Digitally Enabled Peer Support Intervention to Address Loneliness and Mental Health: Prospective Cohort Analysis
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».