Technology-Enhanced Peer Support for Depression in Older Adults: Single-Arm Mixed Methods Feasibility Study (Preprint)
Bibliographic record
Abstract
BACKGROUND: Depression in late life is often compounded by social isolation and barriers to care. There is limited study of technology-enhanced peer support for depression among older adults. OBJECTIVE: This study aimed to assess the feasibility and acceptability of a technology-enhanced peer support intervention to decrease depression among older adults. METHODS: We used a mixed methods pilot study among adults aged 50 years and older with depression who received a peer support intervention called Peers+. The intervention consisted of 8 weekly video chats and unidirectional texts focused on increasing depression self-care and coping. Data obtained from screening, baseline, postintervention, and 3-month follow-up were used in the analysis to assess preliminary outcomes of the intervention. Mixed effects longitudinal models were used to assess change in depression, and qualitative data were collected and analyzed to identify key themes related to participant experiences. RESULTS: =26.0; β=-.14, 95% CI -0.20 to 0.09; P<.001). Emotional well-being (β=.48, 95% CI 0.26-0.70; P<.001), social functioning (β=.71, 95% CI 0.33-1.09; P<.001), self-efficacy (β=2.29, 95% CI 0.83-3.75; P<.001), and coping (β=2.90, 95% CI 0.24-5.55; P<.001) improved throughout the study period. Participants perceived supportive texts as reinforcing trust between peer coaches, using coping strategies, increasing social connection, and providing accountability for improving self-care. Peer coaches and older adults needed technology support for participation in the study. CONCLUSIONS: This study demonstrated the feasibility and acceptability of a peer support intervention enhanced by video chats and texts, delivered by older adult peer coaches to an ethnically diverse group of older adults with depression. Study findings indicate that ongoing and accessible technology support contributed to older adult participation and engagement.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".