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Record W4388521708 · doi:10.2196/53602

Behavior Change Techniques for the Maintenance of Physical Activity in Cancer

2023· article· en· W4388521708 on OpenAlexvenueno aff
Lara Edbrooke, Tamara L. Jones

Bibliographic record

VenueJMIR Cancer · 2023
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsBehavior changePhysical activityIntervention (counseling)Randomized controlled trialCancerPsychologyBehaviour changeCluster (spacecraft)GerontologyComputer scienceMedicinePhysical therapySocial psychologyOperating systemPsychiatry

Abstract

fetched live from OpenAlex

Ester et al report the findings from a 2-arm cluster randomized controlled trial nested within a hybrid effectiveness-implementation study, which involved a 12-week exercise and behavior change program for rural and remote Canadians (Exercise for Cancer to Enhance Living Well [EXCEL]). The addition of 23 weeks of app-based physical activity monitoring to the EXCEL program did not result in significant between-group differences in physical activity at 6 months. While several behavior change techniques were included in the initial 12-week intervention, additional techniques were embedded within the mobile app. However, there is currently a lack of evidence regarding how many and which behavior change techniques are the most effective for people with cancer and if these differ based on individual characteristics. Potentially, the use of the mobile app was not required in addition to the behavior change support delivered to both groups as part of the EXCEL program. Further research should involve participants who may be in most need of behavioral support, for example, those with lower levels of self-efficacy. Suggestions for future research to tailor behavior change support for people with cancer are discussed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.904
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.083
GPT teacher head0.415
Teacher spread0.331 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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