Multifactorial e- and mHealth interventions for cardiovascular disease primary prevention: Protocol for a systematic review and meta-analysis of randomised controlled trials

Objective: Cardiovascular diseases (CVD) are a leading cause of mortality and disease burden. Preventative interventions to augment the population-level adoption of health lifestyle behaviours that reduce CVD risk are a priority. Face-to-face interventions afford individualisation and are effective for improving health-related behaviours and outcomes, but they are costly and resource intensive. Electronic and mobile health (e-and mHealth) approaches aimed at modifying lifestyle risk factors may be an effective and scalable approach to reach many individuals while preserving individualisation. This systematic review aims to (a) determine the effectiveness of multifactorial e-and mHealth interventions on CVD risk and on lifestyle-related cardiometabolic risk factors and self-management behaviours among adults without CVD; and (b) describe the evidence on adverse events and on the cost-effectiveness of these interventions. Methods: Methods were detailed prior to the start of the review in order to improve conduct and prevent inconsistent decision making throughout the review. This protocol was prepared following the PRISMA-P 2015 statement. MEDLINE, CINAHL, Embase, PsycINFO, Web of Science, Cochrane Public Health Group Specialised Register and CENTRAL electronic databases will be searched between 1991 and September 2019. Eligibility criteria are: (a) population: community-dwelling adults; (b) intervention/comparison: randomised controlled trials comparing e-or mHealth CVD risk preventative interventions with usual care; and (c) outcomes: modifiable CVD risk factors. Selection of study reports will involve two authors independently screening titles and abstracts, followed by a full-text review of potentially eligible reports. Two authors will independently undertake data extraction and assess risk of bias. Where appropriate, meta-analysis of outcome data will be performed. Discussion: This protocol describes the pre-specified methods for a systematic review that will provide quantitative and narrative syntheses of current multifactorial e-and mHealth CVD preventative interventions. A systematic review and meta-analysis will be conducted following the methods outlined in the Cochrane Handbook for Systematic Reviews of Interventions and reported according to PRISMA guidelines

Just-In-Time Adaptive Intervention to Sit Less and Move More in People With Type 2 Diabetes: Protocol for a Microrandomized Trial

BackgroundReducing sedentary behavior and increasing physical activity in people with type 2 diabetes (T2D) are associated with various positive health benefits. Just-in-time adaptive interventions offer the potential to target both of these behaviors through more contextually aware, tailored, and personalized support. We have developed a just-in-time adaptive intervention to promote sitting less and moving more in people with T2D. ObjectiveThis paper presents the study protocol for a microrandomized trial to investigate whether motivational messages are effective in reducing time spent sitting in people with T2D and to determine what behavior change techniques are effective and in which context (eg, location, etc). MethodsWe will use a 6-week microrandomized trial design. A total of 22 adults with T2D will be recruited. The intervention aims to reduce sitting time and increase time spent standing and walking and comprises a mobile app (iMove), a bespoke activity sensor called Sedentary Behavior Detector (SORD), a messaging system, and a secured database. Depending on the randomization sequence, participants will potentially receive motivational messages 5 times a day. ResultsRecruitment was initiated in October 2022. As of now, 6 participants (2 female and 4 male) have consented and enrolled in the study. Their baseline measurements have been completed, and they have started using iMove. The mean age of 6 participants is 56.8 years, and they were diagnosed with T2D for 9.4 years on average. ConclusionsThis study will inform the optimization of digital behavior change interventions to support people with T2D Sit Less and Move More to increase daily physical activity. This study will generate new evidence about the immediate effectiveness of sedentary behavior interventions, their active ingredients, and associated factors. Trial RegistrationAustralian New Zealand Clinical Trial Registry ACTRN12622000426785; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=383664 International Registered Report Identifier (IRRID)DERR1-10.2196/4150

Development of an Android Mobile Application for Reducing Sitting Time and Increasing Walking Time in People with Type 2 Diabetes

Breaking up prolonged sitting with short bouts of light physical activity including standing and walking has been shown to be beneficial for people with type 2 diabetes (T2D). This paper presents the development of an android mobile app to deliver a just-in-time adaptive intervention (JITAI) to reduce sedentary time in people with T2D. A total of six design workshops were conducted with seven experts to identify design requirements, a behavioural framework, and required contextual adaptations for the development of a bespoke mobile app (iMOVE). Moreover, a focus group was conducted among people with T2D as potential end-users (N = 10) to ascertain their perceptions of the app. Feedback from the focus group was used in subsequent iterations of the iMOVE app. Data were analysed using an inductive qualitative thematic analysis. Based on workshops, key features of iMOVE were developed, including simplicity (e.g., navigation, login), colours and font sizes, push notifications, messaging algorithms, and a triggering system for breaking up sitting time and moving more. Based on the user testing results, a goal-setting tab was added, font sizes were made larger, the brightness of colours was reduced, and a colour indicator was used to indicate device connectivity with an activity tracker. A user-centric app was developed to support people with T2D to transition from sedentary to active lifestyles

Development of an Android Mobile Application for Reducing Sitting Time and Increasing Walking Time in People with Type 2 Diabetes

Breaking up prolonged sitting with short bouts of light physical activity including standing and walking has been shown to be beneficial for people with type 2 diabetes (T2D). This paper presents the development of an android mobile app to deliver a just-in-time adaptive intervention (JITAI) to reduce sedentary time in people with T2D. A total of six design workshops were conducted with seven experts to identify design requirements, a behavioural framework, and required contextual adaptations for the development of a bespoke mobile app (iMOVE). Moreover, a focus group was conducted among people with T2D as potential end-users (N = 10) to ascertain their perceptions of the app. Feedback from the focus group was used in subsequent iterations of the iMOVE app. Data were analysed using an inductive qualitative thematic analysis. Based on workshops, key features of iMOVE were developed, including simplicity (e.g., navigation, login), colours and font sizes, push notifications, messaging algorithms, and a triggering system for breaking up sitting time and moving more. Based on the user testing results, a goal-setting tab was added, font sizes were made larger, the brightness of colours was reduced, and a colour indicator was used to indicate device connectivity with an activity tracker. A user-centric app was developed to support people with T2D to transition from sedentary to active lifestyles

A control system model of capability-opportunity-motivation and behaviour (COM-B) framework for sedentary and physical activity behaviours

Objective Theoretical frameworks are essential for understanding behaviour change, yet their current use is inadequate to capture the complexity of human behaviour such as physical activity. Real-time and big data analytics can assist in the development of more testable and dynamic models of current theories. To transform current behavioural theories into more dynamic models, it is recommended that researchers adopt principles such as control systems engineering. In this article, we aim to describe a control system model of capability-opportunity-motivation and behaviour (COM-B) framework for reducing sedentary behaviour (SB) and increasing physical activity (PA) in adults. Methods The COM-B model is explained in terms of control systems. Examples of effective behaviour change techniques (BCTs) (e.g. goal setting, problem-solving and social support) for reducing SB and increasing PA were mapped to the COM-B model for illustration. Result A fluid analogy of the COM-B system is presented. Conclusions The proposed integrated model will enable empirical testing of individual behaviour change components (i.e. BCTs) and contribute to the optimisation of digital behaviour change interventions

Application of machine learning in predicting non-alcoholic fatty liver disease using anthropometric and body composition indices

Abstract Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, which can progress from simple steatosis to advanced cirrhosis and hepatocellular carcinoma. Clinical diagnosis of NAFLD is crucial in the early stages of the disease. The main aim of this study was to apply machine learning (ML) methods to identify significant classifiers of NAFLD using body composition and anthropometric variables. A cross-sectional study was carried out among 513 individuals aged 13 years old or above in Iran. Anthropometric and body composition measurements were performed manually using body composition analyzer InBody 270. Hepatic steatosis and fibrosis were determined using a Fibroscan. ML methods including k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Radial Basis Function (RBF) SVM, Gaussian Process (GP), Random Forest (RF), Neural Network (NN), Adaboost and Naïve Bayes were examined for model performance and to identify anthropometric and body composition predictors of fatty liver disease. RF generated the most accurate model for fatty liver (presence of any stage), steatosis stages and fibrosis stages with 82%, 52% and 57% accuracy, respectively. Abdomen circumference, waist circumference, chest circumference, trunk fat and body mass index were among the most important variables contributing to fatty liver disease. ML-based prediction of NAFLD using anthropometric and body composition data can assist clinicians in decision making. ML-based systems provide opportunities for NAFLD screening and early diagnosis, especially in population-level and remote areas

A Bluetooth-Enabled Device for Real-Time Detection of Sitting, Standing, and Walking: Cross-Sectional Validation Study

Background This study assesses the accuracy of a Bluetooth-enabled prototype activity tracker called the Sedentary behaviOR Detector (SORD) device in identifying sedentary, standing, and walking behaviors in a group of adult participants. Objective The primary objective of this study was to determine the criterion and convergent validity of SORD against direct observation and activPAL. Methods A total of 15 healthy adults wore SORD and activPAL devices on their thighs while engaging in activities (lying, reclining, sitting, standing, and walking). Direct observation was facilitated with cameras. Algorithms were developed using the Python programming language. The Bland-Altman method was used to assess the level of agreement. Results Overall, 1 model generated a low level of bias and high precision for SORD. In this model, accuracy, sensitivity, and specificity were all above 0.95 for detecting sitting, reclining, standing, and walking. Bland-Altman results showed that mean biases between SORD and direct observation were 0.3% for sitting and reclining (limits of agreement [LoA]=–0.3% to 0.9%), 1.19% for standing (LoA=–1.5% to 3.42%), and –4.71% for walking (LoA=–9.26% to –0.16%). The mean biases between SORD and activPAL were –3.45% for sitting and reclining (LoA=–11.59% to 4.68%), 7.45% for standing (LoA=–5.04% to 19.95%), and –5.40% for walking (LoA=–11.44% to 0.64%). Conclusions Results suggest that SORD is a valid device for detecting sitting, standing, and walking, which was demonstrated by excellent accuracy compared to direct observation. SORD offers promise for future inclusion in theory-based, real-time, and adaptive interventions to encourage physical activity and reduce sedentary behavior