MétaCan
Menu
Back to cohort
Record W4388092104 · doi:10.2196/50573

Building on Existing Classifications of Behavior Change Techniques to Classify Planned Coping Strategies: Physical Activity Diary Study

2023· article· en· W4388092104 on OpenAlexvenueno aff
Maya Braun, Helene Schroé, Annick De Paepe, Geert Crombez

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsnot available
FundersBijzonder Onderzoeksfonds UGentUniversiteit Gent
KeywordsCoping (psychology)CompendiumPsychologyCovertAction planInter-rater reliabilityComputer scienceApplied psychologyMedicineClinical psychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

BACKGROUND: When trying to be more physically active, preparing for possible barriers by considering potential coping strategies increases the likelihood of plan enactment. Digital interventions can support this process by providing personalized recommendations for coping strategies, but this requires that possible coping strategies are identified and classified. Existing classification systems of behavior change, such as the compendium of self-enactable techniques, may be reused to classify coping strategies in the context of physical activity (PA) coping planning. OBJECTIVE: This study investigated whether coping strategies created by a student population to overcome barriers to be physically active can be mapped onto the compendium of self-enactable techniques and which adaptations or additions to the frameworks are needed. METHODS: In total, 359 Flemish university students created action and coping plans for PA for 8 consecutive days in 2020, resulting in 5252 coping plans. A codebook was developed iteratively using the compendium of self-enactable techniques as a starting point to code coping strategies. Additional codes were added to the codebook iteratively. Interrater reliability was calculated, and descriptive statistics were provided for the coping strategies. RESULTS: Interrater reliability was moderate (Cohen κ=0.72) for the coded coping strategies. Existing self-enactable techniques covered 64.6% (3393/5252) of the coded coping strategies, and added coping strategies covered 28.52% (n=1498). The remaining coping strategies could not be coded as entries were too vague or contained no coping strategy. The added classes covered multiple ways of adapting the original action plan, managing one's time, ensuring the availability of required material, and doing the activity with someone else. When exploring the data further, we found that almost half (n=2371, 45.1%) of the coping strategies coded focused on contextual factors. CONCLUSIONS: The study's objective was to categorize PA coping strategies. The compendium of self-enactable techniques addressed almost two-thirds (3393/5252, 64.6%) of these strategies, serving as valuable starting points for classification. In total, 9 additional strategies were integrated into the self-enactable techniques, which are largely absent in other existing classification systems. These new techniques can be seen as further refinements of "problem-solving" or "coping planning." Due to data constraints stemming from the COVID-19 pandemic and the study's focus on a healthy Flemish student population, it is anticipated that more coping strategies would apply under normal conditions, in the general population, and among clinical groups. Future research should expand to diverse populations and establish connections between coping strategies and PA barriers, with ontologies recommended for this purpose. This study is a first step in classifying the content of coping strategies for PA. We believe this is an important and necessary step toward digital health interventions that incorporate personalized suggestions for PA coping plans.

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)0.019
metaresearch head score (Gemma)0.053
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.053
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.005
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.531
GPT teacher head0.621
Teacher spread0.090 · 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 designQualitative
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

Citations6
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJMIR Formative ResearchSame topicBehavioral Health and InterventionsFrench-language works237,207