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Record W4389190806 · doi:10.2196/53619

A Remotely Delivered, Semaglutide-Supported Specialist Weight Management Program: Preliminary Findings From a Retrospective Service Evaluation

2023· article· en· W4389190806 on OpenAlexvenueno aff
Rebecca Richards, Gina Wren, P.J. Campion, Michael Whitman

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
Fundersnot available
KeywordsWeight lossMedicineWeight changeWeight managementPsychological interventionObesityPhysical therapyRetrospective cohort studyInternal medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Digital weight management interventions have the potential to increase access to novel pharmacotherapy for people living with obesity. At present, there is limited real-world evidence on the effectiveness, feasibility, and acceptability of this type of intervention. OBJECTIVE: This retrospective service evaluation examines real-world data to evaluate the preliminary impact of Second Nature's 24-month, remotely delivered, semaglutide-supported weight management intervention for adults living with obesity at 12 weeks. METHODS: Retrospective data were extracted in October 2023 for participants who started the intervention between June 8, 2023, and July 22, 2023. The primary outcomes were weight change (kg) and percentage of weight change at 12 weeks. The secondary outcomes were the proportion of participants who achieved ≥5% and ≥10% weight loss and the feasibility and acceptability of this type of intervention. Descriptive statistics were used to evaluate the baseline characteristics, retention, engagement, prevalence of side effects, and weight change. A paired 2-tailed t test was used to determine the significance of weight change. Content analysis was used to analyze the free-text questionnaire responses. RESULTS: (SD 7.3) were included in the analysis (n=102, 90.4% women, mean age 46.6, SD 11.1 years). Over 12 weeks, 23% (n=26) of participants withdrew from the intervention. A total of 70.8% (n=80) of participants provided weight data at 12 weeks. The average weight loss observed over this 12-week period was 6.5 (SD 4.4) kg (P<.001) or 6.4% (SD 4.2%) of their starting weight (P<.001). Of the 80 participants who recorded weight readings, 62.5% (n=50) achieved ≥5% weight loss, and 11.3% (n=9) achieved ≥10% weight loss. Engagement with the app-based program declined from a mean of 131 (SD 142.6) home screen views in week 0 to 35 (SD 57.1) in week 11. Common side effects reported over 12 weeks included feeling more tired than usual, constipation, and feeling sick. However, a significant proportion of participants reported no side effects. Most participants (n=106, 93.8%) did not experience any difficulties in medication administration. Qualitative data showed that most participants had a positive or neutral experience of the intervention, with some reporting perceived benefits as early as 4 weeks. Most participants did not feel that improvements in the intervention were needed; however, some participants faced issues with medication shipping or logistics. CONCLUSIONS: This retrospective preliminary service evaluation suggests that a remotely delivered semaglutide-supported weight management intervention has the potential to be effective, feasible, and acceptable for self-paying consumer adults with obesity in the United Kingdom. Areas for further improvement were highlighted, including user engagement in an app-based program. A full-service evaluation at the end of the 24-month intervention with a larger sample size is required to support these early findings.

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)0.010
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.140
GPT teacher head0.523
Teacher spread0.383 · 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 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".

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Citations12
Published2023
Admission routes1
Has abstractyes

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