In the article “Practice parameter: Evidence-based guidelines for migraine headache (an evidence-based review): Report of the Quality Standards Subcommittee of the American Academy …

Practice parameter: Evidence-based guidelines for migraine headache (an evidence-based review): Report of the Quality Standards Subcommittee of the American Academy of Neurology

Background: Mobile apps for mental health have the potential to overcome access barriers to mental health care, but there is little information on whether patients use the interventions as intended and the impact they have on mental health outcomes. Objective: The objective of our study was to document and compare use patterns and clinical outcomes across the United States between 3 different self-guided mobile apps for depression. Methods: Participants were recruited through Web-based advertisements and social media and were randomly assigned to 1 of 3 mood apps. Treatment and assessment were conducted remotely on each participant’s smartphone or tablet with minimal contact with study staff. We enrolled 626 English-speaking adults (≥18 years old) with mild to moderate depression as determined by a 9-item Patient Health Questionnaire (PHQ-9) score ≥5, or if their score on item 10 was ≥2. The apps were (1) Project: EVO, a cognitive training app theorized to mitigate depressive symptoms by improving cognitive control, (2) iPST, an app based on an evidence-based psychotherapy for depression, and (3) Health Tips, a treatment control. Outcomes were scores on the PHQ-9 and the Sheehan Disability Scale. Adherence to treatment was measured as number of times participants opened and used the apps as instructed. Results: We randomly assigned 211 participants to iPST, 209 to Project: EVO, and 206 to Health Tips. Among the participants, 77.0% (482/626) had a PHQ-9 score >10 (moderately depressed). Among the participants using the 2 active apps, 57.9% (243/420) did not download their assigned intervention app but did not differ demographically from those who did. Differential treatment effects were present in participants with baseline PHQ-9 score >10, with the cognitive training and problem-solving apps resulting in greater effects on mood than the information control app (χ22=6.46, P=.04). Conclusions: Mobile apps for depression appear to have their greatest impact on people with more moderate levels of depression. In particular, an app that is designed to engage cognitive correlates of depression had the strongest effect on depressed mood in this sample. This study suggests that mobile apps reach many people and are useful for more moderate levels of depression. ClinicalTrial: Clinicaltrials.gov NCT00540865; https://www.clinicaltrials.gov/ct2/show/NCT00540865 (Archived by WebCite at http://www.webcitation.org/6mj8IPqQr) [J Med Internet Res 2016;18(12):e330]

/pdf/the-use-and-effectiveness-of-mobile-apps-for-depression-4nhpet7nip.pdf

The Use and Effectiveness of Mobile Apps for Depression: Results From a Fully Remote Clinical Trial.

There is great interest in and excitement about the concept of personalized or precision medicine and, in particular, advancing this vision via various ‘big data’ efforts While these methods are necessary, they are insufficient to achieve the full personalized medicine promise A rigorous, complementary ‘small data’ paradigm that can function both autonomously from and in collaboration with big data is also needed By ‘small data’ we build on Estrin’s formulation and refer to the rigorous use of data by and for a specific N-of-1 unit (ie, a single person, clinic, hospital, healthcare system, community, city, etc) to facilitate improved individual-level description, prediction and, ultimately, control for that specific unit The purpose of this piece is to articulate why a small data paradigm is needed and is valuable in itself, and to provide initial directions for future work that can advance study designs and data analytic techniques for a small data approach to precision health Scientifically, the central value of a small data approach is that it can uniquely manage complex, dynamic, multi-causal, idiosyncratically manifesting phenomena, such as chronic diseases, in comparison to big data Beyond this, a small data approach better aligns the goals of science and practice, which can result in more rapid agile learning with less data There is also, feasibly, a unique pathway towards transportable knowledge from a small data approach, which is complementary to a big data approach Future work should (1) further refine appropriate methods for a small data approach; (2) advance strategies for better integrating a small data approach into real-world practices; and (3) advance ways of actively integrating the strengths and limitations from both small and big data approaches into a unified scientific knowledge base that is linked via a robust science of causality Small data is valuable in its own right That said, small and big data paradigms can and should be combined via a foundational science of causality With these approaches combined, the vision of precision health can be achieved

/pdf/why-we-need-a-small-data-paradigm-2df8d8tmdt.pdf

Why we need a small data paradigm

Food is essential for human life and it is fundamental to the human experience. Food-related study may support multifarious applications and services, such as guiding human behavior, improving human health, and understanding the culinary culture. With the rapid development of social networks, mobile networks, and Internet of Things (IoT), people commonly upload, share, and record food images, recipes, cooking videos, and food diaries, leading to large-scale food data. Large-scale food data offers rich knowledge about food and can help tackle many central issues of human society. Therefore, it is time to group several disparate issues related to food computing. Food computing acquires and analyzes heterogenous food data from different sources for perception, recognition, retrieval, recommendation, and monitoring of food. In food computing, computational approaches are applied to address food-related issues in medicine, biology, gastronomy, and agronomy. Both large-scale food data and recent breakthroughs in computer science are transforming the way we analyze food data. Therefore, a series of works has been conducted in the food area, targeting different food-oriented tasks and applications. However, there are very few systematic reviews that shape this area well and provide a comprehensive and in-depth summary of current efforts or detail open problems in this area. In this article, we formalize food computing and present such a comprehensive overview of various emerging concepts, methods, and tasks. We summarize key challenges and future directions ahead for food computing. This is the first comprehensive survey that targets the study of computing technology for the food area and also offers a collection of research studies and technologies to benefit researchers and practitioners working in different food-related fields.

A Survey on Food Computing

Previous research suggests an important role for self-tracking in promoting mental wellness. Recent studies with college student populations have examined the feasibility of collecting everyday mood, activity, and social data. However, these studies do not account for students' experiences and challenges adopting self-tracking technologies to support mental wellness goals. We present two studies conducted to better understand self-tracking for stress management and mental wellness in student populations. First, focus groups and card sorting activities with 14 student health professionals reveal expert perspectives on the usefulness of tracking for three scenarios. Second, an online survey of 297 students examines personal experiences with self-tracking and attitudes toward sharing self-tracked data with others. We draw on findings from these studies to characterize students' motivations, challenges and preferences in collecting and viewing self-tracked data related to mental wellness, and we compare findings between students with diagnosed mental illnesses and those without. We conclude with a discussion of challenges and opportunities in leveraging self-tracking for mental wellness, highlighting several design considerations.

/pdf/self-tracking-for-mental-wellness-understanding-expert-3gpunpambm.pdf

Self-tracking for Mental Wellness: Understanding Expert Perspectives and Student Experiences

Deploying and Examining Beacon for At-Home Patient Self-Monitoring with Critical Flicker Frequency

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Deploying and Examining Beacon for At-Home Patient Self-Monitoring with Critical Flicker Frequency