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The purpose of this study was to introduce a simple gait training method using real-time gait modification to reduce the peak knee adduction moment during walking by producing a subtle weight bearing shift to the medial side of the foot. The hypothesis of this study was that this weight shift could be achieved via either verbal instruction or an active feedback system, and that the weight shift would result in a reduction in the first peak knee adduction moment compared with the control tests. Nine individuals were tested during walking using two intervention methods: verbal instruction and an active feedback system placed on the right shoe. The first peak of the knee adduction moment for each condition was assessed using a motion capture system and force plate. The active feedback system significantly reduced (14.2%) the peak knee adduction moment relative to the control. This study demonstrated that a subtle weight bearing shift to the medial side of the foot produced with an active feedback system during walking reduced the first peak of the knee adduction moment and suggests the potential application of this method to slow the rate of progression of medial compartment knee osteoarthritis. [DOI: 10.1115/1.4001584] Keywords: gait modification, osteoarthritis, knee adduction moment, active feedback

This study demonstrated the feasibility of a device for monitoring pressure relief maneuvers and physical activity for wheelchair users. The device counts the number of wheel pushes based on wheelchair acceleration and measures pressure relief maneuvers using a seat sensor consisting of three force sensing resistors (FSRs). To establish the feasibility of the seat sensor for the detection of pressure relief maneuvers, 10 wheelchair users and 10 non-disabled controls completed a series of wheelchair depression raises, forward trunk leans, and lateral trunk leans. The seat sensor was placed underneath the user’s seat cushion. To establish the feasibility of wheel push counting, 10 full-time wheelchair users navigated a flat 50-m outdoor track and a 100-m outdoor obstacle course during self-propulsion (e.g., wheel pushes) and during assisted-propulsion (e.g., no wheel pushes). Of the 240 performed pressure relief, 225 were properly classified by the seat sensor (accuracy: 94%, sensitivity: 96%, specificity: 80%). Sensitivity was highest for depression raises (98%) and lowest for front lean maneuvers (80%). The wheelchair activity monitor measured 2,112 pushes during the self-propulsion trials compared to 2,162 pushes measured with the instrumented push-rim (97.7%). During assisted-propulsion trials, there were 477 incorrectly identified pushes (8.0 per trial). [ABSTRACT FROM PUBLISHER]

Considering sex as a biological variable in modern digital health solutions, we investigated sex-specific differences in the trajectory of four physiological parameters across a COVID-19 infection. A wearable medical device measured breathing rate, heart rate, heart rate variability, and wrist skin temperature in 1163 participants (mean age = 44.1 years, standard deviation [SD] = 5.6; 667 [57%] females). Participants reported daily symptoms and confounders in a complementary app. A machine learning algorithm retrospectively ingested daily biophysical parameters to detect COVID-19 infections. COVID-19 serology samples were collected from all participants at baseline and follow-up. We analysed potential sex-specific differences in physiology and antibody titres using multilevel modelling and t-tests. Over 1.5 million hours of physiological data were recorded. During the symptomatic period of infection, men demonstrated larger increases in skin temperature, breathing rate, and heart rate as well as larger decreases in heart rate variability than women. The COVID-19 infection detection algorithm performed similarly well for men and women. Our study belongs to the first research to provide evidence for differential physiological responses to COVID-19 between females and males, highlighting the potential of wearable technology to inform future precision medicine approaches., Competing Interests: The authors have read the journal’s policy and have the following competing interests: Lorenz Risch, and Martin Risch are key shareholders of the Dr Risch Medical Laboratory. David Conen has received consulting fees from Roche Diagnostics, outside of the current work. Andjela Markovic, Vladimir Kovacevic, Martina Rothenbühler, Brianna Goodale and Maureen Cronin are past employees of Ava AG. Brianna Goodale and Timo Brakenhoff are current employees of Julius Clinical BV. Billy Franks is a former employee of Julius Clinical BV and now an employee of Haleon. Paul Klaver and Duco Veen are former employees of Julius Clinical BV. Marianna Mitratza is a current employee of P95 CVBA. There are no patents, products in development or marketed products associated with this research to declare. These competing interests do not alter our adherence to PLOS ONE policies on sharing data and materials., (Copyright: © 2024 Grossmann et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objectives: It is currently thought that most-but not all-individuals infected with SARS-CoV-2 develop symptoms, but the infectious period starts on average 2 days before the first overt symptoms appear. It is estimated that pre- and asymptomatic individuals are responsible for more than half of all transmissions. By detecting infected individuals before they have overt symptoms, wearable devices could potentially and significantly reduce the proportion of transmissions by pre-symptomatic individuals. Using laboratory-confirmed SARS-CoV-2 infections (detected via serology tests [to determine if there are antibodies against the SARS-CoV-2 in the blood] or SARS-CoV-2 infection tests such as polymerase chain reaction [PCR] or antigen tests) as the gold standard, we will determine the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for the following two algorithms to detect first time SARS-CoV-2 infection including early or asymptomatic infection: • The algorithm using Ava bracelet data when coupled with self-reported Daily Symptom Diary data (Wearable + Symptom Data Algo; experimental condition) • The algorithm using self-reported Daily Symptom Diary data alone (Symptom Only Algo; control condition) In addition, we will determine which of the two algorithms has superior performance characteristics for detecting SARS-CoV-2 infection including early or asymptomatic infection as confirmed by SARS-CoV-2 virus testing., Trial Design: The trial is a randomized, single-blinded, two-period, two-sequence crossover trial. The study will start with an initial learning phase (maximum of 3 months), followed by period 1 (3 months) and period 2 (3 months). Subjects entering the study at the end of the recruitment period may directly start with period 1 and will not be part of the learning phase. Each subject will undergo the experimental condition (the Wearable + Symptom Data Algo) in either period 1 or period 2 and the control condition (Symptom Only Algo) in the other period. The order will be randomly assigned, resulting in subjects being allocated 1:1 to either sequence 1 (experimental condition first) or sequence 2 (control condition first). Based on demographics, medical history and/or profession, each subject will be stratified at baseline into a high-risk and normal-risk group within each sequence., Participants: The trial will be conducted in the Netherlands. A target of 20,000 subjects will be enrolled. Based on demographics, medical history and/or profession, each subject will be stratified at baseline into a high-risk and normal-risk group within each sequence. This results in approximately 6500 normal-risk individuals and 3500 high-risk individuals per sequence. Subjects will be recruited from previously studied cohorts as well as via public campaigns and social media. All data for this study will be collected remotely through the Ava COVID-RED app, the Ava bracelet, surveys in the COVID-RED web portal and self-sampling serology and PCR kits. More information on the study can be found in www.covid-red.eu . During recruitment, subjects will be invited to visit the COVID-RED web portal. After successfully completing the enrolment questionnaire, meeting eligibility criteria and indicating interest in joining the study, subjects will receive the subject information sheet and informed consent form. Subjects can enrol in COVID-RED if they comply with the following inclusion and exclusion criteria: Inclusion criteria: • Resident of the Netherlands • At least 18 years old • Informed consent provided (electronic) • Willing to adhere to the study procedures described in the protocol • Must have a smartphone that runs at least Android 8.0 or iOS 13.0 operating systems and is active for the duration of the study (in the case of a change of mobile number, the study team should be notified) • Be able to read, understand and write Dutch Exclusion criteria: • Previous positive SARS-CoV-2 test result (confirmed either through PCR/antigen or antibody tests; self-reported) • Current suspected (e.g. waiting for test result) COVID-19 infection or symptoms of a COVID-19 infection (self-reported) • Participating in any other COVID-19 clinical drug, vaccine or medical device trial (self-reported) • Electronic implanted device (such as a pacemaker; self-reported) • Pregnant at the time of informed consent (self-reported) • Suffering from cholinergic urticaria (per the Ava bracelet's user manual; self-reported) • Staff involved in the management or conduct of this study INTERVENTION AND COMPARATOR: All subjects will be instructed to complete the Daily Symptom Diary in the Ava COVID-RED app daily, wear their Ava bracelet each night and synchronize it with the app each day for the entire period of study participation. Provided with wearable sensor and/or self-reported symptom data within the last 24 h, the Ava COVID-RED app's underlying algorithms will provide subjects with a real-time indicator of their overall health and well-being. Subjects will see one of three messages, notifying them that no seeming deviations in symptoms and/or physiological parameters have been detected; some changes in symptoms and/or physiological parameters have been detected and they should self-isolate; or alerting them that deviations in their symptoms and/or physiological parameters could be suggestive of a potential COVID-19 infection and to seek additional testing. We will assess the intraperson performance of the algorithms in the experimental condition (Wearable + Symptom Data Algo) and control conditions (Symptom Only Algo). Note that both algorithms will also instruct to seek testing when any SARS-CoV-2 symptoms are reported in line with those defined by the Dutch national institute for public health and the environment 'Rijksinstituut voor Volksgezondheid en Milieu' (RIVM) guidelines., Main Outcomes: The trial will evaluate the use and performance of the Ava COVID-RED app and Ava bracelet, which uses sensors to measure breathing rate, pulse rate, skin temperature and heart rate variability for the purpose of early and asymptomatic detection and monitoring of SARS-CoV-2 in general and high-risk populations. Using laboratory-confirmed SARS-CoV-2 infections (detected via serology tests, PCR tests and/or antigen tests) as the gold standard, we will determine the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for each of the following two algorithms to detect first-time SARS-CoV-2 infection including early or asymptomatic infection: the algorithm using Ava bracelet data when coupled with the self-reported Daily Symptom Diary data and the algorithm using self-reported Daily Symptom Diary data alone. In addition, we will determine which of the two algorithms has superior performance characteristics for detecting SARS-CoV-2 infection including early or asymptomatic infection as confirmed by SARS-CoV-2 virus testing. The protocol contains an additional twenty secondary and exploratory objectives which address, among others, infection incidence rates, health resource utilization, symptoms reported by SARS-CoV-2-infected participants and the rate of breakthrough and asymptomatic SARS-CoV-2 infections among individuals vaccinated against COVID-19. PCR or antigen testing will occur when the subject receives a notification from the algorithm to seek additional testing. Subjects will be advised to get tested via the national testing programme and report the testing result in the Ava COVID-RED app and a survey. If they cannot obtain a test via the national testing programme, they will receive a nasal swab self-sampling kit at home, and the sample will be tested by PCR in a trial-affiliated laboratory. In addition, all subjects will be asked to take a capillary blood sample at home at baseline (between month 0 and 3.5 months after the start of subject recruitment), at the end of the learning phase (month 3; note that this sampling moment is skipped if a subject entered the study at the end of the recruitment period), period 1 (month 6) and period 2 (month 9). These samples will be used for SARS-CoV-2-specific antibody testing in a trial-affiliated laboratory, differentiating between antibodies resulting from a natural infection and antibodies resulting from COVID-19 vaccination (as vaccination will gradually be rolled out during the trial period). Baseline samples will only be analysed if the sample collected at the end of the learning phase is positive, or if the subject entered the study at the end of the recruitment period, and samples collected at the end of period 1 will only be analysed if the sample collected at the end of period 2 is positive. When subjects obtain a positive PCR/antigen or serology test result during the study, they will continue to be in the study but will be moved into a so-called COVID-positive mode in the Ava COVID-RED app. This means that they will no longer receive recommendations from the algorithms but can still contribute and track symptom and bracelet data. The primary analysis of the main objective will be executed using the data collected in period 2 (months 6 through 9). Within this period, serology tests (before and after period 2) and PCR/antigen tests (taken based on recommendations by the algorithms) will be used to determine if a subject was infected with SARS-CoV-2 or not. Within this same time period, it will be determined if the algorithms gave any recommendations for testing. The agreement between these quantities will be used to evaluate the performance of the algorithms and how these compare between the study conditions., Randomization: All eligible subjects will be randomized using a stratified block randomization approach with an allocation ratio of 1:1 to one of two sequences (experimental condition followed by control condition or control condition followed by experimental condition). Based on demographics, medical history and/or profession, each subject will be stratified at baseline into a high-risk and normal-risk group within each sequence, resulting in approximately equal numbers of high-risk and normal-risk individuals between the sequences., Blinding (masking): In this study, subjects will be blinded to the study condition and randomization sequence. Relevant study staff and the device manufacturer will be aware of the assigned sequence. The subject will wear the Ava bracelet and complete the Daily Symptom Diary in the Ava COVID-RED app for the full duration of the study, and they will not know if the feedback they receive about their potential infection status will only be based on the data they entered in the Daily Symptom Diary within the Ava COVID-RED app or based on both the data from the Daily Symptom Diary and the Ava bracelet., Numbers to Be Randomized (sample Size): A total of 20,000 subjects will be recruited and randomized 1:1 to either sequence 1 (experimental condition followed by control condition) or sequence 2 (control condition followed by experimental condition), taking into account their risk level. This results in approximately 6500 normal-risk and 3500 high-risk individuals per sequence., Trial Status: Protocol version: 3.0, dated May 3, 2021. Start of recruitment: February 19, 2021. End of recruitment: June 3, 2021. End of follow-up (estimated): November 2021 TRIAL REGISTRATION: The Netherlands Trial Register on the 18 th of February, 2021 with number NL9320 ( https://www.trialregister.nl/trial/9320 ) FULL PROTOCOL: The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). In the interest in expediting dissemination of this material, the familiar formatting has been eliminated; this letter serves as a summary of the key elements of the full protocol., (© 2021. The Author(s).)

Innovative tools are urgently needed to accelerate the evaluation and subsequent approval of novel treatments that may slow, halt, or reverse the relentless progression of Parkinson disease (PD). Therapies that intervene early in the disease continuum are a priority for the many candidates in the drug development pipeline. There is a paucity of sensitive and objective, yet clinically interpretable, measures that can capture meaningful aspects of the disease. This poses a major challenge for the development of new therapies and is compounded by the considerable heterogeneity in clinical manifestations across patients and the fluctuating nature of many signs and symptoms of PD. Digital health technologies (DHT), such as smartphone applications, wearable sensors, and digital diaries, have the potential to address many of these gaps by enabling the objective, remote, and frequent measurement of PD signs and symptoms in natural living environments. The current climate of the COVID-19 pandemic creates a heightened sense of urgency for effective implementation of such strategies. In order for these technologies to be adopted in drug development studies, a regulatory-aligned consensus on best practices in implementing appropriate technologies, including the collection, processing, and interpretation of digital sensor data, is required. A growing number of collaborative initiatives are being launched to identify effective ways to advance the use of DHT in PD clinical trials. The Critical Path for Parkinson's Consortium of the Critical Path Institute is highlighted as a case example where stakeholders collectively engaged regulatory agencies on the effective use of DHT in PD clinical trials. Global regulatory agencies, including the US Food and Drug Administration and the European Medicines Agency, are encouraging the efficiencies of data-driven engagements through multistakeholder consortia. To this end, we review how the advancement of DHT can be most effectively achieved by aligning knowledge, expertise, and data sharing in ways that maximize efficiencies., Competing Interests: Critical Path Institute staff (D. Stephenson, V. Aggarawal, R. Bhatnagar, K. Romero, J. Burton, S. Sadar, and M. Minchick) and R. Badawy, L. Bain, J.M. Cedarbaum, D.T. Dexter, M. Frasier, D. Hill Michael, and P. Lawton have no conflict of interests to declare. R. Alexander, N. Zach, and R. Rubens are full-time employees of Takeda. A.V. Dowling is an employee of Takeda Pharmaceuticals and also serves on the Strategic Advisory Board of the DiMe Society. B.R. Bloem currently serves as associate editor for the Journal of Parkinson Disease, serves on the editorial board of Practical Neurology and Digital Biomarkers, has received honoraria from serving on the scientific advisory board for AbbVie, Biogen, UCB, and Walk with Path, has received fees for speaking at conferences from AbbVie, Zambon, Roche, GE Healthcare, and Bial, and has received research support from The Netherlands Organisation for Scientific Research, the Michael J. Fox Foundation, UCB, AbbVie, the Stichting Parkinson-Fonds, the Hersenstichting Nederland, the Parkinson Foundation, Verily Life Sciences, Horizon 2020, Topsector Life Sciences and Health, and the Parkinson Vereniging. B. Boroojerdi and E. Sengoku are employed by UCB. J. Cosman was a full-time employee of Biogen, Inc., and is currently a full-time employee of AbbVie, Inc. M. Dockendorf, S.J. Lee, and N. Mehrotra are employees of Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc. (Kenilworth, NJ, USA), and may own stock/stock options in Merck & Co., Inc. E.R. Dorsey has received honoraria for speaking at American Academy of Neurology courses, the American Neurological Association, and the University of Michigan. E.R. Dorsey received compensation for consulting services from: 23andMe; Abbott; AbbVie; American Well; Biogen; BrainNeuroBio; Clintrex; Curasen Therapeutics; DeciBio; Denali Therapeutics; GlaxoSmithKline; Grand Rounds; Karger; Lundbeck; MC10; MedAvante; Medical-Legal Services; Mednick Associates; the National Institute of Neurological Disorders and Stroke; the Olson Research Group; Optio; Origent Data Sciences, Inc.; Otsuka, Prilenia; Putnam Associates; Roche; Sanofi; Shire; Spark; Sunovion Pharma; Teva; Theravance; UCB; and Voyager Therapeutics. E.R. Dorsey also received research support from AbbVie, Acadia Pharmaceuticals, AMC Health, Biosensics, the Burroughs Wellcome Fund, the Davis Phinney Foundation, Duke University, the FDA, GlaxoSmithKline, the Greater Rochester Health Foundation, the Huntington Study Group, the Michael J. Fox Foundation, the National Institutes of Health/National Institute of Neurological Disorders and Stroke, the National Science Foundation, Nuredis Pharmaceuticals, the Patient-Centered Outcomes Research Institute, Pfizer, Prana Biotechnology, Raptor Pharmaceuticals, Roche, Safra Foundation, Teva Pharmaceuticals, and the University of California, Irvine; has provided editorial services for Karger Publications; and has ownership interests with Blackfynn (data integration company) and Grand Rounds (second opinion service). L.J.W. Evers has received research support from the Michael J. Fox Foundation, UCB, the Stichting Parkinson Fonds, The Netherlands Organisation for Scientific Research, and Topsector Life Sciences and Health. L. Garcia-Gancedo is a full-time employee of GSK. J.C. Goldsack is a part-time employee of HealthMode, Inc. J. Hitchcock is an employee of Hitchcock Regulatory Consulting, Inc., and has consulted for: Acumen Pharmaceuticals; Axon Advisors LLC; the Critical Path Institute; the Gerson Lehrman Group; H. Lundbeck A/S; the High Lantern Group LLC; Regenera Pharma LTD; UCB Biopharma SPRL; Vaccinex, Inc.; and Washington University. She is retired from Eli Lilly and Company and holds Lilly shares. M.T. Hu serves as a consultant for Biogen and Roche Advisory Boards and for CuraSen Therapeutics, Inc. Prof. Hu has received funding and support from Parkinson's UK, Oxford NIHR BRC, the University of Oxford, NIHR, the Michael J Fox Foundation, H2020 European Union, GE Healthcare, and the PSP Association. M. Lindemann is a consultant for F. Hoffmann-La Roche through Inovigate. M.J. Meinders has received research support from the Michael J. Fox Foundation, UCB, the Stichting Parkinson Fonds, Horizon 2020, and Topsector Life Sciences and Health. L. Oliva and K. Fisher are employees of Biogen. G. Roussos acknowledges funding from the Michael J. Fox Foundation and is a joint patent holder for UK patent GB2433856 (which is not related to healthcare). J. Scheeren was formerly employed by Bayer AG-Pharmaceuticals and as of 2019 is the president and CEO of C-Path. Dr. J. Scheeren is also currently an adjunct professor of regulatory sciences at Peking University in Beijing, China, the chair of the fellows for the Drug Information Association (DIA), a member of the advisory board for the Centre of Regulatory Excellence (CoRE), a special advisor to the scientific advisory council of the Centre for Innovation in Regulatory Science (CIRS), a lecturer at Yale University, and a foreign corresponding member of the French Académie Nationale de Pharmacie. T. Simuni, MD, has served as a consultant for Acadia, AbbVie, Accorda, Adamas, Allergan, Amneal, Aptinyx, Denali, General Electric (GE), Kyowa, Neuroderm, Neurocrine, Sanofi, Sinopia, Sunovion, Roche, Takeda, Voyager, US World Meds, the Parkinson's Foundation, and the Michael J. Fox Foundation for Parkinson research. Dr. T. Simuni has served as a speaker and received an honorarium from Acadia and Adamas, is on the scientific advisory board for Neuroderm and Sanofi, and has received research funding from the NINDS, the Parkinson's Foundation, MJFF, Biogen, Roche, Neuroderm, Sanofi, Sun Pharma, AbbVie, IMPAX, and Prevail. G. Stebbins is an employee of Rush University. Dr. G. Stebbins has consulting and advisory board membership with honoraria for: Acadia Pharmaceuticals; Adamas Pharmaceuticals, Inc.; Biogen, Inc.; Ceregene, Inc.; CHDI Management, Inc.; the Cleveland Clinic Foundation; Ingenix Pharmaceutical Services (i3 Research); MedGenesis Therapeutix, Inc.; Neurocrine Biosciences, Inc.; Pfizer, Inc.; Tools-4-Patients; Ultragenyx, Inc.; and the Sunshine Care Foundation. He has received grants from and done research for: the National Institutes of Health, the Department of Defense, the Michael J. Fox Foundation for Parkinson's Research, the Dystonia Coalition, CHDI, the Cleveland Clinic Foundation, the International Parkinson and Movement Disorder Society, and CBD Solutions, and has received honoraria from: the International Parkinson and Movement Disorder Society, the American Academy of Neurology, the Michael J. Fox Foundation for Parkinson's Research, the FDA, the National Institutes of Health, and the Alzheimer's Association. K.I. Taylor is a full-time employee and shareholder of F. Hoffmann-La Roche Ltd. B. Yang is a full-time employee at Lundbeck A/S., (Copyright © 2020 by S. Karger AG, Basel.)