Applying Machine Learning to Daily-Life Data From the TrackYourTinnitus Mobile Health Crowdsensing Platform to Predict the Mobile Operating System Used With High Accuracy: Longitudinal Observational Study

Background: Tinnitus is often described as the phantom perception of a sound and is experienced by 5.1% to 42.7% of the population worldwide, at least once during their lifetime. The symptoms often reduce the patient’s quality of life. TheTrackYourTinnitus (TYT) mobile health (mHealth) crowdsensing platform was developed for two operating systems (OS)—Android and iOS—to help patients demystify the daily moment-to-moment variations of their tinnitus symptoms. In all platforms developed for more than one OS, it is important to investigate whether the crowdsensed data predicts the OS that was used in order to understand the degree to which the OS is a confounder that is necessary to consider. Objective: In this study, we explored whether the mobile OS—Android and iOS—used during user assessments can be predicted by the dynamic daily-life TYT data. Methods: TYT mainly applies the paradigms ecological momentary assessment (EMA) and mobile crowdsensing to collect dynamic EMA (EMA-D) daily-life data. The dynamic daily-life TYT data that were analyzed included eight questions as part of the EMA-D questionnaire. In this study, 518 TYT users were analyzed, who each completed at least 11 EMA-D questionnaires. Out of these, 221 were iOS users and 297 were Android users. The iOS users completed, in total, 14,708 EMA-D questionnaires; the number of EMA-D questionnaires completed by the Android users was randomly reduced to the same number to properly address the research question of the study. Machine learning methods—a feedforward neural network, a decision tree, a random forest classifier, and a support vector machine—were applied to address the research question. Results: Machine learning was able to predict the mobile OS used with an accuracy up to 78.94% based on the provided EMA-D questionnaires on the assessment level. In this context, the daily measurements regarding how users concentrate on the actual activity were particularly suitable for the prediction of the mobile OS used. Conclusions: In the work at hand, two particular aspects have been revealed. First, machine learning can contribute to EMA-D data in the medical context. Second, based on the EMA-D data of TYT, we found that the accuracy in predicting the mobile OS used has several implications. Particularly, in clinical studies using mobile devices, the OS should be assessed as a covariate, as it might be a confounder

Determinants of Longitudinal Adherence in Smartphone-Based Self-Tracking for Chronic Health Conditions: Evidence from Axial Spondyloarthritis

The use of interactive mobile and wearable technologies for understanding and managing health conditions is a growing area of interest for patients, health professionals and researchers. Self-tracking technologies such as smartphone apps and wearabledevices for measuring symptoms and behaviours generate a wealth of patient-centric data with the potential to support clinical decision making. However, the utility of self-tracking technologies for providing insight into patients’ conditions is impacted by poor adherence with data logging. This paper explores factors associated with adherence in smartphone-based tracking, drawing on two studies of patients living with axial spondyloarthritis (axSpA), a chronic rheumatological condition. In Study1, 184 axSpA patients used the uMotif health tracking smartphone app for a period of up to 593 days. In Study 2, 108 axSpA patients completed a survey about their experience of using self-tracking technologies. We identify six significant correlates of self-tracking adherence, providing insight into the determinants of tracking behaviour. Specifically, our data provides evidence that adherence correlates with the age of the user, the types of tracking devices that are being used (smartphone OS and physical activity tracker), preferences for types of data to record, the timing of interactions with a self-tracking app, and the reported symptom severity of the user. We discuss how these factors may have implications for those designing, deploying or using mobile and wearable tracking technologies to support monitoring and management of chronic diseases

Determinants of Longitudinal Adherence in Smartphone-Based Self-Tracking for Chronic Health Conditions: Evidence from Axial Spondyloarthritis

The use of interactive mobile and wearable technologies for understanding and managing health conditions is a growing area of interest for patients, health professionals and researchers. Self-tracking technologies such as smartphone apps and wearabledevices for measuring symptoms and behaviours generate a wealth of patient-centric data with the potential to support clinical decision making. However, the utility of self-tracking technologies for providing insight into patients’ conditions is impacted by poor adherence with data logging. This paper explores factors associated with adherence in smartphone-based tracking, drawing on two studies of patients living with axial spondyloarthritis (axSpA), a chronic rheumatological condition. In Study1, 184 axSpA patients used the uMotif health tracking smartphone app for a period of up to 593 days. In Study 2, 108 axSpA patients completed a survey about their experience of using self-tracking technologies. We identify six significant correlates of self-tracking adherence, providing insight into the determinants of tracking behaviour. Specifically, our data provides evidence that adherence correlates with the age of the user, the types of tracking devices that are being used (smartphone OS and physical activity tracker), preferences for types of data to record, the timing of interactions with a self-tracking app, and the reported symptom severity of the user. We discuss how these factors may have implications for those designing, deploying or using mobile and wearable tracking technologies to support monitoring and management of chronic diseases

Developing Transferable Deep Models for Mobile Health

Human behavior is one of the key facets of health. A major portion of healthcare spending in the US is attributed to chronic diseases, which are linked to behavioral risk factors such as smoking, drinking, unhealthy eating. Mobile devices that are integrated into people's everyday lives make it possible for us to get a closer look into behavior. Two of the most commonly used sensing modalities include Ecological Momentary Assessments (EMAs): surveys about mental states, environment, and other factors, and wearable sensors that are used to capture high frequency contextual and physiological signals. One of the main visions of mobile health (mHealth) is sensor-based behavior modification. Contextual data collected from participants is typically used to train a risk prediction model for adverse events such as smoking, which can then be used to inform intervention design. However, there are several choices in an mHealth study such as the demographics of the participants in the study, the type of sensors used, the questions included in the EMA. This results in two technical challenges to using machine learning models effectively across mHealth studies. The first is the problem of domain shift where the data distribution varies across studies. This would result in models trained on one study to have sub-optimal performance on a different study. Domain shift is common in wearable sensor data since there are several sources of variability such as sensor design, the placement of the sensor on the body, demographics of the users, etc. The second challenge is that of covariate-space shift where the input-space changes across datasets. This is common across EMA datasets since questions can vary based on the study. This thesis studies the problem of covariate-space shift and domain shift in mHealth data. First, I study the problem of domain shift caused by differences in the sensor type and placement in ECG and PPG signals. I propose a self-supervised learning based domain adaptation method that captures the physiological structure of these signals to improve transfer performance of predictive models. Second, I present a method to find a common input representation irrespective of the fine-grained questions in EMA datasets to overcome the problem of covariate-space shift. The next challenge to the deployment of ML models in health is explainability. I explore the problem of bridging the gap between explainability methods and domain experts and present a method to generate plausible, relevant, and convincing explanations.Ph.D