Harnessing Mobile Technologies to Improve Physical Activity, Lipids and CVD Risk
Editor's Note: Commentary based on Feldman DI, Robison WT, Pacor JM, et al. Harnessing mHealth technologies to increase physical activity and prevent cardiovascular disease. Clin Cardiol 2018. [Epub ahead of print].
The spread of technology has contributed to the progressively sedentary lifestyle of the average adult, but it seems that technology also has the potential to ameliorate this dangerous trend and improve cardiovascular health.
Physical activity is a marker for life expectancy and an intervention that improves cardiovascular outcomes. A recent review highlights the progress made in the use of mobile health (mHealth), including smartphone-based applications (apps) and wearable fitness trackers, to empower patients to increase their daily level of physical activity and improve cardiovascular disease (CVD) risk factors. Among the advantages of this emerging field over traditional behavioral interventions are automated tracking of real-time data and two-way communication outside of the traditional appointment-based health care model.
Efficacy of mHealth Interventions
Studies in pedometry, the simplest of mHealth interventions, have demonstrated the impact of self-monitoring of physical activity. In the weeks to months after participants start tracking their physical activity, step counts tend to increase.1 Even more importantly, increased step counts correlate with an improvement in CV health parameters such as body mass index (BMI) and blood pressure.2 The NAVIGATOR (Long-term Study of Nateglinide+Valsartan to Prevent or Delay Type II Diabetes Mellitus and Cardiovascular Complications) trial even demonstrated reduction in rate of CV events with improvement from baseline activity level over an average 6-year follow-up, an effect margin that can be expected to widen if activity level improvements are sustained over decades.3
The app market is gaining progressively complex, interactive and personalized health tracking and intervention resources with widely varying levels of clinician involvement and formal outcome testing. There is, therefore, a need to hone in on what patients need most from their fitness tracker technology and how clinicians can efficiently and effectively integrate mHealth tools into their practice.
Qualities of Effective mHealth Interventions for Physical Activity
The authors delineated the various domains in which effective mHealth interventions work to improve activity habits: user knowledge, social support, behavior change and decision support and self-efficacy and motivation. The most common feature of apps currently on the market is user education, while decision support and self-motivation techniques are under-utilized, particularly in free apps.
Thus far, studies have demonstrated that the most successful technology-based interventions provide real-time feedback, offer social support, have a user-friendly interface and facilitate personalized and actionable goal setting.1,4,5 The pilot study for the MyBehavior app suggested that users prefer automated but personalized messages over standard clinician-scripted messages.4 An automated messaging algorithm was tested in the mActive trial, with personalization based on 16 factors (e.g., name, pet, favorite athlete, cardiologist) and targeting a step goal of 10,000 steps per day. Patients were recruited from a preventive cardiology clinic and improved step counts by over 2,000 steps per day (1 mile per day) when receiving the smart texts, predominantly due to an increase in aerobic activity time. Those receiving the texts were twice as likely to achieve the 10,000 step/day goal. The mActive trial highlights the optimal role of mHealth: as an extension of the clinician rather than as an independent tool.6
Next Steps in mHealth for Physical Activity
These findings prompt more questions. The first pertains to social integration. Social support is understood as the informational, emotional and logistical resources gained from a community to facilitate a behavior, while social influence is the change in one's thinking (and accordingly, behavior) as a result of interaction with others. Perceived social support is generally positive to the receiver, while social influences may either promote or detract from motivation to improve physical fitness.
As mHealth becomes more sophisticated, developers are exploring the best methods to capitalize on social support while avoiding the pitfalls of negative social influence, as evidenced by several ongoing studies. The FAITH! App will leverage the pre-existing social connectedness of church communities to facilitate group lifestyle intervention.7 Results are expected soon from the PennFit platform investigating the motivational value of randomly-assigned four-woman social networks for motivating physical activity compared to self-monitoring alone.8 The Columbia Moves intervention will evaluate the effect of adding a team competition component using self-selected teams to the platform's standard self-monitoring and app-based feedback.9
Another opportunity to achieve balance will be the ratio of active vs passive involvement. Participant engagement is linked to behavioral change, but applications with significant time demands can be expected to be ineffective and underused by many. Just how much active engagement is necessary to promote motivation is uncertain and is likely user-specific.
Barriers to Adoption
Some concerns about fitness tracker apps may be generalized to users of all mobile health technology. With the recent rapid expansion of the capabilities of mHealth, agencies from software developers to healthcare administrators to clinicians are forced to address novel questions related to the collection, storage, and transmission of increasingly detailed and comprehensive health data.
The first is assurance of scientific accuracy. Physical activity data collected by various wearable trackers has been reported to have widely varying accuracy in analysis of energy expenditure, from 52% to 100%.1 For example, FitBit devices were found to closely approximate indirect calorimetry during flat ground activity, but less so with incline.10 Testing of these devices in healthy adults may also limit generalizability to those with multiple medical comorbidities, particularly cardiovascular disease which could impair heart rate interpretation or mobility disorders which lead to mis-interpretation of the accelerometer or impaired extrapolation of accelerometer data to energy expenditure.
An important issue is the true cost of long-term use of mHealth. Some of the more sophisticated apps and all wearable trackers require up-front monetary investment. Hidden costs to participants may include expanded data plans as well as tools for supplementing phone battery life, both made necessary by the demands of continuous monitoring and transmission of information.
Mobile tracking and transmission of health data requires consideration of security and privacy. There was concern raised with early fitness tracker apps about data security, the potential for that data to be hacked or manipulated. Data transmitted to a clinician, however, falls under HIPAA regulations, which demands the involvement of health care entities to ensure patient privacy in accordance with HIPAA.
The burden falls on both developers and consumers to confirm that appropriate encrypted and multi-layer security is in place. The American Medical Association has proposed an independent app store for apps which integrate with electronic health records in accordance with HIPAA, a move which would streamline the utilization of mHealth in the realm of privacy as well as improve ease of clinical integration for providers.
Integrating mHealth in Practice
Close to 99% of clinicians surveyed indicated that they plan to integrate mHealth or telehealth into their practice in the near future. What strategy should we employ? Methods for structuring increased demands on clinicians' time outside of the traditional office visit environment to address mobile health data are being widely explored from the system-wide perspectives of reimbursement and time-effectiveness. As mentioned above, emphasis on apps which integrate with EHR would streamline this process.
The review authors proposed a 'starting place' framework, which can be applied to a variety of mHealth platforms, to simplify the initiation of basic technology-based patient behavioral change therapy for the practicing clinician. The basic model emphasizes continuous collection of activity data by clinician-patient teams, optimization of social, technological, and decision support, and generation of individualized patient goals to produce behavioral changes.
This model is simplified by a checklist for clinicians wishing to adapt mHealth into their patients' plan of care: 1) Assess current physical activity levels and compare these to AHA/ACC recommendations; 2) Implement technology-driven physical activity tracking intervention; 3) Recommend simple daily living and workplace modifications to increase activity levels; 4) Develop a personalized exercise prescription. The efficacy of exercise prescriptions is supported in literature.11 The exercise prescription should be staged in order to reach a long term goal which will reduce cardiovascular risk, beginning with as little as 10 minutes of walking per day for previously sedentary individuals. Both the model and the checklist will undoubtedly be fine-tuned as research accumulates.
One potential step forward in exercise prescribing is tailoring of recommendations to the individual's environment. An ongoing National Heart, Lung, and Blood Institute study will provide low-resource users recommendations about exercise opportunities specific to their own neighborhood.12 The ENCOURAGE App will provide prompts specific to the user-provided workplace schedule to encourage appropriately-timed breaks from sedentary time.13
There is great potential to leverage mHealth tools for promotion and monitoring of physical activity habits. Ongoing research, evidence-based application, and collaborative integration between developers, health care entities, and clinicians will ensure that mHealth practice progresses on pace with mobile technology.
- Bort-Roig J, Gilson ND, Puig-Ribera A, Contreras RS, Trost SG. Measuring and influencing physical activity with smartphone technology: a systematic review. Sports Med 2014;44:671-86.
- Bravata DM, Smith-Spangler C, Sundaram V, et al. Using pedometers to increase physical activity and improve health. JAMA 2007;298:2296.
- Yates T, Haffner SM, Schulte PJ, et al. Association between change in daily ambulatory activity and cardiovascular events in people with impaired glucose tolerance (NAVIGATOR trial): a cohort analysis. Lancet 2014;3831059-66.
- Rabbi M, Pfammatter A, Zhang M, Spring B, Choudhury T. Automated personalized feedback for physical activity and dietary behavior change with mobile phones: a
- Kirwan M, Duncan MJ, Vandelanotte C, Mummery WK. Using smartphone technology to monitor physical activity in the 10,000 Steps program: a matched case-control trial. J Med Internet Res 2012;14:e55.
- Martin SS, Feldman DI, Blumenthal RS, et al. mActive: a randomized clincial trial of an automated mHealth intervention for physical activity promotion. J Am Heart Assoc 2015;4:e002239.
- Cardiovascular Health Promotion Among African-Americans by FAITH! ClinicalTrials.gov. https://clinicaltrials.gov/ct2/show/NCT03084822. Accessed May 18, 2018.
- Mobile-based Online Social Network Intervention to Increase Physical Activity (PennFit). ClinicalTrials.gov. https://clinicaltrials.gov/ct2/show/NCT02736903. Accessed May 20, 2018.
- Columbia Moves Physical Activity. ClinicalTrials.gov. https://clinicaltrials.gov/ct2/show/NCT03509129. Accessed May 20, 2018.
- Adam Noah J, Spierer DK, Gu J, Bronner S. Comparison of steps and energy expenditure assessment in adults of Fitbit Tracker and Ultra to the Actical and indirect calorimetry. J Med Eng Technol 2013;37:456-62.
- Metkus TS, Baughman KL, Thompson PD. Exercise prescription and primary prevention of cardioavascular disease. Circulation 2010;121:2601-4.
- National Heart, Lung and BI (NHLBI). Tailoring Mobile Health Technology to Reduce Obesity and Improve Cardiovascular Health in Resource-Limited Neighborhood Environments. https://clinicaltrials.gov/ct2/show/NCT03288207. Accessed May 17, 2018.
- A Mobile Health App to Reduce Sedentary Time in Inactive Employees. ClinicalTrials.gov. https://clinicaltrials.gov/ct2/show/NCT03403270. Accessed May 19, 2018.
Keywords: Dyslipidemias, Sedentary Lifestyle, Body Mass Index, Self Efficacy, Heart Rate, Calorimetry, Indirect, Motivation, Risk Factors, Diabetes Mellitus, Type 2, Blood Pressure, Athletes, Physical Fitness, Exercise, Telemedicine, Cyclohexanes, Phenylalanine, Computer Security, Habits, Social Support, Comorbidity, Biomedical Technology, Energy Metabolism, Accelerometry, Lipids, Primary Prevention, Secondary Prevention, Risk Reduction Behavior
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