MétaCan
Menu
← Back to cohort
Record W4415711087 · doi:10.1101/2025.10.29.25339035

<i>Boosting Everyday Movement</i> : Co-designing a digital micropatterns intervention with socioeconomically diverse UK and Australian Women

2025· preprint· W4415711087 on OpenAlexaff
Maria Bissett, Nicholas A. Koemel, Matthew Ahmadi, Kristina Atsiaris, Sam Liu, Cecilie Thøgersen‐Ntoumani, Cindy M. Gray, Jason M. R. Gill, Emmanuel Stamatakis, Gemma C. Ryde

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Language
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of Victoria
FundersNational Health and Medical Research CouncilMedical Research CouncilUniversity of Glasgow
KeywordsIntervention (counseling)Thematic analysisMoodPhysical activitySociocultural evolutionMental healthBehaviour changeQualitative research

Abstract

fetched live from OpenAlex

Abstract Background Vigorous (VILPA) and moderate-to-vigorous (MV-ILPA) intermittent lifestyle physical activity refer to brief bouts of physical activity (<1 and <3 minutes, respectively) embedded in daily routines. Evidence suggests that 4–6 daily bursts of VILPA/MV-ILPA can significantly reduce the risk of cardiovascular disease and some cancers. These “micropatterns” of activity may offer a time-efficient and accessible alternative to structured exercise, particularly for women from socioeconomically diverse backgrounds who face intersecting barriers to traditional forms of physical activity. This study aimed to explore women’s perspectives and experiences of VILPA/MV-ILPA and co-design a micropatterns intervention to promote these behaviours among socioeconomically diverse women. Methods The study involved a series of three co-design workshops with women in Glasgow (N=19) and Sydney (N=31). Workshops incorporated participatory activities, education, training, discussion, and reflection to explore the concept of micropatterns, related facilitators and barriers and co-design the intervention. This study was guided by the Behaviour Change Wheel and MRC and 6SQuID intervention development frameworks. Data were audio-recorded, transcribed, and analysed using thematic framework analysis. Results Participants identified a range of barriers (e.g. concerns about ability and safety, low mood, sociocultural norms) and facilitators (e.g. adaptability, dual-purpose activities, minimal time commitment) to engaging in micropatterns. Following reflection on the barriers and facilitators, six modifiable factors were identified to be addressed in the intervention, these included: lack of knowledge and awareness, concerns about ability and safety, low mood and poor mental health, sociocultural norms and environmental constraints. Participants identified thirteen intervention components that utilized seven intervention functions (education, persuasion, training, environmental restructuring, modelling, incentivisation, and enablement) to promote VILPA/MV-ILPA activities. Participants emphasised the importance of educational content, social support, and inclusive delivery formats (e.g. short videos, visual materials). Terminology such as “Mindful Movement” and “Boosting Everyday Movement” were preferred over technical acronyms and jargon. Conclusions The final intervention involved a six-week programme of education, training, goal setting and VILPA/MV-ILPA tracking. Due to the popularity of social components and mixed perceptions of the accessibility of digital technology, the intervention was designed with three different delivery mechanisms:1) a smartphone application, 2) a smartphone application and a wearable device (e.g. Fitbit) and 3) workshops, a smartphone application and a wearable device. With further testing, this co-designed intervention could offer a feasible approach to promoting physical activity micropatterns among women from diverse socioeconomic backgrounds.

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)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.030
GPT teacher head0.300
Teacher spread0.270 · 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 designNot applicable
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

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venuemedRxiv→Same topicPhysical Activity and Health→French-language works237,207→