Background Mood disorders are commonly underrecognized and undertreated, as diagnosis is reliant on self-reporting and clinical assessments that are often not timely. Speech characteristics of those with mood disorders differs from healthy individuals. With the wide use of smartphones, and the emergence of machine learning approaches, smartphones can be used to monitor speech patterns to help the diagnosis and monitoring of mood disorders. Objective The aim of this review is to synthesize research on using speech patterns from smartphones to diagnose and monitor mood disorders. Methods Literature searches of major databases, Medline, PsycInfo, EMBASE, and CINAHL, initially identified 832 relevant articles using the search terms “mood disorders”, “smartphone”, “voice analysis”, and their variants. Only 13 studies met inclusion criteria: use of a smartphone for capturing voice data, focus on diagnosing or monitoring a mood disorder(s), clinical populations recruited prospectively, and in the English language only. Articles were assessed by 2 reviewers, and data extracted included data type, classifiers used, methods of capture, and study results. Studies were analyzed using a narrative synthesis approach. Results Studies showed that voice data alone had reasonable accuracy in predicting mood states and mood fluctuations based on objectively monitored speech patterns. While a fusion of different sensor modalities revealed the highest accuracy (97.4%), nearly 80% of included studies were pilot trials or feasibility studies without control groups and had small sample sizes ranging from 1 to 73 participants. Studies were also carried out over short or varying timeframes and had significant heterogeneity of methods in terms of the types of audio data captured, environmental contexts, classifiers, and measures to control for privacy and ambient noise. Conclusions Approaches that allow smartphone-based monitoring of speech patterns in mood disorders are rapidly growing. The current body of evidence supports the value of speech patterns to monitor, classify, and predict mood states in real time. However, many challenges remain around the robustness, cost-effectiveness, and acceptability of such an approach and further work is required to build on current research and reduce heterogeneity of methodologies as well as clinical evaluation of the benefits and risks of such approaches.

BACKGROUND Mood disorders are commonly underrecognized and undertreated as diagnosis is reliant on self-reporting and clinical assessments that are often not timely. Speech characteristics of those with mood disorders differs from healthy individuals. With the wide use of smartphones, and the emergence of machine learning approaches, smartphones can be used to monitor speech patterns to help the diagnosis and monitoring of mood disorders. OBJECTIVE The aim of this review is to synthesize research on using speech patterns from smartphones to diagnose and/or monitor mood disorders. METHODS Literature searches of major databases, Medline, PsycInfo, EMBASE and CINAHL initially identified 440 relevant articles using the search terms ‘mood disorders’, ‘smartphone’, ‘voice analysis’ and their variants. Only 14 studies met inclusion criteria: Use of a smartphone for capturing voice data, focus on diagnosing or monitoring a mood disorder(s) (not exclusive to clinical populations), capturing voice data, and in the English language only. Articles were assessed by two reviewers, and data extracted included data type, classifiers used, methods of capture and study results. Studies were analyzed using a narrative synthesis approach. RESULTS Studies showed that voice data alone had reasonable accuracy in predicting mood states and mood fluctuations based on objectively monitored speech patterns. While a fusion of different sensor modalities revealed the highest accuracy (97.4%), nearly 80% of included studies were pilot trials or feasibility studies without control groups and had small sample sizes ranging from 1 to 73 participants. Studies were also carried out over short or varying timeframes and had significant heterogeneity of methods in terms of the types of audio data captured, environmental contexts, classifiers, and measures to control for privacy and ambient noise. CONCLUSIONS Approaches that allow smartphone-based monitoring of speech patterns in mood disorders is rapidly growing. The current body of evidence supports the value of speech patterns to monitor, classify and predict mood states in real-time. However, many challenges remain around the robustness, cost-effectiveness and acceptability of such an approach and further work is required to build on current research and reduce heterogeneity of methodologies as well as clinical evaluation of the benefits and risks of such approaches.