Design and rationale of the MyHeartMate study: a randomised controlled trial of a game-based app to promote behaviour change in patients with cardiovascular disease

Attraction and achievement as 2 attributes of gamification in healthcare: an evolutionary concept analysis

This study conducted a conceptual analysis of gamification in healthcare utilizing Rogers’ evolutionary concept analysis methodology to identify its attributes and provide a method for its applications in the healthcare field. Gamification has recently been used as a health intervention and education method, but the concept is used inconsistently and confusingly. A literature review was conducted to derive definitions, surrogate terms, antecedents, influencing factors, attributes (characteristics with dimensions and features), related concepts, consequences, implications, and hypotheses from various academic fields. A total of 56 journal articles in English and Korean, published between August 2 and August 7, 2023, were extracted from databases such as PubMed Central, the Institute of Electrical and Electronics Engineers, the Association for Computing Machinery Digital Library, the Research Information Sharing Service, and the Korean Studies Information Service System, using the keywords “gamification” and “healthcare.” These articles were then analyzed. Gamification in healthcare is defined as the application of game elements in health-related contexts to improve health outcomes. The attributes of this concept were categorized into 2 main areas: attraction and achievement. These categories encompass various strategies for synchronization, enjoyable engagement, visual rewards, and goal-reinforcing frames. Through a multidisciplinary analysis of the concept’s attributes and influencing factors, this paper provides practical strategies for implementing gamification in health interventions. When developing a gamification strategy, healthcare providers can reference this analysis to ensure the game elements are used both appropriately and effectively.

Knowledge mapping of digital medicine in cardiovascular diseases from 2004 to 2022: A bibliometric analysis

Objective

To review studies on digital medicine in cardiovascular diseases (CVD), discuss its development process, knowledge structure and research hotspots, and provide a perspective for researchers in this field.

Methods

The relevant literature in recent 20 years (January 2004 to October 2022) were retrieved from the Web of Science Core Collection (WoSCC). CiteSpace was used to demonstrate our knowledge of keywords, co-references and speculative frontiers. VOSviewer was used to chart the contributions of authors, institutions and countries and incorporates their link strength into the table.

Results

A total of 5265 English articles in set timespan were included. The number of publications increased steadily annually. The United States (US) produced the highest number of publications, followed by England. Most publications were from Harvard Medicine School, followed by Massachusetts General Hospital and Brigham Women's Hospital. The most authoritative academic journal was JMIR mHealth and uHealth. Noseworthy PA may have the highest influence in this intersected field with the highest number of citations and total link strength. The utilization of wearable mobile devices in the context of CVD, encompassing the identification of risk factors, diagnosis and prevention of diseases, as well as early intervention and remote management of diseases, has been widely acknowledged as a knowledge base and an area of current interest. To investigate the impact of various digital medicine interventions on chronic care and assess their clinical effectiveness, examine the potential of machine learning (ML) in delivering clinical care for atrial fibrillation (AF) and identifying early disease risk factors, as well as explore the development of disease prediction models using neural networks (NNs), ML and unsupervised learning in CVD prognosis, may emerge as future trends and areas of focus.

Conclusion

Recently, there has been a significant surge of interest in the investigation of digital medicine in CVD. This initial bibliometric study offers a comprehensive analysis of the research landscape pertaining to digital medicine in CVD, thereby furnishing related scholars with a dependable reference to facilitate further progress in this domain.

A review of the factors influencing adoption of digital health applications for people living with dementia

Objective

Researchers have used various theories and models to understand technology adoption, however, with the growing interest and availability of mobile applications (apps) for people living with dementia, it is desirable to have a broader insight into how technology adoption may be further improved. This paper aims to explore the factors influencing the adoption of digital health applications for people living with dementia and add to the current literature on this topic.

Methods

Searches were conducted in CINHAL, Web of Science, Psych Info, ProQuest Health and Medical, IEEE Xplore and Scopus. Citation searching and handsearching were used in the identification of other studies.

Results

Following an assessment of relevancy, nine studies remained and are included within this review. Methodological quality was assessed using The Mixed Methods Appraisal Tool (MMAT). A thematic analysis was used for the data synthesis of included studies. Each study reported on different types of apps.

Conclusion

From the synthesis of included studies, four analytic themes were identified; Theme 1: Personal and contextual factors; Theme 2: Perceived value and benefit; Theme 3: Design and content of app; and Theme 4: Digital Literacy and Confidence. People are diverse and so are their reasons for the adoption of apps. These findings provide an insight into the range of factors that impact the adoption of apps for people living with dementia. Understanding the factors that impact the adoption of mobile applications is critical to their success. These findings can be beneficial for app developers and for people living with dementia and their carers.

The effect of a game-based mobile app 'MyHeartMate' to promote lifestyle change in coronary disease patients: a randomized controlled trial

Aims

Secondary prevention reduces coronary heart disease (CHD) progression. Traditional prevention programs including cardiac rehabilitation are under-accessed, which smartphone apps may overcome. To evaluate the effect of a game-based mobile app intervention (MyHeartMate) to improve cardiovascular risk factors and lifestyle behaviours.

Methods and results

Single-blind randomized trial of CHD patients in Sydney, 2017-2021. Intervention group were provided the MyHeartMate app for 6 months. Co-designed features included an avatar of the patient's heart and tokens earned by risk factor work (tracking, challenges, and quizzes). The control group received usual care. Primary outcome was self-reported physical activity [metabolic equivalents (METs), Global Physical Activity Questionnaire] and secondary outcomes included lipid levels, blood pressure (BP), body mass index, and smoking. Pre-specified sample size was achieved (n = 390), age 61.2 ± 11.5 years; 82.5% men and 9.2% current smokers. At 6 months, adjusted for baseline levels, the intervention group achieved more physical activity than control (median difference 329 MET mins/wk), which was not statistically significant (95% CI -37.4, 696; P = 0.064). No differences occurred between groups on secondary outcomes except for lower triglyceride levels in the intervention [mean difference -0.3 (95% CI -0.5, -0.1 mmoL/L, P = 0.004)]. Acceptability was high: 94.8% of intervention participants engaged by tracking exercise or BP and completing missions; 26.8% continued to engage for ≥30 days. Participants (n = 14) reported the app supported tracking behaviours and risk factors, reinforcing and improving self-care confidence, and decreasing anxiety.

Conclusion

A game-based app proved highly acceptable for patients with CHD but did not improve risk factors or lifestyle behaviours other than triglyceride levels.

Understanding and assessing gamification in digital healthcare interventions for patients with cardiovascular disease

Gamification is defined as the use of game design elements in contexts other than gaming to increase user engagement and experience. Gamification in cardiovascular care can contribute to positively change health behaviour with possible effects and benefits on physical health and mental well-being. Based on previous literature, in this article we describe: the conceptualization of gamification, the five gamification principles for gamified digital health programmes or applications, the six most common game elements used to impact health behaviour applied in gamified digital health interventions and finally scientifically validated instruments to use for assessment of gamification in terms of self-reported psychological outcomes.

Evaluating mobile health technologies: does the traditional randomized controlled trial serve our needs?

Mobile health technologies are increasingly used by cardiovascular nurses and allied health clinicians to support behaviour change and self-care in patients. Randomized controlled trials provide rigorous methodology for evaluation but progress slowly, so the technology is often outdated by completion. A helpful modification to the trial design includes identification of the behaviour intervention principles involved and concurrently track in-app usage data, allowing updates to be made. These data collected alongside user experiences enables analysis of effects on outcomes and determination of the relative influence of component parts. Cardiovascular and mental health mHealth strategies are used to exemplify these recommendations.

Development of a Digital Lifestyle Modification Intervention for Use after Transient Ischaemic Attack or Minor Stroke: A Person-Based Approach

This paper describes the development of the ‘Brain-Fit’ app, a digital secondary prevention intervention designed for use in the early phase after transient ischaemic attack (TIA) or minor stroke. The aim of the study was to explore perceptions on usability and relevance of the app in order to maximise user engagement and sustainability. Using the theory- and evidence-informed person-based approach, initial planning included a scoping review of qualitative evidence to identify barriers and facilitators to use of digital interventions in people with cardiovascular conditions and two focus groups exploring experiences and support needs of people (N = 32) with a history of TIA or minor stroke. The scoping review and focus group data were analysed thematically and findings were used to produce guiding principles, a behavioural analysis and explanatory logic model for the intervention. Optimisation included an additional focus group (N = 12) and individual think-aloud interviews (N = 8) to explore perspectives on content and usability of a prototype app. Overall, thematic analysis highlighted uncertainty about increasing physical activity and concerns that fatigue might limit participation. Realistic goals and progressive increases in activity were seen as important to improving self-confidence and personal control. The app was seen as a useful and flexible resource. Participant feedback from the optimisation phase was used to make modifications to the app to maximise engagement, including simplification of the goal setting and daily data entry sections. Further studies are required to examine efficacy and cost-effectiveness of this novel digital intervention.

Digital Technology Interventions for Risk Factor Modification in Patients With Cardiovascular Disease: Systematic Review and Meta-analysis

BACKGROUND

Approximately 50% of cardiovascular disease (CVD) cases are attributable to lifestyle risk factors. Despite widespread education, personal knowledge, and efficacy, many individuals fail to adequately modify these risk factors, even after a cardiovascular event. Digital technology interventions have been suggested as a viable equivalent and potential alternative to conventional cardiac rehabilitation care centers. However, little is known about the clinical effectiveness of these technologies in bringing about behavioral changes in patients with CVD at an individual level.

OBJECTIVE

The aim of this study is to identify and measure the effectiveness of digital technology (eg, mobile phones, the internet, software applications, wearables, etc) interventions in randomized controlled trials (RCTs) and determine which behavior change constructs are effective at achieving risk factor modification in patients with CVD.

METHODS

This study is a systematic review and meta-analysis of RCTs designed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analysis) statement standard. Mixed data from studies extracted from selected research databases and filtered for RCTs only were analyzed using quantitative methods. Outcome hypothesis testing was set at 95% CI and P=.05 for statistical significance.

RESULTS

Digital interventions were delivered using devices such as cell phones, smartphones, personal computers, and wearables coupled with technologies such as the internet, SMS, software applications, and mobile sensors. Behavioral change constructs such as cognition, follow-up, goal setting, record keeping, perceived benefit, persuasion, socialization, personalization, rewards and incentives, support, and self-management were used. The meta-analyzed effect estimates (mean difference [MD]; standard mean difference [SMD]; and risk ratio [RR]) calculated for outcomes showed benefits in total cholesterol SMD at -0.29 [-0.44, -0.15], P<.001; high-density lipoprotein SMD at -0.09 [-0.19, 0.00], P=.05; low-density lipoprotein SMD at -0.18 [-0.33, -0.04], P=.01; physical activity (PA) SMD at 0.23 [0.11, 0.36], P<.001; physical inactivity (sedentary) RR at 0.54 [0.39, 0.75], P<.001; and diet (food intake) RR at 0.79 [0.66, 0.94], P=.007. Initial effect estimates showed no significant benefit in body mass index (BMI) MD at -0.37 [-1.20, 0.46], P=.38; diastolic blood pressure (BP) SMD at -0.06 [-0.20, 0.08], P=.43; systolic BP SMD at -0.03 [-0.18, 0.13], P=.74; Hemoglobin A1C blood sugar (HbA1c) RR at 1.04 [0.40, 2.70], P=.94; alcohol intake SMD at -0.16 [-1.43, 1.10], P=.80; smoking RR at 0.87 [0.67, 1.13], P=.30; and medication adherence RR at 1.10 [1.00, 1.22], P=.06.

CONCLUSIONS

Digital interventions may improve healthy behavioral factors (PA, healthy diet, and medication adherence) and are even more potent when used to treat multiple behavioral outcomes (eg, medication adherence plus). However, they did not appear to reduce unhealthy behavioral factors (smoking, alcohol intake, and unhealthy diet) and clinical outcomes (BMI, triglycerides, diastolic and systolic BP, and HbA1c).

Lifestyle intervention using mobile technology and smart devices in patients with high cardiovascular risk: A pragmatic randomised clinical trial

BACKGROUND AND AIMS

Mobile technology-based solutions present promising effects against cardiovascular diseases. Long-term follow-up in mobile phone-based interventions has not yet been elucidated as a primary prevention technique for cardiovascular diseases. The objective of the present trial is to evaluate the effectiveness of lifestyle intervention on the change in the atherosclerotic cardiovascular disease (ASCVD) risk score in a 1-year follow-up using smart phone technology in patients with high cardiovascular risk.

METHODS

This prospective, randomised, single-centre clinical investigation enrolled 242 and 241 patients to the intervention plus usual care and usual care arms, respectively. The primary outcome of this study was the 1-year ASCVD risk score adjusted to baseline ASCVD risk score. ASCVD risk scores were calculated for every study participant at the randomisation and final stages.

RESULTS

After a 1-year follow-up, the intervention plus usual care reduced the ASCVD score by 2.7% (adjusted treatment effect -2.7, 95% confidence interval (CI): -2.2,-3.3, p<0.0001). An improvement was observed in favour of the intervention plus usual care arm in the majority of the pre-specified secondary endpoints. The high sensitive troponin and high sensitive C-reactive protein levels at 1 year were similar between the two arms. The treatment effect was homogenous for diabetes mellitus, gender, systolic blood pressure, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides and body mass index; however, heterogeneity in the treatment effect was observed for age.

CONCLUSIONS

The lifestyle intervention using smart phone technology compared to usual care alone in patients with high cardiovascular risk reduced the ASCVD score at 1-year follow-up.