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Record W3208184390 · doi:10.2196/31801

Development of a Web-Based, Guided Self-help, Acceptance and Commitment Therapy–Based Intervention for Weight Loss Maintenance: Evidence-, Theory-, and Person-Based Approach

2021· article· en· W3208184390 on OpenAlexvenueno aff
Rebecca Richards, Rebecca A. Jones, Fiona Whittle, Carly A. Hughes, Andrew J. Hill, Emma Lawlor, Jennifer Bostock, Sarah Bates, Penny Breeze, Alan Brennan, Chloe Thomas, Marie Stubbings, Jennifer Woolston, Simon J. Griffin, Amy L. Ahern

Bibliographic record

VenueJMIR Formative Research · 2021
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
FundersDepartment of Health and Social CareMedical Research CouncilNational Institute for Health and Care Research
KeywordsPsychological interventionIntervention (counseling)Acceptance and commitment therapyWeight lossStakeholderBest practiceWeight managementPsychologyMedicineProcess managementNursingEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: The long-term impact and cost-effectiveness of weight management programs depend on posttreatment weight maintenance. There is growing evidence that interventions based on third-wave cognitive behavioral therapy, particularly acceptance and commitment therapy (ACT), could improve long-term weight management; however, these interventions are typically delivered face-to-face by psychologists, which limits the scalability of these types of intervention. OBJECTIVE: The aim of this study is to use an evidence-, theory-, and person-based approach to develop an ACT-based intervention for weight loss maintenance that uses digital technology and nonspecialist guidance to minimize the resources needed for delivery at scale. METHODS: Intervention development was guided by the Medical Research Council framework for the development of complex interventions in health care, Intervention Mapping Protocol, and a person-based approach for enhancing the acceptability and feasibility of interventions. Work was conducted in two phases: phase 1 consisted of collating and analyzing existing and new primary evidence and phase 2 consisted of theoretical modeling and intervention development. Phase 1 included a synthesis of existing evidence on weight loss maintenance from previous research, a systematic review and network meta-analysis of third-wave cognitive behavioral therapy interventions for weight management, a qualitative interview study of experiences of weight loss maintenance, and the modeling of a justifiable cost for a weight loss maintenance program. Phase 2 included the iterative development of guiding principles, a logic model, and the intervention design and content. Target user and stakeholder panels were established to inform each phase of development, and user testing of successive iterations of the prototype intervention was conducted. RESULTS: This process resulted in a guided self-help ACT-based intervention called SWiM (Supporting Weight Management). SWiM is a 4-month program consisting of weekly web-based sessions for 13 consecutive weeks followed by a 4-week break for participants to reflect and practice their new skills and a final session at week 18. Each session consists of psychoeducational content, reflective exercises, and behavioral experiments. SWiM includes specific sessions on key determinants of weight loss maintenance, including developing skills to manage high-risk situations for lapses, creating new helpful habits, breaking old unhelpful habits, and learning to manage interpersonal relationships and their impact on weight management. A trained, nonspecialist coach provides guidance for the participants through the program with 4 scheduled 30-minute telephone calls and 3 further optional calls. CONCLUSIONS: This comprehensive approach facilitated the development of an intervention that is based on scientific theory and evidence for supporting people with weight loss maintenance and is grounded in the experiences of the target users and the context in which it is intended to be delivered. The intervention will be refined based on the findings of a planned pilot randomized controlled trial.

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)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.540
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.175
GPT teacher head0.505
Teacher spread0.330 · 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.

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

Citations19
Published2021
Admission routes1
Has abstractyes

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