Utilizing Technology for Diet and Exercise Change in Complex Chronic Conditions Across Diverse Environments (U-DECIDE): Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: The metabolic syndrome is common across many complex chronic disease groups. Advances in health technology have provided opportunities to support lifestyle interventions. OBJECTIVE: The purpose of this study is to test the feasibility of a health technology-assisted lifestyle intervention in a patient-led model of care. METHODS: The study is a single-center, 26-week, randomized controlled trial. The setting is specialist kidney and liver disease clinics at a large Australian tertiary hospital. The participants will be adults with a complex chronic condition who are referred for dietetic assessment and display at least one feature of the metabolic syndrome. All participants will receive an individualized assessment and advice on diet quality from a dietitian, a wearable activity monitor, and standard care. Participants randomized to the intervention group will receive access to a suite of health technologies from which to choose, including common base components (text messages) and optional components (online and mobile app-based nutrition information, an online home exercise program, and group-based videoconferencing). Exposure to the optional aspects of the intervention will be patient-led, with participants choosing their preferred level of engagement. The primary outcome will be the feasibility of delivering the program, determined by safety, recruitment rate, retention, exposure uptake, and telehealth adherence. Secondary outcomes will be clinical effectiveness, patient-led goal attainment, treatment fidelity, exposure demand, and participant perceptions. Primary outcome data will be assessed descriptively and secondary outcomes will be assessed using an analysis of covariance. This study will provide evidence on the feasibility of the intervention in a tertiary setting for patients with complex chronic disease exhibiting features of the metabolic syndrome. RESULTS: The study was funded in 2019. Enrollment has commenced and is expected to be completed by June 2022. Data collection and follow up are expected to be completed by December 2022. Results from the analyses based on primary outcomes are expected to be submitted for publication by June 2023. CONCLUSIONS: The study will test the implementation of a health technology-assisted lifestyle intervention in a tertiary outpatient setting for a diverse group of patients with complex chronic conditions. It is novel in that it embeds patient choice into intervention exposure and will inform health service decision-makers in regards to the feasibility of scale and spread of technology-assisted access to care for a broader reach of specialist services. TRIAL REGISTRATION: Australian New Zealand Clinical Trial Registry ACTRN12620001282976; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=378337. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/37556.
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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 | 0.052 | 0.046 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.007 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.102 | 0.018 |
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".