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Record W4410793951 · doi:10.2196/56905

Design, Implementation, and Evaluation of a Community-Based Phygital Telemonitoring Program for Older Adults: Multisite Retrospective Pilot Study in Singapore

2025· article· en· W4410793951 on OpenAlexvenueno aff
Yichi Zhang, Michelle Cheok Yien Law, Soon Keong Wee, B.S. Lee, Bing Liang Alvin Chew, Wei‐Peng Teo, Edmund W. J. Lee

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintGerontologyComputer scienceMedicineWorld Wide Web

Abstract

fetched live from OpenAlex

Background: Noncommunicable diseases, particularly hypertension, diabetes, hyperlipidemia, and obesity, are on the rise among older adults in Singapore, emphasizing the need for effective screening, monitoring, and educational interventions. The traditional health care model, relying on in-person visits to review patients, poses risks of underreported cases and missed opportunities for early interventions to manage complications. Community-based telemonitoring programs present promising opportunities to extend telehealth services to underserved populations, thereby mitigating the digital divide and addressing health inequalities. Objective: This study aimed to retrospectively evaluate the implementation of a community-based telemonitoring program, the Community Telehealth Service (CTS), developed to reduce digital barriers and raise awareness for regular health screening among older adults in the Singapore community. It also aimed to generate insights for scaling up similar telehealth initiatives in the community. Methods: This retrospective study used the (1) Implementation Research Logic Model, (2) Reach, Effectiveness, Adoption, Implementation, and Maintenance framework, and the Implementation Outcomes Framework to guide the design and evaluation of CTS' implementation strategies. Outcomes covered implementation outcomes (reach, adoption, feasibility, and cost), service outcomes (safety and preliminary effectiveness), and user outcomes (satisfaction). Data were collected from operational statistics and structured user feedback surveys across 3 phases of implementation at different community sites. Results: Over the course of the 3 phases, CTS has reached more than 800 older individuals and 147 health ambassadors, with the participation of community organizations, health care institutions, academic collaborators, corporate sponsors, and government agencies. Operational statistics indicated that CTS was delivered consistently across 3 sites, with improving show-up rates and stable service hours. User feedback was generally positive, citing convenience, perceived value, and appreciation for health ambassador support. However, challenges were noted in referral tracking due to differing workflows across partners and in collecting user feedback, particularly in later phases where the survey was perceived as lengthy and complex for older users. Several areas for improvement were identified, such as incorporating more health assessments, providing more health-related information, and improving the referral process. Conclusions: This early-stage retrospective study suggests that community-based telehealth programs may be a feasible and acceptable approach to delivering preventive services in community settings. While initial findings are promising, further rigorous research is needed to evaluate long-term outcomes, integration with health systems, and potential for scale-up.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.184
GPT teacher head0.559
Teacher spread0.376 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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