User Engagement With Smartphone Apps and Cardiovascular Disease Risk Factor Outcomes: Systematic Review

Abstract

Background The use of mobile health (mHealth) interventions, including smartphone apps, for the prevention of cardiovascular disease (CVD) has demonstrated mixed results for obesity, hypercholesterolemia, diabetes, and hypertension management. A major factor attributing to the variation in mHealth study results may be mHealth user engagement. Objective This systematic review aims to determine if user engagement with smartphone apps for the prevention and management of CVD is associated with improved CVD health behavior change and risk factor outcomes. Methods We conducted a comprehensive search of PubMed, CINAHL, and Embase databases from 2007 to 2020. Studies were eligible if they assessed whether user engagement with a smartphone app used by an individual to manage his or her CVD risk factors was associated with the CVD health behavior change or risk factor outcomes. For eligible studies, data were extracted on study and sample characteristics, intervention description, app user engagement measures, and the relationship between app user engagement and the CVD risk factor outcomes. App user engagement was operationalized as general usage (eg, number of log-ins or usage days per week) or self-monitoring within the app (eg, total number of entries made in the app). The quality of the studies was assessed. Results Of the 24 included studies, 17 used a randomized controlled trial design, 4 used a retrospective analysis, and 3 used a single-arm pre- and posttest design. Sample sizes ranged from 55 to 324,649 adults, with 19 studies recruiting participants from a community setting. Most of the studies assessed weight loss interventions, with 6 addressing additional CVD risk factors, including diabetes, sleep, stress, and alcohol consumption. Most of the studies that assessed the relationship between user engagement and reduction in weight (9/13, 69%), BMI (3/4, 75%), body fat percentage (1/2, 50%), waist circumference (2/3, 67%), and hemoglobin A1c (3/5, 60%) found statistically significant results, indicating that greater app user engagement was associated with better outcomes. Of 5 studies, 3 (60%) found a statistically significant relationship between higher user engagement and an increase in objectively measured physical activity. The studies assessing the relationship between user engagement and dietary and diabetes self-care behaviors, blood pressure, and lipid panel components did not find statistically significant results. Conclusions Increased app user engagement for prevention and management of CVD may be associated with improved weight and BMI; however, only a few studies assessed other outcomes, limiting the evidence beyond this. Additional studies are needed to assess user engagement with smartphone apps targeting other important CVD risk factors, including dietary behaviors, hypercholesterolemia, diabetes, and hypertension. Further research is needed to assess mHealth user engagement in both inpatient and outpatient settings to determine the effect of integrating mHealth interventions into the existing clinical workflow and on CVD outcomes.

The Relationship Between Weight Loss Outcomes and Engagement in a Mobile Behavioral Change Intervention: Retrospective Analysis

“…Our findings corroborate past work that found significant associations between the frequency of food logging and weigh-ins and weight loss outcomes [ 10 , 17 - 22 ]. Departing from some past studies, we found that weight loss outcomes were associated with all engagement measures over all time periods, with one exception.…”

“…Previous work has found positive associations between engagement and weight loss outcomes [ 10 , 17 - 22 ]. In a digital commercial program, the number of weigh-ins per week, steps per day, active minutes per week, days logging meals per week, and the percentage of weeks with 5 or more meal logs were associated with weight loss at 6 months [ 19 ].…”

Remote Healthcare for Elderly People Using Wearables: A Review

“…Medical data processing is an important aspect that several wearable device manufacturers have addressed through a cloud storage scheme, where specialized applications can later query the data. Furthermore, literature reviews on cardiovascular diseases [ 128 , 129 , 130 , 131 , 132 , 133 ] and diabetes [ 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 ] have described apps designed for specific diseases that were not addressed here, as they were outside the scope of this research.…”

Contrasting a Mobile App With a Conversational Chatbot for Reducing Alcohol Consumption: Randomized Controlled Pilot Trial

“…For example, conversational agents that use artificial intelligence to proactively guide, prompt, and check in with participants and encourage general use of the app have been found to improve the therapeutic alliance between the participant and the intervention, resulting in more engagement [ 36 ]. In other studies, the ability to set goals, self-monitor progress, and receive feedback increased engagement across a variety of health behaviors, including alcohol use [ 37 ]. An advantage of apps is their ability to generate utilization data, which are commonly used as a proxy for quantifying engagement.…”