Comparing Implementation Strategies for an Evidence-Based Weight Management Program Delivered in Community Mental Health Programs: Protocol for a Pilot Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Among people with serious mental illness (SMI), obesity contributes to increased cardiovascular disease (CVD) risk. The Achieving Healthy Lifestyles in Psychiatric Rehabilitation (ACHIEVE) randomized controlled trial (RCT) demonstrated that a behavioral intervention tailored to the needs of individuals with SMI results in clinically significant weight loss. While the research team delivered the ACHIEVE intervention in the trial, community mental health program staff are needed to deliver sessions to make scale-up feasible. Therefore, we adapted the ACHIEVE-Dissemination (ACHIEVE-D) curriculum to ease adoption and implementation in this setting. Designing and testing of implementation strategies is now needed to understand how to support ACHIEVE-D delivery by community mental health program staff coaches. OBJECTIVE: This study aims to conduct a pilot trial evaluating standard and enhanced implementation interventions to support the delivery of ACHIEVE-D in community mental health programs by examining effects on staff coaches' knowledge, self-efficacy, and delivery fidelity of the curriculum. We will also examine the effects on outcomes among individuals with SMI taking part in the curriculum. METHODS: The trial will be a cluster-randomized, 2-arm parallel pilot RCT comparing standard and enhanced implementation intervention at 6 months within community mental health programs. We will randomly assign programs to either the standard or enhanced implementation interventions. The standard intervention will combine multimodal training for coaches (real-time initial training via videoconference, ongoing virtual training, and web-based avatar-assisted motivational interviewing practice) with organizational strategy meetings to garner leadership support for implementation. The enhanced intervention will include all standard strategies, and the coaches will receive performance coaching. At each program, we will enroll staff to participate as coaches and clients with SMI to participate in the curriculum. Coaches will deliver the ACHIEVE-D curriculum to the clients with SMI. Primary outcomes will be coaches' knowledge, self-efficacy, and fidelity to the ACHIEVE-D curriculum. We will also examine the acceptability, feasibility, and appropriateness of ACHIEVE-D and the implementation strategies. Secondary outcomes among individuals with SMI will be weight and self-reported lifestyle behaviors. RESULTS: Data collection started in March 2021, with completion estimated in March 2023. We recruited 9 sites and a total of 20 staff coaches and 72 clients with SMI. The expected start of data analyses will occur in March 2023, with primary results submitted for publication in April 2023. CONCLUSIONS: Community mental health programs may be an ideal setting for implementing an evidence-based weight management curriculum for individuals with SMI. This pilot study will contribute knowledge about implementation strategies to support the community-based delivery of such programs, which may inform future research that definitively tests the implementation and dissemination of behavioral weight management programs. TRIAL REGISTRATION: ClinicalTrials.gov NCT03454997; https://clinicaltrials.gov/ct2/show/NCT03454997. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/45802.
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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 | 0.061 | 0.056 |
| Meta-epidemiology (narrow) | 0.008 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.011 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.084 | 0.014 |
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".