Evidence map›Paper›PMID 37043276›Full record

SynthesisJournal of medical Internet research2023

Time-Dependent Changes in Depressive Symptoms Among Control Participants in Digital-Based Psychological Intervention Studies: Meta-analysis of Randomized Controlled Trials.

Alan Cy Tong, Florence Sy Ho, Owen Hh Chu, Winnie Ws Mak

Open access · goldAbstract readMeta-AnalysisReview
In one paragraph

Synthesis in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
3.2field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.

  1. Pooled it
  2. Trial
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  4. Article
  5. Article
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  7. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Alan Cy TongDepartment of Psychology, The Chinese University of Hong Kong, New Territories, Hong Kong.ORCID 0000-0002-3158-0939
Florence Sy HoDepartment of Psychology, The Chinese University of Hong Kong, New Territories, Hong Kong.ORCID 0000-0002-7102-9105
Owen Hh ChuDepartment of Psychology, The Chinese University of Hong Kong, New Territories, Hong Kong.ORCID 0000-0002-9280-6161
Winnie Ws MakDepartment of Psychology, The Chinese University of Hong Kong, New Territories, Hong Kong.ORCID 0000-0002-9714-7847
Chinese University of Hong Kong · HK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDigital-based psychological interventions (DPIs) have been shown to be efficacious in many randomized controlled trials (RCTs) in dealing with depression in adults. However, the effects of control comparators in these DPI studies have been largely overlooked, and they may vary in their effects on depression management.

objectiveThis meta-analytical study aimed to provide a quantitative estimate of the within-subject effects of control groups across different time intervals and explore the moderating effects of control types and symptom severity at baseline.

methodsA systematic literature search was conducted in late September 2021 on selected electronic databases: PubMed; ProQuest; Web of Science; and the Ovid system with MEDLINE, PsycINFO, and Embase. The control conditions in 107 RCTs with a total of 11,803 adults with depressive symptoms were included in the meta-analysis, and effect sizes (Hedges g) were calculated using the standardized mean difference approach. Study quality was assessed using the Cochrane risk-of-bias tool for randomized trials version 2.

resultsThe control conditions collectively yielded small to moderate effects in reducing depressive symptoms within 8 weeks since the baseline assessment (g=-0.358, 95% CI -0.434 to -0.281). The effects grew to moderate within 9 to 24 weeks (g=-0.549, 95% CI -0.638 to -0.460) and peaked at g=-0.810 (95% CI -0.950 to -0.670) between 25 and 48 weeks. The effects were maintained at moderate to large ranges (g=-0.769, 95% CI -1.041 to -0.498) beyond 48 weeks. The magnitude of the reduction differed across the types of control and severity of symptoms. Care as usual was the most powerful condition of all and produced a large effect (g=-0.950, 95% CI -1.161 to -0.739) in the medium term. The findings showed that waitlist controls also produced a significant symptomatic reduction in the short term (g=-0.291, 95% CI -0.478 to -0.104), refuting the previous suspicion of a nocebo effect. In addition, a large effect on depressive symptom reduction in the long term (g=-1.091, 95% CI -1.210 to -0.972) was noted among participants with severe levels of depressive symptoms at baseline.

conclusionsThis study provided evidence that depressive symptoms generally reduced over time among control conditions in research trials of DPIs. Given that different control conditions produce variable and significant levels of symptomatic reduction, future intervention trials must adopt an RCT design and should consider the contents of control treatments when investigating the efficacy of DPIs. The results of waitlist controls confirmed previous findings of spontaneous recovery among people with mild to moderate depressive symptoms in face-to-face studies. Researchers may adopt watchful waiting as participants wait for the availability of digital-based psychological services.

Indexed as

DepressionPsychosocial InterventionAdultAffectHumansRandomized Controlled Trials as TopicWaiting Listscontrol groupsdepressiondepressive symptomsdigital-based psychological interventionmeta-analysismobile phone

Identifiers

PMID37043276
PMCPMC10134030
OpenAlexW4321462465

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.