MHealth Monitoring of Acoustic and Behavioral Patterns in Bipolar Disorder Across Cultures

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Abstract: The ability to prioritize individuals for health care based on behavioral and acoustic patterns in speech will allow for efficient use of health care resources. The ability to predict mood states using daily monitoring of acoustics derived from mobile technology provides the basis for a real-time proxy measure of moods and affective states. Identification and monitoring of these and other dimensional features of human disease is the base for anticipating outcomes, offering the future possibility of timely and mitigating interventions. Technological and mHealth methods are well suited for the global health community due to the flexibility and adaptability of the approach; the capacity to reach large numbers of patients can be easily amplified with modest increase in infrastructure. We have developed an accurate prediction model for mood states in bipolar (BP) individuals using machine-learning strategies and established a process that involves preprocessing, feature extraction, and an integrated data analysis of clinical and acoustic data gathered from personal use of a mobile device for up to one year. The results show mood states are predicted with an AUC statistic of 0.74 (mania) and 0.77 (depression). We hypothesize that analyses across cultures will identify common features of illness that can be identified using our methods. BP is ideal for study because of the wide range of mood states and temperamental traits. This study aims to 1) ascertain 30 individuals with BP and 10 healthy controls from Lebanon and a multilingual community in SE Michigan, recording daily acoustic and behavioral data using a smart-phone, all outgoing speech from the device is gathered and all personal digital activity is recorded from the device. We propose to study participants in Lebanon and SE Michigan in order to identify the fundamental acoustic elements of mood variation among bipolar patients. 2) apply integrated computational analyses using static (Gaussian Mixture Models and Support Vector Machines) and dynamic (Hidden Markov Models) modeling of categorical, dimensional and derived features from clinical, acoustic, and behavioral signals; we will compare data from the 15 BP from Lebanon and 15 BP from SE Michigan that have been resident in USA >2 years but originate from a geographical region comparable to Lebanon in language and culture, and 15 American born BP Caucasians (from our current cohort). Our hypothesis is that there are fundamental elements of acoustics that associate with mood states regardless of the culture. The impact is the longitudinal use of mobile technology to passively gather personal data to establish computational models that use extensive individual state and trait data to accurately predict mood and health states. This provides a foundation for predictive modeling that can be integrated into subsequent clinical interventional studies to predict and test causal effects of specific interventions on disease mechanisms. Expertise in clinical, computational, and technology disciplines form the team to realize these goals.