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Record W4312191769 · doi:10.1093/ehjdh/ztac076.2801

The effectiveness of eHealth interventions on moderate-to-vigorous intensity physical activity among cardiac rehabilitation participants: a systematic review and meta-analysis

2022· review· en· W4312191769 on OpenAlexaff
Tianhua Yu, Rui-Jia Gao, Linqi Xu, Xianwei Zhang, Ting Yu, Xiaoqian Lian, Fudong Li

Bibliographic record

VenueEuropean Heart Journal - Digital Health · 2022
Typereview
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineeHealthRehabilitationPsychological interventionPhysical therapyCochrane LibraryRandomized controlled trialSystematic reviewMeta-analysisMEDLINEHealth careInternal medicineNursing

Abstract

fetched live from OpenAlex

Abstract Background Cardiac rehabilitation is a class IA recommendation for patients with cardiovascular diseases. Physical activity is the core component and core competency of cardiac rehabilitation programs. Cardiac rehabilitation guidelines recommend patients achieve at least thirty minutes of moderate-to-vigorous intensity physical activity five or more days per week. However many patients with cardiovascular diseases are failing to meet recommended daily physical activity levels. eHealth interventions may increase moderate-to-vigorous intensity physical activity among cardiac rehabilitation participants. Objectives The objective of this study was to review the evidence of the effectiveness of eHealth interventions to increase moderate-to-vigorous intensity physical activity among cardiac rehabilitation participants. The secondary objective was to examine the effectiveness of eHealth interventions on improving exercise capacity, cardiovascular risk profile and health-related quality of life. Methods A comprehensive search strategy was developed for four electronic databases (PubMed, Web of Science, Embase and The Cochrane Library); through December 16, 2021. All studies reporting on eHealth interventions designed to increase moderate-to-vigorous physical activity among cardiac rehabilitation participants were included. Multiple not-blind reviewers determined study eligibility and extracted data. Risk of bias was evaluated using the Cochrane Collaboration Tool for randomized controlled trials and using the Effective Practice and Organisation of Care Cochrane Review Group for non-randomized controlled trials. Data quality using the Grading of Recommendations Assessment, Development and Evaluation professional guideline development tool. Data were pooled using a random-effects model. All statistical analyses were performed using Stata 17. Results Twenty-one studies were included in the review which fifteen were in the meta-analysis. The meta-analysis demonstrated eHealth interventions improved moderate-to-vigorous physical activity (standard mean difference = 0.17, 95% confidence interval: 0.07, 0.27, P<0.001). No changes were observed in exercise capacity (P=0.09), cardiovascular risk profile (body mass index, P=0.19; waist circumference, P=0.7; systolic blood pressure, P=0.36; total cholesterol, P=0.72; plasma glucose, P=0.41) and health-related quality of life (P=0.12). Conclusions eHealth interventions are effective at increasing minutes/week of moderate-to-vigorous intensity physical activity among cardiac rehabilitation participants. Future high-quality empirical studies are needed to evaluate the effectiveness of eHealth interventions delivered, with these sub-types (wearable devices, online web portal, smart phone application, messaging services and telephone calls) delivered alone or in combination delivered more conducive to moderate-to-high intensity physical activity. Funding Acknowledgement Type of funding sources: None.

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)0.015
metaresearch head score (Gemma)0.038
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 score0.024
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.038
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0240.033
Bibliometrics0.0070.006
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.187
GPT teacher head0.462
Teacher spread0.275 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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