The Wild Wild West: A Framework to Integrate mHealth Software Applications and Wearables to Support Physical Activity Assessment, Counseling and Interventions for Cardiovascular Disease Risk Reduction

Abstract

Physical activity (PA) interventions constitute a critical component of cardiovascular disease (CVD) risk reduction programs. Objective mobile health (mHealth) software applications (apps) and wearable activity monitors (WAMs) can advance both assessment and integration of PA counseling in clinical settings and support community-based PA interventions. The use of mHealth technology for CVD risk reduction is promising, but integration into routine clinical care and population health management has proven challenging. The increasing diversity of available technologies and the lack of a comprehensive guiding framework are key barriers for standardizing data collection and integration. This paper reviews the validity, utility and feasibility of implementing mHealth technology in clinical settings and proposes an organizational framework to support PA assessment, counseling and referrals to community resources for CVD risk reduction interventions. This integration framework can be adapted to different clinical population needs. It should also be refined as technologies and regulations advance under an evolving health care system landscape in the United States and globally.

Keywords: Cardiovascular disease; Clinical counseling; Mobile health; Physical activity; Population health management.

Fig 1

mHealth Integration Framework. The proposed model provides a framework for collecting valuable physical activity (PA) data from mobile health (mHealth) applications (apps) and wearable activity monitors (WAMs) to be analyzed for meaningful use and summarized into actionable metrics to guide PA assessment and counseling in healthcare settings. The communication between each entity is compliant with HIPAA/HITECH regulations to assure privacy and security of healthcare data. The first step of the model is the collection of real-time PA data from the patient through apps and WAMs. The data will be downloaded, processed and standardized by the proposed digital ecosystem platform. The data analytic provides a standardized format to integrate key summary data into the electronic medical record (EMR) or make it available to healthcare providers via 3rd party software. The integration step has multi-dimensional purposes, which can be specified by the healthcare team according to the clinical need and specialty. Data visualization needs to provide meaningful use and actionable metrics to guide patient care.

Fig 2

Case study—inpatient vs. outpatient models. The following case study presents the integration of mobile health (mHealth) data into the inpatient and outpatient settings. The initial steps for data collection and analytics are similar to those in Fig 1 for the proposed mHealth integration model. The summarized and processed data can be provided for different stakeholders in the clinical or community care team based on pre-specified use-case scenarios. For example, the inpatient setting will require short term monitoring of patient’s state by collecting data on physical activity (PA) to implement acute interventions for faster recovery and improvement [i.e., integrating early ambulation markers into early recovery after surgery (ERAS) protocols for improved outcomes]. The data are specified and analyzed based on the patient’s clinical status and PA needs. Once the patient is discharged, the outpatient provider can continue to monitor PA and health status of the patient through the integrated mHealth system. For prevention-oriented outpatient care (preventive cardiology, lifestyle medicine, and primary care clinics) the mHealth integration model follows the same initial flow. In this model, after the healthcare provider initiates PA assessment and counseling, a referral to the community care team (i.e., certified fitness professionals) is recommended. These teams deliver evidence-based interventions and work closely with each patient or small groups of patients based on their clinical needs and goals. However, the challenge is to track and analyze not only PA data but also behavioral change precursors that will lead to the adoption of improved PA and lifestyle and reduced CVD risk. The community care team is able to use mHealth data for increased engagement and real-time monitoring in order to implement clinician’s recommendations and relay summary behavior change outcome data as the clinician follows-up with the patient.