Mobile and wearable devices and applications have led to changes that positively affect patient behavior, not only for patients, but also for providers. Insurer with care and opportunities. But the surprising results regarding the effectiveness of personal and non-personal products mean that there is no clear way to remind patients to use healthy behaviors. This suggests that providers should experiment to find out what works best with patients.
Over the years, many companies have marketed wearable devices and mobile applications that can track our personal health data. These “MHealth” devices and applications led to the birth of so-called “self-calculations” – a phenomenon in which individuals begin to monitor their behavior, physiology, biology, and other types of health scores. An important question of interest in this ecosystem has not been answered until recently: is there scientific evidence that consumer adoption and use of these wearable devices and mobile healthcare applications have indeed led to a clear change in their behavior, and Are decisive health care decisions that they may reflect? This is a question my colleagues and I are asking in a recent study.
The first study of this type uses data from key stakeholders (digital application sites, hospitals, clinics, physicians, nutritionists, pharmacists, etc.). Emerging technologies mHealth effectively motivate people to change their lifestyles, thus reducing hospital visits and medical costs over time. MHealth’s relatively new field includes mobile computing, medical sensors, and communications services that are used for health services (eg, management of chronic diseases). MHealth applications can run on smart phones, tablets, sensors, and cloud-based computing systems, which all collect personal health data.
In collaboration with a large MHealth utility site in Asia, we designed a large-scale randomized field trial based on extensive patient lifestyle activities (eg, walking, training time and calorie expenditure, sleep patterns and food quality and quantity) And implemented. Blood sugar values within 15 months from patients with chronic diabetes. Randomization allowed some patients to access mHealth applications, web-based versions of some applications, and the rest (control panels) without access to any applications or devices.
Acceptance of MHealth use has led to improvements in both short-term measurements (such as lowering patients’ blood sugar and glycated hemoglobin levels) and long-term measurements (such as reducing hospital visits and medical expenses). ) Belongs to. Patients who accepted using MHealth consumed more calories, ate fewer calorie-rich foods, walked more steps, and slept more every day.
Some other interesting findings consider the effect on patients in the group who used the MHealth app via SMS compared to patients receiving personalized reminders via SMS. An example of a personal reminder is: “Dear Mr. **, you did not exercise yesterday. Now take a 45-minute walk because it will help control blood sugar levels.” A general reminder to the contrary may say: Is Regular exercise at moderate intensity is beneficial to control blood sugar. “
Such general news may be 18% more effective with general guidance on diabetes than individual reports in reducing glucose levels over time. Studies conducted after the trial provide an explanation: Some patients found the accuracy of individual messages to be intrusive and annoying, and some said they felt compelled to follow up. Health recommendations, which were initiated and led to lower levels of health activities (eg, less exercise, less healthy eating habits and less sleep at night).
Our randomized trials suggest that personalized messages can be very effective in reducing individual physician visits and converting them to telehealth services compared to general news. Experimental content studies have shown that the accuracy of personalized messages has, in fact, made it easier for patients to use the telehealth services used on the platform. Thus, they replace their offline physician contacts with those online and reduce their overall medical expenses. This is the silver lining of customization.
Our findings have several implications:
First, our study suggests that users of MHealth devices and applications may be more autonomous and are motivated to adjust their health behaviors and be more focused and consistent in their behavior. Lifestyle behavior and well-being, which leads to better health outcomes. This implies that government and private insurers and technology companies would benefit from subsidizing the prices of these devices to encourage their use. In fact, Apple has recently partnered with medical program providers to help watches for the elderly.
Second, adaptation is a double-edged sword. On the one hand, it leads some patients to reduce contact with wearable techniques and reduce their well-being behavior. On the other hand, adaptation accelerates the increased use of telemedicine in patients, reducing medical costs. Health ecosystem trainers will benefit from taking these effects into consideration when designing their communication strategies. For example, they may conduct tests on local populations or market research to assess the impact of adaptation on patients’ preferences for individual and telehelm counseling. By requesting feedback on patients’ preferences, they can predict that person’s net gain