Aileen C. Naef

Background: Real-world gait assessment has gained momentum in populations with walking impairments, offering insights beyond standardized tests and supporting the integration of remote monitoring into clinical care. However, its potential remains limited by the lack of validated population- and context-specific digital biomarkers. Objective: The primary objective was to summarize and critically evaluate the current state of real-world gait assessment using wearable sensors in Parkinson disease (PD) and stroke, addressing methodological approaches, sensor configurations, validation strategies, and outcome reporting. The secondary objective was to quantify methodological heterogeneity by reporting pooled means with 95% confidence intervals (CIs) and prediction intervals (PIs) of commonly reported gait parameters. Methods: PubMed, Scopus, Web of Science, and Embase were searched for English-language studies published up to April 20, 2026. Eligible studies used wearable sensors to assess gait quality parameters in real-world settings in at least 5 individuals with PD or poststroke. Laboratory- or rehabilitation-only studies, non–peer-reviewed articles, abstracts, and studies before 2014 were excluded. Methodological quality was assessed with a checklist adapted from Hubble et al. Gait features available in at least 5 reports were included in a meta-analysis using a random-effects model with Hartung-Knapp-Sidik-Jonkman adjustment, deriving 95% CIs and PIs to characterize the pooled estimate and its dispersion. Results: Of 2489 records, 43 reports were included: 37 on PD (2774 participants; mean age 67.89, SD 8.68 years) and 6 on stroke (217 participants; mean age 63.83, SD 11.84 years). Methodological quality was high, but external validity was consistently the weakest domain in both populations. Sensor configurations most commonly consisted of a lower back accelerometer. In total, 13 distinct walking bout definitions were identified, whereas 17 reports provided none, indicating substantial terminological heterogeneity. In PD, 4 gait parameters met the meta-analysis threshold, each showing a PI far wider than its CI, precluding generalization to future settings. For example, gait speed pooled at 0.84 m/s (95% CI 0.77‐0.91; 95% PI 0.56‐1.12), and number of steps per day at 7202 (95% CI 3975‐10,429), the latter with a PI extending below zero, a mathematically impossible range reflecting extreme dispersion. No stroke parameter was reported consistently enough to permit meta-analysis, underscoring a critically underdeveloped evidence base. Conclusions: Unlike previous reviews focusing on single diseases or specific hardware, this review contrasts PD and stroke to highlight shared methodological challenges. Real-world gait assessment in PD is advancing but fragmented, whereas the stroke evidence base remains sparse. The observed variance is compounded by procedural and algorithmic inconsistencies that cannot currently be separated from genuine clinical differences. The methodological groundwork established in PD offers a foundation for early adoption of standardized terminology, validation procedures, and core outcomes, essential for transitioning wearable sensors from exploratory tools to reliable clinical and remote-monitoring instruments.

BACKGROUND Real-world gait assessment has gained momentum in populations with walking impairments, offering insights beyond standardized tests and supporting the integration of remote monitoring into clinical care. However, the full potential of wearable sensors remains limited by the lack of validated population- and context-specific digital biomarkers. OBJECTIVE The primary objective was to update the state of the art and summarize challenges in real-world gait assessment for PD and stroke. The secondary objective was to report pooled means and standard deviations of gait parameters. METHODS PubMed and Scopus were searched for English-language studies published up to December 31, 2024. Eligible studies included a minimum of five individuals with Parkinson’s disease (PD) or post-stroke and used wearable sensors to assess gait in real-world settings. Studies conducted solely in laboratory or rehabilitation environments, non-peer-reviewed articles, abstracts, or studies before 2014 were excluded. RESULTS Of 167 records identified, 34 studies were included, comprising 30 on PD (n=209; 812 [37%] female, 1359 [63%] male, mean age 68,59 years [SD 7,86]) and four on stroke (n=159; 77 [49%] female, 80 [51%] male, mean age 64,16 years [SD 10,51]). The meta-analysis for PD covered seven gait parameters with high heterogeneity across outcomes (I²>97%). CONCLUSIONS Wearable sensors show strong potential for real-world gait assessment, but inconsistent methods call for standardization in sensor placement, algorithm validation, and metric definitions. Stroke populations are underrepresented, highlighting the need for targeted validation. CLINICALTRIAL This rapid review and meta-analysis was registered with PROSPERO, CRD42024531665.

BACKGROUND Stroke affects over 12 million people annually, with up to 80% experiencing gait impairments three months post-event. While rehabilitation can improve mobility, outcomes often vary due to limited personalization of conventional therapy. Real-time gait biofeedback, defined as the delivery of immediate sensory information about motor performance to guide movement, shows promise for enhancing motor learning. However, current biofeedback designs are typically developed without end-user input, potentially reducing end-users’ understanding, enjoyment, and engagement. OBJECTIVE The objective of this study was to investigate the user experience of people with stroke involved in co-designing personalized real-time gait biofeedback and its associated virtual environment for rehabilitation. A mixed-methods approach was used to identify preferred biofeedback features and assess the impact of personalization on task load and motivation. METHODS Ten post-stroke participants completed multiple sessions categorized as kick-off, discovery, or consolidation, each lasting approximately 1 hour. After an initial assessment of gait deficits, an iterative co-design process was used to progressively refine the biofeedback and virtual environment based on individual input. During these sessions, participants walked in the CAREN while receiving real-time biofeedback based on a therapist-selected gait parameter and collaborated with the experimenter to modify feedback modalities and design. Qualitative data were collected via unstructured interviews to determine the preferred biofeedback features, while quantitative data were collected through the raw NASA-Task Load Index and Intrinsic Motivation Inventory. RESULTS Each participant developed a unique biofeedback and virtual environment setup. Despite this, common preferences emerged, including positive feedback, avatars that mirrored participant’s movements, and multimodal visual-auditory hit/error indicators. Age-related differences in motivation were observed across sessions, with participants over 60 showing greater motivation as personalization evolved. When examining the percent change in scores between the first discovery and last consolidation sessions, no significant changes were found in task load (t₈=–1.13, adjusted P=.366), value/usefulness (z=21.00, adjusted P=.858), or effort/importance (t₉=1.29, adjusted P=.366). However, a marginally significant 13% increase in perceived competence (z=0.00, adjusted P=.058) suggested participants felt more capable of achieving the target parameter value with personalized feedback. Success/error counters led to minor individual-level changes in enjoyment, effort, and competence, though no single feature significantly influenced overall outcomes. CONCLUSIONS This study highlights the value of co-design in developing personalized biofeedback for gait rehabilitation, revealing consistent preferences for features such as positive feedback, avatars, and multimodal cues. Age-related differences in motivation also emerged, suggesting that personalization may be particularly beneficial for older adults. CLINICALTRIAL Ethical approval was obtained from the ETH Zurich Ethics Commission (EK-2022-N-173).

Background: Gait impairments are common in stroke survivors, negatively impacting their overall quality of life. Therefore, gait rehabilitation is often targeted during in-clinic rehabilitation. While standardized assessments are available for inpatient evaluation, the literature often reports variable results when these assessments are conducted in a home environment. Several factors, such as the presence of an observer, the environment itself, or the technology used, may contribute to these differing results. Therefore, it is relevant to establish unsupervised capacity assessments for both in-clinic use and across the continuum of care. Objective: This study aimed to investigate the effect of supervision on the outcomes of a sensor-based 10-meter walk test conducted in a clinical setting, maintaining a controlled environment and setup. Methods: In total, 21 stroke survivors (10 female, 11 male; age: mean 63.9, SD 15.5 years) were assigned alternately to one of two data collection sequences and tested over 4 consecutive days, alternating between supervised test (ST) and unsupervised test (UST) assessments. For both assessments, participants were required to walk a set distance of 10 meters as fast as possible while data were collected using a single wearable sensor (Physilog 5) attached to each shoe. After each walking assessment, the participants completed the Intrinsic Motivation Inventory. Statistical analyses were conducted to examine the mean speed, stride length, and cadence, across repeated measurements and between assessment conditions. Results: The intraclass correlation coefficient indicated good to excellent reliability for speed (ST: κ=0.93, P<.001; UST: κ=0.93, P<.001), stride length (ST: κ=0.92, P<.001; UST: κ=0.88, P<.001), and cadence (ST: κ=0.91, P<.001; UST: κ=0.95, P<.001) across repeated measurements for both ST and UST assessments. There was no significant effect of testing order (ie, sequence A vs B). Comparing ST and UST, there were no significant differences in speed (t39=–0.735, P=.47, 95% CI 0.06-0.03), stride length (z=0.835, P=.80), or cadence (t39=–0.501, P=.62, 95% CI 3.38-2.04) between the 2 assessments. The overall motivation did not show any significant differences between the ST and UST conditions (P>.05). However, the self-reported perceived competence increased during the unsupervised assessment from the first to the second measurement. Conclusions: Unsupervised gait capacity assessments offer a reliable alternative to supervised assessments in a clinical environment, showing comparable results for gait speed, stride length, and cadence, with no differences in overall motivation between the two. Future work should build upon these findings to extend unsupervised assessment of both capacity and performance in home environments. Such assessments could allow improved and more specific tracking of rehabilitation progress across the continuum of care.

BACKGROUND Gait impairments are common in stroke survivors, negatively impacting their overall quality of life. Therefore, gait rehabilitation is often targeted during in-clinic rehabilitation. While standardized assessments are available for inpatient evaluation, the literature often reports variable results when these assessments are conducted in a home environment. Several factors, such as the presence of an observer, the environment itself, or the technology used, may contribute to these differing results. Therefore, it is relevant to establish unsupervised capacity assessments for both in-clinic use and across the continuum of care. OBJECTIVE This study aimed to investigate the effect of supervision on the outcomes of a sensor-based 10-meter walk test conducted in a clinical setting, maintaining a controlled environment and setup. METHODS In total, 21 stroke survivors (10 female, 11 male; age: mean 63.9, SD 15.5 years) were assigned alternately to one of two data collection sequences and tested over 4 consecutive days, alternating between supervised test (ST) and unsupervised test (UST) assessments. For both assessments, participants were required to walk a set distance of 10 meters as fast as possible while data were collected using a single wearable sensor (Physilog 5) attached to each shoe. After each walking assessment, the participants completed the Intrinsic Motivation Inventory. Statistical analyses were conducted to examine the mean speed, stride length, and cadence, across repeated measurements and between assessment conditions. RESULTS The intraclass correlation coefficient indicated good to excellent reliability for speed (ST: κ=0.93, P<.001; UST: κ=0.93, P<.001), stride length (ST: κ=0.92, P<.001; UST: κ=0.88, P<.001), and cadence (ST: κ=0.91, P<.001; UST: κ=0.95, P<.001) across repeated measurements for both ST and UST assessments. There was no significant effect of testing order (ie, sequence A vs B). Comparing ST and UST, there were no significant differences in speed (t39=–0.735, P=.47, 95% CI 0.06-0.03), stride length (z=0.835, P=.80), or cadence (t39=–0.501, P=.62, 95% CI 3.38-2.04) between the 2 assessments. The overall motivation did not show any significant differences between the ST and UST conditions (P>.05). However, the self-reported perceived competence increased during the unsupervised assessment from the first to the second measurement. CONCLUSIONS Unsupervised gait capacity assessments offer a reliable alternative to supervised assessments in a clinical environment, showing comparable results for gait speed, stride length, and cadence, with no differences in overall motivation between the two. Future work should build upon these findings to extend unsupervised assessment of both capacity and performance in home environments. Such assessments could allow improved and more specific tracking of rehabilitation progress across the continuum of care.

The use of virtual reality (VR) stimulation in clinical settings has increased in recent years. In particular, there has been increasing interest in the use of VR stimulation for a variety of purposes, including medical training, pain therapy, and relaxation. Unfortunately, there is still a limited amount of real-world 360-degree content that is both available and suitable for these applications. Therefore, this tutorial paper describes a pipeline for the creation of custom VR content. It covers the planning and designing of content; the selection of appropriate equipment; the creation and processing of footage; and the deployment, visualization, and evaluation of the VR experience. This paper aims to provide a set of guidelines, based on first-hand experience, that readers can use to help create their own 360-degree videos. By discussing and elaborating upon the challenges associated with making 360-degree content, this tutorial can help researchers and health care professionals anticipate and avoid common pitfalls during their own content creation process.

Background: Digital sensing devices have become an increasingly important component of modern biomedical research, as they help provide objective insights into individuals’ everyday behavior in terms of changes in motor and nonmotor symptoms. However, there are significant barriers to the adoption of sensor-enhanced biomedical solutions in terms of both technical expertise and associated costs. The currently available solutions neither allow easy integration of custom sensing devices nor offer a practicable methodology in cases of limited resources. This has become particularly relevant, given the need for real-time sensor data that could help lower health care costs by reducing the frequency of clinical assessments performed by specialists and improve access to health assessments (eg, for people living in remote areas or older adults living at home). Objective: The objective of this paper is to detail the end-to-end development of a novel sensor recording software system that supports the integration of heterogeneous sensor technologies, runs as an on-demand service on consumer-grade hardware to build sensor systems, and can be easily used to reliably record longitudinal sensor measurements in research settings. Methods: The proposed software system is based on a server-client architecture, consisting of multiple self-contained microservices that communicated with each other (eg, the web server transfers data to a database instance) and were implemented as Docker containers. The design of the software is based on state-of-the-art open-source technologies (eg, Node.js or MongoDB), which fulfill nonfunctional requirements and reduce associated costs. A series of programs to facilitate the use of the software were documented. To demonstrate performance, the software was tested in 3 studies (2 gait studies and 1 behavioral study assessing activities of daily living) that ran between 2 and 225 days, with a total of 114 participants. We used descriptive statistics to evaluate longitudinal measurements for reliability, error rates, throughput rates, latency, and usability (with the System Usability Scale [SUS] and the Post-Study System Usability Questionnaire [PSSUQ]). Results: Three qualitative features (event annotation program, sample delay analysis program, and monitoring dashboard) were elaborated and realized as integrated programs. Our quantitative findings demonstrate that the system operates reliably on consumer-grade hardware, even across multiple months (>420 days), providing high throughput (2000 requests per second) with a low latency and error rate (<0.002%). In addition, the results of the usability tests indicate that the system is effective, efficient, and satisfactory to use (mean usability ratings for the SUS and PSSUQ were 89.5 and 1.62, respectively). Conclusions: Overall, this sensor recording software could be leveraged to test sensor devices, as well as to develop and validate algorithms that are able to extract digital measures (eg, gait parameters or actigraphy). The proposed software could help significantly reduce barriers related to sensor-enhanced biomedical research and allow researchers to focus on the research questions at hand rather than on developing recording technologies.

Purpose: The noise levels in intensive care units have been repeatedly reported to exceed the recommended guidelines and yield negative health outcomes among healthcare professionals. However, it is unclear which sound sources within this environment are perceived as disturbing. Therefore, this study aimed to evaluate how healthcare professionals in Germany, Switzerland, and Austria perceive the sound levels and the associated sound sources within their work environment and explore sound reduction strategies. Material and methods: An online survey was conducted among 350 healthcare professionals working in intensive care units. The survey consisted of items on demographic and hospital data and questions about the perception of the sound levels [1 (strongly disagree) to 5 (strongly agree)], disturbance from sound sources [1 (not disturbing at all) to 5 (very disturbing)], and implementation potential, feasibility, and motivation to reduce sound reduction measures [1 (not high at all) to 5 (very high)]. Results: Approximately 69.3% of the healthcare professionals perceived the sound levels in the ICUs as too high. Short-lasting human sounds (e.g. moans or laughs) [mean (M) ± standard deviation (SD) = 3.30 ± 0.81], devices and alarms (M ± SD = 2.67 ± 0.59), and short-lasting object sounds (M ± SD = 2.55 ± 0.68) were perceived as the most disturbing sounds. Reducing medical equipment alarms was considered to have greater implementation potential [M ± SD = 3.62 ± 0.92, t(334) = -7.30, p < 0.001], feasibility [M ± SD = 3.19 ± 0.93, t(334) = -11.02, p < 0.001], and motivation [M ± SD = 3.85 ± 0.89, t(334) = -10.10, p < 0.001] for reducing the sound levels. Conclusion: This study showed that healthcare professionals perceive short-lasting human sounds as most disturbing and rated reducing medical equipment alarms as the best approach to reduce the sound levels in terms of potential, feasibility, and motivation for implementation.

BACKGROUND Digital sensing devices have become an increasingly important component of modern biomedical research, as they help provide objective insights into individuals’ everyday behavior in terms of changes in motor and nonmotor symptoms. However, there are significant barriers to the adoption of sensor-enhanced biomedical solutions in terms of both technical expertise and associated costs. The currently available solutions neither allow easy integration of custom sensing devices nor offer a practicable methodology in cases of limited resources. This has become particularly relevant, given the need for real-time sensor data that could help lower health care costs by reducing the frequency of clinical assessments performed by specialists and improve access to health assessments (eg, for people living in remote areas or older adults living at home). OBJECTIVE The objective of this paper is to detail the end-to-end development of a novel sensor recording software system that supports the integration of heterogeneous sensor technologies, runs as an on-demand service on consumer-grade hardware to build sensor systems, and can be easily used to reliably record longitudinal sensor measurements in research settings. METHODS The proposed software system is based on a server-client architecture, consisting of multiple self-contained microservices that communicated with each other (eg, the web server transfers data to a database instance) and were implemented as Docker containers. The design of the software is based on state-of-the-art open-source technologies (eg, Node.js or MongoDB), which fulfill nonfunctional requirements and reduce associated costs. A series of programs to facilitate the use of the software were documented. To demonstrate performance, the software was tested in 3 studies (2 gait studies and 1 behavioral study assessing activities of daily living) that ran between 2 and 225 days, with a total of 114 participants. We used descriptive statistics to evaluate longitudinal measurements for reliability, error rates, throughput rates, latency, and usability (with the System Usability Scale [SUS] and the Post-Study System Usability Questionnaire [PSSUQ]). RESULTS Three qualitative features (event annotation program, sample delay analysis program, and monitoring dashboard) were elaborated and realized as integrated programs. Our quantitative findings demonstrate that the system operates reliably on consumer-grade hardware, even across multiple months (>420 days), providing high throughput (2000 requests per second) with a low latency and error rate (<0.002%). In addition, the results of the usability tests indicate that the system is effective, efficient, and satisfactory to use (mean usability ratings for the SUS and PSSUQ were 89.5 and 1.62, respectively). CONCLUSIONS Overall, this sensor recording software could be leveraged to test sensor devices, as well as to develop and validate algorithms that are able to extract digital measures (eg, gait parameters or actigraphy). The proposed software could help significantly reduce barriers related to sensor-enhanced biomedical research and allow researchers to focus on the research questions at hand rather than on developing recording technologies. CLINICALTRIAL