MotivATE: A Pretreatment Web-Based Program to Improve Attendance at UK Outpatient Services Among Adults With Eating Disorders
Bibliographic record
Abstract
BACKGROUND: In the UK, eating disorders affect upward of 725,000 people per year, and early assessment and treatment are important for patient outcomes. Around a third of adult outpatients in the UK who are referred to specialist eating disorder services do not attend, which could be related to patient factors related to ambivalence, fear, and a lack of confidence about change. This lack of engagement has a negative impact on the quality of life of patients and has implications for service costs. OBJECTIVE: To describe the development of a Web-based program ("MotivATE") designed for delivery at the point of referral to an eating disorder service, with the aim of increasing service attendance. METHODS: We used intervention mapping and a person-based approach to design the MotivATE program and conducted a needs assessment to determine the current impact of service nonattendance on patients (via a review of the qualitative evidence) and services (through a service provision survey to understand current issues in UK services). Following the needs assessment, we followed the five steps of program development outlined by Bartholomew et al (1998): (1) creating a matrix of proximal program objectives; (2) selecting theory-based intervention methods and strategies; (3) designing and organizing the program; (4) specifying adoption and implementation plans; and (5) generating program evaluation plans. RESULTS: The needs assessment identified current nonattendance rates of 10%-32%. We defined the objective of MotivATE as increasing attendance rates at an eating disorder service and considered four key determinants of poor attendance: patient ambivalence about change, low patient self-efficacy, recognition of the need to change, and expectations about assessment. We chose aspects of motivational interviewing, self-determination theory, and the use of patient stories as the most appropriate ways to enable change. Think-aloud piloting with people with lived experience of an eating disorder resulted in positive feedback on the MotivATE program. Participants related well to the stories used. Nonetheless, because of feedback, we further modified the program in line with patients' stage of change and addressed issues with the language used. A consultation with service staff meant that we could make clear implementation plans. Finally, a randomized controlled trial is currently underway to evaluate the MotivATE program. CONCLUSIONS: Using intervention mapping, we have developed a novel pretreatment Web-based program that is acceptable to people with eating disorders. To our knowledge, this is the first such program. The model of development described here could be a useful template for designing further programs for other difficult-to-engage populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".