Publications by authors named "Ariel V Dowling"

16 Publications

  • Page 1 of 1

A prospective, randomized, single-blinded, crossover trial to investigate the effect of a wearable device in addition to a daily symptom diary for the Remote Early Detection of SARS-CoV-2 infections (COVID-RED): a structured summary of a study protocol for a randomized controlled trial.

Trials 2021 Oct 11;22(1):694. Epub 2021 Oct 11.

Julius Clinical, Zeist, the Netherlands.

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 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.
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http://dx.doi.org/10.1186/s13063-021-05643-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8503725PMC
October 2021

A prospective, randomized, single-blinded, crossover trial to investigate the effect of a wearable device in addition to a daily symptom diary for the remote early detection of SARS-CoV-2 infections (COVID-RED): a structured summary of a study protocol for a randomized controlled trial.

Trials 2021 Jun 22;22(1):412. Epub 2021 Jun 22.

Julius Clinical, Zeist, the Netherlands.

Objectives: It is currently thought that most-but not all-individuals infected with SARS-CoV-2 develop symptoms, but that the infectious period starts on average two 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. All subjects will participate in an initial Learning Phase (varying from 2 weeks to 3 months depending on enrolment date), followed by two contiguous 3-month test phases, Period 1 and Period 2. Each subject will undergo the experimental condition (the Wearable + Symptom Data Algo) in one of these periods 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 6,500 normal-risk individuals and 3,500 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. During recruitment, subjects will be invited to visit the COVID-RED web portal ( www.covid-red.eu ). 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, 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) Previously received a vaccine developed specifically for COVID-19 or in possession of an appointment for vaccination in the near future (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 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 synchronise 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 hours, 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 intraperson performance of the algorithms in the experimental condition (Wearable + Symptom Data Algo) and control conditions (Symptom Only Algo).

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 seventeen secondary outcomes which address 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 (Month 0), and at the end of the Learning Phase (Month 3), 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, 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 data collected in Period 2 (Month 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.

Randomisation: 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 equal numbers of high-risk and normal-risk individuals between the sequences.

Blinding (masking): In this study, subjects will be blinded as to 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 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 Randomised (sample Size): 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 6,500 normal-risk and 3,500 high-risk individuals per sequence.

Trial Status: Protocol version: 1.2, dated January 22, 2021 Start of recruitment: February 22, 2021 End of recruitment (estimated): April 2021 End of follow-up (estimated): December 2021 TRIAL REGISTRATION: The trial has been registered at the Netherlands Trial Register on the 18 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.
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http://dx.doi.org/10.1186/s13063-021-05241-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8218271PMC
June 2021

Evaluation, Acceptance, and Qualification of Digital Measures: From Proof of Concept to Endpoint.

Digit Biomark 2021 Jan-Apr;5(1):53-64. Epub 2021 Mar 23.

Evidation Health Inc., San Mateo, California, USA.

To support the successful adoption of digital measures into internal decision making and evidence generation for medical product development, we present a unified lexicon to aid communication throughout this process, and highlight key concepts including the critical role of participant engagement in development of digital measures. We detail the steps of bringing a successful proof of concept to scale, focusing on key decisions in the development of a new digital measure: asking the right question, optimized approaches to evaluating new measures, and whether and how to pursue qualification or acceptance. Building on the V3 framework for establishing verification and analytical and clinical validation, we discuss strategic and practical considerations for collecting this evidence, illustrated with concrete examples of trailblazing digital measures in the field.
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http://dx.doi.org/10.1159/000514730DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8077605PMC
March 2021

Precompetitive Consensus Building to Facilitate the Use of Digital Health Technologies to Support Parkinson Disease Drug Development through Regulatory Science.

Digit Biomark 2020 26;4(Suppl 1):28-49. Epub 2020 Nov 26.

Critical Path Institute, Tucson, Arizona, USA.

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.
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http://dx.doi.org/10.1159/000512500DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7768153PMC
November 2020

Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs).

NPJ Digit Med 2020 14;3:55. Epub 2020 Apr 14.

5Biomedical Engineering Department, Duke University, Durham, NC USA.

Digital medicine is an interdisciplinary field, drawing together stakeholders with expertize in engineering, manufacturing, clinical science, data science, biostatistics, regulatory science, ethics, patient advocacy, and healthcare policy, to name a few. Although this diversity is undoubtedly valuable, it can lead to confusion regarding terminology and best practices. There are many instances, as we detail in this paper, where a single term is used by different groups to mean different things, as well as cases where multiple terms are used to describe essentially the same concept. Our intent is to clarify core terminology and best practices for the evaluation of Biometric Monitoring Technologies (BioMeTs), without unnecessarily introducing new terms. We focus on the evaluation of BioMeTs as fit-for-purpose for use in clinical trials. However, our intent is for this framework to be instructional to all users of digital measurement tools, regardless of setting or intended use. We propose and describe a three-component framework intended to provide a foundational evaluation framework for BioMeTs. This framework includes (1) verification, (2) analytical validation, and (3) clinical validation. We aim for this common vocabulary to enable more effective communication and collaboration, generate a common and meaningful evidence base for BioMeTs, and improve the accessibility of the digital medicine field.
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http://dx.doi.org/10.1038/s41746-020-0260-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7156507PMC
April 2020

Enabling Stroke Rehabilitation in Home and Community Settings: A Wearable Sensor-Based Approach for Upper-Limb Motor Training.

IEEE J Transl Eng Health Med 2018 2;6:2100411. Epub 2018 May 2.

BioSensicsLLCWatertownMA02472USA.

High-dosage motor practice can significantly contribute to achieving functional recovery after a stroke. Performing rehabilitation exercises at home and using, or attempting to use, the stroke-affected upper limb during Activities of Daily Living (ADL) are effective ways to achieve high-dosage motor practice in stroke survivors. This paper presents a novel technological approach that enables 1) detecting goal-directed upper limb movements during the performance of ADL, so that timely feedback can be provided to encourage the use of the affected limb, and 2) assessing the quality of motor performance during in-home rehabilitation exercises so that appropriate feedback can be generated to promote high-quality exercise. The results herein presented show that it is possible to detect 1) goal-directed movements during the performance of ADL with a [Formula: see text]-statistic of 87.0% and 2) poorly performed movements in selected rehabilitation exercises with an [Formula: see text]-score of 84.3%, thus enabling the generation of appropriate feedback. In a survey to gather preliminary data concerning the clinical adequacy of the proposed approach, 91.7% of occupational therapists demonstrated willingness to use it in their practice, and 88.2% of stroke survivors indicated that they would use it if recommended by their therapist.
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http://dx.doi.org/10.1109/JTEHM.2018.2829208DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5951609PMC
May 2018

Telehealth monitor to measure physical activity and pressure relief maneuver performance in wheelchair users.

Assist Technol 2017 29;29(4):202-209. Epub 2016 Sep 29.

a BioSensics LLC , Cambridge , Massachusetts , USA.

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).
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http://dx.doi.org/10.1080/10400435.2016.1220993DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5629123PMC
July 2018

Wearable Sensors in Huntington Disease: A Pilot Study.

J Huntingtons Dis 2016 06;5(2):199-206

Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA.

Background: The Unified Huntington's Disease Rating Scale (UHDRS) is the principal means of assessing motor impairment in Huntington disease but is subjective and generally limited to in-clinic assessments.

Objective: To evaluate the feasibility and ability of wearable sensors to measure motor impairment in individuals with Huntington disease in the clinic and at home.

Methods: Participants with Huntington disease and controls were asked to wear five accelerometer-based sensors attached to the chest and each limb for standardized, in-clinic assessments and for one day at home. A second chest sensor was worn for six additional days at home. Gait measures were compared between controls, participants with Huntington disease, and participants with Huntington disease grouped by UHDRS total motor score using Cohen's d values.

Results: Fifteen individuals with Huntington disease and five controls completed the study. Sensor data were successfully captured from 18 of the 20 participants at home. In the clinic, the standard deviation of step time (time between consecutive steps) was increased in Huntington disease (p < 0.0001; Cohen's d = 2.61) compared to controls. At home with additional observations, significant differences were observed in seven additional gait measures. The gait of individuals with higher total motor scores (50 or more) differed significantly from those with lower total motor scores (below 50) on multiple measures at home.

Conclusions: In this pilot study, the use of wearable sensors in clinic and at home was feasible and demonstrated gait differences between controls, participants with Huntington disease, and participants with Huntington disease grouped by motor impairment.
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http://dx.doi.org/10.3233/JHD-160197DOI Listing
June 2016

Modification of Knee Flexion Angle Has Patient-Specific Effects on Anterior Cruciate Ligament Injury Risk Factors During Jump Landing.

Am J Sports Med 2016 Jun 16;44(6):1540-6. Epub 2016 Mar 16.

Department of Mechanical Engineering, Stanford University, Stanford, California, USA Department of Orthopedic Surgery, Stanford University, Stanford, California, USA.

Background: The incidence of anterior cruciate ligament (ACL) injuries may be decreased through the use of intervention programs that focus on increasing the knee flexion angle during jump landing, which decreases strain on the ACL.

Purpose: To investigate whether intervention training designed to change the knee flexion angle during landing causes secondary changes in other known measures associated with the risk of ACL injuries and to examine the time points when these secondary measures change.

Study Design: Controlled laboratory study.

Methods: A total of 39 healthy recreational athletes performed a volleyball block jump task in an instrumented gait laboratory. The participants first completed the jumps without any modification to their normal landing technique. They were then given oral instruction to land softly and to increase their knee flexion angle during landing. Lower body kinematics and kinetics were measured before and after the modification using an optoelectronic motion capture system.

Results: The knee flexion angle after the modification significantly increased from 11.2° to 15.2° at initial contact and from 67.8° to 100.7° at maximum flexion, and the time between initial contact and maximum flexion increased from 177.4 to 399.4 milliseconds. The flexion modification produced a substantial reduction in vertical ground-reaction force (243.1 to 187.8 %BW) with a concomitant reduction in the maximum flexion moment. Interestingly, the flexion modification only affected the abduction angle and abduction moment for the group of participants that landed in an initial adducted position before the modification and had no significant effect on the group that landed in an abducted position.

Conclusion: Increasing the knee flexion angle during jump landing may be an effective intervention to improve knee biomechanical risk factors associated with an ACL injury. However, the fact that the flexion modification only influenced critical risk factors (the abduction angle and abduction moment) in participants who initially landed in an adducted position suggests that the selection of interventions to prevent ACL injuries should account for patient-specific characteristics.

Clinical Relevance: The study helps elucidate how increasing the knee flexion angle affects lower body biomechanics and provided evidence for the need to introduce patient-specific strategies for preventing ACL injuries.
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http://dx.doi.org/10.1177/0363546516634000DOI Listing
June 2016

Optimization of the anterior cruciate ligament injury prevention paradigm: novel feedback techniques to enhance motor learning and reduce injury risk.

J Orthop Sports Phys Ther 2015 Mar 27;45(3):170-82. Epub 2015 Jan 27.

Center for Human Movement Sciences, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.

Synopsis: Primary anterior cruciate ligament (ACL) injury prevention programs effectively reduce ACL injury risk in the short term. Despite these programs, ACL injury incidence is still high, making it imperative to continue to improve current prevention strategies. A potential limitation of current ACL injury prevention training may be a deficit in the transfer of conscious, optimal movement strategies rehearsed during training sessions to automatic movements required for athletic activities and unanticipated events on the field. Instructional strategies with an internal focus of attention have traditionally been utilized, but may not be optimal for the acquisition of the control of complex motor skills required for sports. Conversely, external-focus instructional strategies may enhance skill acquisition more efficiently and increase the transfer of improved motor skills to sports activities. The current article will present insights gained from the motor-learning domain that may enhance neuromuscular training programs via improved skill development and increased retention and transfer to sports activities, which may reduce ACL injury incidence in the long term.
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http://dx.doi.org/10.2519/jospt.2015.4986DOI Listing
March 2015

An Adaptive Home-Use Robotic Rehabilitation System for the Upper Body.

IEEE J Transl Eng Health Med 2014 27;2:2100310. Epub 2014 Mar 27.

Robotic rehabilitation systems have been developed to treat musculoskeletal conditions, but limited availability prevents most patients from using them. The objective of this paper was to create a home-use robotic rehabilitation system. Data were obtained in real time from a Microsoft [Formula: see text] and a wireless surface electromyograph system. Results from the [Formula: see text] sensor were compared to a standard motion capture system. A subject completed visual follow exercise tasks in a 3-D visual environment. Data from two training exercises were used to generate a neural network, which was then used to simulate the subject's individual performance. The subjects completed both the exercise task output from the neural network (custom), and the unmodified task (standard). In addition, a wearable arm robot prototype was built. Basic system identification was completed, and a control algorithm for the robot based on pressure control was designed and tested. The subjects had greater root-mean-square error for position and velocity variables during the custom exercise tasks. These results suggest that the custom task was difficult to complete, possibly because the neural network was unconstrained. Finally, the robot prototype was able to mimic changes in a subject's elbow angle in real time, demonstrating the feasibility of the robotic rehabilitation system.
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http://dx.doi.org/10.1109/JTEHM.2014.2314097DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4848105PMC
May 2016

Characterization of thigh and shank segment angular velocity during jump landing tasks commonly used to evaluate risk for ACL injury.

J Biomech Eng 2012 Sep;134(9):091006

Department of Mechanical Engineering, Stanford University, Stanford, CA 94305-4008, USA.

The dynamic movements associated with anterior cruciate ligament (ACL) injury during jump landing suggest that limb segment angular velocity can provide important information for understanding the conditions that lead to an injury. Angular velocity measures could provide a quick and simple method of assessing injury risk without the constraints of a laboratory. The objective of this study was to assess the inter-subject variations and the sensitivity of the thigh and shank segment angular velocity in order to determine if these measures could be used to characterize jump landing mechanisms. Additionally, this study tested the correlation between angular velocity and the knee abduction moment. Thirty-six healthy participants (18 male) performed drop jumps with bilateral and unilateral landing. Thigh and shank angular velocities were measured by a wearable inertial-based system, and external knee moments were measured using a marker-based system. Discrete parameters were extracted from the data and compared between systems. For both jumping tasks, the angular velocity curves were well defined movement patterns with high inter-subject similarity in the sagittal plane and moderate to good similarity in the coronal and transverse planes. The angular velocity parameters were also able to detect differences between the two jumping tasks that were consistent across subjects. Furthermore, the coronal angular velocities were significantly correlated with the knee abduction moment (R of 0.28-0.51), which is a strong indicator of ACL injury risk. This study suggested that the thigh and shank angular velocities, which describe the angular dynamics of the movement, should be considered in future studies about ACL injury mechanisms.
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http://dx.doi.org/10.1115/1.4007178DOI Listing
September 2012

Inertial sensor-based feedback can reduce key risk metrics for anterior cruciate ligament injury during jump landings.

Am J Sports Med 2012 May 28;40(5):1075-83. Epub 2012 Mar 28.

Stanford Biomotion Laboratory, Stanford, CA 94305, USA.

Background: The incidence of anterior cruciate ligament (ACL) injury can be decreased through the use of intervention programs. However, the success of these programs is dependent on access to a skilled trainer who provides feedback; as such, these programs would benefit from a simple device with the capacity to provide high-quality feedback.

Hypothesis: Feedback based on kinematic measurements from a simple inertial sensor-based system can be used to modify key ACL injury risk metrics (knee flexion angle, trunk lean, knee abduction moment) during jump landing.

Study Design: Controlled laboratory study.

Methods: Seventeen subjects (7 male) were tested during drop jump tasks. Their movements were measured simultaneously with inertial, optoelectronic, and force platform systems. Feedback provided to the subjects was based only on measurements from the inertial sensor-based system (knee flexion angle, trunk lean, and thigh coronal velocity). The subjects conducted a baseline session (without landing instructions), then a training session (with immediate feedback), and finally an evaluation session (without feedback). The baseline and evaluation sessions were then tested for changes in the key risk metrics.

Results: The subjects increased their knee flexion angle (16.2°) and trunk lean (17.4°) after the training. They also altered their thigh coronal angular velocity by 29.4 deg/s and reduced their knee abduction moment by 0.5 %BW·Ht. There was a significant correlation (R (2) = 0.55) between the change in thigh coronal angular velocity and the change in knee abduction moment.

Conclusion: Subjects reduced key risk metrics for ACL injury after training with the system, suggesting the potential benefit of instrumented feedback for interventional training.

Clinical Relevance: Interventional training for reducing the risk of ACL injury could be improved with a simple device that provides immediate feedback.
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http://dx.doi.org/10.1177/0363546512437529DOI Listing
May 2012

A wearable system to assess risk for anterior cruciate ligament injury during jump landing: measurements of temporal events, jump height, and sagittal plane kinematics.

J Biomech Eng 2011 Jul;133(7):071008

Department of Mechanical Engineering, Stanford University, Stanford, CA 94305-4308, USA.

The incidence of anterior cruciate ligament (ACL) injury remains high, and there is a need for simple, cost effective methods to identify athletes at a higher risk for ACL injury. Wearable measurement systems offer potential methods to assess the risk of ACL injury during jumping tasks. The objective of this study was to assess the capacity of a wearable inertial-based system to evaluate ACL injury risk during jumping tasks. The system accuracy for measuring temporal events (initial contact, toe-off), jump height, and sagittal plane angles (knee, trunk) was assessed by comparing results obtained with the wearable system to simultaneous measurements obtained with a marker-based optoelectronic reference system. Thirty-eight healthy participants (20 male and 18 female) performed drop jumps with bilateral and unilateral support landing. The mean differences between the temporal events obtained with both systems were below 5 ms, and the precisions were below 24 ms. The mean jump heights measured with both systems differed by less than 1 mm, and the associations (Pearson correlation coefficients) were above 0.9. For the discrete angle parameters, there was an average association of 0.91 and precision of 3.5° for the knee flexion angle and an association of 0.77 and precision of 5.5° for the trunk lean. The results based on the receiver-operating characteristic (ROC) also demonstrated that the proposed wearable system could identify movements at higher risk for ACL injury. The area under the ROC plots was between 0.89 and 0.99 for the knee flexion angle and between 0.83 and 0.95 for the trunk lean. The wearable system demonstrated good concurrent validity with marker-based measurements and good discriminative performance in terms of the known risk factors for ACL injury. This study suggests that a wearable system could be a simple cost-effective tool for conducting risk screening or for providing focused feedback.
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http://dx.doi.org/10.1115/1.4004413DOI Listing
July 2011

Gait modification via verbal instruction and an active feedback system to reduce peak knee adduction moment.

J Biomech Eng 2010 Jul;132(7):071007

Department of Mechanical Engineering, Stanford University, Durand Building 061, 496 Lomita Mall, Stanford, CA 94305-4308, USA.

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.
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http://dx.doi.org/10.1115/1.4001584DOI Listing
July 2010

Shoe-surface friction influences movement strategies during a sidestep cutting task: implications for anterior cruciate ligament injury risk.

Am J Sports Med 2010 Mar;38(3):478-85

Department of Mechanical Engineering, Stanford University, Durand Building 061, Stanford, CA 94305-4308, USA.

Background: Increasing the coefficient of friction of the shoe-surface interaction has been shown to lead to increased incidence of anterior cruciate ligament (ACL) injuries, but the causes for this increase are unknown. Previous studies indicate that specific biomechanical measures during landing are associated with an increased risk for ACL injury.

Hypothesis: At foot contact during a sidestep cutting task, subjects use different movement strategies for shoe-surface conditions with a high coefficient of friction (COF) relative to a low friction condition. Specifically, the study tested for significant differences in knee kinematics, external knee moments, and the position of the center of mass for different COFs.

Study Design: Controlled laboratory study.

Methods: Twenty-two healthy subjects (11 male) were evaluated performing a 30 degrees sidestep cutting task on a low friction surface (0.38) and a high friction surface (0.87) at a constant speed. An 8-camera markerless motion capture system combined with 2 force plates was used to measure full-body kinematics, kinetics, and center of mass.

Results: At foot contact, subjects had a lower knee flexion angle (P = .01), lower external knee flexion moment (P < .001), higher external knee valgus moment (P < .001), and greater medial distance of the center of mass from the support limb (P < .001) on the high friction surface relative to the low friction surface.

Conclusion: The high COF shoe-surface condition was associated with biomechanical conditions that can increase the risk of ACL injury. The higher incidence of ACL injury observed on high friction surfaces could be a result of these biomechanical changes. The differences in the biomechanical variables were the result of an anticipated stimulus due to different surface friction, with other conditions remaining constant.

Clinical Relevance: The risk analysis of ACL injury should consider the biomechanical movement changes that occur for a shoe-surface condition with high friction.
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http://dx.doi.org/10.1177/0363546509348374DOI Listing
March 2010
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