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Record W4381804855 · doi:10.1186/s40814-023-01326-x

Keeping Active with Texting after Stroke (KATS): development of a text message intervention to promote physical activity and exercise after stroke

2023· article· en· W4381804855 on OpenAlexaff
Linda Irvine, Jacqui Morris, Stephan U Dombrowski, Jenna Breckenridge, Albert Farré, Gözde Özakinci, Thérèse Lebedis, Claire Jones

Bibliographic record

VenuePilot and Feasibility Studies · 2023
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of New Brunswick
FundersChief Scientist Office, Scottish Government Health and Social Care DirectorateChest Heart and Stroke ScotlandStroke AssociationScottish Government
KeywordsIntervention (counseling)Stroke (engine)RehabilitationFormative assessmentPsychologyMedicinePhysical medicine and rehabilitationPhysical therapyMedical educationNursingPedagogyEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: Post-stroke physical activity reduces disability and risk of further stroke. When stroke rehabilitation ends, some people feel abandoned by services and struggle to undertake physical activities that support recovery and health. The aim of this study was to codesign a novel text message intervention to promote physical activity among people with stroke and provide support when formal rehabilitation ends. This manuscript describes the intervention development processes that will inform future pilot and feasibility studies. METHODS: The planned intervention was a series of text messages to be sent in a predetermined sequence to people with stroke at the end of rehabilitation. The intervention, underpinned by behaviour change theory and using salient behaviour change techniques, would provide daily messages offering encouragement and support for the uptake and maintenance of physical activity following stroke. The intervention was codesigned by a Collaborative Working Group, comprised of people with stroke, rehabilitation therapists, representatives from stroke charities and academics. A four-step framework was used to design the intervention: formative research on physical activity post-stroke, creation of the behaviour change text message intervention, pre-testing and refinement. Formative research included a review of the scientific evidence and interviews with community-dwelling people with stroke. Data generated were used by the Collaborative Working Group to identify topics to be addressed in the intervention. These were mapped to constructs of the Health Action Process Approach, and salient behaviour change techniques to deliver the intervention were identified. The intervention was rendered into a series of text messages to be delivered over 12 weeks. The draft intervention was revised and refined through an iterative process including review by people with stroke, their spouses, rehabilitation therapists and experts in the field of stroke. The messages encourage regular physical activity but do not prescribe exercise or provide reminders to exercise at specific times. They use conversational language to encourage engagement, and some are personalised for participants. Quotes from people with stroke provide encouragement and support and model key behaviour change techniques such as goal setting and coping planning. DISCUSSION: Co-design processes were critical in systematically developing this theory and evidence-based intervention. People with stroke and rehabilitation therapists provided insights into perceived barriers post-rehabilitation and identified strategies to overcome them. The structured multistep approach highlighted areas for improvement through successive rounds of review. The intervention will be tested for acceptability, feasibility and effectiveness in future studies. This co-design approach could be used for interventions for other heath behaviours and with different populations.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.834
Threshold uncertainty score0.698

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 head0.169
GPT teacher head0.442
Teacher spread0.273 · 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 designObservational
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

Citations12
Published2023
Admission routes1
Has abstractyes

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