Welcome to Patient-Centered Care Activated Patients. This is Lecture b. In this lecture, the Quantified Self and mHealth, we will learn more about activated patients through the usage of mHealth and wearable technologies. The objective of this unit, activated patients are to bring a global perspective to Do-It-Yourself or DIY medicine, and describe the factors influencing it's expansion. Discuss the impact of DIY medicine on both clinical practice and clinical research. Discuss the potential promise and peril of the changes that DIY will bring to healthcare. And discuss the role of the Quantified Self and mobile health applications in person-centered care. Quantified Self is a movement to incorporate technology into data acquisition on aspects of a person's life in terms of inputs, such as food consumed, quality of surrounding air. States such as mood, arousal, blood oxygen levels, and performance, mental and physical. Self monitoring often combines wearable sensors and wearable computing for data collection. It may be conjoined with gamification approaches, that allow everyday activities to be turned into games. Rewarding points to encourage people to compete with friends. It is, in many ways a natural extension, of the trend of patients taking more responsibility for their own health. And reflects patients now collecting data, that may have been previously collected by health professionals. The combination of wearable sensors and wearable computing has been known by many names over time. Life logging, self-tracking, auto-analytics, body hacking, self-quantifying, self-surveillance, and personal informatics are all forms of the quantified self using wearables. Patients often collect data using self quantification devices in the service of DIY medicine. Patients may use these data to monitor a chronic illness and to ascertain the impact of their daily activities on such an illness as they seek subtle clues on how to improve their functional status and quality of life. Such activated patients often seek to share and discuss these data with their clinicians. Yet, in an industry where standardized practices are still being formed, it is sometimes difficult for clinicians to engage with patients on the optimal way to interpret and act on their data Data that are collected by wearable sensors or data that are logged manually can be analyzed by researchers using traditional techniques such as linear regression to establish correlations among the variables. In essence, these innovative technologies are applying quantitative methods used in science and business to personal health data, often with the goal of detecting opportunities for health improvement. Data visualization techniques can also suggest hypotheses that can be tested via more vigorous methods. It is estimated that nearly 60% of US adults track at least one health metric. Mobile health application adoption doubled from 2013 to 2015 in the PWC Health Research Institute consumer survey. In regard to sharing data 83% of respondents were willing to share data to aid in diagnosing and treating themselves. While 73% were willing to do the same to aid others. Shifting attitudes to data tracking and sharing, and readily available wearables connected to networks, smartphone or otherwise, empower this growing movement. With the current technologies, there are multiple aspects of human activities that can be tracked and monitored. We will now explore some facets of the quantified self movement, and currently available tools and biosensors across the following domains. Mood, activity, sleep, biological and other. There are applications that allow patients to track their mood for a variety of reasons. There's a spectrum of users from those seeking to understand or analyze factors that influence their mood for self-discovery, to those diagnosed with mental health disorders like depression. Several apps allow users to journal about their mood, either by answering questions or logging data. Many allow users to share mood data over social media, and even have some interactions with other users. For example, Mood Panda allows for sharing of virtual hugs. Some apps seek to pull user data to glean insight into factors associated with happiness with a goal of improving this outcome in the general population. There are many of these apps available some examples include Track Your Happiness, MoodPanda, Moodscope, Moodjam, and Optimism, among others. Activity trackers are wearable devices that monitor and record a person's fitness activity and biologic response. Sensors are used to calculate mileage, caloric expenditure, physical activity, heart rate, temperature, and so forth. Sensor examples include pedometers for counting steps, accelerometers to detect acceleration, altimeters, which measure the altitude of an object above a fixed level, and GPS for geolocation. Many commercial devices for sale include companion apps to track, visualize, analyze and share data. Gamification may also be used to promote competition, with the goal of enhancing use. Available examples include FitBit, Amiigo, Jawbone, Strava, RunKeeper Pebble and Apple Watch. The overall goal of sleep monitoring sensors is to monitor and analyze sleep patterns providing insight into ways to optimize sleep and waking. Multiple products that use different approaches, from smart phone sensors to separate alarm clocks to monitor sleep cycles have been utilized. Several products have been discontinued like Zeo and Wake Mate, while sleep-specific apps continue to exist, such as SleepBot and Sleep Cycle. And sleep monitoring has been included in many activity trackers. For example, Fitbit. Biological tracking can involve any physiological output as long as there are sensors on the devices that can capture relevant data. Data can be captured with components available on smartphones as part of independent sensors or as unique combinations of both. Some common biological tracking apps collect data on heart rate, blood pressure and weight. Cardio uses a smartphone's camera to detect heart rate. With each heart beat, more blood is pumped into our faces, which changes how much light is absorbed and reflected back to the camera. These subtle changes are detected by the app and converted to an accurate heart rate. Emwave2 is a separate device with sensors for respiratory and heart rate, that allow their correlation with stress and encourage interventions to decrease it. IThlete uses a chest strap or finger sensor, that transmits data over Bluetooth to the device that runs the app. To visualize heart rate and help with training, Withings combines a watch which tracks activity and sleep and the scale. Data are wirelessly transmitted to a smartphone, the Withings app visualizes the data and allows sharing of data with friends. MyFitnessPal is an app where one can easily enter foods consumed. It keeps track of prior entries, and builds lists over time to facilitate subsequent data entry. Using data from MyFitnessPal, or any such associated caloric intake and weight tracking app allows users to quantify, monitor, and share their caloric intake and promote weight loss. There are also multiple podcasts and web communities such as the quantified body dedicated to self quantification of different diets and DIY approaches to weight loss and improving health. Many examples exist of remote monitoring of chronic diseases such as congestive heart failure. Data are typically gathered by sensors at the patient's residence and communicated wirelessly to a central location where the data are visualized by providers who are sometimes alerted to issues via built-in clinical decision support. Such systems may spread further, as the shift to accountable care organizations expands the emphasis for effective home monitoring solutions for early detection or exacerbation of chronic illness. The opportunities for quantifying different facets of our lives will only grow as new sensors to collect new types of data become available. Some companies are providing quantification without sensor devices. For instance, there has been growing interest in research in the microbiome. The microbiome refers to the genome of the microbial population of parts of our anatomy such as the nose, gut, genitals and so forth, these microbes may perform essential functions. For example, in the gut they digest food and synthesize vitamins. uBiome allows sample swabs to be mailed in. Additional survey data collected the company sequences the users microbiome and users can compare their data to others. LumoLift has a sensor that attaches to clothing near the collarbone for postural and activity monitoring. It vibrates if the person wearing it is slouching to remind them to sit differently. HapiFork includes sensors in a fork to provide feedback for eating speed. And, Belty is a belt buckle sensor that adjusts at mealtime and signals for activity after long sedentary periods. What is interesting about these devices is they both track data and provide feedback in prompts based on the data. Self quantification opens the door to home follow up at a hit or to unimaginable scale. Patients may capture and share more information than ever before on how their condition is doing at home. Home monitoring technologies enable patient-centered care with previously unavailable monitoring, early detection and intervention possibilities that improve patient morbidity. That is, less severe exacerbation's of chronic illness, cost and potentially mortality. A looming challenges how to integrate these devices into clinical care in a non-disruptive manner. How do we integrate these data in a way that it does not cause alert fatigue which may lead to clinicians ignoring important alerts. How do we incentivize the uptake of these technologies? And how do we determine and establish fair compensation for clinician time to review the data. On anticipated impact on patients must also be considered. Will those who complete newly monitored home goals such as compliance with exercise or diet. Be three of the same by their insurance providers or will failure to meet this goals lead to financial penalties. If patients are going to be using these devices, and potentially sharing the data with their clinicians, one needs assurance that the data are accurate. Unfortunately, self quantification experiments lack the rigorous controls and methodology of traditional research. Such controls might help mitigate placebo effects, which could be a significant factor. Scant data exists comparing these devices and their accuracy. Chase and colleagues published a study in Jama in 2015, that showed the variability in step count trials for 500 and 1500 step trials across various commercially available wearable devices and smartphone apps. They found consistency between the 500 and 1500 step trials, and generally found the smartphone apps to be very close to the observed step count. However, the wearable devices showed the most variability. Standard for scientific validation and comparisons of these technologies are not fully developed and our needed to identify best practices. Increasingly, randomized control trials to validate them are underway. One of the major problems is that most of these technologies exist independent of electronic health records, and cannot easily be imported into them. Even if the clinician and patient were interested in having them be part of the record. In addition, even if the raw data could be imported, there would also need to be good visualization tools within the EHR to make the data interpretable. Although, the devices and apps often have such tools, they may not transfer to the EHR. Their integration into the larger Health IT ecosystem and bidirectional data exchange between patient and providers remains limited and is an opportunity to maximize the impact of these tools and involve individuals more closely in their care. Recent interest in standardizing the APIs of the major electronic health records systems, may make it easier to incorporate these data. APIs are the application programming interfaces which can allow different apps to interface with the EHRs. Another challenge relates to the digital divide. Significant sections of the population either do not have access or do not have the health and computer literacy to use these devices. How will we bring the benefits of these new technologies to economically disadvantaged populations who may not have access? Well, the digital divide usually refers to disadvantaged populations the elderly are another population group that can benefit greatly. Yet, may not use the devices in apps for self monitoring. How do we bring the benefits of these technologies to elderly patients who often lag in regards to their adoption when compared to younger Americans? Once in the hands of users how will we ensure long term user engagement in the capture and sharing of data. Gamification has been proposed as one strategy, but we need data to further understand how to maximize longitudinal utilization. This concludes lecture b, the quantified self and M health. In summary, the quantified self refers to self-tracking of activities through a combination of wearable devices and wearable computing. There are growing number of applications to measure a wide variety of body functions. Data captured in this fashion can be used by individuals, clinicians, and health care systems, to optimize care and health management. Many challenges remain, including establishing robust methodologies to validate these technologies, the bidirectional exchange of data with the EHRs and establishing how to bring the benefits of these technologies to all individuals. Regardless of socioeconomic or other barriers to adoption.
