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Record W4405352233 · doi:10.2196/59504

A Remotely Delivered Light-Intensity Physical Activity Intervention for Older Cancer Survivors: Protocol for a Feasibility Randomized Controlled Trial

2024· article· en· W4405352233 on OpenAlexvenueno aff
Cindy K. Blair, Ursa Brown-Glaberman, Scott T. Walters, Claire Pestak, Tawny W. Boyce, Laura Barriga, Ellen S. Burgess, Bernard Tawfik, Cynthia Killough, Anita Y. Kinney, Wendy Demark‐Wahnefried, Angela L. W. Meisner, Charles L. Wiggins, V. Shane Pankratz, Sally M. Davis

Bibliographic record

VenueJMIR Research Protocols · 2024
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Cancer Institute
KeywordsHealth coachingMotivational interviewingRandomized controlled trialMedicinePhysical therapyIntervention (counseling)Activity trackerCoachingSocial cognitive theoryQuality of life (healthcare)GerontologyActivity monitorPhysical medicine and rehabilitationPhysical activityPsychologyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Older cancer survivors face age- and treatment-related comorbidities, including physical functional impairment, which are exacerbated by physical inactivity and sedentary behavior. Regular physical activity can reduce this risk, yet less than 30% of older cancer survivors meet the recommended guidelines for physical activity. OBJECTIVE: This study aims to describe the design, methods, and rationale for a remotely delivered intervention that uses a whole-of-day approach to physical activity in older cancer survivors. This approach focuses on the accumulation of intermittent bouts of light-intensity activity throughout the entire day by disrupting and reducing sedentary activity. The intervention was guided by social cognitive and self-determination theories and incorporated motivational interviewing. METHODS: The 12-week Move for Your Health trial randomly assigned 64 older cancer survivors to a theory-based physical activity intervention or a waitlist control. A Fitbit (Google) activity tracker and smartphone app were used to promote awareness of activity levels and enable self-monitoring of both activity and inactivity in tandem with health coaching phone calls. Motivational interviewing was used to engage participants and tailor strategies to achieve goals during the 12-week intervention. Data were collected at baseline, immediately after the intervention, and at longer-term follow-up (3 months thereafter). Feasibility outcomes included recruitment, retention, adherence, adverse events, and acceptability. Other outcomes included obtaining the parameter estimates for changes in physical function, physical performance, physical activity, sedentary behavior, and quality of life. RESULTS: Recruitment for the Move for Your Health randomized controlled trial was completed in June 2023. Data collection was completed in March 2024. Data analyses are ongoing. CONCLUSIONS: The results of this trial will provide information on the feasibility of implementing this intervention in the target patient population, as well as data that will provide information about the potential impact of the intervention on the outcomes. Both of these outcomes will inform the design of a larger randomized controlled trial to more fully test a physical activity intervention in an older cancer survivor population. TRIAL REGISTRATION: ClinicalTrials.gov NCT05582889; https://clinicaltrials.gov/study/NCT05582889. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/59504.

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)0.028
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.080
Threshold uncertainty score0.267

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.021
Meta-epidemiology (narrow)0.0050.003
Meta-epidemiology (broad)0.0100.005
Bibliometrics0.0030.003
Science and technology studies0.0040.004
Scholarly communication0.0050.004
Open science0.0030.003
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0800.013

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 head0.213
GPT teacher head0.569
Teacher spread0.356 · 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 designRandomized 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

Citations0
Published2024
Admission routes1
Has abstractyes

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