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Usability, Acceptability, and Feasibility of a Personalized Adaptive Mirror Therapy for Upper-Limb Poststroke Rehabilitation Using Immersive Virtual Reality and Myoelectric Control: Single-Arm Pre-Post Study

<strong>Background:</strong> Stroke remains a primary cause of long-term disability worldwide, with upper-limb deficits affecting up to 80% of survivors acutely and 40% chronically. These deficits lead to considerable effects on their independence and overall quality of life. Conventional rehabilitation therapies are most effective when initiated shortly after a stroke, yet many patients face barriers to ongoing therapy post discharge. Recent advancements in low-cost rehabilitation systems, particularly those using virtual reality (VR) technologies, offer promising alternatives for enhancing upper-limb recovery. <strong>Objective:</strong> Given the burden on health care systems and the limitations in access to high-intensity postdischarge rehabilitation, this study aimed to evaluate the feasibility, acceptability, and usability of an upper-limb adaptive mirror therapy using VR and myoelectric control for the rehabilitation of patients with chronic stroke developed through a user-centered design approach. <strong>Methods:</strong> In this study, a total of 12 community-dwelling survivors of chronic stroke (mean age 52.9, SD 16.0 years; 4 female) with moderate to severe upper-limb impairments were enrolled. Participants were stratified by age (young: 18-55 years; older: 56-80 years) and impairment level (Fugl-Meyer Assessment-Upper Extremity score: 18-36=severe; 37-54=moderate). Acceptability was assessed for each session by patient self-evaluation of satisfaction and motivation through a visual analog scale, while the therapist assessed the patient’s participation in therapy using the Pittsburgh Participation Rehabilitation Scale. Usability was measured with the User Satisfaction Evaluation Questionnaire scale and feasibility through the NASA (National Aeronautics and Space Administration) Task Load Index cognitive workload indices. <strong>Results:</strong> Patients reported a significant increase in satisfaction from the intermediate to the final assessment (T1: 72% vs T2: 85%; <i>P</i>=.01) and stable high motivation levels. Differences in participation and motivation were observed based on impairment levels, with no effect of age. Usability ratings remained high (&gt;80%) throughout the intervention, with no significant differences between baseline and end line (<i>P</i>=.56). Cognitive workload assessments showed a significant reduction over time, in perceived cognitive (<i>P</i>=.04), performance (<i>P</i>=.007), and effort demands (<i>P</i>&lt;.001). Impairment level significantly influenced perceived workload, with participants with more severe impairment reporting higher cognitive, physical, temporal, and effort demands (all <i>P</i>&lt;.001), while age did not contribute to variability in acceptability, usability, or workload measures. <strong>Conclusions:</strong> VR therapy was found to be feasible, under adaptive task conditions, ensuring stable performance across patients. The protocol was usable and acceptable among patients with chronic stroke, especially those with moderate impairment, supporting its potential as a user-centered digital rehabilitation tool, warranting further investigation in controlled and home-based settings. <strong>Trial Registration:</strong> ClinicalTrials.gov NCT07103122; https://clinicaltrials.gov/study/NCT07103122
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Cultural Relevance and Acceptability of Cognitive Behavioral Therapy Techniques Adapted by AI or a Human Psychologist: Experimental Study

Background: Evidence-based psychological interventions are usually not accessed by marginalized groups such as refugees. Culturally adapted psychological interventions have reported larger effect sizes than nonadapted psychological interventions. However, the cultural adaptation of interventions is a lengthy process, entailing a challenge. One potential solution to overcome this challenge is the use of artificial intelligence (AI). Objective: The aim of this study was to investigate and compare the perceived cultural relevance and acceptability of 2 common cognitive behavioral therapy (CBT) techniques when translated and culturally adapted by AI versus a human psychologist. Methods: In a 2×2 factorial design, the text generator type (AI vs human psychologist) and the CBT technique (cognitive restructuring vs behavior modification) were compared. CBT technique texts translated and culturally adapted either by AI or by a human psychologist were blindly rated using the Cultural Relevance Questionnaire and the Theoretical Framework of Acceptability. Raters were Arabic-speaking refugees and immigrants, aged between 18 and 69 years, residing in Sweden, Denmark, and Germany. Raters were randomly allocated to 1 of 4 conditions. Each condition consisted of 2 stimuli. Two-factor between-subject design analyses were used to analyze the data. Results: A significant main effect of the text generator domain type (=.02; η²=0.045) was found in the first rating, with texts adapted by the AI domain perceived as more culturally relevant than those adapted by the human domain. No significant main effect of the CBT technique was found in the first rating (=.10; η²=0.022). There were no differences in the second rating. Regarding acceptability, no significant main effects of text generator domain type (=.09; η²=0.024) or the CBT technique (=.88; η²=0.001) were found in either of the ratings. Conclusions: CBT technique materials adapted by AI may be perceived as similarly culturally relevant as those adapted by a human psychologist. This finding implies the potential to accelerate the cultural adaptation of psychological interventions. However, AI still needs to be used with caution and in accordance with rigorous safety standards and robust frameworks.
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Addressing the Psychological Needs of Adolescents During the Wait Time for Mental Health Treatment: Service Design Study

<strong>Background:</strong> Adolescents waiting for mental health treatment often experience significant unmet psychological needs, including severe psychological distress, increased use of maladaptive coping strategies, and feelings of abandonment. However, current wait time support offerings across the mental health sector are sparse and lack clear evidence of effectiveness. <strong>Objective:</strong> Using design thinking, this early report describes the development of a service blueprint for a new model of care (<i>While We Wait</i>) designed to address the psychological needs of adolescents during the wait time for mental health treatment in Australia through targeted support from general practitioners (GPs) and brief, self-directed digital interventions. <strong>Methods:</strong> In partnership with health service designers from Deloitte Digital Australia, we conducted a rapid 6-week health service design sprint. This industry-led methodology involved iterative weekly activities, including the development of service user personas and service experience principles, consultation sessions with 12 youth with lived experience experts (aged 18 to 20 years) and 15 GPs, insight synthesis, and service blueprint development. <strong>Results:</strong> The design sprint produced a service blueprint anchored in 5 service experience principles: “I’m never alone,” “It’s for me,” “I’m in control,” “It’s easy to use,” and “It lifts me up.” The proposed service model incorporated a five-stage service journey: (1) recognition (the adolescent acknowledges the need for support), (2) initial consultation and onboarding with the GP, (3) support and monitoring, (4) preparation for treatment, and (5) transition to specialist care and follow-up. Key adolescent service outcomes included uptake, acceptability, self-advocacy, mental health and well-being, perceived quality of care, and help seeking intentions and behaviors. For GPs, outcomes included uptake, feasibility, acceptability, and confidence in supporting adolescents during the wait time. <strong>Conclusions:</strong> This work demonstrates that a rapid, industry-led design thinking approach may help identify priorities for developing services that address adolescents’ needs during the wait time for mental health treatment. The project also highlights the value of co-designing mental health services with lived experience experts and service providers. Together, these findings suggest that the wait time may represent an important opportunity for early therapeutic engagement rather than a passive delay before treatment.
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Older Adults’ Experiences Navigating Setup of Digital Health Technology: Implementation Report

<strong>Background:</strong> Digital health and connected technologies may support better health outcomes among older adults, including those with multiple chronic conditions or low engagement in health behaviors. However, initial experiences with technology, including during unboxing, setup, and first use, can influence emotional reactions and perceptions and can ultimately determine sustained, meaningful use. Older adults with low technology experience or poor health may be particularly vulnerable to frustration, stress, or abandonment of devices when early interactions are negative. <strong>Objective:</strong> The purpose of this implementation study was to closely observe the initial engagements with a telehealth treatment app and connected blood pressure monitor (BPM) among a group of older adults with low prior technology use and reported low health behavior engagement. The goal was to identify setup “pain points” that may influence initial impressions and intention to use the technology over time. <strong>Methods:</strong> A total of 24 older adults (aged ≥65 years) were recruited for a 4-week trial of a telehealth app. Participants were provided with a box containing a tablet preloaded with the app, paper instructions, and a BPM and cuff. Researchers first conducted in-home ethnographic interviews with participants to observe the unboxing and setup process, documenting experiences with reading instructions, using the BPM, and engaging with customer support. Weekly check-in calls and a final exit interview captured ongoing experiences and likelihood of continued use. Interview recordings were transcribed and independently coded, guided by the unified theory of acceptance and use of technology. <strong>Results:</strong> Most of the sample were White (20/24, 83%) and female (14/24, 58%). Negative experiences with the app’s customer support were the top challenge for participants, with representatives providing confusing steps or conflicting terminology. Other common challenges were understanding instructions, connecting to Bluetooth, and correctly using the BPM. While 67% (16/24) of the participants indicated that they were likely or very likely to continue to use the app after the study ended at the end of week 1, this number dropped to 54% (13/24) by the end of the 4 weeks. Participants who reported lower technology self-efficacy at the beginning of the study also experienced frustration, anxiety, and embarrassment as friction with the setup process continued. <strong>Conclusions:</strong> First impressions of digital health apps play a critical role in influencing older adults’ emotions and perceptions regarding the technology and may impact the likelihood of longer-term engagement. Those with lower technology self-efficacy are particularly susceptible to experiencing negative emotions such as frustration, stress, or shame. Mobile health apps and interventions targeting older adults should incorporate simplified instructions with clear, consistent terminology and well-trained customer support staff to improve the onboarding experience.
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Peer Support in Online Women’s Health Communities: Mixed Methods Formative Analysis of Reddit Discourse

Background: Stigmatized women’s health issues, such as polycystic ovary syndrome (PCOS) and endometriosis, are often marginalized or dismissed in traditional clinical settings. This drives individuals to seek peer support in anonymous online communities such as Reddit. While these digital platforms host critical discussions, they are often designed as static information repositories, failing to account for the complex emotional, temporal, and cultural dynamics that shape users’ support needs. There is a disconnect between the lived experiences of users—particularly feelings of clinical dismissal and the need for culturally specific advice—and the design of the sociotechnical systems they rely on. Objective: This study aimed to deconstruct support practices in online women’s health forums to provide a formative basis for designing more responsive digital health systems. We analyzed the intersections of discussion topics, emotional expression, temporal shifts (specifically the impact of the COVID-19 pandemic), and culturally situated discourse to identify unmet user needs and effective peer-support patterns. Methods: We conducted a large-scale, mixed-methods analysis of 4995 posts and 460,317 comments from 5 major women’s health subreddits (r/WomensHealth, r/TwoXChromosomes, r/BirthControl, r/Endometriosis, and r/PCOS). Computational methods included Latent Dirichlet Allocation for topic modeling, Valence Aware Dictionary for Sentiment Reasoning for sentiment analysis, and the NRC Emotion Lexicon for granular emotion classification. We segmented the data into pre-, during-, and post–COVID-19 periods to analyze temporal shifts. This quantitative analysis was complemented by a 2-phase qualitative thematic analysis to identify and characterize engagement patterns within 147 validated culturally situated threads. Results: Our analysis revealed that the most prevalent and emotionally negative topic was “Pain & Doctor Visits,” which was uniquely characterized by high levels of fear and sadness linked to systemic clinical dismissal. The COVID-19 pandemic triggered a significant topical “turn inward,” with discussions shifting away from social or political issues and toward somatic concerns (eg, “PCOS” “Pain & Doctor Visits”). Paradoxically, this period saw a simultaneous rise in both negative emotions (eg, fear and sadness) and expressions of community trust. Critically, our qualitative analysis of culturally situated discourse uncovered a consistent three-stage “playbook” for effective support: (1) to establish psychological safety and validate cultural experiences; (2) to provide actionable, culturally tailored advice; and (3) to facilitate community-wide learning and empathy. Conclusions: Online health forums operate as essential, resilient sociotechnical infrastructures that actively compensate for failures and gaps in formal health care. The “Affirmation-Scaffolding-Bridging” model identified in our research provides a clear, formative framework for designing future digital health interventions. These findings can guide the development of new platforms that are emotionally aware, culturally responsive, and adaptive to user needs and external crises.
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At-Home Sleep Electroencephalography Assessment in Young and Older Adults Using a Novel Wireless Soft Electronics Sleep Monitoring System: Experimental Study

Background: Sleep quality declines with age and is a known contributor to multiple chronic health conditions, including Alzheimer disease. Emerging evidence suggests that certain electroencephalography (EEG) neural signatures measured during sleep may be predictive of cognitive decline in older adults. Sleep EEG signals are traditionally measured using bulky, rigid, and uncomfortable equipment in an unfamiliar laboratory setting, which can negatively impact sleep signals. Due to these limitations, sleep EEG data acquisition is typically limited to a single night. Objective: This study aimed to validate our recently developed portable, skin-like EEG monitoring patch for 7 nights in the home environment in a pilot sample of young and older adults by evaluating usability and acceptance, and replicating age-related differences in sleep architecture observed in the polysomnography literature. Methods: Eighteen young adults and 18 cognitively unimpaired older adults without sleep disorders were enrolled (data from 11 young adults and 12 older adults were included in the analyses) in a 7-night study during which they wore novel, gel-free, wireless, ultrathin, skin-conforming, sleep monitoring, fabric-based patches. These patches were self-applied to the forehead and face for optimal usability and comfort. The patches incorporate laser-cut mesh electrodes with low-profile electronics (including a rechargeable battery and amplifier) and transmit EEG signals to a participant-controlled, Bluetooth-enabled, tablet-based data acquisition app. An automated algorithm was used to stage sleep and assess microarchitecture features from the EEG commonly impacted for each participant. Averages across nights were computed for these sleep features for each participant. Results: Young and older adults reported that the sleep patch was easy to use and comfortable to wear. There was no loss of signal power over 7 nights of wear across participants (retained-data signal-to-noise ratio over the 7-d period: young adult, mean 20.69, SD 12.78, maximum 52.13, minimum 5.19; older adult, mean 22.10, SD 9.39, maximum 49.96, minimum 13.79). Most datasets not retained were lost due to poor reference electrode adhesion on the nose (75/101, 74% of lost datasets in young adults and 57/88, 65% in older adults). Trained sleep technologists verified that the retained datasets were of sufficient quality to be scored without difficulty. Expected age-group differences in sleep features were observed, including age-related reductions in stage N3 sleep (young adult, mean 18.55, SD 6.70; older adult, mean 10.40, SD 6.43; Mann-Whitney =42.0; =.01) and reduced sleep spindle density (young adult, mean 2.92, SD 2.24; older adult, mean 0.94, SD 1.33; Mann-Whitney =45.0; =.006). Conclusions: This study demonstrates that our novel, comfortable, wearable patch can reliably measure physiological sleep data over multiple nights at home in adults across the lifespan, thereby making multinight sleep assessment in cognitive aging studies and clinical research more accessible than traditional polysomnography. In future studies, the small, lightweight system, which is highly scalable, can be shipped inexpensively to participants’ homes, making this technology and research accessible to individuals who may have difficulty traveling or who are hesitant to travel to a laboratory or clinic.
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Protein Biomarkers in Practice: Strategies to Reduce Drug Development Risk

Drug development demands scientific rigor, sustained investment, and confident decision-making under uncertainty. As programs move from early discovery into clinical development, teams must balance biological complexity, timelines, and capital allocation— often without sufficient translational insight. Selecting the wrong target or patient population can result in costly delays and increased clinical risk.

Protein biomarkers are becoming central to how pharmaceutical leaders reduce that risk and guide strategy. Unlike static genomic associations, proteins provide dynamic, functional insights into disease biology, reflecting pathway activity, target engagement, and treatment response in real-time. Advances in high-throughput proteomic technologies have transformed protein biomarkers from exploratory tools into strategic assets applied across the drug development lifecycle.

When integrated early, biomarker-driven approaches can strengthen target validation, support proof-of-mechanism studies, enable more precise patient segmentation, and provide measurable indicators of efficacy and safety. The result is more informed decision-making, improved trial design, and greater confidence as programs advance.

This eBook is designed to deliver both strategic insight and practical guidance. It opens with a White Paper informed by expert perspectives from senior translational leaders at leading pharmaceutical organizations. These experts explore how protein biomarkers mitigate risk across the drug development continuum, from early target validation to clinical trial design, by strengthening biological confidence and enhancing decision quality.

Building on these strategic insights, the eBook presents seven real-world application examples that illustrate how these approaches are implemented in practice. Together, these perspectives provide readers with actionable frameworks and concrete use cases to help reduce uncertainty, optimize patient selection, improve trial efficiency, and make more confident, data-driven decisions earlier in development.

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The Role of Trust in Text Messaging for Promoting Patient Portal Activation Among Low-Income Patients: Quality Improvement Project

Background: The increasing reliance on patient portals for electronic health records has widened the digital health care access gap, particularly among low-income and Medicaid-insured populations. However, resources exist to assist low-income patients with portal enrollment; in obtaining a free smartphone; and, in New York, in obtaining low-cost internet. Automated bidirectional SMS text messaging offers a scalable and cost-effective strategy for identifying low-income patients’ digital health needs and eligibility for resources by using screening questions and providing tailored information on how to access available resources. Objective: This study aimed to increase portal access among low-income patients using automated bidirectional SMS text messaging and assess its feasibility and acceptability. Methods: This quality improvement initiative involved sending automated, bidirectional SMS text messages in English to 12,381 Medicaid-insured and/or low-income patients from a primary care practice. Messages assessed patients’ digital health needs and provided adaptive, personalized resources and assistance for enrolling in the patient portal and for accessing digital technology. We assessed response rates and follow-up portal enrollment rates. We surveyed participants regarding the acceptability, appropriateness, and usability of the SMS text messaging intervention, as well as their subsequent use of the patient portal. We performed descriptive statistics and a binomial probability test. Results: In total, 9.2% (1140/12,381) of patients responded to the SMS text messages, with 3.9% (481/12,381) opting out and 5.3% (659/12,381) actively engaging. Among respondents, 71.1% (469/659) completed the follow-up survey. Respondents were predominantly female (336/469, 71.6%), with ages ranging from 18 to 65 years or older. Most respondents rated the message’s clarity (420/469, 89.6%), its usefulness (400/469, 85.2%), and the demonstration of care by their health team (350/469, 74.6%) favorably. Concerns regarding privacy (61/469, 13%) and trustworthiness (71/469, 15%) were noted. Notably, 71% of initially unenrolled patients activated their patient portals after the intervention (=.007), exceeding the hypothesized expectations. Conclusions: Automated bidirectional SMS text messaging had mixed effects on promoting patient portal use among low-income patients. Response rates to SMS text messages were low when delivered from an unknown phone number. Among responders, most reported that these messages were useful and that they would recommend them to others. Research is needed to determine optimal strategies for introducing the program and vendor phone numbers to patients to improve engagement.
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Curiosity in a Novel Virtual Reality Scenario and Its Association With Symptoms of Depression: Observational Pilot Investigation

Background: Curiosity plays a fundamental role in human learning, development, and motivation, and emerging evidence suggests that reduced curiosity is linked to poorer mental health outcomes, including depressive symptoms (DS). However, to date, the majority of curiosity research relies on self-report assessments and thus risks biased reporting. Virtual reality (VR), a novel tool increasingly used within mental health research and treatment, might represent a potent tool for offering ecologically valid insights into curiosity-driven behaviors while circumventing issues related to self-report assessments, including demand characteristics and recall bias. Objective: The study aimed to enhance the assessment of curiosity by using a novel VR environment and to examine its relevance to DS. Specifically, we tested 2 hypotheses using a novel VR environment: first, that curiosity, as assessed through spontaneous exploratory interactions and behaviors in VR, positively correlates with self-reported curiosity, and second, that VR-based curiosity is inversely associated with DS. Methods: This exploratory study used an observational design that included 100 volunteers. All participants completed self-reported assessments of DS and curiosity before engaging in a novel VR scenario. Although progression in the virtual environment required solving cognitive tasks, these were embedded as structural elements rather than framed as the primary objective. Instead, participants’ free explorations and interactions with objects formed the basis for the 4 curiosity metrics used in this study. After VR exposure, participants completed a questionnaire assessing cybersickness symptoms. Results: Hypothesis 1 was not supported, as only one curiosity metric, namely object interactions, was positively associated with one aspect of curiosity relating to motivation to seek new knowledge and experiences. Further, diminishing significance after correction for multiple testing warranted caution. Results relating to hypothesis 2 indicated partial support, in that object interaction was significantly associated with DS while controlling for age, sex, and cybersickness levels. Sensitivity analyses showed no associations between object interactions and self-reported anxiety and stress symptoms. Conclusions: VR may be a potent tool for assessing exploratory behaviors in a controlled, yet ecologically valid, environment that avoids issues related to self-report. However, whether such motivations translate to established curiosity constructs warrants further research. This study also provided preliminary insights into how assessing exploratory interactions in VR may be a promising avenue that could enhance the understanding of the etiology and assessment of DS—particularly its early stages.
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TRACS Enables Strain-Level Tracking of Microbial Transmission

Tracking microbes is challenging, particularly when there are coexisting strains of the same species within metagenomic data. However, overcoming that challenge is important for inferring transmission of both pathogenic and commensal microbes.

A new tool, called TRAnsmision Clustering of Strains (TRACS), distinguishes between closely related bacterial strains. The “highly accurate algorithm” can be used for “estimating genetic distances between strains at the level of individual single nucleotide polymorphisms, which is robust to intra-species diversity within the host.”

Researchers used the TRACS tool to map the transmission of SARS-CoV-2, Streptococcus pneumoniae, and Plasmodium falciparum (the causative agent of malaria) across different populations. The tool may play an important role in infection prevention, outbreak response, and the development of treatments designed to help the human microbiome fight infection. They note that this tool can be used across microbial kingdoms to uncover strain dynamics.

“Traditionally, this has been very difficult for us to achieve, yet it is incredibly important to know, as people can carry several slightly different versions or strains of the same species at once, which makes it challenging to understand how microbes move between individuals,” notes Gerry Tonkin-Hill, PhD, group leader at the the Peter MacCallum Cancer Centre and the Peter Doherty Institute at the University of Melbourne, Australia. “Using this new technology, we can now overcome this challenge and gain a clearer picture of how microbes are shared between people. This will give us a better understanding of how microbes spread to help us prevent infection in vulnerable populations, like our cancer patients.”

This work is published in Nature Microbiology in the paper, “Strain-level transmission inference across multi-kingdom metagenomic data using TRACS.”

Being able to track the spread of pathogens using genomics has become a major tool in public health and can help inform new ways to prevent transmission. Additionally, it can help understand more about how lifestyle and environmental factors are involved in the transmission of these pathogens, and their role in the microbiome.

Currently, genomic tools used to track multiple bacterial species do not have the speed and flexibility required for routine public health monitoring and can struggle to distinguish between samples transmitted recently and those transmitted years ago. Furthermore, it can be difficult to continuously add in new samples, making real-time surveillance difficult.

The TRACS algorithm identifies and analyzes Single Nucleotide Polymorphisms (SNPs) to estimate how closely related the pathogens are, and if they are likely to have recently been transmitted. This approach allows for the continuous integration of new samples, making it an ideal tool for accurately identifying transmission networks and ruling out transmission events in ongoing public health applications.

In this new study, the team used TRACS to map pathogen transmission networks across three different populations, all of which had different genomic data. They applied it to SARS-CoV-2 data from U.K. hospitals, deep population sequencing data of Streptococcus pneumoniae and single-cell genome sequencing data from malaria patients infected with Plasmodium falciparum. They found that the tool was able to identify different pathogens in one sample and infer where these were each transmitted.

They also used TRACS to study how microbes are passed from mothers to infants and found that one beneficial bacterium, Bifidobacterium breve, persisted in infants longer than previously recognized, something that previous methods have missed.

More superficially, the authors note that “applying TRACS to gut metagenomic samples from a mother–infant cohort revealed species-specific transmission rates and identified increased the persistence of Bifidobacterium breve in infants, a finding previously missed owing to the presence of multiple strains.”

“This research could support the development of new treatments that use beneficial microbes to improve health,” notes Trevor Lawley, PhD, group leader at the Wellcome Sanger Institute. “By understanding exactly how microbes move between people and which of them are more likely to thrive in their microbiome, we could design better ways to increase helpful gut microbes and investigate whether there are ways to use these to help prevent infections, opening the door to safer healthcare environments and new microbiome-based therapies.”

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