Mental health ubiquitous monitoring supported by social situation awareness: A systematic review

Digital Phenotyping of Mental Health using multimodal sensing of multiple situations of interest: A Systematic Literature Review

Many studies have used Digital Phenotyping of Mental Health (DPMH) to complement classic methods of mental health assessment and monitoring. This research area proposes innovative methods that perform multimodal sensing of multiple situations of interest (e.g., sleep, physical activity, mobility) to health professionals. In this paper, we present a Systematic Literature Review (SLR) to recognize, characterize and analyze the state of the art on DPMH using multimodal sensing of multiple situations of interest to professionals. We searched for studies in six digital libraries, which resulted in 1865 retrieved published papers. Next, we performed a systematic process of selecting studies based on inclusion and exclusion criteria, which selected 59 studies for the data extraction phase. First, based on the analysis of the extracted data, we describe an overview of this field, then presenting characteristics of the selected studies, the main mental health topics targeted, the physical and virtual sensors used, and the identified situations of interest. Next, we outline answers to research questions, describing the context data sources used to detect situations, the DPMH workflow used for multimodal sensing of situations, and the application of DPMH solutions in the mental health assessment and monitoring process. In addition, we recognize trends presented by DPMH studies, such as the design of solutions for high-level information recognition, association of features with mental states/disorders, classification of mental states/disorders, and prediction of mental states/disorders. We also recognize the main open issues in this research area. Based on the results of this SLR, we conclude that despite the potential and continuous evolution for using these solutions as medical decision support tools, this research area needs more work to overcome technology and methodological rigor issues to adopt proposed solutions in real clinical settings.

Language-agnostic deep learning framework for automatic monitoring of population-level mental health from social networks

In many countries, mental health issues are among the most serious public health concerns. National mental health statistics are frequently collected from reported patient cases or government-sponsored surveys, which have restricted coverage, frequency, and timeliness. Many domains of study, including public healthcare and biomedical informatics, have recently adopted social media data as a feasible real-time alternative to traditional methods of gathering representative information at the population level in a variety of contexts. However, because of the limits of fundamental natural language processing tools and labeled corpora in countries with limited natural language resources, such as Thailand, implementing social media systems to monitor mental health signals could be challenging. This paper presents LAPoMM, a novel framework for monitoring real-time mental health indicators from social media data without using labeled datasets in low-resource languages. Specifically, we use cross-lingual methods to train language-agnostic models and validate our framework by examining cross-correlations between the aggregate predicted mental signals and real-world administrative data from Thailand's Department of Mental Health, which includes monthly depression patients and reported cases of suicidal attempts. A combination of a language-agnostic representation and a deep learning classification model outperforms all other cross-lingual techniques for recognizing various mental signals in Tweets, such as emotions, sentiments, and suicidal tendencies. The correlation analyses discover a strong positive relationship between actual depression cases and the predicted negative sentiment signals as well as suicide attempts and negative signals (e.g., fear, sadness, and disgust) and suicidal tendency. These findings establish the effectiveness of our proposed framework and its potential applications in monitoring population-level mental health using large-scale social media data. Furthermore, because the language-agnostic model utilized in the methodology is capable of supporting a wide range of languages, the proposed LAPoMM framework can be easily generalized for analogous applications in other countries with limited language resources.

Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review

Background

Mental disorders are normally diagnosed exclusively on the basis of symptoms, which are identified from patients’ interviews and self-reported experiences. To make mental health diagnoses and monitoring more objective, different solutions have been proposed such as digital phenotyping of mental health (DPMH), which can expand the ability to identify and monitor health conditions based on the interactions of people with digital technologies.

Objective

This article aims to identify and characterize the sensing applications and public data sets for DPMH from a technical perspective.

Methods

We performed a systematic review of scientific literature and data sets. We searched 8 digital libraries and 20 data set repositories to find results that met the selection criteria. We conducted a data extraction process from the selected articles and data sets. For this purpose, a form was designed to extract relevant information, thus enabling us to answer the research questions and identify open issues and research trends.

Results

A total of 31 sensing apps and 8 data sets were identified and reviewed. Sensing apps explore different context data sources (eg, positioning, inertial, ambient) to support DPMH studies. These apps are designed to analyze and process collected data to classify (n=11) and predict (n=6) mental states/disorders, and also to investigate existing correlations between context data and mental states/disorders (n=6). Moreover, general-purpose sensing apps are developed to focus only on contextual data collection (n=9). The reviewed data sets contain context data that model different aspects of human behavior, such as sociability, mood, physical activity, sleep, with some also being multimodal.

Conclusions

This systematic review provides in-depth analysis regarding solutions for DPMH. Results show growth in proposals for DPMH sensing apps in recent years, as opposed to a scarcity of public data sets. The review shows that there are features that can be measured on smart devices that can act as proxies for mental status and well-being; however, it should be noted that the combined evidence for high-quality features for mental states remains limited. DPMH presents a great perspective for future research, mainly to reach the needed maturity for applications in clinical settings.

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

Traditionally, mental health specialists monitor their patients' social behavior by applying subjective self-report questionnaires in face-to-face meetings. Usually, the application of the self-report questionnaire is limited by cognitive biases (e.g., memory bias and social desirability). As an alternative, we present a solution to detect context-aware sociability patterns and behavioral changes based on social situations inferred from ubiquitous device data. This solution does not focus on the diagnosis of mental states, but works on identifying situations of interest to specialized professionals. The proposed solution consists of an algorithm based on frequent pattern mining and complex event processing to detect periods of the day in which the individual usually socializes. Social routine recognition is performed under different context conditions to differentiate abnormal social behaviors from the variation of usual social habits. The proposed solution also can detect abnormal behavior and routine changes. This solution uses fuzzy logic to model the knowledge of the mental health specialist necessary to identify the occurrence of behavioral change. Evaluation results show that the prediction performance of the identified context-aware sociability patterns has strong positive relation (Pearson's correlation coefficient >70%) with individuals' social routine. Finally, the evaluation conducted recognized that the proposed solution leading to the identification of abnormal social behaviors and social routine changes consistently.