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Record W4311291695 · doi:10.2196/38049

The Effectiveness of Internet-Guided Self-help Interventions to Promote Physical Activity Among Individuals With Depression: Systematic Review

2022· review· en· W4311291695 on OpenAlexafffundvenue
Yiling Tang, Madelaine Gierc, Raymond W. Lam, Sam Liu, Guy Faulkner

Bibliographic record

VenueJMIR Mental Health · 2022
Typereview
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsUniversity of VictoriaUniversity of British Columbia
FundersCanadian Institutes of Health ResearchUniversity of British ColumbiaMichael Smith Health Research BC
KeywordsPsychological interventionRandomized controlled trialDepression (economics)MedicineIntervention (counseling)Clinical psychologyPhysical therapyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Depression is a prevalent and debilitating mental disorder and a leading cause of disability worldwide. Physical activity (PA) interventions have been shown to alleviate depressive symptoms. However, not all patients have access to PA programing tailored for depression. Internet-guided self-help (IGSH) interventions may be an effective option for increasing PA among people with depression who cannot or prefer not to access supervised exercise treatment. OBJECTIVE: We aimed to evaluate the effectiveness of IGSH interventions in increasing PA and alleviating depressive symptoms in people with depression. METHODS: A systematic literature search was conducted for randomized controlled trials and quasiexperimental studies using 9 electronic databases. The review was registered in PROSPERO (2020 CRD42020221713). RESULTS: A total of 4 randomized controlled trials (430 participants) met the inclusion criteria. Of these, 3 were web-based and 1 was app-based. Three studies found IGSH interventions to have medium to large effects on decreasing depressive symptoms but not on increasing PA compared with waitlist or usual care. One study showed increased self-reported PA but no significant difference in depressive symptoms in the intervention group compared with the control group. Goal setting was the most common behavior change technique used in the interventions. Dropout rates within the intervention groups were relatively low (0%-19%). CONCLUSIONS: Our findings suggested that IGSH PA interventions are feasible and have the potential to reduce depressive symptoms in people with depression. More well-designed and tailored interventions with different combinations of behavior change techniques, particularly those targeting the emotion domain, are needed to assess the overall effectiveness and feasibility of using IGSH interventions to increase PA among people with depression. TRIAL REGISTRATION: PROSPERO CRD42020221713; https://tinyurl.com/ysaua5bu.

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)0.005
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.007
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.001
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 head0.074
GPT teacher head0.479
Teacher spread0.405 · 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 designSystematic review
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

Citations16
Published2022
Admission routes3
Has abstractyes

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