MétaCan
Menu
Back to cohort
Record W4311219606 · doi:10.2196/40271

Assessing the Acceptability and Effectiveness of Mobile-Based Physical Activity Interventions for Midlife Women During Menopause: Systematic Review of the Literature

2022· review· en· W4311219606 on OpenAlexvenueno aff
Ghada AlSwayied, Haoyue Guo, Tasmin Rookes, Rachael Frost, Fiona Hamilton

Bibliographic record

VenueJMIR mhealth and uhealth · 2022
Typereview
Languageen
FieldMedicine
TopicMenopause: Health Impacts and Treatments
Canadian institutionsnot available
FundersKing Saud UniversityUniversity College London
KeywordsCINAHLPsychological interventionPsycINFOMedicineBehavior change methodsMEDLINESystematic reviewScopusmHealthGerontologyRandomized controlled trialMeta-analysisPhysical therapyInternal medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Midlife women with menopausal symptoms are less likely to meet the recommended level of physical activity (PA). Promoting PA among women in midlife could reduce their risk of cardiovascular diseases and perhaps improve menopausal symptoms. Mobile PA interventions in the form of smartphone apps and wearable activity trackers can potentially encourage users to increase PA levels and address time and resource barriers to PA. However, evidence on the acceptability and effectiveness of these interventions among midlife women is unclear. OBJECTIVE: This systematic review evaluated the effectiveness, acceptability, and active behavior change techniques (BCTs) of mobile PA technologies among midlife menopausal women. METHODS: A mixed methods systematic review of qualitative and quantitative studies was conducted. MEDLINE (Ovid), Embase, Scopus, CINAHL, Web of Science, SPORTDiscus, CENTRAL, PsycINFO, and the ProQuest Sports Medicine and Education Index were systematically searched. Studies were selected and screened according to predetermined eligibility criteria. In total, 2 reviewers independently assessed the risk of bias using the Mixed Methods Appraisal Tool and completed BCT mapping of the included interventions using the BCT Taxonomy v1. RESULTS: A total of 12 studies were included in this review. Overall risk of bias was "Moderate to high" in 58% (7/12) of the included studies and "low" in 42% (5/12) of the studies. Of the 12 studies, 7 (58%) assessed changes in PA levels. The pooled effect size of 2 randomized controlled trials resulted in a small to moderate increase in moderate to vigorous PA of approximately 61.36 weekly minutes among midlife women, at least in the short term (95% CI 17.70-105.01; P=.006). Although a meta-analysis was not feasible because of heterogeneity, positive improvements were also found in a range of menopause-related outcomes such as weight reduction, anxiety management, sleep quality, and menopause-related quality of life. Midlife women perceived mobile PA interventions to be acceptable and potentially helpful in increasing PA and daily steps. The average number of BCTs per mobile PA intervention was 8.8 (range 4-13) according to the BCT Taxonomy v1. "Self-monitoring of behaviour," "Biofeedback," and "Goal setting (behaviour)" were the most frequently described BCTs across the included interventions. CONCLUSIONS: This review demonstrated that mobile PA interventions in the form of smartphone apps and wearable trackers are potentially effective for small to moderate increases in moderate to vigorous PA among midlife women with menopausal symptoms. Although menopause is a natural condition affecting half the population worldwide, there is a substantial lack of evidence to support the acceptability and effectiveness of mobile PA interventions on menopause-related outcomes, which needs further investigation. TRIAL REGISTRATION: PROSPERO CRD42021273062; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=273062.

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)0.026
metaresearch head score (Gemma)0.119
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 score0.026
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.119
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0090.008
Bibliometrics0.0080.008
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.106
GPT teacher head0.493
Teacher spread0.386 · 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

Citations25
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJMIR mhealth and uhealthSame topicMenopause: Health Impacts and TreatmentsFrench-language works237,207