings align with previous studies [21,22], but the comparisons suggest that longer durations may be required to achieve more substantial improvements in digital literacy. For example, Xie’s study [21] involved 2 weekly classes over a total of 4 weeks and showed significant improvements in participants’ computer knowledge (P<.01). Additionally, our sample was noticeably older (mean age: 80.42, SD 5.72 years) with lower baseline eHEALS scores (mean 21.41, SE 0.79) compared to that of Chang et al [22], whose participants (mean age: 74.64 years) started with higher baseline eHEALS scores (mean 28.31) and achieved statistically significant improvements 2 months postintervention (P<.001). These comparisons suggest that the older age and limited digital experience of our participants may have constrained the impact of the intervention within the 2-week timeframe. Previous literature [45,46] suggests that longer follow-up time periods may be required to observe noticeable changes as older adults require time to learn and put knowledge to use.

The home-based design of the intervention was effective in reaching underprivileged older adults, a population often overlooked by traditional classroom-based programs. Only 4.0% (4/101) of participants had tertiary education, and just 3.0% (3/101) had a household income above the local median (HKD $25,000 per month). Many participants had limited prior exposure to eHealth technologies; fewer than 30% had ever searched for health information online, and most had not used mobile health apps, except for the government-mandated COVID-tracking app, LeaveHomeSafe. Conducting the intervention in participants’ homes allowed them to learn comfortably at their own pace, free from the fear of judgment by peers, an essential factor given their low baseline eHEALS scores (mean 21.41, SE 0.79). The home-based setting also helped minimize stress and boost confidence, enhanced by positive language and a personalized pace set by student interventionists. This approach removed several key barriers commonly identified in older adults’ learning of digital technologies [20].

We also recruited 57 nursing students (Table S6 in Multimedia Appendix 1) with varying levels of study and background knowledge, all of whom received specialized training covering primary health care, aging populations in Hong Kong, health assessments, intervention delivery, and communication skills with older adults. This experience not only provided student interventionists with an extracurricular opportunity to develop practical skills but also enhanced their ability to communicate with older adults, an important component of geriatric care. Programs like the Generations Connect Project demonstrate the potential of intergenerational interventions to support older adults while enriching health care students’ education and professional development [14].

This study had 3 limitations. First, the short follow-up period may have limited our ability to observe significant changes in health-related indicators and eHealth literacy. Future studies with longer follow-up periods are needed to better assess the sustained effects of interventions. Second, the follow-up surveys were conducted by telephone due to privacy concerns, differing from the home-based baseline assessments, which may have affected consistency. Third, the low educational attainment of participants (60/101, 60.0% having received only primary school education or below) may have hindered their engagement with the intervention content, reducing its effectiveness. These limitations highlight the need for materials tailored to the educational level of older adults and for more standardized intervention protocols in future research.

This pilot study showed that an intergenerational, home-based eHealth intervention may be an effective approach to improving older adults’ engagement with digital health care resources while simultaneously supporting their physical and psychological well-being. The findings suggest that such interventions can help bridge the digital divide for older adult populations and promote healthier behaviors.

Future research should consider extending the intervention duration, increasing the frequency of home visits, and developing more tailored educational materials. Additionally, collaborations with local health care agencies could help address financial barriers by providing affordable mobile devices and internet access. The broader implications of this research extend to health care policy and practice, highlighting the potential of community-based, intergenerational approaches to address the growing health care needs of aging populations while preparing health care students for geriatric care through practical, community-oriented training experiences.