Over 35% of the world's population uses social media. Platforms like Facebook, Twitter, and Instagram have radically influenced the way individuals interact and communicate. These platforms facilitate both public and private communication with strangers and friends alike, providing rich insight into an individual's personality, health, and wellbeing. To date, many researchers have employed a variety of methods for extracting mental health-centric features from digital text communication (DTC) data, including natural language processing, social network analysis, and extraction of temporal discourse patterns. However, none have explored a hierarchical framework for extracting features from private messages with the goal of unifying approaches across methodological domains. Furthermore, while analyses of large, public corpora abound in existing literature, limited work has been done to explore the relationship between of private textual communications, personality traits, and symptoms of mental illness. We present a framework for constructing rich feature spaces from digital text communications. We then demonstrate the efficacy of our framework by applying it to a dataset of private Facebook messages in a college student population (N=103). Our results reveal key individual differences in temporal and relational behaviors, as well as language usage in relation to validated measures of trait-level anxiety, loneliness, and personality. This work represents a critical step forward in linking features of private social media messages to validated measures of mental health, wellbeing, and personality.

Mental illness is widespread in our society, yet remains difficult to treat due to challenges such as stigma and overburdened health care systems. New paradigms are needed for treating mental illness outside the practitioner’s office. We propose a framework to guide the design of mobile sensing systems for personalized mental health interventions. This framework guides researchers in constructing interventions from the ground up through four phases: sensor data collection, digital biomarker extraction, health state detection, and intervention deployment. We highlight how this framework advances research in personalized mHealth and address remaining challenges, such as ground truth fidelity and missing data.

Background Health interventions delivered via smart devices are increasingly being used to address mental health challenges associated with cancer treatment. Engagement with mobile interventions has been associated with treatment success; however, the relationship between mood and engagement among patients with cancer remains poorly understood. A reason for this is the lack of a data-driven process for analyzing mood and app engagement data for patients with cancer. Objective This study aimed to provide a step-by-step process for using app engagement metrics to predict continuously assessed mood outcomes in patients with breast cancer. Methods We described the steps involved in data preprocessing, feature extraction, and data modeling and prediction. We applied this process as a case study to data collected from patients with breast cancer who engaged with a mobile mental health app intervention (IntelliCare) over 7 weeks. We compared engagement patterns over time (eg, frequency and days of use) between participants with high and low anxiety and between participants with high and low depression. We then used a linear mixed model to identify significant effects and evaluate the performance of the random forest and XGBoost classifiers in predicting weekly mood from baseline affect and engagement features. Results We observed differences in engagement patterns between the participants with high and low levels of anxiety and depression. The linear mixed model results varied by the feature set; these results revealed weak effects for several features of engagement, including duration-based metrics and frequency. The accuracy of predicting depressed mood varied according to the feature set and classifier. The feature set containing survey features and overall app engagement features achieved the best performance (accuracy: 84.6%; precision: 82.5%; recall: 64.4%; F1 score: 67.8%) when used with a random forest classifier. Conclusions The results from the case study support the feasibility and potential of our analytic process for understanding the relationship between app engagement and mood outcomes in patients with breast cancer. The ability to leverage both self-report and engagement features to analyze and predict mood during an intervention could be used to enhance decision-making for researchers and clinicians and assist in developing more personalized interventions for patients with breast cancer.

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Music therapists provide critical, evidence-based care to a diverse range of clients. However, despite their active role in empowering individuals affected by disability, stigma, grief, and trauma, music therapists remain understudied by the HCI community. We present the results of a mixed methods study of 10 interviewees and 20 survey respondents in the U.S., all of whom are practicing music therapists. Our results show that music therapists engage in technology-aided practices such as making personalized connections with clients, assisting in identity formation, encouraging musicking (music-making), and preserving legacies. Results also show that music therapists face key challenges such as environmental, societal, and financial constraints, including high workload, lack of awareness of the value of music therapy among the general community, and limited access to secure technologies for remote client care. In light of these challenges, we present a set of design implications for creating future technologies for music therapists. This work diverges from previous studies on music therapy technologies, which focus largely on interventions with music therapy clients, by highlighting the often-neglected perspectives from music therapists.

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