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Record W4413022858 · doi:10.1111/hex.70368

Adaptation of the Content of a Behavioural Text Message Delivered Weight Management Intervention for a Socio‐Culturally and Geographically Diverse Population of Postpartum Women in the UK: The Supporting MumS (SMS) Intervention

2025· article· en· W4413022858 on OpenAlexaff
Eleni Spyreli, Lizzie Caperon, Emma Ansell, Sara Ahern, Sally Bridges, Elinor Coulman, Stephan U Dombrowski, Frank Kee, Jayne V. Woodside, Dunla Gallagher, Michelle C. McKinley

Bibliographic record

VenueHealth Expectations · 2025
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsUniversity of New Brunswick
FundersNational Institute for Health and Care Research
KeywordsIntervention (counseling)PopulationWeight managementSession (web analytics)Adaptation (eye)PsychologyMedicineMedical educationNursingWorld Wide WebComputer scienceWeight lossObesity

Abstract

fetched live from OpenAlex

BACKGROUND: The Supporting MumS (SMS) intervention, originally piloted in Northern Ireland, United Kingdom (UK), uses automated text messages aiming to support diet and physical activity behaviour change for weight management in the postpartum period. Before testing the effectiveness of the SMS intervention in a UK-wide randomised controlled trial, it was important to ensure that the core component of the intervention was acceptable and culturally relevant for a diverse range of women across different regions of the UK. OBJECTIVE: to undertake Personal and Public Involvement (PPI) to adapt the content of the previously developed library of text messages for a socio-culturally and geographically diverse population of postpartum women. SETTING AND PARTICIPANTS: Recruitment focused on mothers who lived in London, Bradford and various locations in Scotland, who had had a child within the last 2 years and had struggled with their weight. Existing PPI networks and community groups helped identify PPI representatives. DESIGN: The PPI activities employed an iterative process including three stages: (1) an online group session to review some of the text messages and provide immediate feedback; (2) online group sessions to review adaptations made to messages; and (3) working remotely on a one-to-one basis with PPI collaborators to review and provide comments and suggestions on the entire text message library (previously modified based on feedback from stages 1 and 2). RESULTS: A total of 19 PPI representatives responded to the invitation and 18 contributed to the review of the SMS text messages: n = 12 from England [n = 4 from London (African-Caribbean ethnicity); n = 8 from Bradford (Asian ethnicity]; n = 6 from Scotland (White ethnicity). During a period of 9 months (July 2021-March 2022), they provided unprompted, positive feedback about the overall concept of a text message-delivered intervention to support postpartum weight management. During review and discussion of the original text message content they suggested minor amendments on the length, language, humour and cultural relevance of the text messages. Overall, no messages needed major re-writing. CONCLUSION: This PPI work provided useful suggestions for the cultural and regional adaptation of the content of a text message library that aims to support postpartum weight management. Minor modifications to the messages were suggested. The effectiveness of the Supporting MumS intervention will be tested in a UK-wide trial. PATIENT OR PUBLIC CONTRIBUTION: Our PPI collaborators were identified through existing PPI networks and community groups. They contributed through online group sessions and on a one-to-one basis through email correspondence. They offered valuable insights into ways of enhancing the cultural and regional relevance of a library of text messages to support diet and physical activity behaviour change for weight loss and weight loss maintenance in the postpartum period.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score0.853

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.061
GPT teacher head0.395
Teacher spread0.334 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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