To overcome the lack of deep personalization in standard biofeedback methods, we introduce ASafePlace, a system utilizing an AI-powered, art-therapy-inspired exercise called The Safe Place, to create a personalized VR biofeedback experience. In our system, users sketch a personal sanctuary from memory, which is then transformed into a customized 360° virtual environment with personalized audio guidance for relaxation training. A study with 52 participants showed this approach effectively reduced anxiety and increased user presence, while the integration of art-therapy-inspired activity and biofeedback produced strong improvements in physiological relaxation, measured by heart rate variability and respiration rate. Qualitative results showed how participants’ sense of familiarity and presence was enhanced by the symbolic elements and natural sanctuaries created from their autobiographical memories. Our findings demonstrate that art-therapy-inspired activity is a powerful tool for creating highly effective and individualized relaxation experiences, naturally connecting the virtual environment to a user’s core memories and emotions.

Chronic stress and anxiety severely affect breast cancer survivors’ (BCSs) mental health and well-being. Peer support has been shown to enhance psychological empowerment, while biofeedback offers a promising approach to improve physiological relaxation through self-regulation. However, few studies explored combining both for BCSs. We conducted a formative study with clinicians and BCSs to identify requirements and preferences for VR biofeedback. Informed by the findings, we proposed a VR-based dyadic biofeedback system, Cobreath, which integrates breathing and heart rate variability (HRV) feedback into a calming virtual environment, allowing two users to practice breathing-focused relaxation simultaneously. Through a clinical user study with ten clinicians and a between-subjects study with 32 BCSs, we demonstrated that Cobreath’s dyadic mode improved biofeedback effectiveness and provided a better user experience compared to the individual mode. We further discuss insights and design considerations for developing dyadic VR-biofeedback applications to support the mental well-being of BCSs and potential applications.

The global incidence of chronic diseases is rising, posing substantial social and economic challenges. These conditions necessitate effective long-term self-care, which can be supported by digital interventions using remote measurement technologies, like smartphones and wearables. This systematic review investigates the motivational strategies within digital technologies to improve self-care adherence for chronic illnesses, particularly cardiovascular diseases and diabetes mellitus. A literature search was conducted, focusing on studies from 2004 to 2024. A total of 17 studies met the inclusion criteria. The reviewed interventions targeted medication adherence, lifestyle modifications, and symptom tracking. Findings suggest that motivational strategies, such as feedback, health literacy, reminders, and motivational messages, goal-setting, social interaction, gamification, and rewards can improve patient adherence to self-care behaviors. However, their effectiveness relies on theoretical grounding, data-driven features, and personalization. Future research should prioritize integrating robust theories and developing standardized metrics for adherence to enhance the reliability and impact of digital interventions.

Breathing exercises and Progressive Muscle Relaxation (PMR) have complementary effects, making their integration a common practice among relaxation techniques. While numerous virtual reality (VR) exercises support breathing exercises in self-training, integrating breathing support into PMR in VR presents challenges, including maintaining the user's sense of presence and ensuring the effectiveness of breathing guidance. In this paper, we present MindFlow, a system design that combines breathing biofeedback with a full-body self-avatar, using mindfulness-based principles to provide effective, breathing-integrated PMR guidance in VR. The system demonstrates effectiveness in enhancing relaxation and reducing anxiety in novice users, based on empirical results from a 24-participant user study, offering generalizable insights for the design of embodied mindfulness systems and further research on mindfulness support in virtual and mixed reality.

Pregnancy can be challenging for women as they experience various physical, psychological, and social changes that can lead to stress and potential mental health concerns. Being neglected in the long-term, sustained stress can increase the likelihood of postpartum depression, which can have significant negative impacts on mothers, families, and society. Therefore, managing stress promptly and maintaining emotional well-being is crucial for pregnant women to give a healthy birth and improve their postpartum life quality. Biofeedback is a secure and effective treatment for anxiety; nevertheless, conventional biofeedback systems often depend on intrusive sensors and require clinician support, thereby restricting their utilization primarily to clinical settings. To address this challenge, in this study, by incorporating biofeedback techniques with wearable sensors, musical displays, and ambient light, we created an immersive biofeedback environment where pregnant women could practice slow-paced resonant breathing to promote relaxation and reduce stress. GlowGrow system has been deployed in a regional hospital's ante-natal clinic and evaluated by 24 pregnant women regarding its effectiveness and user experience. The results show that GlowGrow, as an effective relaxation intervention, could efficiently guide pregnant women to perform deep breathing and manage physiological stress.

Zoos play a crucial role in public education and wildlife engagement, yet traditional visits often lack interactive elements that foster meaningful connections between visitors and animals. In this paper, we introduce ZooWear, a head-mounted wearable featuring cartoon-style animal ears that provides haptic feedback patterns simulating an animal's reactions to other species in the food chain. With ZooWear, we focused on examining its effectiveness in affording perspective-taking during human-animal encounters, promoting embodied knowledge retention, and enriching the zoo experiences. We first conducted a between-subject experiment in lab-based virtual zoo visits to evaluate its effectiveness in creating effective learning experiences and enhancing connections to wildlife. This was followed by a real-world zoo experiment, which showed that ZooWear promoted nature connectedness and enabled more emotionally and socially engaging experiences. Our findings highlight the potential of integrating perspective-taking into zoo experiences through animal-inspired wearables, embodied sensory feedback, and narrative-driven experiences.

Integrated speech and hand-motor training is an effective post-stroke rehabilitation method. However, few interactive systems and assistive technologies were developed in this field. Driven by this challenge, we leverage Mixed Reality technology, which merges immersive virtual scenarios with physical hands-on tools in the real world, to provide patients with multi-modal interactions and engaging training experiences. Following a user-centered design approach, we first interviewed seven therapists to identify user requirements and design considerations. We further designed MRehab, an interactive rehabilitation system that allows patients to regain speech and hand skills through MR scenarios that depict daily living activities. We conducted a preliminary user test with 12 patients and 5 therapists to validate the feasibility and understand the user experience with MRehab. The results confirmed its feasibility for hand-motor training. Additionally, the patients expressed high motivation, engagement, and a positive attitude toward using MRehab. Our findings demonstrate the potential of MR technology in integrated speech and hand function rehabilitation training.

Background Many promising artificial intelligence (AI) and computer-aided detection and diagnosis systems have been developed, but few have been successfully integrated into clinical practice. This is partially owing to a lack of user-centered design of AI-based computer-aided detection or diagnosis (AI-CAD) systems. Objective We aimed to assess the impact of different onboarding tutorials and levels of AI model explainability on radiologists’ trust in AI and the use of AI recommendations in lung nodule assessment on computed tomography (CT) scans. Methods In total, 20 radiologists from 7 Dutch medical centers performed lung nodule assessment on CT scans under different conditions in a simulated use study as part of a 2×2 repeated-measures quasi-experimental design. Two types of AI onboarding tutorials (reflective vs informative) and 2 levels of AI output (black box vs explainable) were designed. The radiologists first received an onboarding tutorial that was either informative or reflective. Subsequently, each radiologist assessed 7 CT scans, first without AI recommendations. AI recommendations were shown to the radiologist, and they could adjust their initial assessment. Half of the participants received the recommendations via black box AI output and half received explainable AI output. Mental model and psychological trust were measured before onboarding, after onboarding, and after assessing the 7 CT scans. We recorded whether radiologists changed their assessment on found nodules, malignancy prediction, and follow-up advice for each CT assessment. In addition, we analyzed whether radiologists’ trust in their assessments had changed based on the AI recommendations. Results Both variations of onboarding tutorials resulted in a significantly improved mental model of the AI-CAD system (informative P=.01 and reflective P=.01). After using AI-CAD, psychological trust significantly decreased for the group with explainable AI output (P=.02). On the basis of the AI recommendations, radiologists changed the number of reported nodules in 27 of 140 assessments, malignancy prediction in 32 of 140 assessments, and follow-up advice in 12 of 140 assessments. The changes were mostly an increased number of reported nodules, a higher estimated probability of malignancy, and earlier follow-up. The radiologists’ confidence in their found nodules changed in 82 of 140 assessments, in their estimated probability of malignancy in 50 of 140 assessments, and in their follow-up advice in 28 of 140 assessments. These changes were predominantly increases in confidence. The number of changed assessments and radiologists’ confidence did not significantly differ between the groups that received different onboarding tutorials and AI outputs. Conclusions Onboarding tutorials help radiologists gain a better understanding of AI-CAD and facilitate the formation of a correct mental model. If AI explanations do not consistently substantiate the probability of malignancy across patient cases, radiologists’ trust in the AI-CAD system can be impaired. Radiologists’ confidence in their assessments was improved by using the AI recommendations.

In both remote and physical work environments, it is commonplace for help-seeking messages to be rejected by other colleagues. This paper investigates how signifying co-workers' stress status would influence the social diction and empathy of help-seekers in the context of rejection. 36 participants were recruited to perform helpseeking tasks with virtual co-workers via a professional mobile messaging application (Trillian). Their device was tailored with a vibrotactile mechanism (TacStatus), which could signify different emotional states of the coworkers: no-cue, relaxed, normal, and stressful. Independent sample Friedman nonparametric tests were conducted to analyze the social diction and empathy of the participants in their messages for help-seeking and responses to the co-workers' rejection. This study revealed that stress cues have observable impacts on the social diction and empathy of help-seekers. Stressful and relaxed cues were found to evidently shape the social diction of help-seekers. When faced with a relaxed co-worker, the help-seeker felt disappointed and unaccepted after being rejected. By contrast, when confronted with a stressful cue, help-seekers tended to exhibit relatively more positive emotions after been rejected. This study attempts to reveal the mechanism through which stress cues influence professional messaging interactions and collaboration. The findings could provide implications for the design of socio-emotional cues in the context of messaging.

Anxiety among pregnant women can significantly impact their overall well-being. However, the development of data-driven HCI interventions for this demographic is often hindered by data scarcity and collection challenges. In this study, we leverage the Empatica E4 wristband to gather physiological data from pregnant women in both resting and relaxed states. Additionally, we collect subjective reports on their anxiety levels. We integrate features from signals including Blood Volume Pulse (BVP), Skin Temperature (SKT), and Inter-Beat Interval (IBI). Employing a Support Vector Machine (SVM) algorithm, we construct a model capable of evaluating anxiety levels in pregnant women. Our model attains an emotion recognition accuracy of 69.3%, marking achievements in HCI technology tailored for this specific user group. Furthermore, we introduce conceptual ideas for biofeedback on maternal emotions and its interactive mechanism, shedding light on improved monitoring and timely intervention strategies to enhance the emotional health of pregnant women.

College students are known to face many stressors, making them one of the most vulnerable groups to mental disorders. To tackle such an issue, this paper presents a design study that investigated data-enabled analogue journaling (DEAJ) to help college students improve their mental wellness through paper-based self-reflection aided by automatic self-tracking. Specifically, based on several design iterations we developed a DEAJ tool, called EmoVis, which could generate a printed visualisation based on daily physiological data and event tags to support doodling or structured writing in analogue journaling for everyday stress management. We conducted a six-day mixed methods field study with 32 college students to evaluate the effectiveness and user experience of EmoVis. Results suggested that EmoVis can significantly improve the engagement and need for self-reflection due to context-based reflection and data-enabled exploratory journaling. Furthermore, the doodling canvas was experienced as a joyful tool for mindfulness and creative DEAJ, while reflecting with a writing template was perceived as efficient. The paper concludes with a discussion of the implications of DEAJ approaches for daily stress coping in students' college life.

The shoulder joint plays a crucial role in the recovery of upper limb function. However, conventional wearable technologies employed for monitoring shoulder joint movements predominantly rely on inertial sensing units (IMUs), which may suffer alignment errors and compromise the freedom and wearability experienced by patients during their daily activities. This paper contributes in two facets, first, it presents the design, implementation, and technical evaluation of a new wearable system, a customized unilateral shoulder wrap that utilizes flexible and breathable textile sensors. Diverging from earlier studies, our system not only facilitates the monitoring of glenohumeral joint angles but also concurrently tracks the movement angles of the scapula. Secondly, to estimate joint angles, we propose a specific model called the Channel-Temporal Encoding Network (CTEN), which leverages Transformer and Long Short-Term Memory (LSTM) architectures. In a preliminary technical evaluation, the results demonstrate root mean square errors (RMSEs) of 2.24°and 1.13°for the glenohumeral joint and scapula, respectively. This study is intended to contribute to the development of more advanced wearables tailored for shoulder joint rehabilitation training.

Running is a highly popular form of exercise, while incorrect running posture over an extended period can lead to severe knee injuries. Smart textiles have recently demonstrated significant potential for continuous motion monitoring. This study involved the design and development of a smart legging with a resistive textile sensor network to monitor lower body motion. The study consists of three main parts. Firstly, we tested textile sensors in terms of linearity and robustness to determine the basic sensor unit that can monitor the characteristics of running postures. Next, optimal sensor placement was determined through comparison experiments, and a sensor network was proposed. Finally, based on the LSTM model with data gathered from 6 participants, we developed the smart legging system that is capable of identifying three types of improper running postures and normal postures with 99.1% accuracy. The evaluation revealed that the smart legging system had the potential to help users adjust their running postures to prevent knee injury through continuous monitoring and multi-modal feedback.

For a small group of office workers who share the same workspace and the task load, leveraging their social skills and awareness could further increase their mutual awareness of each other's work-related stress. This paper presents a case study of a one-week deployment of a shared, anonymous heart rate variability (HRV) data visualization system at six workplaces with 24 office workers, who were closely collaborated in four-person groups. We collected stress-related physiology data (i.e., heart-rate variability) from wearable sensors and anonymously visualized them on a shared display. Although the physiological data collection where noisy due to the practical constraints in the field settings, we found the participants still increasingly agreed with the systems and used the visualization as a reference for their subjective stress assessment. We also present and discuss how groups of office workers individually and socially reflect on their one-week experiences and then summarize takeaways for designing shared physiological data visualization systems for group stress management in the long term.

Ballistocardiography (BCG) is considered a good alternative to HRV analysis with its non-contact and unobtrusive acquisition characteristics. However, consensus about its validity has not yet been established. In this study, 50 healthy subjects (26.2 ± 5.5 years old, 22 females, 28 males) were invited. Comprehensive statistical analysis, including Coefficients of Variation (CV), Lin’s Concordance Correlation Coefficient (LCCC), and Bland-Altman analysis (BA ratio), were utilized to analyze the consistency of BCG and ECG signals in HRV analysis. If the methods gave different answers, the worst case was taken as the result. Measures of consistency such as Mean, SDNN, LF gave good agreement (the absolute value of CV difference < 2%, LCCC > 0.99, BA ratio < 0.1) between J-J (BCG) and R-R intervals (ECG). pNN50 showed moderate agreement (the absolute value of CV difference < 5%, LCCC > 0.95, BA ratio < 0.2), while RMSSD, HF, LF/HF indicated poor agreement (the absolute value of CV difference ≥ 5% or LCCC ≤ 0.95 or BA ratio ≥ 0.2). Additionally, the R-R intervals were compared with P-P intervals extracted from the pulse wave (PW). Except for pNN50, which exhibited poor agreement in this comparison, the performances of the HRV indices estimated from the PW and the BCG signals were similar.

Slow breathing guiding applications increasingly emerge, showing promise for helping knowledge workers to better cope with workaday stress. However, standard breathing guidance is non-interactive, with rigid paces. Despite their effects being proved, they could cause respiratory fatigue, or lack of training motivation, especially for novice users. To explore new design possibilities, we investigate using heart rate variability (HRV) data to mediate breathing guidance, which results in two HRV-enhanced guidance modes: (i) responsive breathing guidance and (ii) adaptive breathing guidance. These guidance modes are implemented on a soft haptic interface named "ViBreathe". We conducted a user test (N = 24), and a one-week field deployment (N = 4) with knowledge workers, to understand the user experience of our design. The HRV-enhanced modes were generally experienced to reduce tiresome and improve engagement and comfort. And Vibreathe showed great potential for seamlessly weaving slow breathing practice into work routines. We thereby summarize related design insights and opportunities.

The accurate and sensitive detection of glucose from secretory clinical samples, such as tears and saliva, remains a great challenge. In this research, a novel ultrasensitive glucose detection method consisting of a glucose oxidase (GOx), pistol-like DNAzyme (PLDz), and CRISPR-Cas12a system is proposed. First, the oxidation of glucose catalyzed by GOx leads to the production of H2O2; the self-cleavage activity of PLDz is activated after recognition of the produced H2O2. The two procedures triggered by COx and PLDz play an important role in accurately identifying glucose and converting glucose signals to nucleic acids. The obtained PLDz fragments can be recognized by the Cas12 enzyme and thus activate the trans-cleavage activity of the Cas12a enzyme. Finally, the surrounding reporter probes are cut by the Cas12a enzyme to produce fluorescence signals. In summary, an ultra-sensitive and specific fluorescence method has been developed for glucose detection from secretory clinical samples, which could potentially contribute to the noninvasive diagnosis of diabetes mellitus.

Participating in physical activity at a moderate-and-vigorous-intensity-level is recommended for teenagers to advance in health benefits. However, due to the fitness differences, the amount of physical activity to reach the recommended intensity varies among teenagers. Therefore, tailoring the physical intensity of each teenager's fitness level is meaningful in physical education. In this paper, we present FitBirds, a multiplayer fitness game to encourage teenagers in physical activity at a recommended intensity level, which is calibrated by their real-time heart rate. The FitBirds game leverages both competition and cooperation game mechanics to enhance teenagers’ playful experiences and social engagement, which could further contribute to their physically active participation in the physical education context.

The authors have collaborated on a project visualizing heart rate variability with the assistance of a pen plotter. The authors present artworks produced at the interface between biofeedback techniques and visual art. This biofeedback project created a new process of drawing and painting driven by participants' heart activity, which itself is influenced by their mental, physical and emotional states.