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.

This paper reintroduces the Effortless Assessment Research System (EARS), 4 years and 10,000 participants after its initial launch. EARS is a mobile sensing tool that affords researchers the opportunity to collect naturalistic, behavioral data via participants’ naturalistic smartphone use. The first section of the paper highlights improvements made to EARS via a tour of EARS’s capabilities—the most important of which is the expansion of EARS to the iOS operating system. Other improvements include better keyboard integration for the collection of typed text; full control of survey design and administration for research teams; and the addition of a researcher-facing EARS dashboard, which facilitates survey design, the enrollment of participants, and the tracking of participants. The second section of the paper goes behind the scenes to describe 3 challenges faced by the EARS developers—remote participant enrollment and tracking, keeping EARS running in the background, and continuous attention and effort toward data protection—and how those challenges shaped the design of the app.

UNSTRUCTURED This paper re-introduces the Effortless Assessment Research System (EARS), four years and 4,000 participants after its initial launch. EARS is a mobile sensing tool that affords researchers the opportunity to collect naturalistic, behavioral data via participants’ normal smartphone use. The first section of the paper highlights improvements made to EARS via a tour of EARS’s capabilities across iOS and Android. The most important improvement is the expansion of EARS to iOS. Other improvements include better keyboard integration for the collection of typed text, full control of survey design and administration for research teams, and the addition of a researcher-facing EARS dashboard, which facilitates survey design, enrollment of participants, and tracking of participants. The second section of the paper goes behind the scenes to describe three challenges faced by the EARS developers –remote participant enrollment and tracking, keeping EARS running on participant phones, and continuous attention and effort toward data protection– and how those challenges shaped the design of the app.

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Background: To 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. Current methods of predicting mental health crises (e.g., clinical monitoring, screening) usually fail on most, if not all, of these criteria. Lucky for us, 77% of Americans carry with them an unprecedented opportunity to detect risk states and provide precise, life-saving interventions. Smart phones represent an opportunity to empower individuals to leverage the data they generate through their normal phone use to predict and prevent mental health crises. Objective: We believe that with enough high-quality, passive mobile sensing data, we may be able to generate predictive machine learning algorithms to solve previously intractable problems and identify risk states before they become crises. To test this hypothesis, our team built the Effortless Assessment of Risk States (EARS) tool. Methods: The EARS tool captures multiple indices of a person's social and affective behavior via their naturalistic use of a smart phone. These indices include facial expressions, acoustic vocal quality, natural language use, physical activity, music choice, and geographical location, among others. 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. Results: In 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 the tool will consume approximately 15% of the battery over a 16 hour period. Installation of the EARS tool causes minimal change in the UI/UX. Once installation is completed, the only difference the user will notice is the custom keyboard. Conclusions: The 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