Laura E. Barnes

Emerging adulthood involves impactful developmental milestones for sexual and gender minority (SGM) individuals and is a critical period for understanding stress processes. Most existing SGM stress research is cross-sectional, with relatively little known about the everyday experiences that may confer mental health risk or resilience for SGM emerging adults. N = 104 SGM emerging adults (age: M = 20.21 years, SD = 1.92 years; 76% assigned female at birth; 35% gender minority; 53% Bisexual+; 55% White) were recruited to codesign (Phases 1-2) or complete for 14 days (Phase 3) a daily diary measure of identity-related and non-identity-related stressors and positive identity-related experiences. Phase 1 participants (n = 8) completed qualitative interviews and provided feedback on an initial draft measure. Phase 2 participants (n = 6) piloted a revised measure for seven days before providing feedback in qualitative interviews. Phase 3 participants (n = 90) completed the final measure for 14 days. Qualitative data from Phases 1-2 were analyzed using the immersion-crystallization framework, and descriptive statistics were computed for Phase 3 quantitative data. In Phases 1-2, participants emphasized the need to capture chronic stressors and stress tied to recent sociopolitical events in the U.S. In Phase 3, non-identity-related stressors were more frequent than identity-related stressors, proximal identity-related stressors were more frequent than distal identity-related stressors, and negative intersectional experiences and positive identity-related experiences were both common. Results highlight the importance of involving participants with lived experiences in all stages of survey design.

This issue includes 121 articles, continuing the growing number of outstanding contributions to PACM IMWUT. As in previous issues, the published articles span several key areas of interest to the IMWUT community.

Social interactions are fundamental to well-being, yet automatically detecting them in daily life—particularly using wearables—remains underexplored. Most existing systems are evaluated in controlled settings, focus primarily on in-person interactions, or rely on restrictive assumptions (e.g., requiring multiple speakers within fixed temporal windows), limiting generalizability to real-world use. We present an on-watch interaction detection system designed to capture diverse interactions in naturalistic settings. A core component is a foreground speech detector trained on a public dataset. Evaluated on over 100,000 labeled foreground speech and background sound instances, the detector achieves a balanced accuracy of 85.51%, outperforming prior work by 5.11%.; AB@We evaluated the system in a real-world deployment (N=38), with over 900 hours of total smartwatch wear time. The system detected 1,691 interactions, 77.28% were confirmed via participant self-report, with durations ranging from under one minute to over one hour. Among correct detections, 81.45% were in-person, 15.7% virtual, and 1.85% hybrid. We further developed a 15-second window-level audio-only model that enables faster interaction prediction, achieving a balanced accuracy of 90.39% and a sensitivity of 91.01% on 33,698 labeled windows. These results demonstrate the feasibility of real-world interaction sensing and open the door to adaptive, context-aware systems responding to users' dynamic social environments.

Social interactions are fundamental to well-being, yet automatically detecting them in daily life—particularly using wearables—remains underexplored. Most existing systems are evaluated in controlled settings, focus primarily on in-person interactions, or rely on restrictive assumptions (e.g., requiring multiple speakers within fixed temporal windows), limiting generalizability to real-world use. We present an on-watch interaction detection system designed to capture diverse interactions in naturalistic settings. A core component is a foreground speech detector trained on a public dataset. Evaluated on over 100,000 labeled foreground speech and background sound instances, the detector achieves a balanced accuracy of 85.51%, outperforming prior work by 5.11%.We evaluated the system in a real-world deployment (N=38), with over 900 hours of total smartwatch wear time. The system detected 1,691 interactions, 77.28% were confirmed via participant self-report, with durations ranging from under one minute to over one hour. Among correct detections, 81.45% were in-person, 15.7% virtual, and 1.85% hybrid. We further developed a 15-second window-level audio-only model that enables faster interaction prediction, achieving a balanced accuracy of 90.39% and a sensitivity of 91.01% on 33,698 labeled windows. These results demonstrate the feasibility of real-world interaction sensing and open the door to adaptive, context-aware systems responding to users’ dynamic social environments.

Effective patient-provider communication is crucial in clinical care, directly impacting patient outcomes and quality of life. Traditional evaluation methods, such as human ratings, patient feedback, and provider self-assessments, are often limited by high costs and scalability issues. Although existing natural language processing (NLP) techniques show promise, they struggle with the nuances of clinical communication and require sensitive clinical data for training, reducing their effectiveness in real-world applications. Emerging large language models (LLMs) offer a new approach to assessing complex communication metrics, with the potential to advance the field through integration into passive sensing and just-in-time intervention systems. This study explores LLMs as evaluators of palliative care communication quality, leveraging their linguistic, in-context learning, and reasoning capabilities. Specifically, using simulated scripts crafted and labeled by healthcare professionals, we test proprietary models (e.g., GPT-4) and fine-tune open-source LLMs (e.g., LLaMA2) with a synthetic dataset generated by GPT-4 to evaluate clinical conversations, to identify key metrics such as “understanding” and “empathy.” Our findings demonstrated LLMs’ superior performance in evaluating clinical communication, providing actionable feedback with reasoning, and demonstrating the feasibility and practical viability of developing in-house LLMs. This research highlights LLMs’ potential to enhance patient-provider interactions and lays the groundwork for downstream steps in developing LLM-empowered clinical health systems.

This issue includes an outstanding collection of manuscripts spanning several key areas of interest to the IMWUT community.

Background. Co-occurrence of depressive and anxiety symptoms is common; however, the shortage of mental health professionals and high treatment costs means additions to in-person care are necessary to meet treatment needs. The present paper evaluates whether a web-based cognitive bias modification for interpretation (CBM-I) intervention intended for anxious adults—but which targets a shared cognitive mechanism of anxiety and depression—is superior to web-based psychoeducation at reducing co-occurring depressive symptoms. Methods. Latent growth curve modeling was used to assess for superiority of CBM-I over psychoeducation at reducing depressive symptoms among adults with elevated anxiety and depressive symptoms. Interventions were completed once weekly for five weeks with a 2-month follow-up assessment. Analyses were replicated in two separate datasets (Study 1: N = 1069; Study 2: N = 517) and employed multiple variations of CBM-I (Study 1: standard, added low-intensity coaching; Study 2: standard, shortened, added self-referential content, added psychoeducation). Results. Study 1 showed significant superiority of CBM-I over psychoeducation at post-intervention (between-group d = 0.31); this was not replicated in Study 2, but the pattern of effect sizes was similar (d = 0.21). Both interventions showed significant reductions in depressive symptoms at post-intervention (within-group ds from baseline = 0.59-0.92) that were maintained at 2-month follow-up (ds from baseline = 0.75-1.04). Conclusions. The current core CBM-I intervention, designed to target experiences of anxiety, appears preliminarily to be effective in reducing co-occurring depressive symptoms, though its effectiveness is not consistently superior to that of psychoeducation and needs replication in randomized trials.

Background: Breast cancer is a significant public health burden. Despite its critical role in preventing the recurrence of breast cancer, rates of long-term adherence to endocrine therapy (ET) remain low among certain breast cancer survivors. Using embedded sensors in smartphones and wearables, ecological momentary assessment data and health behavior theory may facilitate a richer understanding of the real-world context of medication-taking behaviors, which can aid in the development of personalized interventions. Objective: The objective of this paper is to describe the development of a multiscale modeling intervention (MMI) system to facilitate adherence to daily oral ET for breast cancer survivors. This represents the first phase of a larger project that aims to use machine learning to predict when breast cancer survivors are most likely to miss their ET medications in order to deploy personalized interventions. The purpose of this paper was (1) to determine the acceptability of the proposed MMI system, (2) to identify modifiable predictors of ET medication adherence among breast cancer survivors, and (3) to select surveys or items measuring constructs associated with ET adherence among breast cancer survivors for inclusion in the MMI system. Methods: Study 1 consisted of usability interviews with a cohort of breast cancer survivors (n=25) prescribed ET. For study 1, all qualitative usability interviews were conducted using a semistructured interview guide and assessed whether breast cancer survivors were willing to use various components of the MMI system. Study 2 consisted of (1) a secondary data analysis of ET adherence data from 32 breast cancer survivors using a social cognitive theory framework and (2) a review of research literature of constructs and surveys measuring constructs associated with ET adherence among breast cancer survivors using a social cognitive theory framework. The secondary data analysis included the use of randomized neural network analysis to predict factors strongly associated with medication adherence. Results: In study 1, usability interview findings suggested that participants were willing to use an ecological momentary assessment smartphone app, a smartwatch and associated smartphone app, a smart pill bottle or smart pill box and associated smartphone app, and the entire MMI system for a 6-month study period. In study 2, the randomized neural network analysis identified 104 survey items with significant contributions to 4-week medication adherence using a threshold of the 70th percentile for feature importance. After a review of peer-reviewed studies, we abstracted modifiable constructs significantly associated with adherence to adjuvant ET and identified 42 surveys used to measure these constructs. When these findings were combined, the final survey for the MMI system consisted of 32 surveys and demographic items. Conclusions: Our research highlights the use of social cognitive theory, data-driven models, and participant feedback to inform the development of a medication adherence monitoring system. Data from studies 1 and 2 were used to develop a prototype MMI system that will be deployed in a future longitudinal study with 20 breast cancer survivors over 6 months.

This issue presents an exciting collection of papers spanning both long-standing core areas of IMWUT—such as human activity recognition, localization, and wearable sensing—and emerging directions involving Artificial Intelligence (AI) and Large Language Models (LLMs).