Smartphone applications for physical activity and sedentary behaviour change in people with cardiovascular disease: A systematic review and meta-analysis

Abstract

Background Smartphone applications provide new opportunities for secondary prevention healthcare. This systematic review and meta-analysis aimed to determine if smartphone applications are effective at changing physical activity and sedentary behaviour in people with cardiovascular disease. Methods Six electronic databases (Medline, CINAHL Plus, Cochrane Library, SCOPUS, Sports Discus and EMBASE) were searched from 2007 to October 2020. Cardiovascular disease secondary prevention physical activity or sedentary behaviour interventions were included where the primary element was a smartphone or tablet computer application (excluding SMS-only text-messaging). Study quality was assessed using validated tools appropriate for each study design. Random effects model was used and the pooled mean difference between post scores were calculated. Subgroup analyses were conducted to examine differences based on diagnosis, sample size, age, intervention duration, activity tracker use, target behaviour, and self-report versus device-measured outcome. Results Nineteen studies with a total of 1,543 participants were included (coronary heart disease, n = 10; hypertension, n = 4; stroke, n = 3; heart failure, n = 1; peripheral artery disease, n = 1). Risk of bias was rated as high. Thirteen studies were included in the meta-analysis. Only two controlled studies reported on sedentary behaviour. Smartphone applications produced a significant increase of 40.35 minutes of moderate-to-vigorous intensity physical activity per week (7 studies; p = 0.04; 95% CI 1.03 to 79.67) and 2,390 steps per day (3 studies; p = 0.0007; 95% CI 1,006.9 to 3,791.2). Subgroup analyses found no difference when comparing diagnoses, sample size, activity tracker use, target behaviour and self-report versus device-measured outcome. Larger improvements in physical activity were noted in intervention durations of ≤3-months and participants ≥60yrs (95.35 mins.week-1; p = 0.05). Conclusions Smartphone applications were effective in increasing physical activity in people with cardiovascular disease. Caution is warranted for the low-quality evidence, small sample and larger coronary heart disease representation. More rigorous research is needed to investigate the effect of smartphone applications across diagnoses and in sedentary behaviour.

Behaviour change techniques in cardiovascular disease smartphone apps to improve physical activity and sedentary behaviour: Systematic review and meta-regression

“…Overall, the cohort studies ( n = 6) [ 34 , 39 , 40 , 45 , 47 , 48 ] were considered moderate risk of bias primarily due to small sample sizes and using self-report outcome measures. Additional risk of bias results are available elsewhere [ 10 ] and further descriptive results are in Supplement 1 .…”

“…The protocol was prospectively registered with PROSPERO (CRD42020189046). The methods have been described in full elsewhere in a prior meta-analysis of physical activity outcomes [ 10 ].…”

“…Previous reviews show reasonable effectiveness of smartphone apps for increasing physical activity in older adults [ 8 ], adults without disease [ 9 ] and people with CVD [ 10 ]. These apps typically produce small to moderate effects.…”

“…These apps typically produce small to moderate effects. The impact of smartphone apps on sedentary behaviour for people with CVD is less clear with very few studies [ 10 ]. Due to the diverse features of smartphone apps it is unclear how these interventions are changing behaviour and is further complicated by the constant advances in technology.…”

A Context Model for Personal Data Streams

“…A lot of prior work has focused on collecting and exploiting massive streams of data, e.g., sensor data and annotations. A first line of work has concentrated on using the streams of personal data for learning daily human behavior, including physical activity, see, e.g., [11], assessment personality states, see, e.g., [12], and visiting points of interest [3]. The Reality Mining project [4] collected smartphone sensors, including call records, cellular tower IDs, and Bluetooth proximity logs to study students' social networks and daily activities.…”