Portable technologies for digital phenotyping of bipolar disorder: A systematic review

Sep 6, 2021Journal of affective disorders

Handheld devices for tracking behavior patterns in bipolar disorder: A systematic review

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Abstract

A total of 62 studies involving 2325 individuals with bipolar disorder were reviewed regarding the use of portable digital devices for assessment.

Digital tools, including smartphone apps and wearable sensors, are being explored for identifying bipolar disorder and its mood states.
Two thirds of the studies employed artificial intelligence-based methods, achieving fair to excellent performance in classifying mood states.
The studies showed significant variability in sample sizes and methodologies, raising concerns about the reliability of findings.
Evidence of overfitting was noted, which may limit the applicability of results to broader populations.
Lack of standardized metrics for reporting classification performance makes it difficult to compare outcomes across studies.

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BACKGROUND: Bias-prone psychiatric interviews remain the mainstay of bipolar disorder (BD) assessment. The development of digital phenotyping promises to improve BD management. We present a systematic review of the evidence about the use of portable digital devices for the identification of BD, BD types and BD mood states and for symptom assessment.

METHODS: We searched Web of Knowledge, Scopus ®, IEEE Xplore, and ACM Digital Library databases (until 5/1/2021) for articles evaluating the use of portable/wearable digital devices, such as smartphone apps, wearable sensors, audio and/or visual recordings, and multimodal tools. The protocol is registered in PROSPERO (CRD42020200086). SM

RESULTS: We included 62 studies (2325 BD; 724 healthy controls, HC): 27 using smartphone apps, either for recording self-assessments (n = 10) or for passively gathering metadata (n = 7) or both (n = 10); 15 using wearable sensors for physiological parameters; 17 analysing audio and/or video recordings; 3 using multiple technologies. Two thirds of the included studies applied artificial intelligence (AI)-based approaches. They achieved fair to excellent classification performances.

LIMITATIONS: The included studies had small sample sizes and marked heterogeneity. Evidence of overfitting emerged, limiting generalizability. The absence of clear guidelines about reporting classification performances, with no shared standard metrics, makes results hardly interpretable and comparable.

CONCLUSIONS: New technologies offer a noteworthy opportunity to BD digital phenotyping with objectivity and high granularity. AI-based models could deliver important support in clinical decision-making. Further research and cooperation between different stakeholders are needed for addressing methodological, ethical and socio-economic considerations.

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Portable technologies for digital phenotyping of bipolar disorder: A systematic review

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