Symptom fluctuations within and between individuals with long COVID are widely reported, but the extent to which severity varies following different types of activity and levels of exertion, and the timing of symptoms and recovery, have not previously been quantified. We aimed to characterise timing, severity, and nature of symptom fluctuations in response to effortful physical, social and cognitive activities, using Ecological Momentary Assessments. We recorded activity, effort, and severity of 8 core symptoms every 3 h for up to 24 days, in cohorts from both clinic and community settings. Symptom severities were jointly modelled using autoregressive and moving average processes. Consent was received from 376 participants providing ≥1 week's measurements (273 clinic-based, 103 community-based). Severity of all symptoms was elevated 30 min after all categories of activity. Increased effort was associated with increased symptom severity. Fatigue severity scores increased by 1.8/10 (95% CI: 1.6–1.9) following the highest physical exertions and by 1.5 (1.4–1.7) following cognitive efforts. There was evidence of only mild delayed fatigue 3 h (0.3, 0.2–0.5) or one day later (0.2, 0.0– 0.5). Fatigue severity increased as the day progressed (1.4, 1.0–1.7), and cognitive dysfunction was 0.2 lower at weekends (0.1–0.3). Cognitive, social, self-care and physical activities all triggered increased severity across every symptom, consistent with associated common pathways as potential therapeutic targets. Clear patterns of symptom fluctuations emerged that support more targeted self-management. 10.13039/501100000272 National Institute for Health and Care Research .

Objectives This study aims to provide an in-depth analysis of the symptoms, coexisting conditions and service utilisation among people with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and long COVID. The major research questions include the clustering of symptoms, the relationship between key factors and diagnosis time, and the perceived impact of National Institute for Health and Care Excellence (NICE) guidelines on patient care. Design Cross-sectional survey using secondary data analysis. Setting Community-based primary care level across the UK, incorporating online survey participation. Participants A total of 10 458 individuals responded to the survey, of which 8804 confirmed that they or a close friend/family member had ME/CFS or long COVID. The majority of respondents were female (83.4%), with participants from diverse regions of the UK. Primary and secondary outcome measures Primary outcomes included prevalence and clustering of symptoms, time to diagnosis, and participant satisfaction with National Health Service (NHS) care, while secondary outcomes focused on symptom management strategies and the perceived effect of NICE guidelines. Results Fatigue (88.2%), postexertional malaise (78.2%), cognitive dysfunction (88.4%), pain (87.6%) and sleep disturbances (88.2%) were the most commonly reported symptoms among participants with ME/CFS, with similar patterns observed in long COVID. Time to diagnosis for ME/CFS ranged widely, with 22.1% diagnosed within 1–2 years of symptom onset and 12.9% taking more than 10 years. Despite updated NICE guidelines, only 10.1% of participants reported a positive impact on care, and satisfaction with NHS services remained low (6.9% for ME/CFS and 14.4% for long COVID). Conclusions ME/CFS and long COVID share overlapping but distinct symptom clusters, indicating common challenges in management. The findings highlight significant delays in diagnosis and low satisfaction with specialist services, suggesting a need for improved self-management resources and better-coordinated care across the NHS.

Introduction A substantial proportion of COVID-19 survivors continue to have symptoms more than 3 months after infection, especially of those who required medical intervention. Lasting symptoms are wide-ranging, and presentation varies between individuals and fluctuates within an individual. Improved understanding of undulation in symptoms and triggers may improve efficacy of healthcare providers and enable individuals to better self-manage their Long Covid. We present a protocol where we aim to develop and examine the feasibility and usability of digital home monitoring for capturing daily fluctuation of symptoms in individuals with Long Covid and provide data to facilitate a personalised approach to the classification and management of Long Covid symptoms. Methods and analysis This study is a longitudinal prospective cohort study of adults with Long Covid accessing 10 National Health Service (NHS) rehabilitation services in the UK. We aim to recruit 400 people from participating NHS sites. At referral to study, 6 weeks and 12 weeks, participants will complete demographic data (referral to study) and clinical outcome measures, including ecological momentary assessment (EMA) using personal mobile devices. EMA items are adapted from the COVID-19 Yorkshire Rehabilitation Scale items and include self-reported activities, symptoms and psychological factors. Passive activity data will be collected through wrist-worn sensors. We will use latent class growth models to identify trajectories of experience, potential phenotypes defined by co-occurrence of symptoms and inter-relationships between stressors, symptoms and participation in daily activities. We anticipate that n=300 participants provide 80% power to detect a 20% improvement in fatigue over 12 weeks in one class of patients relative to another. Ethics and dissemination The study was approved by the Yorkshire & The Humber—Bradford Leeds Research Ethics Committee (ref: 21/YH/0276). Findings will be disseminated in peer-reviewed publications and presented at conferences. Trial registration number ISRCTN15022307.

When collecting marker-based motion capture data from clinical populations, speed of collection and comfort for the participant is a priority. This could be achieved by attaching markers to motion capture Velcro suits, as opposed to the skin. This study aimed to ascertain the reliability of sagittal-plane gait parameters estimated using Plug-in Gait (PiG) and Conventional Gait Model 2 (CGM2) marker sets from data collected in Suited and Non-suited (markers placed onto skin) conditions. For ten participants, markers were placed based on PiG and CGM2 models and data captured during a 2-min treadmill walk. Trials were repeated in suited and non-suited conditions. PiG ankle flexion/extension measurements had poor/moderate reliability (Non-suited ICC = 0.531, Suited ICC = 0.435). CGM2 ankle flexion/extension measurements had good/excellent reliability (Non-suited ICC = 0.916, Suited ICC = 0.900). There were significant differences in minimal detectable change (MDC) between conditions at the ankle for PiG (Non-suited MDC = 2.32°, Suited MDC = 18.90°), but not for CGM2 (Non-suited MDC = 0.63°, Suited MDC = 0.95°). When using CGM2, knee (Non-suited ICC = 0.878, Suited ICC = 0.855) and hip (Non-suited ICC = 0.897, Suited ICC = 0.948) showed good/excellent reliability in both conditions. A motion capture suit is not a reliable solution when collecting joint angle data using the PiG model but is reliable enough to consider when using the CGM2 model.

Pain and knee stiffness are common problems following total knee replacement surgery, with 10–20% of patients reporting dissatisfaction following their procedure. A remote assessment of knee stiffness could improve outcomes through continuous monitoring, facilitating timely intervention. Using machine learning algorithms, computer vision can extract joint angles from video footage, offering a method to monitor knee range of motion in patients’ homes. This study outlines a protocol to provide proof of concept and validate a computer vision-based approach for measuring knee range of motion in individuals who have undergone total knee replacement. The study also explores the feasibility of integrating this technology into clinical practice, enhancing post-operative care. The study is divided into three components: carrying out focus groups, validating the computer vision-based software, and home testing. The focus groups will involve five people who underwent total knee replacement and ten healthcare professionals or carers who will discuss the deployment of the software in clinical settings. For the validation phase, 60 participants, including 30 patients who underwent total knee replacement surgery five to nine weeks prior and 30 healthy controls, will be recruited. The participants will perform five tasks, including the sit-to-stand test, where knee range of motion will be measured using computer vision-based markerless motion capture software, marker-based motion capture, and physiotherapy assessments. The accuracy and reliability of the software will be evaluated against these established methods. Participants will perform the sit-to-stand task at home. This will allow for a comparison between home-recorded and lab-based data. The findings from this study have the potential to significantly enhance the monitoring of knee stiffness following total knee replacement. By providing accurate, remote measurements and enabling the early detection of issues, this technology could facilitate timely referrals to non-surgical treatments, ultimately reducing the need for costly and invasive procedures to improve knee range of motion.

Adaptive walking relies on proactive visual search to plan foot placement and maintain stability. This study examined how cognitive load and task complexity affect gaze behaviour and gait biomechanics during a precision target-stepping task in healthy young adults. We also quantified the frequency of cross-stepping during the experimental task. Twenty-three participants (18–23 years) walked along an L-shaped pathway containing raised stepping targets under single-task (ST) and dual-task (DT) conditions. Targets had four different layouts to create high and low difficulty conditions. Eye movements were recorded using mobile eye-tracking, and gait kinematics were captured via motion capture. Compared with ST, DT walking produced slower walking speeds, longer stance times, and reduced velocity between stepping targets, indicating a more inefficient gait strategy. Eye-tracking analyses revealed fewer and shorter fixations on task-relevant targets and a greater number of fixations directed toward task-irrelevant areas under DT conditions. In DT, saccadic amplitudes were reduced despite increased outside fixations, suggesting a breakdown in visual exploration between proximal and distal regions of the walkway. Cross-stepping was more frequent in ST conditions than in DT. These findings indicate that increased cognitive load compromises proactive visual search, likely through working-memory and attentional limitations that disrupt feedforward gait planning. Contrary to expectation, cross-stepping occurred more often during ST than DT walking, suggesting that in this population cross-stepping may not be a maladaptive strategy. Overall, these results highlight the cognitive demands of adaptive walking even in young, low-risk individuals and underscore the importance of preserving visual–motor coordination under cognitive stress. Highlights: Cognitive load during walking restricts proactive visual search and alters gait dynamics in healthy young adults. Dual-tasking led to slower walking speeds, longer stance times, and reduced step-to-step velocity. Eye-tracking showed fewer and shorter fixations on stepping targets, and more gaze directed toward task-irrelevant areas under dual tasking. Reduced proactive gaze under dual-task conditions likely reflects limitations of available cognitive resources rather than task-specific prioritisation. Findings highlight how cognitive demands compromise visual–motor coordination, supporting interventions that promote gait automaticity and reduce cognitive load. Cross-stepping was more frequent in ST than in DT conditions, suggesting that cross-stepping may not be maladaptive in healthy young people.