The Effortless Assessment of Risk States (EARS) Tool: An Interpersonal Approach to Mobile Sensing

Abstract

BackgroundTo predict and prevent mental health crises, we must develop new approaches that can provide a dramatic advance in the effectiveness, timeliness, and scalability of our interventions. However, current methods of predicting mental health crises (eg, clinical monitoring, screening) usually fail on most, if not all, of these criteria. Luckily for us, 77% of Americans carry with them an unprecedented opportunity to detect risk states and provide precise life-saving interventions. Smartphones present an opportunity to empower individuals to leverage the data they generate through their normal phone use to predict and prevent mental health crises.ObjectiveTo facilitate the collection of high-quality, passive mobile sensing data, we built the Effortless Assessment of Risk States (EARS) tool to enable the generation of predictive machine learning algorithms to solve previously intractable problems and identify risk states before they become crises.MethodsThe EARS tool captures multiple indices of a person’s social and affective behavior via their naturalistic use of a smartphone. Although other mobile data collection tools exist, the EARS tool places a unique emphasis on capturing the content as well as the form of social communication on the phone. Signals collected include facial expressions, acoustic vocal quality, natural language use, physical activity, music choice, and geographical location. Critically, the EARS tool collects these data passively, with almost no burden on the user. We programmed the EARS tool in Java for the Android mobile platform. In building the EARS tool, we concentrated on two main considerations: (1) privacy and encryption and (2) phone use impact.ResultsIn a pilot study (N=24), participants tolerated the EARS tool well, reporting minimal burden. None of the participants who completed the study reported needing to use the provided battery packs. Current testing on a range of phones indicated that the tool consumed approximately 15% of the battery over a 16-hour period. Installation of the EARS tool caused minimal change in the user interface and user experience. Once installation is completed, the only difference the user notices is the custom keyboard.ConclusionsThe EARS tool offers an innovative approach to passive mobile sensing by emphasizing the centrality of a person’s social life to their well-being. We built the EARS tool to power cutting-edge research, with the ultimate goal of leveraging individual big data to empower people and enhance mental health.

Beyond Screen Time: Identity Development in the Digital Age

“…New, sophisticated, simple-to-use, passive mobile phone applications allow researchers to track words and semantic categories and combine this text-based digital activity with biomarkers, GPS, scores on psychological assessment tools, and social network engagement (e.g. Schmid Mast, Gatica -Perez, Frauendorfer, Nguyen, & Choudhury, 2015;Lind, Byrne, Wicks, Smidt, & Allen, 2018). These methods not only eliminate the overreliance on self-reports that has marred past research.…”

Using Mobile Sensing Data to Assess Stress: Associations with Perceived and Lifetime Stress, Mental Health, Sleep, and Inflammation

“…Although phrase-and vector-based approaches tend to outperform unigram-based approaches, there is a rich literature of robust unigram-based findings through using the Linguistic Inquiry and Word Count (LIWC 50 ), including research showing that unigram-based approaches can detect differences in stress (e.g., 51,52 ). For more information on the EARS tool and changes to newer versions, see 21 .…”

“…First, there is accumulating evidence that natural language processing may detect critical health events, including elevated suicide risk 19,20 . Second, few mobile sensing tools capture the content of communication, opting instead to focus on communication frequency 21 (e.g., number of texts sent, number of phone calls made). As such, focusing on the content of the language represents an important innovation in assessing stress-related signals that may be useful for noninvasively estimating stress-related health risks on a continual basis.…”

“…To accomplish this goal, we conducted a preliminary longitudinal study of undergraduate students in which we used a mobile sensing collection tool called Effortless Assessment of Risk States 21 (EARS) to validate naturalistic text collection from smartphones as an objective behavioral indicator of stress on both a short-term (perceived and contextual stress, mental health symptoms, biological stress markers) and long-term (cumulative life stress) basis. Data were collected twice during the school term: once during a period that was expected to be relatively less academically stressful and then again during a final exam period that was expected to be more stressful.…”

A randomized controlled trial to test the effectiveness of a peer-based social mobile game intervention to reduce smoking in youth

“…However, biochemical verification often contrasts with other intervention objectives we have, such as anonymity and accessibility, especially in future studies testing the effects of HitnRun "in the wild." Therefore, we also suggest efforts to work towards the use of ecological momentary assessment (Shiffman, 2009) and, in the near future, passive assessment of smoking behavior through mobile phones (Lind, Byrne, Wicks, Smidt, & Allen, 2018).…”