Evidence map›Paper›PMID 38109171›Full record

ArticleJMIR formative research2023

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

Maya Braun, Helene Schroé, Annick L De Paepe, Geert Crombez

Open access · goldAbstract read
In one paragraph

Article in JMIR formative research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.2field-weighted citation impact, top 19% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 6 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Maya BraunDepartment of Experimental-Clinical and Health Psychology, Ghent University, Gent, Belgium.ORCID https://orcid.org/0000-0002-1240-4550
Helene SchroéDepartment of Movement and Sports Sciences, Ghent University, Ghent, Belgium.ORCID https://orcid.org/0000-0001-8473-552X
Annick L De PaepeDepartment of Experimental-Clinical and Health Psychology, Ghent University, Gent, Belgium.ORCID https://orcid.org/0000-0002-9366-3692
Geert CrombezDepartment of Experimental-Clinical and Health Psychology, Ghent University, Gent, Belgium.ORCID https://orcid.org/0000-0002-4744-8561
Ghent University · BE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWhen 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.

objectiveThis 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.

methodsIn 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.

resultsInterrater 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.

conclusionsThe 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.

Indexed as

behavior changeclassificationdigital interventionpersonalizationphysical activityphysically activeplanproblem-solvingsolutiontechniques

Identifiers

PMID38109171
PMCPMC10758936
OpenAlexW4388092104

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.