With advances in electronic-skin and wearable technologies, it is possible to continuously measure stress markers from the skin and sweat to monitor and improve wellbeing and health. Understandably, the sensor's engineering and resolution are important towards its function. However, we find that people looking for an e-skin stress sensor may look beyond measurement precision, demanding a private and stealth design to reduce, for example, social stigmatization. We introduce the idea of a stress sensing "wear index," created from the combination of human-centered design (n=24), physiological (n=10), and biochemical (n=16) data. This wear index can inform the design of stress wearables to fit specific applications, e.g., human factors may be relevant for a wellbeing application, versus a relapse prevention application that may require more sensing precision. Our wear index idea can be further generalized as a method to close gaps between design and engineering practices.
doi.org/10.1145/3613904.3643473
Stratifying depressed individuals may help to improve recovery rates by identifying the subgroups who would benefit from targeted treatments. Detecting depressed individuals with prominent anhedonia (i.e. lack of pleasure) may be one effective approach, given these individuals experience poorer treatment outcomes. This paper explores the linguistic features associated with anhedonia among depressed adults. Over 9 weeks, 218 individuals with depressive symptoms completed a fortnightly psychometric measure of depression (PHQ-9) and provided text data (SMS, social media posts, expressive essays, emotion diaries, personal letters). Linguistic features were examined using LIWC-22. Greater use of discrepancy words was significantly associated with higher anhedonia, but in SMS data only. Machine learning showed some utility for predicting increased anhedonia, with discrepancy words the most important linguistic feature in the model. Discrepancy words were not found to be associated with overall depression scores. These results suggest that this linguistic feature may show some promise for the stratification of anhedonic depression.
doi.org/10.1145/3613904.3642478
Despite the fact that spatio-temporal patterns of vibration, characterized as rhythmic compositions of tactile content, have exhibited an ability to elicit specific emotional responses and enhance the emotion conveyed by music, limited research has explored their underlying mechanism in regulating emotional states within the pre-sleep context. Aiming to investigate whether synergistic spatio-temporal tactile displaying of music can facilitate relaxation before sleep, we developed 16 vibration patterns and an audio-tactile prototype for presenting an ambient experience in a pre-sleep scenario. The stress-reducing effects were further evaluated and compared via a user experiment. The results showed that the spatio-temporal tactile display of music significantly reduced stress and positively influenced users' emotional states before sleep. Furthermore, our study highlights the therapeutic potential of incorporating quantitative and adjustable spatio-temporal parameters correlated with subjective psychophysical perceptions in the audio-tactile experience for stress management.
doi.org/10.1145/3613904.3642736
MoodCapture presents a novel approach that assesses depression based on images automatically captured from the front-facing camera of smartphones as people go about their daily lives. We collect over 125,000 photos in the wild from N=177 participants diagnosed with major depressive disorder for 90 days. Images are captured naturalistically while participants respond to the PHQ-8 depression survey question: "I have felt down, depressed, or hopeless''. Our analysis explores important image attributes, such as angle, dominant colors, location, objects, and lighting. We show that a random forest trained with face landmarks can classify samples as depressed or non-depressed and predict raw PHQ-8 scores effectively. Our post-hoc analysis provides several insights through an ablation study, feature importance analysis, and bias assessment. Importantly, we evaluate user concerns about using MoodCapture to detect depression based on sharing photos, providing critical insights into privacy concerns that inform the future design of in-the-wild image-based mental health assessment tools.
doi.org/10.1145/3613904.3642680
Self-monitoring of mood and lifestyle habits is the cornerstone of many therapies, but it is still hindered by persistent issues including inaccurate records, gaps in the monitoring, patient burden, and perceived stigma. Smartwatches have potential to deliver enhanced self-reports, but their acceptance in clinical mental health settings is unexplored and rendered difficult by a complex theoretical landscape and need for a longitudinal perspective. We present the Mood Monitor smartwatch application for mood and lifestyle habits self-monitoring. We investigated patient acceptance of the app within a routine 8-week digital therapy. We recruited 35 patients of the UK's National Health Service and evaluated their acceptance through three online questionnaires and a post-study interview. We assessed the clinical feasibility of the Mood Monitor by comparing clinical, usage, and acceptance metrics obtained from the 35 patients with smartwatch with those from an additional 34 patients without smartwatch (digital treatment as usual). Findings showed that the smartwatch app was highly accepted by patients, revealed which factors facilitated and impeded this acceptance, and supported clinical feasibility. We provide guidelines for the design of self-monitoring on smartwatch and reflect on the conduct of HCI research evaluating user acceptance of mental health technologies.