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Record W4405185206 · doi:10.2196/59478

A Novel Telehealth Exercise Program Designed for Rural Survivors of Cancer With Cancer-Related Fatigue: Single-Arm Feasibility Trial

2024· article· en· W4405185206 on OpenAlexvenueno aff
Ryan J. Marker, Andrew J. Kittelson, Jared J. Scorsone, Ian A. Moran, John C. Quindry, Heather J. Leach

Bibliographic record

VenueJMIR Cancer · 2024
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Institute of General Medical SciencesNational Cancer Institute
KeywordsPreprintTelehealthGerontologyCancer survivorMedicineCancerPhysical therapyTelemedicineHealth careComputer scienceWorld Wide WebPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Exercise interventions are among the best-known interventions for cancer-related fatigue (CRF). Rural survivors of cancer, however, report specific barriers to engaging in exercise programs and lack overall access to effective programs. OBJECTIVE: The purpose of this investigation was to assess the feasibility of a novel telehealth exercise program designed specifically for rural survivors of cancer with CRF. METHODS: A single-arm clinical trial of the BfitBwell Telehealth Program was performed. Based on an established clinical program, this adapted 12-week program addressed barriers previously reported by rural survivors by providing synchronous videoconference exercise sessions (2 per program), asynchronous exercise sessions using a personal training smartphone or internet app (3-5 per week), and regular symptom (CRF) monitoring using automated emailed surveys (every 2 weeks). Personalized exercise prescriptions containing aerobic and resistance activities were implemented by cancer exercise specialists. Symptom-triggered synchronous sessions were initiated for participants failing to improve in CRF, as identified by a reference chart of CRF improvements observed during a supervised exercise program. Eligible participants were adult survivors of any cancer diagnosis who had completed treatment with curative intent in the past 12 months or had no planned changes in treatment for the duration of the study, lived in a rural area, and were currently experiencing CRF. Feasibility was assessed by objective measures of recruitment, data collection, intervention acceptability and suitability, and preliminary evaluations of participant responses. CRF was the primary clinical outcome (assessed using the Functional Assessment of Chronic Illness Therapy-Fatigue Scale [FACIT-Fatigue]) and was measured before, after, and 6 months after program completion. RESULTS: In total, 19 participants enrolled in the study, 16 initiated the exercise program, and 15 completed the program. A total of 14 participants were recruited through internet advertisements, and the total recruitment rate peaked at 5 participants per month. Participants completed 100% of initial and final assessments (30 assessments across all participants) and 93% (70/75 possible surveys across all participants) of emailed surveys and attended 97% (29/30 possible sessions across all participants) of synchronous exercise sessions. In total, 6 participants initiated symptom-triggered sessions, with 6 of 7 initiated sessions attended. The mean FACIT-Fatigue scores significantly improved (P=.001) by 11.2 (SD 6.8) points following the completion of the program. A total of 13 participants demonstrated at least a minimal clinically important difference in FACIT-Fatigue scores (≥ +3 points) at this time. FACIT-Fatigue scores did not significantly change from program completion to 6-month follow-up (n=13; mean change -1.1, SD 3.4 points; P=.29). CONCLUSIONS: Results from this investigation support the feasibility of the BfitBwell Telehealth Program and a subsequent efficacy trial. Novel program components also provide potential models for improving exercise program efficacy and efficiency through asynchronous exercise prescription and symptom monitoring. TRIAL REGISTRATION: ClinicalTrials.gov NCT04533165; https://clinicaltrials.gov/study/NCT04533165.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.785
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.065
GPT teacher head0.389
Teacher spread0.324 · 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.

Study designOther design
Domainnot available
GenreEmpirical

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

Citations5
Published2024
Admission routes1
Has abstractyes

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