MoveMentor-examining the effectiveness of a machine learning and app-based digital assistant to increase physical activity in adults: protocol for a randomised controlled trial.

Background: Physical inactivity is prevalent, leading to a high burden of disease and large healthcare costs. Thus, there is a need for affordable, effective and scalable interventions. However, interventions that are affordable and scalable are beset with modest effects and engagement.

Impact of Iterative Development and Beta-Testing on the Usability and Acceptability of a Novel Just-in-Time Adaptive Digital Physical Activity Intervention.

Background: The search for cost-effective population-based physical activity interventions continues. Therefore, we developed a novel just-in-time adaptive digital assistant supported by machine learning (ie, MoveMentor). Beta-testing is essential to evaluate both technical performance and user acceptance.

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.

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.

How do economic and public finance statuses affect policy responses during a pandemic? - learning from the COVID-19 first wave.

Background: In the time of a pandemic, it is typical for public health bodies to collaborate with epidemiologists to design health policies both at national and international levels for controlling the spread. A point largely overlooked in literature is the extent economic capability and public finance status can influence the policy responses of countries during a pandemic situation. This article fills this gap by considering 12 public health and 7 economic measures (i.

Intelligent type 2 diabetes risk prediction from administrative claim data.

Type 2 diabetes is a chronic, costly disease and is a serious global population health problem. Yet, the disease is well manageable and preventable if there is an early warning. This study aims to apply supervised machine learning algorithms for developing predictive models for type 2 diabetes using administrative claim data.

How did socio-demographic status and personal attributes influence compliance to COVID-19 preventive behaviours during the early outbreak in Japan? Lessons for pandemic management.

This study focuses on how socio-demographic status and personal attributes influence self-protective behaviours during a pandemic, with protection behaviours being assessed through three perspectives - social distancing, personal protection behaviour and social responsibility awareness. The research considers a publicly available and recently collected dataset on Japanese citizens during the COVID-19 early outbreak and utilises a data analysis framework combining Classification and Regression Tree (CART), a data mining approach, and regression analysis to gain deep insights. The analysis reveals Socio-demographic attributes - sex, marital family status and having children - as having played an influential role in Japanese citizens' abiding by the COVID-19 protection behaviours.

Cyberattacks Detection in IoT-Based Smart City Applications Using Machine Learning Techniques.

In recent years, the widespread deployment of the Internet of Things (IoT) applications has contributed to the development of smart cities. A smart city utilizes IoT-enabled technologies, communications and applications to maximize operational efficiency and enhance both the service providers' quality of services and people's wellbeing and quality of life. With the growth of smart city networks, however, comes the increased risk of cybersecurity threats and attacks.

Research interdisciplinarity: STEM versus non-STEM.

Research collaboration among interdisciplinary teams has become a common trend in recent days. However, there is a lack of evidence in literature regarding which disciplines play dominant roles in interdisciplinary research settings. It is also unclear whether the dominant role of disciplines vary between STEM (Science, Technology, Engineering, and Mathematics) and non-STEM focused research.

Onslaught of COVID-19: How Did Governments React and at What Point of the Crisis?

The COVID-19 outbreak has taken many governments by surprise. While the crisis unfolds, it is instructive to explore how different governments reacted to the onslaught of an unknown disease. This research, using very recently collected and open-source data, meets this objective.

A Systematic Review of Network Studies Based on Administrative Health Data.

Effective and efficient delivery of healthcare services requires comprehensive collaboration and coordination between healthcare entities and their complex inter-reliant activities. This inter-relation and coordination lead to different networks among diverse healthcare stakeholders. It is important to understand the varied dynamics of these networks to measure the efficiency of healthcare delivery services.

Issues of data governance associated with data mining in medical research: experiences from an empirical study.

This chapter is a review of data mining techniques used in medical research. It will cover the existing applications of these techniques in the identification of diseases, and also present the authors' research experiences in medical disease diagnosis and analysis. A computational diagnosis approach can have a significant impact on accurate diagnosis and result in time and cost effective solutions.