A pilot randomised controlled trial exploring the feasibility and efficacy of a human-AI sleep coaching model for improving sleep among university students

Author:

Liu Jintana1ORCID,Ito Sakura1ORCID,Ngo Tra My1,Lawate Ashwini1,Ong Qi Chwen1ORCID,Fox Tatiana Erlikh1,Chang Si Yuan1,Phung Duy1,Nair Elizabeth2,Palaiyan Malar3,Joty Shafiq45,Abisheganaden John6,Lee Chuen Peng6ORCID,Lwin May Oo7,Theng Yin Leng7,Ho Moon-Ho Ringo8,Chia Michael9ORCID,Bojic Iva1,Car Josip110

Affiliation:

1. Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore

2. Work & Health Psychologists, Singapore, Singapore

3. University Counselling Centre, Nanyang Technological University, Singapore, Singapore

4. Salesforce AI Research, San Francisco, CA, USA

5. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

6. Department of Respiratory and Critical Care Medicine, Tan Tock Seng Hospital, Singapore, Singapore

7. Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore

8. School of Social Sciences, Nanyang Technological University, Singapore, Singapore

9. Physical Education and Sports Science, National Institute of Education, Nanyang Technological University, Singapore, Singapore

10. School of Life Course & Population Sciences, King's College London, London, UK

Abstract

Objective Sleep quality is a crucial concern, particularly among youth. The integration of health coaching with question-answering (QA) systems presents the potential to foster behavioural changes and enhance health outcomes. This study proposes a novel human-AI sleep coaching model, combining health coaching by peers and a QA system, and assesses its feasibility and efficacy in improving university students’ sleep quality. Methods In a four-week unblinded pilot randomised controlled trial, 59 university students (mean age: 21.9; 64% males) were randomly assigned to the intervention (health coaching and QA system; n = 30) or the control conditions (QA system; n = 29). Outcomes included efficacy of the intervention on sleep quality (Pittsburgh Sleep Quality Index; PSQI), objective and self-reported sleep measures (obtained from Fitbit and sleep diaries) and feasibility of the study procedures and the intervention. Results Analysis revealed no significant differences in sleep quality (PSQI) between intervention and control groups (adjusted mean difference = −0.51, 95% CI: [−1.55–0.77], p = 0.40). The intervention group demonstrated significant improvements in Fitbit measures of total sleep time (adjusted mean difference = 32.5, 95% CI: [5.9–59.1], p = 0.02) and time in bed (adjusted mean difference = 32.3, 95% CI: [2.7–61.9], p = 0.03) compared to the control group, although other sleep measures were insignificant. Adherence was high, with the majority of the intervention group attending all health coaching sessions. Most participants completed baseline and post-intervention self-report measures, all diary entries, and consistently wore Fitbits during sleep. Conclusions The proposed model showed improvements in specific sleep measures for university students and the feasibility of the study procedures and intervention. Future research may extend the intervention period to see substantive sleep quality improvements.

Funder

Accelerating Creativity & Excellence award

Publisher

SAGE Publications

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