Background Behavior change methods involving new ambulatory technologies may improve lifestyle and cardiovascular disease outcomes. Objective This study aimed to provide proof-of-concept analyses of an intervention aiming to increase (1) behavioral flexibility, (2) lifestyle change, and (3) quality of life. The feasibility and patient acceptance of the intervention were also evaluated. Methods Patients with cardiovascular disease (N=149; mean age 63.57, SD 8.30 years; 50/149, 33.5% women) were recruited in the Do Cardiac Health Advanced New Generation Ecosystem (Do CHANGE) trial and randomized to the Do CHANGE intervention or care as usual (CAU). The intervention involved a 3-month behavioral program in combination with ecological momentary assessment and intervention technologies. Results The intervention was perceived to be feasible and useful. A significant increase in lifestyle scores over time was found for both groups (F2,146.6=9.99; P<.001), which was similar for CAU and the intervention group (F1,149.9=0.09; P=.77). Quality of life improved more in the intervention group (mean 1.11, SD 0.11) than CAU (mean −1.47, SD 0.11) immediately following the intervention (3 months), but this benefit was not sustained at the 6-month follow-up (interaction: P=.02). No significant treatment effects were observed for behavioral flexibility (F1,149.0=0.48; P=.07). Conclusions The Do CHANGE 1 intervention was perceived as useful and easy to use. However, no long-term treatment effects were found on the outcome measures. More research is warranted to examine which components of behavioral interventions are effective in producing long-term behavior change. Trial Registration ClinicalTrials.gov NCT02946281; https://www.clinicaltrials.gov/ct2/show/NCT02946281

on behalf of the Do CHANGE consortium The importance of modifying lifestyle factors in order to improve prognosis in cardiac patients is well-known. Current study aims to evaluate the effects of a lifestyle intervention on changes in lifestyle-and health data derived from wearable devices. Cardiac patients from Spain (n = 34) and The Netherlands (n = 36) were included in the current analysis. Data were collected for 210 days, using the Fitbit activity tracker, Beddit sleep tracker, Moves app (GPS tracker), and the Careportal home monitoring system. Locally Weighted Error Sum of Squares regression assessed trajectories of outcome variables. Linear Mixed Effects regression analysis was used to find relevant predictors of improvement deterioration of outcome measures. Analysis showed that Number of Steps and Activity Level significantly changed over time (F = 58.21, p < 0.001; F = 6.33, p = 0.01). No significant changes were observed on blood pressure, weight, and sleep efficiency. Secondary analysis revealed that being male was associated with higher activity levels (F = 12.53, p < 0.001) and higher number of steps (F = 8.44, p < 0.01). Secondary analysis revealed demographic (gender, nationality, marital status), clinical (co-morbidities, heart failure), and psychological (anxiety, depression) profiles that were associated with lifestyle measures. In conclusion results showed that physical activity increased over time and that certain subgroups of patients were more likely to have a better lifestyle behaviors based on their demographic, clinical, and psychological profile. This advocates a personalized approach in future studies in order to change lifestyle in cardiac patients.

Objective Unhealthy life-style factors have adverse outcomes in cardiac patients. However, only a minority of patients succeed to change unhealthy habits. Personalization of interventions may result in critical improvements. The current randomized controlled trial provides a proof of concept of the personalized Do Cardiac Health Advanced New Generation Ecosystem (Do CHANGE) 2 intervention and evaluates effects on a) life-style and b) quality of life over time. Methods Cardiac patients (n = 150; mean age = 61.97 ± 11.61 years; 28.7% women; heart failure, n = 33; coronary artery disease, n = 50; hypertension, n = 67) recruited from Spain and the Netherlands were randomized to either the “Do CHANGE 2” or “care as usual” group. The Do CHANGE 2 group received ambulatory health-behavior assessment technologies for 6 months combined with a 3-month behavioral intervention program. Linear mixed-model analysis was used to evaluate the intervention effects, and latent class analysis was used for secondary subgroup analysis. Results Linear mixed-model analysis showed significant intervention effects for life-style behavior (F interaction(2,138.5) = 5.97, p = .003), with improvement of life-style behavior in the intervention group. For quality of life, no significant main effect (F(1,138.18) = .58, p = .447) or interaction effect (F(2,133.1) = 0.41, p = .67) was found. Secondary latent class analysis revealed different subgroups of patients per outcome measure. The intervention was experienced as useful and feasible. Conclusions The personalized eHealth intervention resulted in significant improvements in life-style. Cardiac patients and health care providers were also willing to engage in this personalized digital behavioral intervention program. Incorporating eHealth life-style programs as part of secondary prevention would be particularly useful when taking into account which patients are most likely to benefit. Trial Registration: https://clinicaltrials.gov/ct2/show/NCT03178305.