MétaCan
Menu
Back to cohort
Record W3003783598 · doi:10.1071/ah19046

Sociodemographic and health risk profile associated with participation in a private health insurance weight loss maintenance and chronic disease management program

2020· article· en· W3003783598 on OpenAlexaff
Bronwyn McGill, Blythe J. O’Hara, Anne Grunseit, Adrian Bauman, Luke Lawler, Philayrath Phongsavan

Bibliographic record

VenueAustralian Health Review · 2020
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsMcGill University
Fundersnot available
KeywordsWeight lossMedicineOverweightWeight managementOdds ratioBody mass indexObesityPopulation healthGerontologyDemographyPhysical therapyEnvironmental healthPopulationInternal medicine

Abstract

fetched live from OpenAlex

Objective Identifying who participates in chronic disease management programs yields insights into program reach and appeal. This study investigated sustained participation in a remotely delivered weight loss maintenance program offered to Australian private health insurance members. Methods All participants completing an initial 18-week weight loss program were eligible for a maintenance phase. A pre-post test design was used and sociodemographic and anthropometric characteristics of those who did and did not opt in to the maintenance phase were compared using binary logistic regression. Results Maintenance phase participants lost more weight during the initial weight loss program (-2.2kg (P<0.001); body mass index -0.8kg/m2 (P<0.001)) than those who did not opt in. Participants who were obese (v. overweight) upon completion of the initial weight loss program were less likely to opt in to the maintenance phase (adjusted odds ratio (aOR) 1.76, 95% confidence interval (CI) 1.35-2.30, P<0.001) and participants aged ≥55 years were more likely to opt in (aOR 0.59, 95% CI 0.44-0.80, P<0.001) than those aged <55 years. Conclusions Understanding why health insurance members opt in to maintenance programs can assist the development of strategies to improve program reach. Younger participants and those who remain obese following a weight loss program may be targeted by private health insurers and service providers to increase weight loss maintenance program participation. What is known about the topic? Australian private health insurers offer chronic disease management programs to support members to manage obesity-related chronic disease. An 18-week weight loss and lifestyle modification program was extended to assist participants maintain weight loss and health benefits resulting from the initial program. This weight loss maintenance phase is novel in the private health insurance setting and is thought to be important to sustained health improvement. Although program reach is important to benefit those most in need, little is known about who sustains the use (or does not) of such programs. What does this paper add? This study provides an insight to the characteristics of participants more likely to opt in to a weight loss maintenance program. It highlights the sociodemographic and anthropometric characteristics associated with maintenance program uptake, identifying the subgroups less likely to opt in. These study findings are novel because they report on participation in a chronic disease management program with a focus on maintenance of weight loss. What are the implications for practitioners? These results will benefit private health insurers and service providers implementing maintenance programs for weight loss, providing an awareness of which participant groups to target to increase maintenance and reach. In addition, they offer avenues for future exploration, such as the generalisability and sustainability of chronic disease management programs. Although those not opting in are a difficult-to-access group, a qualitative study of reasons for not opting in to such a program would provide further information for program design, recruitment and retention.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.579
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.118
GPT teacher head0.476
Teacher spread0.358 · 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 designObservational
Domainnot available
GenreReview

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

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueAustralian Health ReviewSame topicObesity and Health PracticesFrench-language works237,207