MétaCan
Menu
Back to cohort
Record W4386604013 · doi:10.1093/eurpub/ckad133.246

O.5.2-5 Piloting a text message intervention to increase physical activity and exercise after post-stroke rehabilitation: the KATS study (Keeping Active with Texting after Stroke)

2023· article· en· W4386604013 on OpenAlexaff
Jacqui Morris, Linda Irvine, Albert Farré, Stephan U Dombrowski, Jenna Breckenridge, Gözde Özakinci, Thérèse Lebedis

Bibliographic record

VenueEuropean Journal of Public Health · 2023
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsRehabilitationPsychological interventionStakeholderIntervention (counseling)Formative assessmentPsychologyApplied psychologyProcess (computing)Medical educationNursingMedicineComputer sciencePublic relationsPedagogy

Abstract

fetched live from OpenAlex

Abstract Purpose After stroke rehabilitation, people feel abandoned by services and struggle to undertake physical activities to support recovery and health. Text messaging interventions can reach many people at low cost but have not been used widely after stroke. This study pilot-tested a text message intervention (KATS) to promote post-rehabilitation activity. Methods We developed KATS using: formative research on post-stroke activity; stakeholder engagement (people with stroke and rehabilitation therapists) to identify priorities; the Health Action Process Approach to structure the behaviour change intervention; and stakeholder review before pre-testing. KATS components included: increasing motivation; goal setting and planning; self-monitoring; coping planning; and maintaining regular activity. KATS comprised 95 messages, delivered by computer programme, over 12 weeks. Text messages explained and modelled KATS components using Behaviour Change Techniques effective for changing health behaviours. Messages used conversational language to encourage engagement, some asked questions on current activities. Quotes from survivors modelled behaviours, providing encouragement and authenticity. Some messages were personalised to include participants’ names. We piloted KATS with community-dwelling stroke survivors, using mid and end-of-intervention interviews to explore experiences of KATS. We analysed qualitative data using Normalisation Process Theory to examine how participants made sense of KATS and embedded it in everyday activities. Results Following piloting, from 24 interviews with 12 participants, we derived four analytical themes: (1) Making sense of KATS: Timing and complementarity in the rehabilitation journey; (2) Engaging with KATS: Connection and identification with others; (3) Making KATS work: flexibility and tailorable guidance; (4) Appraising the worth of KATS: encouragement and friendliness. Participants differentiated KATS from current rehabilitation practice, finding it relevant, fitting, and worthwhile. They reported variations in engagement with behaviour change techniques, but participants could tailor KATS use, making it work for them in different ways. Conclusions KATS was seen as useful and acceptable to stroke survivors. Perceived benefits went beyond promoting physical activity, to feeling supported and connected. Future research will test effectivenes of KATS in promoting physical activities and explore associations with functional, social and emotional secondary outcomes. The study illustrates the importance of co-design in interventions for clinical populations. Funder Scottish Government Chief Scientist Office.

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)0.006
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0140.002

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 head0.053
GPT teacher head0.379
Teacher spread0.325 · 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 designNon-randomized trial
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

Explore more

Same venueEuropean Journal of Public HealthSame topicBehavioral Health and InterventionsFrench-language works237,207