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Record W4206023308 · doi:10.2196/32331

The Effectiveness of Serious Games for Alleviating Depression: Systematic Review and Meta-analysis

2021· review· en· W4206023308 on OpenAlexvenueno aff
Alaa Abd‐Alrazaq, Eiman Al-Jafar, Mohannad Alajlani, Carla Toro, Dari Alhuwail, Arfan Ahmed, Shuja Reagu, Najeeb Al-Shorbaji, Mowafa Househ

Bibliographic record

VenueJMIR Serious Games · 2021
Typereview
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsPsycINFOMeta-analysisRandomized controlled trialMEDLINESystematic reviewMoodDepression (economics)PsychologyClinical psychologyCritical appraisalCochrane LibraryMedicinePsychiatryAlternative medicineInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Depression is a common mental disorder characterized by disturbances in mood, thoughts, or behaviors. Serious games, which are games that have a purpose other than entertainment, have been used as a nonpharmacological therapeutic intervention for depression. Previous systematic reviews have summarized evidence of effectiveness of serious games in reducing depression symptoms; however, they are limited by design and methodological shortcomings. OBJECTIVE: This study aimed to assess the effectiveness of serious games in alleviating depression by summarizing and pooling the results of previous studies. METHODS: A systematic review of randomized controlled trials (RCTs) was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. The search sources included 6 bibliographic databases (eg, MEDLINE, PsycINFO, IEEE Xplore), the search engine "Google Scholar," and backward and forward reference list checking of the included studies and relevant reviews. Two reviewers independently carried out the study selection, data extraction, risk of bias assessment, and quality of evidence appraisal. Results of the included studies were synthesized narratively and statistically, as appropriate, according to the type of serious games (ie, exergames or computerized cognitive behavioral therapy [CBT] games). RESULTS: From an initial 966 citations retrieved, 27 studies met the eligibility criteria, and 16 studies were eventually included in meta-analyses. Very low-quality evidence from 7 RCTs showed no statistically significant effect of exergames on the severity of depressive symptoms as compared with conventional exercises (P=.12). Very low-quality evidence from 5 RCTs showed a statistically and clinically significant difference in the severity of depressive symptoms (P=.004) between exergame and control groups, favoring exergames over no intervention. Very low-quality evidence from 7 RCTs showed a statistically and clinically significant effect of computerized CBT games on the severity of depressive symptoms in comparison with no intervention (P=.003). CONCLUSIONS: Serious games have the potential to alleviate depression as other active interventions do. However, we could not draw definitive conclusions regarding the effectiveness of serious games due to the high risk of bias in the individual studies examined and the low quality of meta-analyzed evidence. Therefore, we recommend that health care providers consider offering serious games as an adjunct to existing interventions until further, more robust evidence is available. Future studies should assess the effectiveness of serious games that are designed specifically to alleviate depression and deliver other therapeutic modalities, recruit participants with depression, and avoid biases by following recommended guidelines for conducting and reporting RCTs. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42021232969; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=232969.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.019
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.039
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0190.034
Bibliometrics0.0080.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.082
GPT teacher head0.461
Teacher spread0.379 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations64
Published2021
Admission routes1
Has abstractyes

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