Having a clearer idea of how these apps and devices do and don’t change behavior can help health care organizations better strategize how to create better care for their patients.
In the past few years, a number of companies have marketed wearable devices and mobile apps that can track our personal health data. These “mHealth” devices and apps have led to the birth of what is known as the “quantified self” — a phenomenon where individuals start tracking their behavioral, physiological, biological, and other kinds of health markers. A key question of interest in this ecosystem remained unanswered up until recently: Is there any scientific evidence that consumer adoption and usage of these wearable devices and mobile health apps actually leads to a tangible change in their behavior, which, in turn, can show up in concrete health care outcomes? This is the question my coauthors and I investigated in a recently published paper.
This first-of-its-kind study uses data from major stakeholders (digital app platforms, hospitals, clinics, doctors, nutritionists, pharmacists, and so on) to examine whether emerging mHealth technologies effectively persuade people to modify their lifestyles and thereby reduce hospital visits and medical expenses over time. The relatively new area of mHealth includes mobile computing, medical sensor, and communications technologies used for health care services (e.g., managing chronic diseases). mHealth applications can operate on smart phones, tablets, sensors, and cloud-based computing systems, all of which collect health data on individuals.
In partnership with a major mHealth app platform in Asia, we designed and implemented a large-scale randomized field experiment based on detailed patient lifestyle activities (e.g., steps walked, exercise time and calories spent, sleeping patterns, and food quality and quantity) and blood glucose values from chronic diabetes patients over a 15-month time frame. The randomization involved some patients having access to the mHealth app, some having access to web-based version of the app, and the rest (the control group) not having access to any of these apps or devices.
The adoption of the mHealth app led to an improvement in both short-term metrics (such as a reduction in patients’ blood glucose and glycated hemoglobin levels) and longer-term metrics (such as a reduction in hospital visits and medical expenses). Patients who adopted the mHealth app undertook higher levels of exercise, consumed healthier food with lower calories, walked more steps, and slept longer on a daily basis.
Some other interesting discoveries concerned the outcomes of patients in the group that used the mHealth app who received personalized reminders via text messages vs. those of patients who received generic reminders. An example of a personalized reminder would go like this: “Dear Mr. XX, you did not exercise at all yesterday. Take a 45-minute walk today as it will help control your blood glucose levels.” In contrast a generic reminder might say: “Regular exercise at moderate intensity is very helpful for controlling blood glucose.”
Such generic messages with generalized guidance about diabetes were 18% more effective than personalized messages at reducing glucose levels over time. Surveys conducted after the experiment offered an explanation: Some patients found the accuracy of the personalized messages to be intrusive and annoying, and some said they made them feel constantly coerced to follow the wellness recommendations, which demotivated them and led to a lower level of wellness activities (e.g., less exercising, fewer healthy eating habits, and shorter sleeping durations at night).
That said, our randomized experiments demonstrated that compared to generic messages, personalized messages were more effective in reducing in-person doctor visits and replacing them with telehealth services. Post-experimental surveys of the experimental subjects revealed that the accuracy of these personalized messages, in fact, made patients comfortable with adopting telehealth services deployed by the platform. Thus, they were substituting their offline physician interactions with online ones, reducing their overall medical expenses. This was a silver lining of personalization.
Our findings have several implications:
First, our study shows that users of mHealth devices and apps can became more autonomous and more motivated in self-regulating their health behavior and more engaged and consistent in their lifestyle and wellness behavior, which leads to improved health outcomes. This suggests that it would be worthwhile for government and private insurers and tech companies to subsidize the prices of these devices in order to encourage their use. Apple, in fact, has been recently collaborating with Medicare plan providers to subsidize its watches for the elderly.
Second, personalization is a double-edged sword. On one hand, it leads to some patients reducing their engagement with wearable technologies and reducing their wellness behaviors. On the other hand, personalization also facilitates an increased usage of telemedicine among patients, which, in turn, leads to lower medical expenses. Practitioners in the health care ecosystem would benefit from keeping these countervailing effects in mind when designing their communications strategies. For instance, they could run experiments or conduct market research on their local populations to examine the effect of personalization on patients’ preferences for in-person vs. telehealth consultations. By soliciting feedback on patients’ preferences, they would be able to predict the net benefit of personalization and adjust the frequency of personalized communications accordingly.
Third, mHealth devices and apps could provide health insurance companies with an opportunity to personalize premiums. They could allow them to reward consumers who make the effort to exercise more often, eat healthier, and sleep longer with lower insurance premiums. This would be similar to what some auto insurance companies are already doing: placing tracking devices in cars to monitor driving behavior and then rewarding better drivers with lower premiums.
That said, such a strategy poses some potential problems or challenges. HIPAA’s privacy rules mean that patients would have to agree to give health insurers companies access to their data. And rewards based on healthier eating habits and lifestyles could end up rewarding the rich and penalizing the poor, which would be wrong. Even so, having a clearer idea of how these apps and devices do and don’t change behavior can help health care organizations better strategize how to create better care for their patients.
Copyright 2022 Harvard Business School Publishing Corp. Distributed by The New York Times Syndicate.