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Record W3132929591 · doi:10.2196/23927

Use of mHealth to Increase Physical Activity Among Breast Cancer Survivors With Fatigue: Qualitative Exploration

2021· article· en· W3132929591 on OpenAlex
Élise Martin, Antonio Di Meglio, Cécile Charles, Arlindo R. Ferreira, Arnauld Gbenou, Marine Blond, Benoit Fagnou, Johanna Arvis, Barbara Pistilli, Mahasti Saghatchian, Inês Vaz-Luís

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJMIR Cancer · 2021
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsmHealthBreast cancerPsychological interventionThematic analysisFocus groupExploratory researchPhysical activityPsychologyMedicineQualitative researchCancerClinical psychologyPhysical therapyNursingInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Physical activity has shown beneficial effects in the treatment of breast cancer fatigue; nevertheless, a significant portion of patients remain insufficiently physically active after breast cancer. Currently most patients have a smartphone, and therefore mobile health (mHealth) holds the promise of promoting health behavior uptake for many of them. OBJECTIVE: In this study, we explored representations, levers, and barriers to physical activity and mHealth interventions among inactive breast cancer patients with fatigue. METHODS: This was an exploratory, qualitative study including breast cancer patients from a French cancer center. A total of 4 focus groups were conducted with 9 patients; 2 independent groups of patients (groups A and B) were interviewed at 2 consecutive times (sessions 1 to 4), before and after their participation in a 2-week mHealth group experience consisting of (1) a competitive virtual exercise group activity (a fictitious world tour), (2) participation in a daily chat network, and (3) access to physical activity information and world tour classification feedback. We used a thematic content analysis. RESULTS: Several physical activity levers emerged including (1) physical factors such as perception of physical benefit and previous practice, (2) psychological factors such as motivation increased by provider recommendations, (3) social factors such as group practice, and (4) organizational factors including preplanning physical activity sessions. The main barriers to physical activity identified included late effects of cancer treatment, lack of motivation, and lack of time. The lack of familiarity with connected devices was perceived as the main barrier to the use of mHealth as a means to promote physical activity. The tested mHealth group challenge was associated with several positive representations including well-being and good habit promotion and being a motivational catalyzer. Following feedback, modifications were implemented into the mHealth challenge. CONCLUSIONS: mHealth-based, easily accessed group challenges were perceived as levers for the practice of physical activity in this population. mHealth-based group challenges should be explored as options to promote physical activity in a population with fatigue after breast cancer.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score0.880

Codex and Gemma teacher scores by category

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

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 head0.102
GPT teacher head0.416
Teacher spread0.314 · 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