Mobile App Use among Persons with Fibromyalgia: A Cross-sectional Survey.

Persons with fibromyalgia experience a diverse set of symptoms. Recommendations for management generally focus on multidisciplinary approaches involving multiple modalities. Mobile apps can be an essential component for self-management, yet little is known about how persons with fibromyalgia use mobile apps for health-related purposes. A cross-sectional survey (N = 663) was conducted to understand the real-world use of apps among persons with fibromyalgia. The survey included 2 main foci: 1) eHealth literacy and use of information sources, and 2) mobile app use patterns and preferences for health-related purposes, including the types of apps used and usage characteristics of apps currently in use, as well as those that had been discontinued. Respondents' average eHealth literacy as measured by eHealth Literacy Scale (eHEALS) was 31.4 (SD = 7.1), and they utilized diverse information sources. Approximately two-thirds of the sample used mobile apps; the remaining one-third did not. Diverse health management needs were represented in the apps reported, including scheduling/time management, notetaking, fitness, and wellness. Compared to apps that had been discontinued, participants rated apps that they still used higher in terms of ease of use and used them more frequently. Reasons for discontinuing app use included issues with privacy, the effort required, lack of interest, and lack of perceived quality. Other reasons for app nonuse were lack of awareness and how-to knowledge, indicating that disseminating information about apps and addressing other barriers, such as providing user support, are critical to increasing uptake. These study findings can inform both app design and dissemination. PERSPECTIVE: This article presents how persons with fibromyalgia use mobile apps to manage their health. The findings could inform the development of digital interventions or programs for this population.

MigraineTracker: Examining Patient Experiences with Goal-Directed Self-Tracking for a Chronic Health Condition.

Self-tracking and personal informatics offer important potential in chronic condition management, but such potential is often undermined by difficulty in aligning self-tracking tools to an individual's goals. Informed by prior proposals of goal-directed tracking, we designed and developed MigraineTracker, a prototype app that emphasizes explicit expression of goals for migraine-related self-tracking. We then examined migraine patient experiences in a deployment study for an average of 12+ months, including a total of 50 interview sessions with 10 patients working with 3 different clinicians. Patients were able to express multiple types of goals, evolve their goals over time, align tracking to their goals, personalize their tracking, reflect in the context of their goals, and gain insights that enabled understanding, communication, and action. We discuss how these results highlight the importance of accounting for distinct and concurrent goals in personal informatics together with implications for the design of future goal-directed personal informatics tools.

Robust Brain State Decoding using Bidirectional Long Short Term Memory Networks in functional MRI.

Decoding brain states of the underlying cognitive processes via learning discriminative feature representations has recently gained a lot of interest in brain imaging studies. Particularly, there has been an impetus to encode the dynamics of brain functioning by analyzing temporal information available in the fMRI data. Long-short term memory (LSTM), a class of machine learning model possessing a "memory" component, to retain previously seen temporal information, is increasingly being observed to perform well in various applications with dynamic temporal behavior, including brain state decoding. Because of the dynamics and inherent latency in fMRI BOLD responses, future temporal context is crucial. However, it is neither encoded nor captured by the conventional LSTM model. This paper performs robust brain state decoding via information encapsulation from both the past and future instances of fMRI data via bi-directional LSTM. This allows for explicitly modeling the dynamics of BOLD response without any delay adjustment. To this end, we utilize a bidirectional LSTM, wherein, the input sequence is fed in normal time-order for one LSTM network, and in the reverse time-order, for another. The two hidden activations of forward and reverse directions in bi-LSTM are collated to build the "memory" of the model and are used to robustly predict the brain states at every time instance. Working memory data from the Human Connectome Project (HCP) is utilized for validation and was observed to perform 18% better than it's unidirectional counterpart in terms of accuracy in predicting the brain states.