The Effortless Assessment of Risk States (EARS) Tool: An Interpersonal Approach to Mobile Sensing
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Abstract
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. 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. Objective: To 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. Methods: The 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. 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 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. 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 power cutting-edge research, with the ultimate goal of leveraging individual big data to empower people and enhance mental health.
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Digital tools offer the promise of taking evidence-based interventions to scale, reducing disparities in access to effective treatments and supports, and removing barriers to treatment resources.
“Digital tools offer the promise of taking evidence-based interventions to scale, reducing disparities in access to effective treatments and supports, and removing barriers to treatment resources”
From Annual Research Review: Adolescent mental health in the digital age: facts, fears, and future directions. (2020) · cites this paper as Lind et al., 2018
The Motivation section (page unavailable) describes mobile interventions as promising and calls for interventions that are scalable and can reach people outside traditional mental health care. It does not say that digital tools reduce disparities in access or remove barriers to treatment resources; its proposed tool primarily assesses risk rather than delivering evidence-based treatment. (LLM, conf=0.83)
Experimental — accuracy not yet measuredEvaluation at the later stage was mostly focused on information available from app providers and app functionalities, such as assessing app quality, intervention relevance, or privacy risks.
“Evaluation at this stage was mostly focused on the information available from app providers and app functionalities, for instance, to assess app quality [74], intervention relevance [76], [77], or privacy risks speci cally [52].”
From Security and Privacy of mHealth Applications: A Scoping Review (2020) · cites this paper as [76]
The citation is attached to the claim about assessing privacy risks. The Considerations section discusses privacy risks, including developers’ terms-of-service agreements and the sale of user data. It does not describe a stage of app evaluation or say that privacy assessments were mostly based on provider information and app functionality. (LLM, conf=0.79)
Experimental — accuracy not yet measuredThe application of design guidelines was described in only five papers, which provided design recommendations to be applied during app development and conducted evaluation studies to validate their findings.
“The application of design guidelines was described in only ve papers, which provided design recommendations to be applied during the process of app development, and conducted evaluation studies to validate their ndings [95], [105], [107] [109].”
From Security and Privacy of mHealth Applications: A Scoping Review (2020) · cites this paper as [107]
The EARS Tool Engineering section describes app-design choices to reduce burden, and What the EARS Tool Does reports a 24-person pilot that assessed participants’ experience. The paper does not describe applying design guidelines or validating design recommendations through that pilot. Its own text also cannot establish the citing review’s count of five papers. (LLM, conf=0.84)
Experimental — accuracy not yet measured
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References 37 · Cited by 84
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- Authors
- Lind, M. N., Byrne, M. L., Wicks, G., Smidt, A. M., & Allen, N. B.
- Affiliations
- University of Oregon (US)
- Corresponding author
- Allen, Nicholas B
- Venue
- JMIR Mental Health
- Publisher
- JMIR Publications Inc.
- Published
- 2018-08-28
- Volume
- 5
- Issue
- 3
- Pages
- e10334
- ISSN
- 2368-7959 (ISSN-L)
- PMC
- PMC6134227
- OpenAlex
- W2886286848
- Cited by
- 84
- References
- 37
- Citations by year
- 2026: 11 · 2025: 9 · 2024: 10 · 2023: 15 · 2022: 13 · 2021: 11 · 2020: 10 · 2019: 5
- FWCI
- 8.20
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Mental Health (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Institute on Aging (R24AG048024)
- National Institute of Mental Health (K01MH111951)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- smartphones, crisis prevention, social communication, facial expression analysis, voice quality, natural language use, physical activity, music selection, geographic location, user burden, battery consumption, Android, user experience
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