TY  - JOUR
AU  - Crocamo, Cristina
AU  - Cioni, Riccardo Matteo
AU  - Canestro, Aurelia
AU  - Nasti, Christian
AU  - Palpella, Dario
AU  - Piacenti, Susanna
AU  - Bartoccetti, Alessandra
AU  - Re, Martina
AU  - Simonetti, Valentina
AU  - Barattieri di San Pietro, Chiara
AU  - Bulgheroni, Maria
AU  - Bartoli, Francesco
AU  - Carrà, Giuseppe
PY  - 2025
DA  - 2025/4/16
TI  - Acoustic and Natural Language Markers for Bipolar Disorder: A Pilot, mHealth Cross-Sectional Study
JO  - JMIR Form Res
SP  - e65555
VL  - 9
KW  - digital mental health
KW  - remote assessment
KW  - mHealth
KW  - speech
KW  - NLP
KW  - natural language processing
KW  - acoustic
KW  - symptom severity
KW  - machine learning
KW  - markers
KW  - mental health
KW  - bipolar disorders
KW  - app
KW  - applications
KW  - multimodal
KW  - mobile health
KW  - voice
KW  - vocal
KW  - bipolar
KW  - verbal
KW  - emotion
KW  - emotional
KW  - psychiatry
KW  - psychiatric
KW  - mental illness
AB  - Background: Monitoring symptoms of bipolar disorder (BD) is a challenge faced by mental health services. Speech patterns are crucial in assessing the current experiences, emotions, and thought patterns of people with BD. Natural language processing (NLP) and acoustic signal processing may support ongoing BD assessment within a mobile health (mHealth) framework. Objective: Using both acoustic and NLP-based features from the speech of people with BD, we built an app-based tool and tested its feasibility and performance to remotely assess the individual clinical status. Methods: We carried out a pilot, observational study, sampling adults diagnosed with BD from the caseload of the Nord Milano Mental Health Trust (Italy) to explore the relationship between selected speech features and symptom severity and to test their potential to remotely assess mental health status. Symptom severity assessment was based on clinician ratings, using the Young Mania Rating Scale (YMRS) and Montgomery-Åsberg Depression Rating Scale (MADRS) for manic and depressive symptoms, respectively. Leveraging a digital health tool embedded in a mobile app, which records and processes speech, participants self-administered verbal performance tasks. Both NLP-based and acoustic features were extracted, testing associations with mood states and exploiting machine learning approaches based on random forest models. Results: We included 32 subjects (mean [SD] age 49.6 [14.3] years; 50% [16/32] females) with a MADRS median (IQR) score of 13 (21) and a YMRS median (IQR) score of 5 (16). Participants freely managed the digital environment of the app, without perceiving it as intrusive and reporting an acceptable system usability level (average score 73.5, SD 19.7). Small-to-moderate correlations between speech features and symptom severity were uncovered, with sex-based differences in predictive capability. Higher latency time (ρ=0.152), increased silences (ρ=0.416), and vocal perturbations correlated with depressive symptomatology. Pressure of speech based on the mean intraword time (ρ=–0.343) and lower voice instability based on jitter-related parameters (ρ ranging from –0.19 to –0.27) were detected for manic symptoms. However, a higher contribution of NLP-based and conversational features, rather than acoustic features, was uncovered, especially for predictive models for depressive symptom severity (NLP-based: R2=0.25, mean squared error [MSE]=110.07, mean absolute error [MAE]=8.17; acoustics: R2=0.11, MSE=133.75, MAE=8.86; combined: R2=0.16; MSE=118.53, MAE=8.68). Conclusions: Remotely collected speech patterns, including both linguistic and acoustic features, are associated with symptom severity levels and may help differentiate clinical conditions in individuals with BD during their mood state assessments. In the future, multimodal, smartphone-integrated digital ecological momentary assessments could serve as a powerful tool for clinical purposes, remotely complementing standard, in-person mental health evaluations. 
SN  - 2561-326X
UR  - https://formative.jmir.org/2025/1/e65555
UR  - https://doi.org/10.2196/65555
DO  - 10.2196/65555
ID  - info:doi/10.2196/65555
ER  -