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Record W4409660795 · doi:10.1371/journal.pone.0321958

Feasibility and preliminary effects of an app-based physical activity intervention for individuals with depression (MoodMover): A protocol for a single-arm, pre-post intervention study

2025· article· en· W4409660795 on OpenAlexafffund
Yiling Tang, Madelaine Gierc, Henry La, Sam Liu, Raymond W. Lam, Eli Puterman, Guy Faulkner

Bibliographic record

VenuePLoS ONE · 2025
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsUniversity of VictoriaUniversity of British Columbia
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BC
KeywordsIntervention (counseling)Psychological interventionProtocol (science)MedicineClinical trialDepression (economics)Randomized controlled trialPatient Health QuestionnairemHealthPhysical therapyDepressive symptomsClinical psychologyAlternative medicinePsychiatryCognition

Abstract

fetched live from OpenAlex

Depression is the leading cause of disability worldwide. Mobile app-based behavior change interventions that promote lifestyle physical activity (PA) may serve as viable alternatives or adjuncts to traditional treatments offering increased reach and accessibility. This protocol describes an experimental, pre-post single-arm trial to investigate the feasibility and preliminary effects of an app-based, 9-week PA intervention (MoodMover) designed for individuals with depression. MoodMover is co-designed with patients and a multidisciplinary research team using a no-code intervention development platform (Pathverse). This study will employ a single-arm pre-post trial with an optional 9-week follow-up, following the Obesity-Related Behavioral Intervention Trials (ORBIT) model. Thirty-six adults who self-report a clinical diagnosis of major depressive disorder or report at least mild depressive symptoms based on the Patient Health Questionnaire - 9 items (PHQ-9) will be recruited. The main outcomes of this study are the feasibility and acceptability of MoodMover, such as the recruitment strategy, assessments (e.g., PHQ-9), and user engagement. Preliminary effects will be assessed by evaluating changes in PA and depressive symptoms. Recruitment is expected to begin on November 1st, 2024, and end on May 1st, 2025. Trial results will be disseminated via publications in peer-reviewed journals and via presentations at academic conferences. This study fits within Phase IIa: Proof-of-concept and Phase IIb: Pilot and Preliminary Testing of the ORBIT model. The robust feasibility and acceptability measures, especially the user engagement data powered by Pathverse, will provide a comprehensive understanding of the MoodMover intervention's feasibility and potential effects. Results will inform potential progression to the next step of the ORBIT model-Phase IIc: Phase II Efficacy Trial-to test MoodMover in a more rigorous randomized controlled trial. This study has been registered at ClinicalTrials.gov (NCT06573125; https://clinicaltrials.gov/study/NCT06573125).

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)0.030
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.039
Threshold uncertainty score0.161

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.026
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0020.001
Science and technology studies0.0040.002
Scholarly communication0.0020.002
Open science0.0030.002
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0390.011

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 head0.084
GPT teacher head0.454
Teacher spread0.370 · 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 designNon-randomized trial
Domainnot available
GenreProtocol

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

Citations2
Published2025
Admission routes2
Has abstractyes

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