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Randomized Experiments

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
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Optimising a digitally delivered behavioural weight loss programme: study protocol for a factorial cluster randomised

Gina M Wren¹, Dimitrios A Koutoukidis², Jadine Scragg²

¹Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK. gina.wren@phc.ox.ac.uk.

|July 13, 2024

Summary

This study optimizes digital weight loss programs by testing four components to improve effectiveness and engagement. Findings will refine interventions for better long-term results and continuous service improvement.

Keywords:

Digital health MOST framework Mobile health Multicomponent Obesity Optimisation Overweight Weight loss mHealth

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Area of Science:

Behavioral Science
Digital Health
Obesity Treatment

Background:

Digital weight loss programs offer convenience and scalability but often show inferior long-term results compared to in-person interventions.
The Multiphase Optimization Strategy (MOST) framework can enhance digital interventions.
This trial aims to optimize a commercial digital behavioral weight loss program.

Purpose of the Study:

To identify an optimized combination of four intervention components to enhance weight loss over 24 weeks.
To explore which components improve participant retention and engagement.
To test the MOST framework in an industry setting for continuous service improvement.

Main Methods:

A 2^4 factorial cluster randomized trial involving approximately 1400 adults with BMI > 21 kg/m².
Testing four components: introductory video call, drop-in webchat, goal setting, and food diary feedback.
Primary outcome: weight change at 16 weeks; secondary outcomes: retention, engagement, fidelity, and acceptability.

Main Results:

The factorial design efficiently tests behavioral components individually and in combination.
Decision-making for program enhancement is based on components yielding ≥ 0.75kg weight loss improvement.
Routinely collected data will be used to refine and evaluate interventions.

Conclusions:

The factorial design is effective for testing behavioral components to improve digital weight loss programs.
This trial demonstrates the implementation of the MOST framework in an industry setting.
The study provides a model for continuous service improvement in digital health interventions.