BackgroundThe emergence of smartphones, wearable sensor technologies, and smart homes allows the nonintrusive collection of activity data. Thus, health-related events, such as activities of daily living (ADLs; eg, mobility patterns, feeding, sleeping, ...) can be captured without patients’ active participation. We designed a system to detect changes in the mobility patterns based on the smartphone’s native sensors and advanced machine learning and signal processing techniques.ObjectiveThe principal objective of this work is to assess the feasibility of detecting mobility pattern changes in a sample of outpatients with depression using the smartphone’s sensors. The proposed method processed the data acquired by the smartphone using an unsupervised detection technique.MethodsIn this study, 38 outpatients from the Hospital Fundación Jiménez Díaz Psychiatry Department (Madrid, Spain) participated. The Evidence-Based Behavior (eB2) app was downloaded by patients on the day of recruitment and configured with the assistance of a physician. The app captured the following data: inertial sensors, physical activity, phone calls and message logs, app usage, nearby Bluetooth and Wi-Fi connections, and location. We applied a change-point detection technique to location data on a sample of 9 outpatients recruited between April 6, 2017 and December 14, 2017. The change-point detection was based only on location information, but the eB2 platform allowed for an easy integration of additional data. The app remained running in the background on patients’ smartphone during the study participation.ResultsThe principal outcome measure was the identification of mobility pattern changes based on an unsupervised detection technique applied to the smartphone’s native sensors data. Here, results from 5 patients’ records are presented as a case series. The eB2 system detected specific mobility pattern changes according to the patients’ activity, which may be used as indicators of behavioral and clinical state changes.ConclusionsThe proposed technique could automatically detect changes in the mobility patterns of outpatients who took part in this study. Assuming these mobility pattern changes correlated with behavioral changes, we have developed a technique that may identify possible relapses or clinical changes. Nevertheless, it is important to point out that the detected changes are not always related to relapses and that some clinical changes cannot be detected by the proposed method.
Key Points
Question
Would a tailored behavioral health intervention reduce substance misuse and mental health symptoms, compared with enhanced usual care, in Latino immigrants with co-occurring mental health and substance misuse symptoms?
Findings
In this randomized clinical trial from 3 sites of 341 immigrants with co-occurring mental health and substance misuse symptoms, the primary outcome of substance misuse did not change in the intent-to-treat analysis. Patients who received the treatment statistically significantly experienced decreased mental health symptoms, compared with controls under enhanced usual care, and only participants with moderate to severe symptoms who received the intervention statistically significantly reduced their substance misuse.
Meaning
The intervention did not change drug misuse in a heterogeneous sample but did improve secondary mental health outcomes, a finding that might provide a path for treating Latino immigrants with co-occurring mental health symptoms whose symptoms are in the moderate-to-severe range.
Salud mental, abuso de sustancias y trastornos duales en el embarazo: tasas de prevalencia y tratamiento en un país desarrollado Substance use, mental health and dual disorders on pregnancy: results of prevalence and treatment rates in a developed country
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