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

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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.

Section snippets

Issues around integration of mHealth into the clinical care workflow

There are many issues and challenges that must be overcome to develop and implement mHealth technology in clinical settings. We searched MEDLINE, PubMed, Web of Science and World Wide Web from inception to December 2015 and selected relevant studies focused on mHealth in relationship to PA assessment, counseling and interventions for CVD risk reduction to include in this review. Specific considerations include privacy and security, validity, clinical utility, clinical integration and behavior

Privacy and security of mHealth technology

Mobile health technology has shown the potential to optimize the management of CVD and other chronic diseases by empowering patients through better health self-monitoring and education.14., 15., 16., 17. However, several challenges are present when patient's data are utilized and monitored in the healthcare setting. A common issue is the privacy and security of health information. A robust framework and guidelines are already established and utilized in software development for electronic

Validity of wearable activity monitors

An advantage of sensors and WAMs is that they provide an objective estimate of PA. The technology to monitor PA with accelerometry-based devices has been available for 25 years, but the explosion of consumer-oriented WAMs is a relatively new phenomenon. A key to the popularity of these devices has been their ability to directly link data to Web and mobile technology to provide direct access to PA patterns as well as estimates of sleep and energy expenditure. Surprisingly, these devices are

Clinical utility

A critical challenge for integrating mHealth apps and WAMs to assess PA as part of routine care is the applicability of such data for clinical decision-making. Several studies in the areas of smoking cessation,39 diabetes40 and weight loss41 have shown a missing link between evidence-based clinical guidelines and mHealth apps and WAMs. This poses a critical issue regarding the meaningful use of integrating PA data generated by mHealth technology to guide behavioral change and lifestyle

EMR and clinical workflow integration

Another barrier for the adoption of mHealth apps and WAMs is the lack of integration of PA data into EMRs, clinical workflow and referrals to the community care team to support lifestyle interventions. These integrated software platforms will be bound by HIPAA and HITECH rules since patient health information would be directly incorporated into the EMR. Security is also a major issue for mHealth apps as they are used on portable devices that may have increased risk for data breach by

Behavior change programming

Systems and structures for behavior change programming are essential to fully realize the potential of mHealth technology. While coaching and brief counseling can be provided directly in the clinical setting, there are also options for referral to community care providers that are well-positioned to provide behavior change programming.65 It is recommended that health systems looking to integrate objective PA data into clinical workflow and EMRs develop comprehensive use-case scenarios as a

Evidence on the use of mHealth technology and CVD risk reduction

Several interventions have shown promising results in promoting PA through digital tracking and personalized feedback.13., 17., 73., 74., 75. The mActive study used real-time smartphone PA tracking and an automated text-based intervention, showing increased steps/day among patients enrolled in the texting feedback system compared to those without the texting component.76 The SMART MOVE randomized trial revealed significant increases in PA with the implementation of a mobile application, the

Future trends in objective PA monitoring

The future of WAM devices is progressing, with advances in simplicity, reliability and automation. Devices will continue to become smaller and more seamlessly integrated into jewelry and clothing, facilitating continuous tracking of PA patterns and other biometric and health outcomes. One direction is for “smart clothing” that can integrate data collection and analysis. AiQ clothing and OMsignal are some of the companies producing biometric garments with the ability to measure vital signs, PA

Conclusion and recommendations

Mobile health is a valuable technology that can help implement clinical guidelines and recommended behavior change strategies to improve the quality of care and health status of patients with CVD risk.1 The rise and rapid evolution of mHealth technology has the potential to guide personalized medicine interventions and strengthen the relationship between patients and healthcare providers, increasing engagement and compliance with healthy lifestyle changes.

Multiple PA stakeholders, including

Statement of conflicts of interest/disclosures

Dr. Lobelo is on the advisory board of the American College of Sports Medicine' Exercise is Medicine initiative; Dr. McConnell is currently on leave from Stanford and employed by Verily Life Sciences.

References (95)

  • J. Myers et al.

    Physical activity and cardiorespiratory fitness as major markers of cardiovascular risk: their independent and interwoven importance to health status

    Prog Cardiovasc Dis

    (2015)
  • S.A. Carlson et al.

    Inadequate physical activity and health care expenditures in the United States

    Prog Cardiovasc Dis

    (2015)
  • R.J. Widmer et al.

    Digital health interventions for the prevention of cardiovascular disease: a systematic review and meta-analysis

    Mayo Clin Proc

    (2015)
  • M.L. LeFevre et al.

    Behavioral counseling to promote a healthful diet and physical activity for cardiovascular disease prevention in adults with cardiovascular risk factors: U.S. Preventive Services Task Force Recommendation Statement

    Ann Intern Med

    (2014)
  • K.J. Coleman et al.

    Initial validation of an exercise “vital sign” in electronic medical records

    Med Sci Sports Exerc

    (2012)
  • R.W. Grant et al.

    Exercise as a vital sign: a quasi-experimental analysis of a health system intervention to collect patient-reported exercise levels

    J Gen Intern Med

    (2014)
  • T.J. Ball et al.

    Validity of two brief primary care physical activity questionnaires with accelerometry in clinic staff

    Prim Health Care Res Dev

    (2015)
  • H.J. Helmerhorst et al.

    A systematic review of reliability and objective criterion-related validity of physical activity questionnaires

    Int J Behav Nutr Phys Act

    (2012)
  • Wearable Tech market

  • P. Krebs et al.

    Health App Use Among US Mobile Phone Owners: a national survey

    JMIR Mhealth Uhealth

    (2015)
  • R. Liamas

    Worldwide wearables market forecast to grow 173.3% in 2015 with 72.1 million units to be shipped, according to IDC 2015

  • mHealth App Developer Economics 2014

  • E. Knight et al.

    Public health guidelines for physical activity: is there an app for that? A review of android and apple app stores

    JMIR Mhealth Uhealth

    (2015)
  • L.E. Burke et al.

    Current science on consumer use of mobile health for cardiovascular disease prevention: a scientific statement from the American Heart Association

    Circulation

    (2015)
  • O. El-Gayar et al.

    Mobile applications for diabetes self-management: status and potential

    J Diabetes Sci Technol

    (2013)
  • R. Hurling et al.

    Using internet and mobile phone technology to deliver an automated physical activity program: randomized controlled trial

    J Med Internet Res

    (2007)
  • G.M. Turner-McGrievy et al.

    Comparison of traditional versus mobile app self-monitoring of physical activity and dietary intake among overweight adults participating in an mHealth weight loss program

    J Am Med Inform Assoc

    (2013)
  • Office of the National Coordinator for Health Information Technology

    Guide to privacy and security of electronic health information

  • U.S. Department of Health and Human Services

    HITECH Act Enforcement Interim Final Rule

  • B. Martinez-Perez et al.

    Privacy and security in mobile health apps: a review and recommendations

    J Med Syst

    (2015)
  • The road ahead in connected health

  • Tactio Health Group

  • Federal Trade Commission

    Complying with the FTC's Health Breach Notification Rule

  • mHealth Laws and Regulations

  • Food and Drug Administration Safety and Innovation Act (FDASIA)

  • Y. Bai et al.

    Comparison of consumer and research monitors under semistructured settings

    (2015)
  • J.M. Lee et al.

    Validity of consumer-based physical activity monitors

    Med Sci Sports Exerc

    (2014)
  • M.A. Case et al.

    Accuracy of smartphone applications and wearable devices for tracking physical activity data

    JAMA

    (2015)
  • M.E. Rosenberger et al.

    24 hours of sleep, sedentary behavior, and physical activity with nine wearable devices

    Med Sci Sports Exerc

    (2016)
  • Y. Kim et al.

    Criterion validity of competing accelerometry-based activity monitoring devices

    Med Sci Sports Exerc

    (2015)
  • A.V. Rowlands et al.

    Sedentary sphere: wrist-worn accelerometer-brand independent posture classification

    Med Sci Sports Exerc

    (2015 Nov 10)
  • Y. Kim et al.

    Examination of different accelerometer cut-points for assessing sedentary behaviors in children

    PLoS One

    (2014)
  • J. Bai et al.

    Normalization and extraction of interpretable metrics from raw accelerometry data

    Biostatistics

    (2013)
  • H. Vähä-Ypyä et al.

    Validation of cut-points for evaluating the intensity of physical activity with accelerometry-based mean amplitude deviation (MAD)

    PLoS One

    (2015)
  • Shimmer

  • Open mHealth

  • T. Chomutare et al.

    Features of mobile diabetes applications: review of the literature and analysis of current applications compared against evidence-based guidelines

    J Med Internet Res

    (2011)
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