Digitally Enabled Peer Support Intervention to Address Loneliness and Mental Health: Prospective Cohort Analysis
Bibliographic record
Abstract
BACKGROUND: Social isolation and loneliness affect 61% of US adults and are associated with significant increases in excessive mental and physical morbidity and mortality. Annual health care spending is US $1643 higher for socially isolated individuals than for those not socially isolated. OBJECTIVE: We prospectively evaluated the effects of participation with a digitally enabled peer support intervention on loneliness, depression, anxiety, and health-related quality of life among adults with loneliness. METHODS: Adults aged 18 years and older living in Colorado were recruited to participate in a peer support program via social media campaigns. The intervention included peer support, group coaching, the ability to become a peer helper, and referral to other behavioral health resources. Participants were asked to complete surveys at baseline, 30, 60, and 90 days, which included questions from the validated University of California, Los Angeles Loneliness Scale, Patient Health Questionnaire 2-Item Scale, General Anxiety Disorder 7-Item Scale, and a 2-item measure assessing unhealthy days due to physical condition and mental condition. A growth curve modeling procedure using multilevel regression analyses was conducted to test for linear changes in the outcome variables from baseline to the end of the intervention. RESULTS: In total, 815 ethnically and socially diverse participants completed registration (mean age 38, SD 12.7; range 18-70 years; female: n=310, 38%; White: n=438, 53.7%; Hispanic: n=133, 16.3%; Black: n=51, 6.3%; n=263, 56.1% had a high social vulnerability score). Participants most commonly joined the following peer communities: loneliness (n=220, 27%), building self-esteem (n=187, 23%), coping with depression (n=179, 22%), and anxiety (n=114, 14%). Program engagement was high, with 90% (n=733) engaged with the platform at 60 days and 86% (n=701) at 90 days. There was a statistically (P<.001 for all outcomes) and clinically significant improvement in all clinical outcomes of interest: a 14.6% (mean 6.47) decrease in loneliness at 90 days; a 50.1% (mean 1.89) decline in depression symptoms at 90 days; a 29% (mean 1.42) reduction in anxiety symptoms at 90 days; and a 13% (mean 21.35) improvement in health-related quality of life at 90 days. Based on changes in health-related quality of life, we estimated a reduction in annual medical costs of US $615 per participant. The program was successful in referring participants to behavioral health educational resources, with 27% (n=217) of participants accessing a resource about how to best support those experiencing psychological distress and 15% (n=45) of women accessing a program about the risks of excessive alcohol use. CONCLUSIONS: Our results suggest that a digitally enabled peer support program can be effective in addressing loneliness, depression, anxiety, and health-related quality of life among a diverse population of adults with loneliness. Moreover, it holds promise as a tool for identifying and referring members to relevant behavioral health resources.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".