Digital Health Sensing for Personalized Dermatology

The rapid evolution of technology, sensors and personal digital devices offers an opportunity to acquire health related data seamlessly, unobtrusively and in real time. In this opinion piece, we discuss the relevance and opportunities for using digital sensing in dermatology, taking eczema as an exemplar.

in machine learning algorithms are now able to differentiate and identify these with increasing sensitivity and specificity [6]. The precision and number of personal digital sensors not only allow for non-invasive collection of activity markers, including itching and sleep, but also evaluation of their severity and aggravating factors such as temperature, medications, locations, etc., and they provide real-time insights into responses to treatment. A recent study reported that these sensors had an almost 90% accuracy for detecting scratching [6], which may improve over time due to more data points. This combined with, for example, microphone, temperature, air quality and other sensor data will give a new dimension to the precision of assessment of scratching and other symptoms. Most fitness wearables also couple actigraphy with heart rate measurement. As eczema is a stress-responsive disorder that involves autonomic nervous system dysfunction, heart rate variability could also indicate itching in individuals with eczema. Subjects exhibit an overactive sympathetic response to itching and scratching, while the parasympathetic tone is persistently and rigidly elevated, showing a lack of adaptability in response to stress.
Sleep disturbance, a common consequence of scratching and often an indicator of eczema severity, could also be measured through actigraphy. A lack of sleep, is known to have negative effects on mental, physiological, emotional and social well-being. Beyond actigraphy and wrist worn devices, studies and several commercially available applications have also experimented with utilizing smartphone data to estimate sleep patterns, with varying degrees of success. These leverage off smartphone sensor data including a combination of device "lock" duration, activity via accelerometers (stationary time), microphone (ambient sounds), and camera (ambient light) to approximate the amount of sleep [7]. The benefit of using a person's phone is the ability to measure without the need or expense of a separate device. However, they rely on the user having the phone next to them during sleep. Advances in IoT further expands on the availability of sensors and provides another means of monitoring. Sensors are being incorporated into everything from appliances to clothing to diapers. Major consumer diaper companies are now venturing into smart diapers to quantify not only babies sleep but also their bowel movements and vital signs [8]. These consumer devices including baby monitors with sound and movement detection, could potentially also be used to detect both itching and sleep. However, regardless of the device or methods used to acquire patient data, this approach to assessment requires validation against a gold standard or validated patient scoring metric. Although there is no globally accepted standardized diagnostic criteria for eczema [9], there are a number of validated instruments for the assessment of severity including the Eczema Area and Severity Index [10] and the Severity Scoring of Atopic Dermatitis Index (SCORAD) [11] which could be used (see Figure 1). in machine learning algorithms are now able to differentiate and identify these with increasing sensitivity and specificity [6]. The precision and number of personal digital sensors not only allow for non-invasive collection of activity markers, including itching and sleep, but also evaluation of their severity and aggravating factors such as temperature, medications, locations, etc., and they provide real-time insights into responses to treatment. A recent study reported that these sensors had an almost 90% accuracy for detecting scratching [6], which may improve over time due to more data points. This combined with, for example, microphone, temperature, air quality and other sensor data will give a new dimension to the precision of assessment of scratching and other symptoms. Most fitness wearables also couple actigraphy with heart rate measurement. As eczema is a stressresponsive disorder that involves autonomic nervous system dysfunction, heart rate variability could also indicate itching in individuals with eczema. Subjects exhibit an overactive sympathetic response to itching and scratching, while the parasympathetic tone is persistently and rigidly elevated, showing a lack of adaptability in response to stress. Sleep disturbance, a common consequence of scratching and often an indicator of eczema severity, could also be measured through actigraphy. A lack of sleep, is known to have negative effects on mental, physiological, emotional and social well-being. Beyond actigraphy and wrist worn devices, studies and several commercially available applications have also experimented with utilizing smartphone data to estimate sleep patterns, with varying degrees of success. These leverage off smartphone sensor data including a combination of device "lock" duration, activity via accelerometers (stationary time), microphone (ambient sounds), and camera (ambient light) to approximate the amount of sleep [7]. The benefit of using a person's phone is the ability to measure without the need or expense of a separate device. However, they rely on the user having the phone next to them during sleep. Advances in IoT further expands on the availability of sensors and provides another means of monitoring. Sensors are being incorporated into everything from appliances to clothing to diapers. Major consumer diaper companies are now venturing into smart diapers to quantify not only babies sleep but also their bowel movements and vital signs [8]. These consumer devices including baby monitors with sound and movement detection, could potentially also be used to detect both itching and sleep. However, regardless of the device or methods used to acquire patient data, this approach to assessment requires validation against a gold standard or validated patient scoring metric. Although there is no globally accepted standardized diagnostic criteria for eczema [9], there are a number of validated instruments for the assessment of severity including the Eczema Area and Severity Index [10] and the Severity Scoring of Atopic Dermatitis Index (SCORAD) [11] which could be used (see Figure 1).  risk factors or exposure plays a substantial role in managing the disease. Environmental factors could be acquired passively, e.g., time of year/season, temperature, humidity, pollen and air pollution as well as location recorded through smartphones, either via sensors, e.g., GPS, Wi-Fi, barometer, etc., or online real-time sources, e.g. weather station reports. Other risk factors could be acquired actively through patient questionnaires. Diaries may be both text-based and visual, i.e., taking images of lesions, meals, medications, etc. In the short term, these images could serve as a visual reminder or reference for the user to evaluate against their objective score. However, with advances in image recognition, future applications may allow for the automatic evaluation of content.
There are important considerations regarding the quality of all this data. Data variability has two sources, including differences in hardware and software (e.g., lack of standardization of sensors or signal processing algorithms) and variability in human behaviour. Sensors in smartphones can vary between manufacturers, models and versions and according to participants' preferences and behaviours. How people carry their smartphone (pocket, handbag or backpack) can profoundly impact the sensor data, for example, accelerometer movement recordings. Thus, this data needs to be captured in raw format and individualized. Another consideration is user compliance or retention.
To capture high quality longitudinal data, the user will need to continuously engage with their device or application.
A number of open source platforms have been developed to do this, including Beiwe [12], LAMP (Learn Assess Manage Prevent) [13] and RADAR-base (Remote Assessment of Disease And Relapses) [14]. Although these platforms were primarily developed for research purposes with mental health in mind, they all perform the same function of amalgamating passively collected sensor data with actively acquired user data from questionnaires and tests to provide a comprehensive picture of an individual's state of health. These platforms could be leveraged for any condition including eczema. The benefit of these insights is evident to practitioners or researchers, however from a patient's perspective the value is not as translatable or easily understood. In order to ensure compliance and user participation, these platforms need to provide feedback or engage users through insights, educational outputs or interventions that encourage retention.
The capacity to measure, capture and interpret multiple sources of data using personal devices and sensors opens up new opportunities for preventative health management. To date, this has been explored within the realm of mental health. However, the long-term nature and frequent need for ongoing monitoring of skin conditions provide an opportunity to develop personalized, patient-centered care delivered through digital devices, assisted by artificial intelligence.