Stress Tracker-Detecting Acute Stress From a Trackpad: Controlled Study.

BACKGROUND: Stress is a risk factor associated with physiological and mental health problems. Unobtrusive, continuous stress sensing would enable precision health monitoring and proactive interventions, but current sensing methods are often inconvenient, expensive, or suffer from limited adherence. Prior work has shown the possibility to detect acute stress using biomechanical models derived from passive logging of computer input devices. OBJECTIVE: Our objective is to detect acute stress from passive movement measurements of everyday interactions on a laptop trackpad: (1) click, (2) steer, and (3) drag and drop. METHODS: We built upon previous work, detecting acute stress through the biomechanical analyses of canonical computer mouse interactions and extended it to study similar interactions with the trackpad. A total of 18 participants carried out 40 trials each of three different types of movement-(1) click, (2) steer, and (3) drag and drop-under both relaxed and stressed conditions. RESULTS: The mean and SD of the contact area under the finger were higher when clicking trials were performed under stressed versus relaxed conditions (mean area: P=.009, effect size=0.76; SD area: P=.01, effect size=0.69). Further, our results show that as little as 4 clicks on a trackpad can be used to detect binary levels of acute stress (ie, whether it is present or not). CONCLUSIONS: We present evidence that scalable, inexpensive, and unobtrusive stress sensing can be done via repurposing passive monitoring of computer trackpad usage.

Evaluating In-Car Movements in the Design of Mindful Commute Interventions: Exploratory Study.

BACKGROUND: The daily commute could be a right moment to teach drivers to use movement or breath towards improving their mental health. Long commutes, the relevance of transitioning from home to work, and vice versa and the privacy of commuting by car make the commute an ideal scenario and time to perform mindful exercises safely. Whereas driving safety is paramount, mindful exercises might help commuters decrease their daily stress while staying alert. Increasing vehicle automation may present new opportunities but also new challenges. OBJECTIVE: This study aimed to explore the design space for movement-based mindful interventions for commuters. We used qualitative analysis of simulated driving experiences in combination with simple movements to obtain key design insights. METHODS: We performed a semistructured viability assessment in 2 parts. First, a think-aloud technique was used to obtain information about a driving task. Drivers (N=12) were given simple instructions to complete movements (configural or breath-based) while engaged in either simple (highway) or complex (city) simulated urban driving tasks using autonomous and manual driving modes. Then, we performed a matching exercise where participants could experience vibrotactile patterns from the back of the car seat and map them to the prior movements. RESULTS: We report a summary of individual perceptions concerning different movements and vibrotactile patterns. Beside describing situations within a drive when it may be more likely to perform movement-based interventions, we also describe movements that may interfere with driving and those that may complement it well. Furthermore, we identify movements that could be conducive to a more relaxing commute and describe vibrotactile patterns that could guide such movements and exercises. We discuss implications for design such as the influence of driving modality on the adoption of movement, need for personal customization, the influence that social perception has on participants, and the potential role of prior awareness of mindful techniques in the adoption of new movement-based interventions. CONCLUSIONS: This exploratory study provides insights into which types of movements could be better suited to design mindful interventions to reduce stress for commuters, when to encourage such movements, and how best to guide them using noninvasive haptic stimuli embedded in the car seat.

A Suite of Mobile Conversational Agents for Daily Stress Management (Popbots): Mixed Methods Exploratory Study.

BACKGROUND: Approximately 60%-80% of the primary care visits have a psychological stress component, but only 3% of patients receive stress management advice during these visits. Given recent advances in natural language processing, there is renewed interest in mental health chatbots. Conversational agents that can understand a user's problems and deliver advice that mitigates the effects of daily stress could be an effective public health tool. However, such systems are complex to build and costly to develop. OBJECTIVE: To address these challenges, our aim is to develop and evaluate a fully automated mobile suite of shallow chatbots-we call them Popbots-that may serve as a new species of chatbots and further complement human assistance in an ecosystem of stress management support. METHODS: After conducting an exploratory Wizard of Oz study (N=14) to evaluate the feasibility of a suite of multiple chatbots, we conducted a web-based study (N=47) to evaluate the implementation of our prototype. Each participant was randomly assigned to a different chatbot designed on the basis of a proven cognitive or behavioral intervention method. To measure the effectiveness of the chatbots, the participants' stress levels were determined using self-reported psychometric evaluations (eg, web-based daily surveys and Patient Health Questionnaire-4). The participants in these studies were recruited through email and enrolled on the web, and some of them participated in follow-up interviews that were conducted in person or on the web (as necessary). RESULTS: Of the 47 participants, 31 (66%) completed the main study. The findings suggest that the users viewed the conversations with our chatbots as helpful or at least neutral and came away with increasingly positive sentiment toward the use of chatbots for proactive stress management. Moreover, those users who used the system more often (ie, they had more than or equal to the median number of conversations) noted a decrease in depression symptoms compared with those who used the system less often based on a Wilcoxon signed-rank test (W=91.50; Z=-2.54; P=.01; r=0.47). The follow-up interviews with a subset of the participants indicated that half of the common daily stressors could be discussed with chatbots, potentially reducing the burden on human coping resources. CONCLUSIONS: Our work suggests that suites of shallow chatbots may offer benefits for both users and designers. As a result, this study's contributions include the design and evaluation of a novel suite of shallow chatbots for daily stress management, a summary of benefits and challenges associated with random delivery of multiple conversational interventions, and design guidelines and directions for future research into similar systems, including authoring chatbot systems and artificial intelligence-enabled recommendation algorithms.