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. 2024 Jul 30;10(15):e35472.
doi: 10.1016/j.heliyon.2024.e35472. eCollection 2024 Aug 15.

Use of smartphone sensor data in detecting and predicting depression and anxiety in young people (12-25 years): A scoping review

Joanne R Beames  1   2 , Jin Han  1 , Artur Shvetcov  1 , Wu Yi Zheng  1 , Aimy Slade  1 , Omar Dabash  1 , Jodie Rosenberg  1 , Bridianne O'Dea  1 , Suranga Kasturi  1 , Leonard Hoon  3 , Alexis E Whitton  1 , Helen Christensen  4 , Jill M Newby  5
Affiliations

Use of smartphone sensor data in detecting and predicting depression and anxiety in young people (12-25 years): A scoping review

Joanne R Beames et al. Heliyon. .

Abstract

Digital phenotyping is a promising method for advancing scalable detection and prediction methods in mental health research and practice. However, little is known about how digital phenotyping data are used to make inferences about youth mental health. We conducted a scoping review of 35 studies to better understand how passive sensing (e.g., Global Positioning System, microphone etc) and electronic usage data (e.g., social media use, device activity etc) collected via smartphones are used in detecting and predicting depression and/or anxiety in young people between 12 and 25 years-of-age. GPS and/or Wifi association logs and accelerometers were the most used sensors, although a wide variety of low-level features were extracted and computed (e.g., transition frequency, time spent in specific locations, uniformity of movement). Mobility and sociability patterns were explored in more studies compared to other behaviours such as sleep, phone use, and circadian movement. Studies used machine learning, statistical regression, and correlation analyses to examine relationships between variables. Results were mixed, but machine learning indicated that models using feature combinations (e.g., mobility, sociability, and sleep features) were better able to predict and detect symptoms of youth anxiety and/or depression when compared to models using single features (e.g., transition frequency). There was inconsistent reporting of age, gender, attrition, and phone characteristics (e.g., operating system, models), and all studies were assessed to have moderate to high risk of bias. To increase translation potential for clinical practice, we recommend the development of a standardised reporting framework to improve transparency and replicability of methodology.

Keywords: Anxiety; Depression; Machine learning; Phone; Sensing; Youth.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
PRISMA flow diagram.
Fig. 2
Fig. 2
Percentage of publications not reporting sample characteristics (by field).
Fig. 3
Fig. 3
Total number of studies using each source of phone data. Note. Accel. = Accelerometer; Gyro. = Gyroscope; BT=Bluetooth; Mic. = Microphone.
Fig. 4
Fig. 4
Number of studies using each sensor type to infer high-level behavioural features. Note. Accel. = Accelerometer; Gyro. = Gyroscope; BT=Bluetooth; Mic. = Microphone.
Fig. 5
Fig. 5
Number of studies inferring high-level behaviours and number of studies using each type of phone sensor. Note. Top panel: Number of studies inferring each type of behaviour. Bottom Panel: Number of studies using each type of phone sensor. Accel = Accelerometer; Gyro. = Gyroscope; BT=Bluetooth; Mic. = Microphone.
See this image and copyright information in PMC

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