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

Jintana Liu; Sakura Ito; Tra My Ngo; Ashwini Lawate; Qi Chwen Ong; Tatiana Erlikh Fox; Si Yuan Chang; Duy Phung; Elizabeth Nair; Malar Palaiyan; Shafiq Joty; John Abisheganaden; Chuen Peng Lee; May Oo Lwin; Yin Leng Theng; Moon-Ho Ringo Ho; Michael Chia; Iva Bojic; Josip Car

doi:10.1177/20552076241241244

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

Digit Health. 2024 Apr 17:10:20552076241241244. doi: 10.1177/20552076241241244. eCollection 2024 Jan-Dec.

Authors

Jintana Liu¹, Sakura Ito¹, Tra My Ngo¹, Ashwini Lawate¹, Qi Chwen Ong¹, Tatiana Erlikh Fox¹, Si Yuan Chang¹, Duy Phung¹, Elizabeth Nair², Malar Palaiyan³, Shafiq Joty^{4

5}, John Abisheganaden⁶, Chuen Peng Lee⁶, May Oo Lwin⁷, Yin Leng Theng⁷, Moon-Ho Ringo Ho⁸, Michael Chia⁹, Iva Bojic¹, Josip Car^{1

10}

Affiliations

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.

Keywords: AI; Fitbit; Health coaching; QA system; sleep intervention; university students.