Quantile Coarsening Analysis of High-Volume Wearable Activity Data in a Longitudinal Observational Study.

Owing to advances in sensor technologies on wearable devices, it is feasible to measure physical activity of an individual continuously over a long period. These devices afford opportunities to understand individual behaviors, which may then provide a basis for tailored behavior interventions. The large volume of data however poses challenges in data management and analysis. We propose a novel quantile coarsening analysis (QCA) of daily physical activity data, with a goal to reduce the volume of data while preserving key information. We applied QCA to a longitudinal study of 79 healthy participants whose step counts were monitored for up to 1 year by a Fitbit device, performed cluster analysis of daily activity, and identified individual activity signature or pattern in terms of the clusters identified. Using 21,393 time series of daily physical activity, we identified eight clusters. Employment and partner status were each associated with 5 of the 8 clusters. Using less than 2% of the original data, QCA provides accurate approximation of the mean physical activity, forms meaningful activity patterns associated with individual characteristics, and is a versatile tool for dimension reduction of densely sampled data.

Efficient goal attainment and engagement in a care manager system using unstructured notes.

OBJECTIVE: To improve efficient goal attainment of patients by analyzing the unstructured text in care manager (CM) notes (CMNs). Our task is to determine whether the goal assigned by the CM can be achieved in a timely manner. MATERIALS AND METHODS: Our data consists of CM structured and unstructured records from a private firm in Orlando, FL. The CM data is based on phone interactions between the CM and the patient. A portion of the data has been manually annotated to indicate engagement. We present 2 machine learning classifiers: an engagement model and a goal attainment model. RESULTS: We can successfully distinguish automatically between engagement and lack of engagement. Subsequently, incorporating engagement and features from textual information from the unstructured notes significantly improves goal attainment classification. DISCUSSION: Two key challenges in this task were the time-consuming annotation effort for engagement classification and the limited amount of data for the more difficult goal attainment class (specifically, for people who take a long time to achieve their goals). We successfully explore domain adaptation and transfer learning techniques to improve performance on the under-represented classes. We also explore the value of using features from unstructured notes to improve the model and interpretability. CONCLUSIONS: Unstructured CMNs can be used to improve accuracy of our classification models for predicting patient self-management goal attainment. This work can be used to help identify patients who may require special attention from CMs to improve engagement in self-management.

Modeling the Personas of Primary Care Communication Modality Usage: Experiences from the R-Health Direct Primary Care Model.

Recent advancements in mediated communication technologies enable unfettered communication between patients and their providers. We explore patients' utilization of different direct and mediated communication modalities in a Direct Primary Care (DPC) clinic and the patient characteristics that predict their communication behavior patterns. Based on this knowledge, we developed 2 patient personas that explicate the nuances of patients who tend to prefer visiting the clinic in person versus those who use mediated modalities more often. We hope this study may inform future work in understanding and supporting patient-provider communication in a new technical environment. The results suggest that patients and their health team alike may be incentivized to voluntarily adopt and utilize multi-modality communication in a DPC setting.

Making Sense of Patient-Generated Health Data for Interpretable Patient-Centered Care: The Transition from "More" to "Better".

The rise of health consumers and the accumulation of patient-generated health data (PGHD) have brought the patient to the centerstage of precision health and behavioral science. In this positional paper we outline an interpretability-aware framework of PGHD, an important but often overlooked dimension in health services. The aim is two-fold: First, it helps generate practice-based evidence for population health management; second, it improves individual care with adaptive interventions. However, how do we check if the evidence generated from PGHD is reliable? Are the evidence directly deployable in realworld applications? How to adapt behavioral interventions for each individual patient at the touchpoint given individual patients' needs? These questions commonly require better interpretability of PGHD-derived patient insights. Yet the definitions of interpretability are often underspecified. In the position paper, we outline an interpretability-aware framework to handle model properties and techniques that affect interpretability in the patient-centered care process. Throughout the positional paper, we contend that making sense of PGHD systematically in such an interpretability-aware framework is preferrable, because it improves on the trustworthiness of PGHD-derived insights and the consequent applications such as person-centered comparative effectiveness in patient-centered care.

Are Nomothetic or Ideographic Approaches Superior in Predicting Daily Exercise Behaviors?

OBJECTIVES: The understanding of how stress influences health behavior can provide insights into developing healthy lifestyle interventions. This understanding is traditionally attained through observational studies that examine associations at a population level. This nomothetic approach, however, is fundamentally limited by the fact that the environment- person milieu that constitutes stress exposure and experience can vary substantially between individuals, and the modifiable elements of these exposures and experiences are individual-specific. With recent advances in smartphone and sensing technologies, it is now possible to conduct idiographic assessment in users' own environment, leveraging the full-range observations of actions and experiences that result in differential response to naturally occurring events. The aim of this paper is to explore the hypothesis that an ideographic N-of-1 model can better capture an individual's stress- behavior pathway (or the lack thereof) and provide useful person-specific predictors of exercise behavior. METHODS: This paper used the data collected in an observational study in 79 participants who were followed for up to a 1-year period, wherein their physical activity was continuously and objectively monitored by actigraphy and their stress experience was recorded via ecological momentary assessment on a mobile app. In addition, our analyses considered exogenous and environmental variables retrieved from public archive such as day in a week, daylight time, temperature and precipitation. Leveraging the multiple data sources, we developed prediction algorithms for exercise behavior using random forest and classification tree techniques using a nomothetic approach and an N-of-1 approach. The two approaches were compared based on classification errors in predicting personalized exercise behavior. RESULTS: Eight factors were selected by random forest for the nomothetic decision model, which was used to predict whether a participant would exercise on a particular day. The predictors included previous exercise behavior, emotional factors (e.g., midday stress), external factors such as weather (e.g., temperature), and self-determination factors (e.g., expectation of exercise). The nomothetic model yielded an average classification error of 36%. The ideographic N-of-1 models used on average about two predictors for each individual, and had an average classification error of 25%, which represented an improvement of 11 percentage points. CONCLUSIONS: Compared to the traditional one-size-fits-all, nomothetic model that generalizes population-evidence for individuals, the proposed N-of-1 model can better capture the individual difference in their stressbehavior pathways. In this paper, we demonstrate it is feasible to perform personalized exercise behavior prediction, mainly made possible by mobile health technology and machine learning analytics.

Progressive testing of health self-efficacy and literacy for personalized engagement.

Patient engagement can be enhanced through continuous monitoring of patient' health knowledge and self-efficacy levels across different co-morbid conditions and key aspects in health improvement and recovery. While self-reported test batteries and computerized instruments have been designed to perform evaluation of patient literacy and self-efficacy, they are not practical to be used in scenarios where concurrent tests are needed to understand the change over time. In this paper we propose an adaptive system that can progressively compose the most pertinent test for each user based on the provisional estimates of a patient's latent trait being measured. This requires modeling not only the latent health literacy and self-efficacy levels of patients and the difficulty and discriminating factors of test items, but also the temporal dependency among concurrent tests. The goal is to account for changes over the course of patient engagement history as the basis for devising personalized patient plans.

Development of temporal context-based feature abstractions for enabling monitoring and managing of interventions.

Disease self-management programs and intervention/care plan monitoring are often unable to systematically leverage patient-generated information, especially those requiring interpretation of the temporal contexts of the measurement. While existing techniques help in capturing and storing the relevant data, their ability to determine appropriate metrics most sensitive to that individual is limited or non-existent. This is attributable to the lack of unifying models for enabling such interpretations and the non-trivial process required to generate meaningful feature abstractions to support individualized prognosis. To address these issues, a data-driven approach designed to identify the right abstractions for key features relevant to personalization and monitoring of care is discussed.