General Medicine · Medical Literature
Enhancing District Health Center membership among older adults through intergenerational home-based and AI-generated digital interventions: protocol for a pilot cluster randomized controlled trial.
Abstract
Hong Kong is experiencing rapid population aging, with the population of older adults expected to reach 36.0% by 2046. The rise in life expectancy is often accompanied by increased chronic diseases and mental health challenges. The healthcare system remains heavily focused on tertiary care, leading to increased system burden and suboptimal health outcomes. Strengthening engagement with primary care for older adults, particularly through community-based services such as District Health Centers, is essential for improving long-term health outcomes. This approach promotes early detection, better management of chronic conditions, and prevention of unnecessary hospitalizations. This pilot study aims to evaluate the feasibility, acceptability, and preliminary effectiveness of a hybrid intergenerational home-based and AI-generated digital intervention for increasing the proportion of District Health Center members among older adults in Hong Kong. This is a two-group parallel cluster randomized controlled trial, involving 180 older adults recruited from 6 clusters. Participants will receive home-based DHC intervention by trained students, followed by 1-month of AI-generated digital interventions, including infographics, videos, and messages on the introduction and promotion of DHC, via WhatsApp. The feasibility indicators include recruitment rate, retention rates, dose delivered, dose received, and participant engagement. Acceptability outcomes are workshop and digital intervention satisfaction. The primary outcome is to increase the proportion of District Health Center members. Secondary outcomes include the proportion of participants utilizing DHC, health status, behavioral modifiable risk factors, eHealth literacy, infectious disease prevention behaviors, and feasibility metrics. Data will be collected via surveys at baseline and follow-ups (1, 3, and 6 months). This proposed pilot study aims to use hybrid intergenerational home-based and AI-generated digital intervention to improve primary healthcare engagement among older adults in Hong Kong. By promoting awareness and utilization of primary healthcare services like District Health Centers, the study seeks to promote healthier aging, improve health outcomes, alleviate hospital strain, and provide a framework for addressing the needs of aging populations. https://clinicaltrials.gov/, NCT07252934.
Clinical relevance
Among community-dwelling older adults in Hong Kong who are not yet enrolled in District Health Centers, does a hybrid intergenerational home-based education intervention delivered by trained healthcare students, followed by one month of AI-generated digital messaging via WhatsApp, increase the proportion of District Health Center members compared to a control condition? __SNAPSHOT__ {"studyDesign":"Protocol for a pilot, two-arm, parallel cluster randomized controlled trial (CONSORT-adherent)","setting":"Community-based; 18 districts across Hong Kong SAR, China; participants recruited through collaborations with local non-governmental organizations (NGOs); affiliated with the School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong","sampleSize":"Target n=180 older adults distributed across 6 clusters (30 participants per cluster; cluster-level allocation); intervention and control arm sizes not explicitly reported per arm","population":"Community-dwelling adults aged ≥65 years, able to speak and read Chinese (Cantonese or Putonghua), physically, mentally, and cognitively capable of participation; draws from the Generations Connect Project data pool. Participants with existing District Health Center membership are implicitly excluded (primary outcome is incident enrollment). Full exclusion criteria not reported in the abstract.","intervention":"Hybrid intervention: (1) intergenerational, home-based District Health Center (DHC) education session delivered by trained healthcare students, followed by (2) one month of AI-generated digital content (infographics, videos, and promotional messages about DHC services) delivered via WhatsApp","comparator":"Control arm specified as a two-group parallel design; nature of the control condition (usual care, waitlist, attention control) not reported in the abstract or available excerpts","primaryOutcome":"Proportion of older adult participants who become enrolled (members) in a District Health Center at follow-up","followUp":"Baseline (face-to-face), 1 month, 3 months, and 6 months (telephone follow-up); data collection window November 2025 – July 2026","fundingSource":"Not reported in the abstract or available excerpts; ethics approval granted by the Human Research Ethics Committee, University of Hong Kong (EA250881); registered on ClinicalTrials.gov (NCT07252934)"}
Key findings
This is a published trial protocol, not a completed study. No efficacy data, effect sizes, confidence intervals, p-values, or outcome results are available. Accordingly, no primary or secondary outcome results can be reported. The protocol specifies that feasibility indicators (recruitment rate, retention rate, dose delivered, dose received, and participant engagement) and acceptability outcomes (workshop and digital intervention satisfaction scores) will be assessed alongside the primary effectiveness endpoint. Secondary outcomes to be measured include DHC service utilization rates, self-reported health status, behavioral modifiable risk factors, eHealth literacy, and infectious disease prevention behaviors. All findings are pending trial completion and subsequent reporting.
Practice implications
This protocol paper establishes the design and rationale for a novel hybrid intervention targeting a well-recognized gap: low primary care engagement among Hong Kong's rapidly aging population, in a healthcare system still heavily weighted toward tertiary care. The study does not yet establish whether the intervention is effective, safe, or superior to any comparator. The target population — Chinese-speaking community-dwelling adults aged ≥65 years in Hong Kong — is reasonably defined, but findings will have uncertain generalizability to other healthcare systems, languages, socioeconomic contexts, or older adults with cognitive impairment or limited digital access. The AI-generated digital component is novel and clinically relevant, but its content generation process, quality assurance, and cultural adaptation are not described in the available abstract. Critically, the nature of the control condition is not reported, which limits interpretation of what 'effect' would actually be attributed to the intervention versus routine exposure. The pilot design is appropriately framed to assess feasibility and acceptability rather than to provide definitive efficacy estimates; clinicians and policymakers should not modify practice based on this protocol alone. Only the abstract and published excerpts were available for this summary, and full-text details on randomization, allocation concealment, blinding, sample size justification, and statistical analysis plan may contain additional relevant information not captured here. __LIMITATIONS__ Protocol paper only — no outcome data are yet available; all effectiveness conclusions are speculative pending trial completion.,Very small number of clusters (n=6), which is a recognized limitation in cluster RCTs and substantially restricts the ability to adjust for between-cluster confounding or detect cluster-level effect modification.,Nature of the control arm is not described in the available abstract, making it impossible to assess what the comparator actually represents (e.g., usual care, attention control, waitlist).,The pilot sample size of 180 participants across 6 clusters is expressly not powered for hypothesis testing on primary effectiveness outcomes; effect size estimates derived from this pilot will carry wide uncertainty intervals.,Telephone-based follow-up assessments introduce potential for social desirability bias, especially for self-reported outcomes such as DHC enrollment and health behaviors.,The AI-generated digital content generation methodology, validation, and cultural appropriateness are not detailed in available excerpts, raising questions about intervention fidelity and reproducibility.,Recruitment through NGO collaborations may favor more socially connected or health-engaged older adults, limiting generalizability to the most isolated or digitally excluded individuals.,Blinding of participants to intervention allocation is not feasible in this behavioral intervention design; assessor blinding status is not reported in the abstract.,The primary outcome (DHC membership enrollment) is a process measure rather than a clinical health outcome; downstream effects on morbidity, hospitalization, or quality of life are not directly assessed.,Funding source is not reported, which precludes assessment of potential conflicts of interest. __BOTTOM_LINE_CATEGORY__ not_ready_for_clinical_application __BOTTOM_LINE__ This is a published protocol for a pilot cluster RCT evaluating a hybrid intergenerational and AI-assisted digital intervention to increase District Health Center enrollment among older adults in Hong Kong; no outcome data currently exist. The study is appropriately designed to assess feasibility and acceptability as precursors to a future definitive trial, and clinicians should not modify practice based on this protocol alone. Results are expected following the July 2026 data collection close, at which point the trial's feasibility metrics and preliminary effectiveness estimates will require independent critical appraisal before any clinical translation.