Wearable Health Trackers: Better behaviors, or Fashion fads?

May 25, 2016

Fitbit_Charge_HRBy David Valentine, MD

Peer Reviewed

Currently, well over one third of US adults use at least one health-related online service or app, with almost half of those focused on physical activity 1. With the growing popularity of wearable health tracking devices such as the Fitbit, Nike Fuel, Jawbone and more, the prevalence of these technologies is only set to grow. However, while more and more people know more and more about their health and habits by the day, little is known about perhaps the most important aspect of these wearables: do they actually promote positive change in the behavior of their users?

Much has been said about the techniques that health related apps and fitness trackers employ to monitor and promote a healthy lifestyle. Generally, they fall into the category of educational or motivational, or something in-between 2, and utilize between 9 and 12 different techniques of behavior change; these include instruction, demonstrations of how to perform a given action or exercise, feedback on performance or progress, goal-setting for a desired outcome, and social support from other users of the app 2. Notably, however, the majority of apps currently available do not include steps for action planning, which is among the most well-established method to effect lasting behavioral change 3,4.

The use of wearable monitors adds a new layer of complexity and opportunity to such traditional fitness apps, via self-monitoring, instant feedback and environmental input. Many of the accompanying apps or online portals for these wearables utilize the same techniques to effect behavioral change that traditional phone-based apps do, but there are some notable differences. Wearables, for instance, tend to follow social cognitive theory—which states that behaviors can be changed by observing a model of that behavior with subsequent results—more than phone apps, and employ a greater number of behavior change techniques, such as real-time feedback and social comparison, to more closely match traditional clinical interventions. They also allow for improved feedback on discrepancies between goals and actual behaviors due to the greater real-world data about such behaviors 5. Furthermore, because of historical trends like steps per day or heart rate, wearables can also reward for past successes, like achieving a certain threshold of steps per day or sleeping for a certain period of uninterrupted time, or prompt one towards activity in periods of prolonged sedentariness.

Despite the abundance of data that wearables collect and then present in charts, tables and summaries, there is a large difference between recording data and acting on it. Thus, the question remains: do people actually change behavior based on a wearable device? A 2012 study sought to answer this question, and broke 51 overweight or obese subjects down into equal groups of in-person, in-person plus wearable, and wearable-only interventions toward health tracking and activity promotion 6. They found that while all groups showed improvement in weight loss, body fat measurements, cardiorespiratory fitness and dietary changes, there was no significant difference in these changes across the groups.  What this suggests is that wearable devices are able to match the efficacy of traditional clinical interventions for weight loss and other positive health changes, at least in the controlled setting of a clinical trial, but may not be superior to current approaches.

Given the above results, a 12-week trial sought to determine if wearables’ efficacy could be improved upon by adding an interpersonal component to health tracking. Investigators looked at the changes in weight, leisure time activity and diet of 58 participants, split into three groups; all groups received regular in-person counseling sessions, but one group also used a wearable monitoring device 25% of the time while another group used this device 100% of the time. While differences in absolute weight loss (in kilograms) were not significant across groups, there was a statistically significant difference in relative weight loss (based on percent lost from starting weight) across groups, with the continuous monitoring group losing far more weight than the other two groups (4.6+3.2% in-person, 3.8+3.8% intermittent, and 7.1+4.6% continuous) 7. Notably, leisure time and energy intake did not significantly differ between the groups, and self-monitoring of eating and exercise decreased with time across all groups.

It seems, therefore, that wearables can match and, possibly, improve upon the efficacy of traditional interpersonal counseling for health and behavior changes. However, they suffer from a dropoff in use that could limit their potential utility. In a survey of over 6000 people, over half of those who bought a wearable device stopped using it over time; a third of these stopped within half a year 8. Part of this falloff may be due to the fact that wearables are typically bought by the people who need them least: young (mostly less than 35 years old), affluent (making over $100,000 per year) and relatively healthy individuals 9. In this population, wearables are most often used to optimize pre-existing fitness regimens rather than begin new ones, and it is possible that once such optimization is achieved, there is less use for them8. As such, broadening of the target markets for wearables might allow for more sustained and dramatic improvements in health and fitness due to their use, but this depends on improvements being made to both the accessibility (mostly via price) of wearables and their consistent use.

While not a study into wearables per say, investigators recently looked into how monetary payment could enhance health improvements due to technology use, by taking 75 patients and splitting them into a control group, a low incentive ($1.40/day) group, and a high incentive ($2.80/day) group 10. Participants used three devices per day—a blood pressure cuff, glucometer, and scale—along with a transmitter to send their measurements to an online server for tracking. While all three groups started with approximately equal levels of adherence, by the end of the tracking period (3 months), the incentive groups showed significantly higher adherence than the controls (81% for low incentive, 78% for high incentive, 58% for control). Interestingly, there was no significant difference between the two incentive groups. This does not mean, however, that a difference in payment does not cause a difference in behavior; rather, in the three months after financial incentives were withdrawn, adherence in the high-level group dropped to levels equal to the control group—around 30%—whereas adherence in the low incentive group remained significantly higher at over 60%. These findings suggest that financial incentives can help to drive behavioral change, but only if the incentive does not outweigh the behavior itself—for the high group, it seemed that the higher dollar amount was more important than healthy changes, whereas the lower dollar amount was enough to drive change but not so much as to supplant its importance.

While the use of financial incentives on a mass scale may be overwhelming at first thought, studies suggest that the cost of such promotions may be covered by consequent cost savings on a population level. A 2014 trial enrolled 74 diabetic patients to participate in an automated messaging system of prompts and questions, sent to their cell phones, pertaining to their diabetes management 11. Patient responses were screened automatically, and anything out of the ordinary range of responses was viewed by a nurse and followed up with the patient. It was found that healthy eating, monitoring of blood glucose, foot care and adherence to medications all improved significantly over the 6-month course of the trial, saving an estimated $30,000 in that time.

The above evidence is not perfect—as previously stated, many studies have focused on populations with chronic disease, while most users of health trackers are relatively healthy, and many previous studies have used only cell phone apps or messaging services. Still, existing data suggests that while wearable health trackers may be an effective tool for weight loss, improved physical activity, and chronic disease management, it is difficult to maintain use of such devices and ensure access for populations that could most benefit from them. Furthermore, while the goal of a wearable device would be to create a new internal motivation for good health rather than depend on long-term external motivators, the lack of information about consequences of behavior, or action planning towards change, might make this difficult12, and results may depend on a supplemental interpersonal interaction or other form of incentive. Still, with big data becoming increasingly manageable and prolific both in and outside of the medical realm, the opportunities that more nuanced and continuous data about patients can afford providers and other caretakers must continue to be investigated.

Dr. David Valentine is a Medicine Internist at NYU Langone Medical Center

Peer reviewed by Neil Shapiro, MD, Editor-In-Chief, Clinical Correlations

Image courtesy of Wikimedia Commons


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