Mobile health application usage and quality of care at a hypertension clinic: an observational cohort study.

OBJECTIVE: The aim of this study was to test whether a physician-supervised web-based app, integrated with an electronic medical record, helps in improving blood pressure (BP) management in clinical practice. MATERIALS AND METHODS: An observational study of 1633 patients seen at a hypertension clinic managed by an endocrinologist with two cohorts (726 adopted the app and 907 had not). The app allowed patients and doctors to monitor BP, blood sugar and other vital signs. Patients decided whether to opt in to using the app and how often to upload their readings. The provider could offer feedback and communicate with patients through the app. We evaluated the change in office-based BP measurement before and after app adoption (at least 12 months apart). We performed a difference-in-difference analysis along with matching based on patient-individual characteristics. RESULTS: The difference-in-difference estimates were 6.23 mmHg systolic [95% confidence interval (95% CI) 0.87-11.59] for patients with SBP 150 mmHg or above, 4.01 mmHg systolic (95% CI 1.11-6.91) for patients with SBP 140 mmHg or above, 4.37 mmHg diastolic (95% CI 1.06-7.68) for patients with DBP 90 mmHg or above, 1.89 mmHg systolic (95% CI 0.58-3.2) and 0.87 mmHg diastolic (95% CI 0.17-1.57) overall for an average patient. Higher frequency of app usage was also associated with a greater reduction in BP. CONCLUSION: Use of an mHealth app in a clinical practice, was associated with a significant reduction in BP for average patients as well as high-severity patients. Physician-supervised mHealth apps in a clinical practice could be instrumental in managing patient BP.

The value of mHealth for managing chronic conditions.

Chronic conditions place a high cost burden on the healthcare system and deplete the quality of life for millions of Americans. Digital innovations such as mobile health (mHealth) technology can be used to provide efficient and effective healthcare. In this research we explore the use of mobile technology to manage chronic conditions such as diabetes and hypertension. There is ample empirical evidence in the healthcare literature showing that patients who use mHealth observe improvement in their health. However, an analytical study that quantifies the benefit of using mHealth is lacking. The benefit of using mHealth depends on many factors such as the current health condition of the patient, pattern of disease progression, frequency of measurement and intervention, the effectiveness of intervention, and the cost of measuring. Stochastic modeling is a suitable approach to take these factors into consideration to evaluate the benefit of mHealth. In this paper, we model the disease progression with the help of a Markov chain and quantify the benefits of measuring and intervention taking into consideration the above-mentioned factors. We compare two different modes for measuring and intervention, mHealth mode and conventional office visit mode, and evaluate the impact of these factors on health outcome.