Leveraging Digital Health Technology to Personalize Lifestyle Interventions

Cardiovascular disease (CVD) is the cause of nearly one third of deaths in the U.S.1 It has been recognized for over fifty years that numerous risk factors, including dyslipidemia, contribute to the development of CVD.2 The publication of the 2013 American College of Cardiology (ACC)/(AHA) guidelines for the management of dyslipidemia and risk assessment generated, in particular, a vigorous debate on the use of pharmacologic therapy for primary and secondary prevention in at risk patients.

Less controversial was the contribution of a variety of lifestyle factors to the development of dyslipidemia and the importance of lifestyle modification in both primary and secondary prevention of CVD. A recent U.S. Preventive Services Task Force guideline recommends intensive behavioral counseling interventions for patients who are overweight or obese and have known CVD risk factors.

It should, however, be noted that nearly half of studies on which this recommendation was based involved interventions requiring more than 360 minutes of interaction with a health care provider over the duration of the intervention.3 That is a tall order when many follow up primary visits are allotted only fifteen minutes.

As a result, there is growing interest in the use of novel strategies to promote effective lifestyle modification. And with smartphones now estimated to be in the pockets of more than half of Americans, leveraging digital health technology has become a particularly appealing strategy.4

In the 2013 AHA/ACC guidelines on lifestyle modification, there are two areas to which particular attention is paid – physical activity and diet.5 Here, we will explore some of the innovative digital health technology available to help drive lifestyle modification with a closing comment on the evidence, or lack thereof, for many of these tools.

Physical Activity

There are a growing number of ways in which patients can track their physical activity. A wide variety of wearable devices are entering the market, from the established Fitbit fitness trackers to newer entrants including Microsoft’s Band. These devices include a variety of capabilities including step counting, heart rate measurement, and more.

Some of these wearable devices also infer intensity of activity and distance, most often by using some variation of a step rate and stride length, respectively. While device makers have little incentive to validate their devices, investigations demonstrating the accuracy (or inaccuracies) of these wearable devices have been published.6

In addition, many smartphones now include embedded functionality that can provide a great deal of activity data, such as the popular Apple iPhone and Samsung Galaxy, using embedded accelerometers, gyroscopes, GPS, and barometers. Data for the accuracy of these embedded tools is even sparser than that for wearable devices.

There are a number of strategies in which these devices are being used by clinicians and patients to promote physical activity. The simplest is suggesting that the patient use a pedometer to increase basal activity level to some target, relying on the impact of clinician counseling and biofeedback from the pedometer.

Many smartphone applications and websites offer embedded functionality to motivate increased activity as well, such as reminders pushed to the user’s smartphone when they are falling short of their target step count. Several smartphone applications also allow users to share their activity level with their social network, attempting to leverage this group to motivate their own behavior change.

Some applications have even gone so far as to create financial incentives and penalties around physical activity targets. Pact Health, for example, asks users to set exercise goals; not meeting that goal leads to a financial penalty while meeting the goal leads to a financial reward, the latter being derived from the penalties paid by others.7


There are similarly a number of options available to patients to aid in dietary tracking. Popular smartphone applications and websites like MyFitnessPal and LoseIt aim to modernize the traditional food diary. These smartphone applications enable users to track calories as well as other nutritional values of the food they eat. By linking to nutritional databases containing data on millions of foods, these applications can help simplify the task of nutritional tracking. For example, these applications can use the smartphone’s camera to scan a package barcode or a QR code associated with a menu item to automatically important nutritional data.

Another interesting approach is the use of social media to help inform nutritional choices. The Eatery smartphone application, recently acquired by popular personal health device company Jawbone, has users take pictures of their plate, rate its nutritional quality, and share it with other users who similarly rate and provide feedback on food choices. One small study found that this crowd-sourced feedback was comparable to that provided by trained dieticians.8

Both of the above-described strategies rely on the user to provide information about their diet in some way. An alternative strategy could leverage passively collected data to inform assessment of diet and subsequent intervention. BagIQ is an interesting startup that uses passively captured data from consumer loyalty programs like those available at most grocery stores to assess users’ food choices and make recommendations on healthier selections.9

Supporting Evidence

While the technology described above certainly presents some enticing opportunities for effective lifestyle modification, it is important to acknowledge the relative paucity of data supporting their efficacy. As these devices fall outside the purview of FDA oversight, there is little incentive by device companies to fund studies evaluating their devices. Additionally, the standard life cycle of a traditional randomized controlled trial is likely to see the tested technology become outdated by study completion. Finally, it is important to note that most studies evaluating these types of technology occur in the context of more complex interventions; as such, separating the contribution of specific facets of the intervention can be quite challenging.

For example, a 2007 meta-analysis of studies evaluating the use of pedometers found a positive effect, with pedometer users increasing their basal activity levels by approximately 25%.10 It should be noted that most of those studies would predate the development of the modern smartphone as well as contemporary activity trackers. A more recent Cochrane Center systematic review of the use of pedometers in workplace interventions found insufficient evidence, identifying only four studies for inclusion; analysis was limited by significant heterogeneity between the studies.11 More recent smaller studies using contemporary devices have generally found conflicting results.

A study evaluating the smartphone application MyFitnessPal found no difference in weight loss between patients utilizing the application and those who did not.12 It should be noted, however, that the intervention was simply recommending the application to patients and that the trial found high dropout rates in the intervention group by the first month.


As described in the 2010 AHA Scientific Statement on promotion of physical activity and dietary modification, there are several strategies with which clinicians and patients can drive positive lifestyle changes. For example, specific goal setting, self-monitoring, feedback and reinforcement, and frequent contact are all cited as important strategies in motivating lifestyle modification. 

 Digital health technology leveraging the growing availability and functionality of consumer mobile devices presents some exciting opportunities for driving the lifestyle changes using several of these strategies. It should be recognized, however, that there is no application or device that will provide a universal solution to achieving this lifestyle change. Assessment of an individual patient’s motivations, health beliefs, and resources are critical in creating an individualized lifestyle intervention, one that may include some or none of these emerging tools.


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  8. Khawar W, Misra S. Crowdsourced feedback on meals provides accurate feedback on dietary choices  (iMedicalApps website). 2014. Available at: http://www.imedicalapps.com/2014/09/crowdsourced-feedback-meals-provides-accurate-feedback-dietary-choices/. Accessed 12/14/2014.
  9. Misra S. Use data from customer loyalty programs to inform better food choices  (iMedicalApps website). 2014. Available at: http://www.imedicalapps.com/2014/10/bagiq-diet-app/. Accessed 12/14/2014.
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  11. Freak-Poli RL, Cumpston M, Peeters A, Clemes SA. Workplace pedometer interventions for increasing physical activity. Cochrane Database Syst Rev 2013;4:CD009209.
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  13. Artinian NT, Fletcher GF, Mozaffarian D, et al. Interventions to promote physical activity and dietary lifestyle changes for cardiovascular risk factor reduction in adults: a scientific statement from the American Heart Association. Circulation 2010;122:406-41.

Keywords: Actigraphy, Biofeedback, Psychology, Biomedical Technology, Cardiovascular Diseases, Cell Phone, Counseling, Diet, Dyslipidemias, Financial Management, Food Habits, Heart Rate, Life Style, Motivation, Motor Activity, Nutritionists, Nutritive Value, Obesity, Overweight, Risk Assessment, Risk Factors, Social Media, Social Support, Weight Loss

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