Effectiveness of Live Health Professional–Led Group eHealth Interventions for Adult Mental Health: Systematic Review of Randomized Controlled Trials
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic has had adverse impacts on mental health and substance use worldwide. Systematic reviews suggest eHealth interventions can be effective at addressing these problems. However, strong positive eHealth outcomes are often tied to the intensity of web-based therapist guidance, which has time and cost implications that can make the population scale-up of more effective interventions difficult. A way to offset cost while maintaining the intensity of therapist guidance is to offer eHealth programs to groups rather than more standard one-on-one formats. OBJECTIVE: This systematic review aims to assess experimental evidence for the effectiveness of live health professional-led group eHealth interventions on mental health, substance use, or bereavement among community-dwelling adults. Within the articles selected for our primary aim, we also seek to examine the impact of interventions that encourage physical activity compared with those that do not. METHODS: Overall, 4 databases (MEDLINE, CINAHL, PsycINFO, and the Cochrane Library) were searched in July 2020. Eligible studies were randomized controlled trials (RCTs) of eHealth interventions led by health professionals and delivered entirely to adult groups by videoconference, teleconference, or webchat. Eligible studies reported mental health, substance use, or bereavement as primary outcomes. The results were examined by outcome, eHealth platform, and intervention length. Postintervention data were used to calculate effect size by study. The findings were summarized using the Synthesis Without Meta-Analysis guidelines. Risk of bias was assessed using the Cochrane Collaboration Tool. RESULTS: Of the 4099 identified studies, 21 (0.51%) RCTs representing 20 interventions met the inclusion criteria. These studies examined mental health outcomes among 2438 participants (sample size range: 47-361 participants per study) across 7 countries. When effect sizes were pooled, live health professional-led group eHealth interventions had a medium effect on reducing anxiety compared with inactive (Cohen d=0.57) or active control (Cohen d=0.48), a medium to small effect on reducing depression compared with inactive (Cohen d=0.61) or active control (Cohen d=0.21), and mixed effects on mental distress and coping. Interventions led by videoconference, and those that provided 8-12 hours of live health professional-led group contact had more robust effects on adult mental health. Risk of bias was high in 91% (19/21) of the studies. Heterogeneity across interventions was significant, resulting in low to very low quality of evidence. No eligible RCT was found that examined substance use, bereavement, or physical activity. CONCLUSIONS: Live eHealth group interventions led by health professionals can foster moderate improvements in anxiety and moderate to small improvements in depression among community-based adults, particularly those delivered by videoconference and those providing 8-12 hours of synchronous engagement. TRIAL REGISTRATION: PROSPERO CRD42020187551; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=187551. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s13643-020-01479-3.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,021 | 0,086 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,017 | 0,016 |
| Bibliométrie | 0,007 | 0,007 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».