Why am I dissatisfied with health devices and applications?

After  2 months of initial excitement with my smart band and related apps, I have stopped wearing the device after. Seems I am not alone in my frustrations: a new study from the Journal of Medical Internet Research (JMIR) shows that even patients who do see value in smartphone and tablet apps nevertheless within 6 months stop engaging with the devices. More than forty percent said that tracking and monitoring apps required too much time to input necessary data, while a similar number simply said that their interest faded over time.

Turn tracking tool into a decision tool

DecisionTools

The enthusiasm about consumer health tracking devices and apps is fading as soon as people get really worried about their health. One of my health applications is measuring calories burned during my exercises. It is also telling me how my heart rate is reacting on the workload. But all those data are not helpful in improving my exercise program: should I increase the workload to burn more calories or, on the contrary, decrease it to sustain my heart rate? What is actually good for my health?

Similarly, millions of individuals are monitoring daily their vital signs such as blood glucose level, heart rate and medications with only one selfish hope: improve their own health – and as soon as possible. Alas, those regularly collected and averaged observations still do not suggest how to make personal health results look better. Should I, e.g., increase my beta-blocker from 0.5 to 10.0 mg, switch to another drug or simply move to the country where the climate is better for me?

The majority of today’s health apps are unable to interpret intricate dependencies between the vast spectrum of personal health data to finally build a full picture of their health puzzle – in order to improve the daily habits or medical treatments.

Unfortunately, the developers are still reluctant to add analytical engines to personal health tracking tools encumbering users with the flood of data that create more questions than provide answers. No surprise that after several months of diligently collecting such data, the enthusiasm is fading and users stop downloading the apps.

Ensure data accuracy

Wearable-device-accuracy-300x300

One can surf the Internet mapping one’s symptoms to potential remedies, but at a certain point you want to share your concerns with a professional physician. Frankly speaking, none of the physicians I met has ever asked me about my Personally Generated Health Data (PGHD). Once I have shared with my GP a nice blood pressure graph stored in my phone app. He looked at the picture briefly pulling out with a sigh his obsolete blood pressure analog gauge with a manually inflated cuff, analog dial and a stethoscope for listening to the noise in the brachial artery (hearing the Korotkov sound). I can understand my doctor. As a consumer, I compared the old-fashioned blood pressure analog monitor having manually inflated cuff with modern ones from such vendors as Sanitas, Microlife and Omron. The difference in measurements between all those well acknowledged and certified devices was about 20-25 mm Hg (colloquially points). For a person with a hypotension a difference in 160 mm Hg or 140 mm Hg of systolic arterial pressure means a lot. How can a doctor trust PGHD when even devices that generate such data are not quite accurate and, therefore, trustworthy?

Data incongruity is typical for many health applications. After comparing several blood pressure applications used by my friends, my husband and myself, I have noticed a great disparity in data interpretation. In the application I was using everything below 140 mm Hg systolic was considered to be normal, everything between 140 and 160 on the borderline and everything higher than 160 mm Hg above the norm. Contrary, the application my husband is using regards values above 140 mm Hg as exceeding the norm.

All those applications are following different medical guidance often disregarding age and gender, not considering the previous health history of a user. Sleep monitoring devices and the respective applications is another example. Some of them are measuring the percentage of your sleep efficiency and of actual sleep, others use intervals or divide the night between light and heavy sleep. One can only guess what exact physical parameters the devices are measuring and with what accuracy.

No wonder that our doctors are reluctant to include PGHD in patient’s health records for a danger of being misled by a flow of non-accurate, non-standardized data. Thus according to the Catalyst Insight Council’s study the majority of the physicians surveyed (56%) stated that “remote monitoring using wireless devices/wearables is the least effective patient engagement initiative. There is also a risk that data inaccuracy will provoke a patient’s anxiety resulting in unnecessary visits and calls to physicians who have limited time even for those who need it most.

Allow patients to engage with physicians

Collaboration with wearable-healthcare

I am not among those conservatives who regard health consumer devices and applications as nothing more but a nice Christmas present. On the contrary, I am absolutely convinced that the Internet of Health and Consumer Generated Personal Health Data are the prerequisites for an efficient collaboration of patients and physicians and ultimately leads to the improvement of the contemporary care system. But those who develop consumer health applications and devices have to seek not only FDA and similar approvals. To ensure the quality of data generated from sensors and wearable devices, the developers have to become an integral part of the entire healthcare process, research and engineering. Until that happens, the bridge between consumers and professional health community will be shaky.

So far the successful examples of patient engagement with smart gadgets are normally generated within the professional medical environment and not by consumers. At least physicians can test data in their premises assuming the responsibility for data quality. Thus the Dartmouth-Hitchcock Health System is piloting ImagineCare, a personalized solution that encompasses physical, mental and emotional health. Patient’s data are taken from sensors and devices such as blood-pressure cuffs, pulse oximeters and activity trackers such as Microsoft Band and transmitted via smartphone to the Azure cloud. From there, the data are pulled into a Cortana Analytics Suite dashboard at a “contact center” staffed 24/7 by registered nurses to provide a particular view of each patient’s personalized care plan. The nurse can reach a patient by phone or video chat, and if some of the patient’s data trespass the norm the care plan can be updated together with the physician. Physicians can view the data within the overall context of patient’s health history thus being able to extract the meaningful information from the buzz.

Consumer engagement in healthcare process takes more than simple tracking of various scattered health parameters. Data accuracy, standardization and analysis are the cornerstones one has to consider even before moving to more sophisticated levels of data interoperability and PGHD/EHR integration. Unless this basic homework is done, medical community will be skeptical in opening up their domain to consumer generated health data, while patients will be locked within the abundance of scattered data that will cause more doubts and frustrations instead of brining vital information.

 

 

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