Voice-Based Remote Care Program for Vulnerable Older Adults in a Rural Community: Single-Arm Pilot Clinical Study
Bibliographic record
Abstract
Background: Voice-based digital health technologies are highly feasible and acceptable tools for supporting older adults. However, their development has rarely focused on caregiving needs, and it is often poorly integrated with existing care services, thereby limiting their sustained effect. Objective: This study aimed to evaluate the feasibility and effectiveness of a comprehensive voice-based remote care program developed in partnership with a local public health center. Methods: A single-center, single-arm clinical study involving community-dwelling, socioeconomically vulnerable older adults was conducted using a Clinical Frailty Scale of 4-5. Participants received a 6-month voice-based care program comprising smart speaker daily check-ins, an emergency response system, and artificial intelligence-driven well-being check calls. These components were integrated with the public health center for continuous monitoring. The primary outcome was caregiver burden, assessed using the Korean version of the Zarit Burden Interview. Secondary outcomes include depression (Patient Health Questionnaire-9), anxiety (Generalized Anxiety Disorder-7), and quality of life (Korean version of the Control, Autonomy, Self-realization, and Pleasure scale). Results: Among 100 enrolled participants, 96 (96%) completed the program. The caregiver burden slightly decreased from 17.1-16.2 points (mean difference -1, 95% CI -2.17 to 0.24; P=.12). However, caregivers reported a significant reduction in their perception of being the sole support provider (P=.003). Among older adults, significant improvements were observed in depression (Patient Health Questionnaire-9; P<.001), anxiety (Generalized Anxiety Disorder-7; P=.008), and quality of life (Korean version of the Control, Autonomy, Self-Realization, and Pleasure scale; P =.048).. Program adherence was high, with participants engaging for a median of 184 (IQR 154-203; 186/214, 87%) days. Conclusions: Whereas the voice-based remote care program did not significantly reduce the overall caregiver burden, it significantly reduced the perception of the caregivers as being the sole support system. Furthermore, it influenced the psychological well-being of older adults by reducing depression and anxiety and enhancing their quality of life. High adherence and engagement enhance the feasibility and acceptability of scalable digital health interventions for vulnerable older adults in rural settings.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".