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Record W2746079584 · doi:10.2196/resprot.7554

Feasibility of a Facebook Intervention for Exercise Motivation and Cardiac Rehabilitation Adherence: Study Protocol

2017· article· en· W2746079584 on OpenAlexvenueno aff
Lee Anne Siegmund, Haitham M. Ahmed, Michael Todd Crawford, James Bena

Bibliographic record

VenueJMIR Research Protocols · 2017
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsnot available
Fundersnot available
KeywordsRehabilitationIntervention (counseling)Protocol (science)Physical therapyMedicinePsychologyApplied psychologyMedical educationNursingAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: While cardiac rehabilitation has been shown to be effective at improving coronary heart disease (CHD), participation is generally poor. Attempts to increase uptake and adherence often fail. Use of a Facebook intervention for this population may be a unique opportunity to support self-determined motivation and affect adherence. OBJECTIVE: To evaluate the impact of a Facebook intervention on motivation for exercise and adherence to cardiac rehabilitation in patients with CHD during a 12-week, Phase II cardiac rehabilitation program. METHODS: A prospective, randomized controlled pilot study, grounded in Self-Determination Theory, will be conducted. Participants will be recruited from inpatient, or the intake visit to outpatient, cardiac rehabilitation, and then randomly assigned to the intervention or comparison group. Participants in the intervention group will take part in a private Facebook group. Weekly posts will be designed to support self-determined motivation, measured at baseline and postcardiac rehabilitation by the Behavioral Regulation in Exercise Questionnaire-3 (BREQ-3). The Psychological Need Satisfaction for Exercise (PNSE) scale will measure fulfillment of needs that affect motivation. Participants in the comparison group will be given the same materials, but these will be supplied via handouts and email. The number of sessions attended will be tallied and analyzed using t tests. Overall motivation will be evaluated using analysis of covariance (ANCOVA) models. Multivariate analysis of variance models will be used to evaluate differences in the change across motivation subtypes. If significant, ANCOVA models for each subtype will be fit. ANCOVA models will be used to compare changes in needs satisfaction, overall and separately among the three subscales, between groups. Engagement in the Facebook group will be measured by number of "likes" and self-report of weekly visits to the group. RESULTS: This project was funded in July 2017 and recruitment is currently underway. The recruitment goal is 60 cardiac rehabilitation patients. Data collection is anticipated to be complete by July 2018. CONCLUSIONS: This pilot study will be the first to examine the effect of a Facebook intervention on patient adherence and motivation for exercise in a cardiac rehabilitation setting. Engagement in the Facebook group and participation in the study will help to determine the feasibility of using Facebook to affect adherence and motivation in cardiac rehabilitation patients, potentially improving outcomes through the use of a unique intervention. TRIAL REGISTRATION: ClinicalTrials.gov NCT02971813; https://clinicaltrials.gov/ct2/show/NCT02971813 (Archived by WebCite at http://www.webcitation.org/6sRsz8Zpa).

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)0.012
metaresearch head score (Gemma)0.010
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: Non-randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.045
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.010
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0020.001
Science and technology studies0.0050.002
Scholarly communication0.0020.002
Open science0.0030.003
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0450.009

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 head0.353
GPT teacher head0.606
Teacher spread0.254 · 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

Citations16
Published2017
Admission routes1
Has abstractyes

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