Award for LvL UP SMART at the ISRII 12th Annual Scientific Meeting, 5 June 2024

Today, in the last session of the 12th Annual Scientific Meeting in Limerick, Ireland, celebrating the International Society of Research on Internet Interventions (ISRII) ‘s 20th anniversary, Dr. Oscar Castro, representing our LvL UP team, received the ISRII Poster Award 2024 for the sequential, multiple assignment, randomized controlled trial protocol of LvL UP, a smartphone-based and chatbot-delivered intervention for the prevention of type-2 diabetes and prevention of depression in Singapore:

Castro, O., Mair, J., Zheng, S., Tan S., Jabir A.I., Yan, X., Chakraborty, B., Tan, S., van Dam, R., Tai E.S., von Wangenheim, F., Fleisch, E., Griva, K.; Kowatsch, T. & F. Mueller-Riemenschneider (2024) The LvL UP trial: Protocol for a sequential, multiple assignment, randomized controlled trial to assess the effectiveness of a blended holistic mobile lifestyle intervention, 12th Annual International Society for Research on Internet Interventions (ISRII) Meeting, Limerick, Ireland, 2-6 June 2024

Congratulations to all team members from LvL UP! This is fantastic and well-deserved.

The LvL UP trial: Protocol for a sequential, multiple assignment, randomized controlled trial to assess the effectiveness of a blended holistic mobile lifestyle intervention

Oscar Castro, PhD; Jacqueline Mair, PhD, Singapore-ETH Centre; Shenglin Zheng, MSc, National University of Singapore; Xiaoxi Yan, PhD; Bibhas Chakraborty, PhD, Duke-NUS; Sarah Tan, PhD; Rob van Dam, PhD, E Shyong Tai, PhD, National University of Singapore; Florian von Wangenheim, PhD; Elgar Fleisch, PhD, ETH Zurich & University of St.Gallen; Konstadina Griva, PhD, Nanyang Technological University; Tobias Kowatsch, PhD, University of Zurich & University of St.Gallen & ETH Zurich; Falk Mueller-Riemenschneider, PhD, National University of Singapore


Context: 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). However, they often rely on highly trained professionals and deliver support to all users regardless of progress, greatly increasing costs and hampering scalability. Evidence is lacking to guide decisions on using lower-cost, more scalable forms of human support, such as leveraging on ‘generalist’ coaches targeting multiple lifestyle domains and providing human support only to selected individuals. This submission describes a sequential, multiple assignment, randomised trial protocol aimed at (i) evaluating the effectiveness and cost-effectiveness of LvL UP, an mHealth lifestyle intervention for the prevention of NCDs and CMDs, and (ii) establishing the optimal blended approach in LvL UP that balances effective personalised lifestyle support with scalability.

Methods: LvL UP is a 6-month mHealth holistic intervention targeting physical activity, diet, and emotional regulation. In this trial, 650 participants (young and middle-aged Singaporean adults at risk of developing NCDs or CMDs) will be randomly allocated to one of two initial conditions (‘LvL UP app’ or ‘control’). After 4 weeks, participants categorised as non-responders from the ‘LvL UP app’ group will be re-randomised into second-stage conditions: (i) continuing with the initial intervention or (ii) additional human-delivered motivational interviewing support sessions. The primary outcome is mental well-being (via the Warwick-Edinburgh Mental Wellbeing Scale). Secondary outcomes include anthropometrics, resting blood pressure, blood metabolic profile, health status, and health behaviours (e.g., diet, physical activity). Outcomes will be measured at baseline, six months (post-intervention), and nine months (follow-up). The research plan includes a pilot study with 120 participants to evaluate the feasibility of the intervention and help inform the sample size for the main trial.

Implications: 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.

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