4371 - THE LVL UP TRIAL: A SEQUENTIAL, MULTIPLE ASSIGNMENT, RANDOMIZED CONTROLLED TRIAL TO ASSESS THE EFFECTIVENESS OF A BLENDED MOBILE LIFESTYLE INTERVENTION

Session: 4356 - HARNESSING BEHAVIORAL INSIGHTS FOR MORE IMPACTFUL HEALTH INTERVENTIONS ACROSS POPULATIONS
AUTHORS:
Griva Konstadina (Lee Kong Chian School of Medicine Nanyang Technological University ~ Singapore ~ Singapore)
Abstract text:
Background: Blended mobile health (mHealth) interventions - combining self-guided and human support components - could play a major role in preventing non-communicable diseases (NCDs) and common mental disorders (CMDs). This sequential, multiple assignment, randomised trial aimed to (i) evaluate the effectiveness and cost-effectiveness of LvL UP, an mHealth lifestyle intervention for the prevention of NCDs and CMDs, and (ii) establish the optimal blended approach in LvL UP for effective personalised lifestyle support and scalability.
Methods: LvL UP is a 6-month mHealth holistic intervention targeting physical activity, diet, and emotional regulation. A total of 640 adults at risk of developing NCDs were randomly allocated to LvL UP' or 'comparison'. At 4 weeks, non-responders were e re-randomised into (i) continuing with the initial intervention (LvL UP) or (ii) additional motivational interviewing (MI) coaching sessions). Assessments were taken at baseline and 6-and 12-months post intervention. These included The primary outcome is mental well-being (primary outcome) and anthropometric measurements, resting blood pressure, blood metabolic profile, health status, and health behaviours (physical activity, diet) (secondary outcomes).
The analyses will follow the intention-to-treat principle. Several process indicators (e.g., engagement) will also be assessed. The findings will have strong implications for the further development of psychosocial adaptive interventions to support mental and physical health outcomes in at risk interventions.
In addition to evaluating the effectiveness of LvL UP, the proposed study design will contribute to increasing evidence on how to introduce human support in mHealth interventions to maximise their effectiveness while remaining scalable