Effectiveness of Live Health Professional–Led Group eHealth Interventions for Adult Mental Health: Systematic Review of Randomized Controlled Trials
Bibliographic record
Abstract
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.
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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 | 0.021 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.016 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".