Increasing physical activity using an just-in-time adaptive digital assistant supported by machine learning: A novel approach for hyper-personalised mHealth interventions.

OBJECTIVE: Physical inactivity is a leading modifiable cause of death and disease worldwide. Population-based interventions to increase physical activity are needed. Existing automated expert systems (e.g., computer-tailored interventions) have significant limitations that result in low long-term effectiveness. Therefore, innovative approaches are needed. This special communication aims to describe and discuss a novel mHealth intervention approach that proactively offers participants with hyper-personalised intervention content adjusted in real-time. METHODS: Using machine learning approaches, we propose a novel physical activity intervention approach that can learn and adapt in real-time to achieve high levels of personalisation and user engagement, underpinned by a likeable digital assistant. It will consist of three major components: (1) conversations: to increase user's knowledge on a wide range of activity-related topics underpinned by Natural Language Processing; (2) nudge engine: to provide users with hyper-personalised cues to action underpinned by reinforcement learning (i.e., contextual bandit) and integrating real-time data from activity tracking, GPS, GIS, weather, and user provided data; (3) Q&A: to facilitate users asking any physical activity related questions underpinned by generative AI (e.g., ChatGPT, Bard) for content generation. RESULTS: The detailed concept of the proposed physical activity intervention platform demonstrates the practical application of a just-in-time adaptive intervention applying various machine learning techniques to deliver a hyper-personalised physical activity intervention in an engaging way. Compared to traditional interventions, the novel platform is expected to show potential for increased user engagement and long-term effectiveness due to: (1) using new variables to personalise content (e.g., GPS, weather), (2) providing behavioural support at the right time in real-time, (3) implementing an engaging digital assistant and (4) improving the relevance of content through applying machine learning algorithms. CONCLUSION: The use of machine learning is on the rise in every aspect of today's society, however few attempts have been undertaken to harness its potential to achieve health behaviour change. By sharing our intervention concept, we contribute to the ongoing dialogue on creating effective methods for promoting health and well-being in the informatics research community. Future research should focus on refining these techniques and evaluating their effectiveness in controlled and real-world circumstances.

Quality of Life Outcomes in Community-based Mental Health Consumers: Comparisons with Population Norms and Changes over Time.

Quality of life is shown to be lower in people diagnosed with mental illness in comparison to the general population. The aim of this study is to examine the Quality of life in a subset of people accessing mental health services in a regional Queensland Centre. Thirty-seven people accessing mental health services completed the SF36 Health Survey on three occasions. Differences and relationships between Physical Composite Scores and Mental Composite Scores, comparisons with Australian population norms, and temporal change in Quality of Life were examined. Physical Composite Scores were significantly different to, but significantly correlated with, Mental Composite Scores on each occasion. Physical Composite Scores and Mental Composite Scores were significantly different to population norms, and did not vary significantly across time. The poor Quality of life of people with mental illness remains a significant challenge for the mental health workforce.

Perceptions and expectations of an artificially intelligent physical activity digital assistant - A focus group study.

Artificially intelligent physical activity digital assistants that use the full spectrum of machine learning capabilities have not yet been developed and examined. This study aimed to explore potential users' perceptions and expectations of using such a digital assistant. Six 90-min online focus group meetings (n = 45 adults) were conducted. Meetings were recorded, transcribed and thematically analysed. Participants embraced the idea of a 'digital assistant' providing physical activity support. Participants indicated they would like to receive notifications from the digital assistant, but did not agree on the number, timing, tone and content of notifications. Likewise, they indicated that the digital assistant's personality and appearance should be customisable. Participants understood the need to provide information to the digital assistant to allow for personalisation, but varied greatly in the extent of information that they were willing to provide. Privacy issues aside, participants embraced the idea of using artificial intelligence or machine learning in return for a more functional and personal digital assistant. In sum, participants were ready for an artificially intelligent physical activity digital assistant but emphasised a need to personalise or customise nearly every feature of the application. This poses challenges in terms of cost and complexity of developing the application.

Lessons Learned From the Trial of a Cardiometabolic Health Nurse.

PURPOSE: This paper examines the findings from an exit interview with a cardiometabolic health nurse (CHN) following a 26-week trial. DESIGN AND METHODS: The CHN participated in a semi-structured exit interview following completion of the 26-week trial. Applied thematic analysis was used to identify themes contained in the resultant transcript. FINDINGS: Contrary to the literature, the CHN did not consider additional training necessary to undertake the role. The CHN felt additional information regarding the research implications of the trial and greater organizational support would contribute to better consumer and health service outcomes. PRACTICE IMPLICATIONS: While personally rewarding, more can be done to help the CHN role reach its potential.