Harnessing Digital Phenotyping for Early Self-Detection of Psychological Distress

Abstract

Psychological distress remains a significant public health concern, particularly among youth. With the growing integration of mobile and wearable technologies into daily life, digital phenotyping has emerged as a promising approach for early self-detection and intervention in psychological distress. Objectives: The study aims to determine how behavioral and device-derived data can be used to identify early signs of emotional distress and to develop and evaluate a prototype system that enables users to self-detect these early warning signs, ultimately supporting early intervention and improved mental health outcomes. Method: To achieve this, this study involved a multi-phase, mixed-method approach, combining literature review, system design, and user evaluation. It started with a scoping review to guide system design, followed by the design and development of a prototype system (ESFY) and a mixed-method evaluation to assess its feasibility and utility in detecting early signs of psychological distress through digital phenotyping. Results: The results demonstrate the potential of digital phenotyping to support early self-detection for psychological distress while highlighting practical considerations for future deployment. Conclusions: The findings highlight the value of integrating active and passive data streams, prioritizing transparency and user empowerment, and designing adaptable systems that respond to the diverse needs and concerns of end users. The recommendations outlined in this study serve as a foundation for the continued development of scalable, trustworthy, and effective digital mental health solutions.

Keywords: anxiety; healthcare; mHealth; mental health; phenotyping; psychological distress.

Figure 1

Relationships between digital phenotyping data, behavioral constructs, and psychological distress outcomes. Sensor inputs (e.g., GPS, accelerometer, screen time) inform behavioral indicators such as location, social activity, sleep, and phone use, which are associated with mental health outcomes, including anxiety, stress, loneliness, and depression. Adapted from Melcher et al. [1].

Figure 2

Flowchart of selection and inclusion process following the PRISMA statement.

Figure 3

System architecture of the ESFY, illustrating the interaction between the user interface, knowledge base, and inference engine for real-time psychological distress detection. The architecture’s core components follow the design methodology outlined by [61]. The system comprises five key modules: (1) a knowledge base, which contains a curated database of digital phenotypes and mental healthcare intervention data; (2) an inference engine with a working memory component that processes inputs and draws conclusions based on stored knowledge; (3) a data translation module that converts raw device data into digital biomarkers; (4) a user interface that mediates communication between the user and the system, collecting behavioral data and delivering personalized outputs; and (5) a digital device user explanation module that presents feedback and insights directly to the user. Solid arrows indicate direct data flow, while dashed lines represent asynchronous or interpretive interactions, such as inference processes and user engagement dynamics.

Figure 4

User interfaces of key features of the ESFY system: (A) home screen with daily prognosis, behavioral insights, and chat access; (B) multi-device integration for data collection; (C) proactive user notifications; (D) direct access to external healthcare interventions.

Figure 5

Key ESFY system interfaces: (A) Health Stats dashboard summarizing physiological and behavioral data; (B) Weekly Insights with visual analytics of screen time and browsing history; (C) Automated Weekly Summary and actionable health recommendations; (D) ESFY Expert Conversational Interface for personalized user support.

Figure 6

The left chart displays key usability metrics, including misclick rate, drop-off rate, direct and indirect task success rates, and overall success rate. The right chart presents the NASA TLX results across six workload dimensions, mental demand, physical demand, temporal demand, performance, effort, and frustration. Error bars represent standard error of the mean for each metric.

Figure 7

User interaction heatmap showing areas of highest attention on the ESFY main screen.