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Record W3042328429 · doi:10.2196/17957

Implementation of Telerehabilitation Interventions for the Self-Management of Cardiovascular Disease: Systematic Review

2020· review· en· W3042328429 on OpenAlexvenueno aff
Narayan Subedi, Jonathan C Rawstorn, Lan Gao, Harriet Koorts, Ralph Maddison

Bibliographic record

VenueJMIR mhealth and uhealth · 2020
Typereview
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsnot available
FundersDeakin University
KeywordsTelerehabilitationMedicinePsychological interventionTelemedicineMEDLINEmHealthPsycINFOPhysical therapySystematic reviewRehabilitationDisease managementHealth carePhysical medicine and rehabilitationDiseaseNursingParkinson's diseasePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Coronary heart disease (CHD) is a leading cause of disability and deaths worldwide. Secondary prevention, including cardiac rehabilitation (CR), is crucial to improve risk factors and to reduce disease burden and disability. Accessibility barriers contribute to underutilization of traditional center-based CR programs; therefore, alternative delivery models, including cardiac telerehabilitation (ie, delivery via mobile, smartphone, and/or web-based apps), have been tested. Experimental studies have shown cardiac telerehabilitation to be effective and cost-effective, but there is inadequate evidence about how to translate this research into routine clinical practice. OBJECTIVE: This systematic review aimed to synthesize research evaluating the effectiveness of implementing cardiac telerehabilitation interventions at scale in routine clinical practice, including factors underlying successful implementation processes, and experimental research evaluating implementation-related outcomes. METHODS: MEDLINE, Embase, PsycINFO, and Global Health databases were searched from 1990 through November 9, 2018, for studies evaluating the implementation of telerehabilitation for the self-management of CHD. Reference lists of included studies and relevant systematic reviews were hand searched to identify additional studies. Implementation outcomes of interest included acceptability, appropriateness, adoption, feasibility, fidelity, implementation cost, penetration, and sustainability. A narrative synthesis of results was carried out. RESULTS: No included studies evaluated the implementation of cardiac telerehabilitation in routine clinical practice. A total of 10 studies of 2250 participants evaluated implementation outcomes, including acceptability (8/10, 80%), appropriateness (9/10, 90%), adoption (6/10, 60%), feasibility (6/10, 60%), fidelity (7/10, 70%), and implementation cost (4/10, 40%), predominantly from the participant perspective. Cardiac telerehabilitation interventions had high acceptance among the majority of participants, but technical challenges such as reliable broadband internet connectivity can impact acceptability and feasibility. Many participants considered telerehabilitation to be an appropriate alternative CR delivery model, as it was convenient, flexible, and easy to access. Participants valued interactive intervention components, such as real-time exercise monitoring and feedback as well as individualized support. The penetration and sustainability of cardiac telerehabilitation, as well as the perspectives of CR practitioners and health care organizations, have received little attention in existing cardiac telerehabilitation research. CONCLUSIONS: Experimental trials suggest that participants perceive cardiac telerehabilitation to be an acceptable and appropriate approach to improve the reach and utilization of CR, but pragmatic implementation studies are needed to understand how interventions can be sustainably translated from research into clinical practice. Addressing this gap could help realize the potential impact of telerehabilitation on CR accessibility and participation as well as person-centered, health, and economic outcomes. TRIAL REGISTRATION: International Prospective Register of Systematic Reviews (PROSPERO) CRD42019124254; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=124254.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.332
Threshold uncertainty score0.901

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.099
GPT teacher head0.489
Teacher spread0.390 · 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 teacher head, 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

Citations130
Published2020
Admission routes1
Has abstractyes

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