Assessment of e-Social Activity in Psychiatric Patients

Abstract

This work introduces a novel method to assess the social activity maintained by psychiatric patients using information and communication technologies. In particular, we model the daily usage patterns of phone calls and social and communication apps using point processes. We propose a novel nonhomogeneous Poisson process model with periodic (circadian) intensity function using a truncated Fourier series expansion, which is inferred using a trust-region algorithm. We also extend the model using a mixture of periodic intensity functions to cope with the different daily patterns of a person. The analysis of the usage of phone calls and social and communication apps of a cohort of 259 patients reveals common patterns shared among patients with relatively high homogeneity and differences among patient pathologies.

Social media and smartphone app use predicts maintenance of physical activity during Covid-19 enforced isolation in psychiatric outpatients

“…Data were drawn from two ongoing studies with psychiatric outpatients in Madrid, Spain that involve remote smartphone monitoring [ 24 , 25 ]. Both studies received ethical approval from the Fundación Jimenez Diaz Hospital Institutional Review Board, and all participants provided written informed consent.…”

Recognizing Context-Aware Human Sociability Patterns Using Pervasive Monitoring for Supporting Mental Health Professionals

“…For example, studies that design machine learning models to classify and predict mental states use metrics such as accuracy, precision, recall, whereas in our unsupervised sociability pattern learning approach, we use methods to assess the ability to model social routine and detect behavior changes. The works [22][23][24] also assess sociability patterns, but they differ from our experiments. Harari et al [23] computed test-retest correlations between the observed behavior durations for adjacent weeks.…”

“…For example, some studies develop machine learning models to classify and predict mental states, while our solution aims to extract sociability patterns and detect behavior changes. The works [22][23][24] also propose solutions capable of detecting sociability patterns, but they differ from our solution. These works design sociability patterns to quantify the duration and frequency of social interactions, while our solution recognizes periods of the day representing individuals' social routine.…”

“…Harari et al [23] identify patterns of stability and changes in social behavior (i.e., daily duration of conversations) of a student group over ten weeks. Bonilla et al [24] found a set of patterns related to intensity functions of all interactions in which patients were involved by analyzing data from the use of their phone calls and social applications.…”

“…To define the size of the slot (i.e., in how many periods should divide the day), the programmer is required to specify a value for the t parameter, which is responsible for creating an array of counters with the total number of slots. For example, if the programmer decides to divide the day into periods of 30 min, the parameter t is equal to 0.5, since 24 t = 48 slots. This equation is responsible for creating the storage structure for counting occurrences of social activities in each slot.…”