124 Using Co-Design Methods to Develop a Theory and Evidence-Based Mother-Daughter mHealth Intervention Prototype Targeting Physical Activity in Pre-Teen Girls
Bibliographic record
Abstract
Abstract Purpose Pre-teen girls of lower socio-economic position are at increased risk of physical inactivity. Parental support, particularly mothers, is positively correlated with girls’ physical activity levels. Consequently, family-based interventions are recognised as a promising approach to improve young people’s physical activity. However, the effects of these interventions on girls’ physical activity are often inconsistent, with calls for more rigorous, theory based and co-designed family-based interventions to promote physical activity in this cohort. Therefore, the aim of this study was to use co-design methods to develop a theory and evidence- based mother daughter mHealth intervention prototype targeting physical activity in pre-teen girls. Method The intervention prototype was developed in accordance with the UK Medical Research Council framework, the Behaviour Change Wheel, the Theoretical Domains Framework, and the Behaviour Change Techniques Taxonomy V1. The co-design process incorporated three phases, (i) behavioural analysis, (ii) the selection of intervention components, and (iii) refinement of the intervention prototype. Across these phases, there were workshops with pre-teen girls (n = 10); mothers of pre-teen girls (n = 9), and primary school teachers (n = 6), with further input from an expert advisory group. Results This three-phase co-design process resulted in the development of a theory-based intervention which targeted two behaviours, (i) mothers’ engagement in a range of supportive behaviours for their daughters’ physical activity, and (ii) daughters’ physical activity behaviour. Formative research identified eleven theoretical domains to be targeted as part of the intervention (e.g., knowledge, skills, and beliefs about capabilities). These were to be targeted by six intervention functions (e.g., education, persuasion, modelling), and 28 behaviours change techniques (e.g., goal setting, self-monitoring). The co-design process resulted in a mobile app being chosen as the mode of delivery for the intervention. Conclusion This paper offers a rich description of using co-design methods to develop a mother-daughter mHealth intervention prototype, that is ready for feasibility and acceptability testing. The use of theoretical frameworks in the co-design process provided a robust and transparent foundation on which to develop the prototype, enabling the evaluation of potential pathways for behaviour change.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.089 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".