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Smartphone-Based Gait Cadence to Identify Older Adults with Decreased Functional Capacity. 基于智能手机的步态节奏识别功能下降的老年人。
Q1 Computer Science Pub Date : 2022-05-01 DOI: 10.1159/000525344
Daniel S Rubin, Sylvia L Ranjeva, Jacek K Urbanek, Marta Karas, Maria Lucia L Madariaga, Megan Huisingh-Scheetz

Background: Functional capacity assessment is a critical step in the preoperative evaluation to identify patients at increased risk of cardiac complications and disability after major noncardiac surgery. Smartphones offer the potential to objectively measure functional capacity but are limited by inaccuracy in patients with poor functional capacity. Open-source methods exist to analyze accelerometer data to estimate gait cadence (steps/min), which is directly associated with activity intensity. Here, we used an updated Step Test smartphone application with an open-source method to analyze accelerometer data to estimate gait cadence and functional capacity in older adults.

Methods: We performed a prospective observational cohort study within the Frailty, Activity, Body Composition and Energy Expenditure in Aging study at the University of Chicago. Participants completed the Duke Activity Status Index (DASI) and performed an in-clinic 6-min walk test (6MWT) while using the Step Test application on a study smartphone. Gait cadence was measured from the raw accelerometer data using an adaptive empirical pattern transformation method, which has been previously validated. A 6MWT distance of 370 m was used as an objective threshold to identify patients at high risk. We performed multivariable logistic regression to predict walking distance using a priori explanatory variables.

Results: Sixty patients were enrolled in the study. Thirty-seven patients completed the protocol and were included in the final data analysis. The median (IQR) age of the overall cohort was 71 (69-74) years, with a body mass index of 31 (27-32). There were no differences in any clinical characteristics or functional measures between participants that were able to walk 370 m during the 6MWT and those that could not walk that distance. Median (IQR) gait cadence for the entire cohort was 110 (102-114) steps/min during the 6MWT. Median (IQR) gait cadence was higher in participants that walked more than 370 m during the 6MWT 112 (108-118) versus 106 (96-114) steps/min; p = 0.0157). The final multivariable model to identify participants that could not walk 370 m included only median gait cadence. The Youden's index cut-point was 107 steps/min with a sensitivity of 0.81 (95% CI: 0.77, 0.85) and a specificity of 0.57 (95% CI: 0.55, 0.59) and an AUCROC of 0.69 (95% CI: 0.51, 0.87).

Conclusions: Our pilot study demonstrates the feasibility of using gait cadence as a measure to estimate functional capacity. Our study was limited by a smaller than expected sample size due to COVID-19, and thus, a prospective study with preoperative patients that measures outcomes is necessary to validate our findings.

背景:功能容量评估是术前评估的关键步骤,用于识别重大非心脏手术后心脏并发症和残疾风险增加的患者。智能手机提供了客观测量功能能力的潜力,但由于功能能力差的患者的不准确性而受到限制。已有开源方法分析加速度计数据以估计与活动强度直接相关的步态节奏(步数/分钟)。在这里,我们使用了更新的Step Test智能手机应用程序和开源方法来分析加速度计数据,以估计老年人的步态节奏和功能能力。方法:我们在芝加哥大学的衰老研究中进行了一项前瞻性观察队列研究,包括虚弱、活动、身体组成和能量消耗。参与者完成了杜克活动状态指数(DASI),并在使用研究智能手机上的步骤测试应用程序时进行了临床6分钟步行测试(6MWT)。从原始加速度计数据中使用自适应经验模式变换方法测量步态节奏,该方法先前已得到验证。以6MWT距离370 m作为识别高危患者的客观阈值。我们使用先验解释变量进行多变量逻辑回归来预测步行距离。结果:60例患者入组研究。37例患者完成了方案,并被纳入最终数据分析。整个队列的中位(IQR)年龄为71(69-74)岁,体重指数为31(27-32)。在6MWT期间能够行走370米的参与者和不能行走370米的参与者之间的任何临床特征或功能测量没有差异。在6MWT期间,整个队列的中位步频(IQR)为110(102-114)步/分钟。在6MWT期间,行走超过370米的参与者的中位步频(IQR)更高,为112(108-118)步/分钟,而106(96-114)步/分钟;P = 0.0157)。最后的多变量模型用于识别不能步行370米的参与者,仅包括中位步速。约登指数切割点为107步/分钟,敏感性为0.81 (95% CI: 0.77, 0.85),特异性为0.57 (95% CI: 0.55, 0.59), AUCROC为0.69 (95% CI: 0.51, 0.87)。结论:我们的初步研究证明了使用步态节奏作为评估功能能力的方法的可行性。由于COVID-19的原因,我们的研究样本量小于预期,因此,有必要对术前患者进行前瞻性研究,以测量结果,以验证我们的发现。
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引用次数: 3
Objective Home-Monitoring of Physical Activity, Cardiovascular Parameters, and Sleep in Pediatric Obesity. 目的对儿童肥胖患者的身体活动、心血管参数和睡眠进行家庭监测。
Q1 Computer Science Pub Date : 2022-03-31 eCollection Date: 2022-01-01 DOI: 10.1159/000522185
Janine M Knijff, Euphemia C A M Houdijk, Daniëlle C M van der Kaay, Youri van Berkel, Luc Filippini, Frederik E Stuurman, Adam F Cohen, Gertjan J A Driessen, Matthijs D Kruizinga

Introduction: Clinical research and treatment of childhood obesity is challenging, and objective biomarkers obtained in a home-setting are needed. The aim of this study was to determine the potential of novel digital endpoints gathered by a home-monitoring platform in pediatric obesity.

Methods: In this prospective observational study, 28 children with obesity aged 6-16 years were included and monitored for 28 days. Patients wore a smartwatch, which measured physical activity (PA), heart rate (HR), and sleep. Furthermore, daily blood pressure (BP) measurements were performed. Data from 128 healthy children were utilized for comparison. Differences between patients and controls were assessed via linear mixed effect models.

Results: Data from 28 patients (average age 11.6 years, 46% male, average body mass index 30.9) and 128 controls (average age 11.1 years, 46% male, average body mass index 18.0) were analyzed. Patients were recruited between November 2018 and February 2020. For patients, the median compliance for the measurements ranged from 55% to 100% and the highest median compliance was observed for the smartwatch-related measurements (81-100%). Patients had a lower daily PA level (4,597 steps vs. 6,081 steps, 95% confidence interval [CI] 862-2,108) and peak PA level (1,115 steps vs. 1,392 steps, 95% CI 136-417), a higher nighttime HR (81 bpm vs. 71 bpm, 95% CI 6.3-12.3) and daytime HR (98 bpm vs. 88 bpm, 95% CI 7.6-12.6), a higher systolic BP (115 mm Hg vs. 104 mm Hg, 95% CI 8.1-14.5) and diastolic BP (76 mm Hg vs. 65 mm Hg, 95% CI 8.7-12.7), and a shorter sleep duration (difference 0.5 h, 95% CI 0.2-0.7) compared to controls.

Conclusion: Remote monitoring via wearables in pediatric obesity has the potential to objectively measure the disease burden in the home-setting. The novel endpoints demonstrate significant differences in PA level, HR, BP, and sleep duration between patients and controls. Future studies are needed to determine the capacity of the novel digital endpoints to detect effect of interventions.

儿童肥胖的临床研究和治疗具有挑战性,需要在家庭环境中获得客观的生物标志物。本研究的目的是确定由家庭监测平台收集的新型数字终点在儿童肥胖中的潜力。方法:在这项前瞻性观察研究中,纳入了28名6-16岁的肥胖儿童,并对其进行了28天的监测。患者戴着智能手表,测量身体活动(PA)、心率(HR)和睡眠。此外,还进行了每日血压(BP)测量。来自128名健康儿童的数据用于比较。通过线性混合效应模型评估患者和对照组之间的差异。结果:分析了28例患者(平均年龄11.6岁,男性46%,平均体重指数30.9)和128例对照组(平均年龄11.1岁,男性46%,平均体重指数18.0)的资料。患者是在2018年11月至2020年2月期间招募的。对于患者,测量的中位依从性范围为55%至100%,与智能手表相关的测量中位依从性最高(81-100%)。患者每日PA水平较低(vs 6081步、4597步95%可信区间[CI] 862 - 2108年)和峰值PA水平(vs 1392步、1115步95%可信区间136 - 417),一个更高的夜间人力资源(81 bpm vs 71 bpm, 95%可信区间6.3 - -12.3)和白天的人力资源(98 bpm vs 88 bpm, 95%可信区间7.6 - -12.6),较高的收缩压(115毫米汞柱和104毫米汞柱,95%可信区间8.1 - -14.5)和舒张压(76毫米汞柱和65毫米汞柱,95%可信区间8.7 - -12.7),和更短的睡眠时间(差异0.5 h, 95%置信区间0.2 - -0.7)相比,控制。结论:通过可穿戴设备对儿童肥胖进行远程监测,有可能客观地衡量家庭环境中的疾病负担。新的终点表明,患者和对照组之间的PA水平、HR、BP和睡眠时间存在显著差异。未来的研究需要确定新型数字端点检测干预措施效果的能力。
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引用次数: 2
Video-Based Pose Estimation for Gait Analysis in Stroke Survivors during Clinical Assessments: A Proof-of-Concept Study 基于视频的步态估计在中风幸存者的临床评估:一项概念验证研究
Q1 Computer Science Pub Date : 2022-01-13 DOI: 10.1159/000520732
L. Lonini, Y. Moon, Kyle R. Embry, R. Cotton, K. McKenzie, Sophia Jenz, A. Jayaraman
Recent advancements in deep learning have produced significant progress in markerless human pose estimation, making it possible to estimate human kinematics from single camera videos without the need for reflective markers and specialized labs equipped with motion capture systems. Such algorithms have the potential to enable the quantification of clinical metrics from videos recorded with a handheld camera. Here we used DeepLabCut, an open-source framework for markerless pose estimation, to fine-tune a deep network to track 5 body keypoints (hip, knee, ankle, heel, and toe) in 82 below-waist videos of 8 patients with stroke performing overground walking during clinical assessments. We trained the pose estimation model by labeling the keypoints in 2 frames per video and then trained a convolutional neural network to estimate 5 clinically relevant gait parameters (cadence, double support time, swing time, stance time, and walking speed) from the trajectory of these keypoints. These results were then compared to those obtained from a clinical system for gait analysis (GAITRite®, CIR Systems). Absolute accuracy (mean error) and precision (standard deviation of error) for swing, stance, and double support time were within 0.04 ± 0.11 s; Pearson’s correlation with the reference system was moderate for swing times (r = 0.4–0.66), but stronger for stance and double support time (r = 0.93–0.95). Cadence mean error was −0.25 steps/min ± 3.9 steps/min (r = 0.97), while walking speed mean error was −0.02 ± 0.11 m/s (r = 0.92). These preliminary results suggest that single camera videos and pose estimation models based on deep networks could be used to quantify clinically relevant gait metrics in individuals poststroke, even while using assistive devices in uncontrolled environments. Such development opens the door to applications for gait analysis both inside and outside of clinical settings, without the need of sophisticated equipment.
深度学习的最新进展在无标记人体姿态估计方面取得了重大进展,使从单摄像机视频中估计人体运动学成为可能,而无需反射标记和配备运动捕捉系统的专业实验室。这种算法有可能从手持摄像机记录的视频中量化临床指标。在这里,我们使用DeepLabCut,一个用于无标记姿势估计的开源框架,对一个深度网络进行微调,以跟踪82个腰部以下视频中的5个身体关键点(髋、膝、踝、脚跟和脚趾),这些视频中有8名中风患者在临床评估期间进行地上行走。我们通过标记每个视频2帧中的关键点来训练姿势估计模型,然后训练卷积神经网络,根据这些关键点的轨迹估计5个临床相关的步态参数(节奏、双支撑时间、摆动时间、站立时间和行走速度)。然后将这些结果与步态分析临床系统(GAITRite®,CIR Systems)获得的结果进行比较。摆动、站立和双支撑时间的绝对精度(平均误差)和精度(误差标准偏差)在0.04±0.11s内;Pearson与参考系统的相关性在摆动时间方面中等(r=0.4-0.66),但在站立和双支撑时间方面更强(r=0.93-0.95)。Cadence平均误差为-0.25步/分钟±3.9步/分钟(r=0.97),而步行速度平均误差为-0.02±0.11m/s(r=0.92)。这些初步结果表明,即使在不受控制的环境中使用辅助设备,基于深度网络的单摄像头视频和姿势估计模型也可以用于量化中风后个体的临床相关步态指标。这样的发展为步态分析在临床环境内外的应用打开了大门,而不需要复杂的设备。
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引用次数: 27
A Smartphone Application as an Exploratory Endpoint in a Phase 3 Parkinson’s Disease Clinical Trial: A Pilot Study 智能手机应用程序作为帕金森病3期临床试验的探索性终点:一项试点研究
Q1 Computer Science Pub Date : 2022-01-10 DOI: 10.1159/000521232
A. Page, Norman C W Yung, P. Auinger, C. Venuto, Alistair Glidden, E. Macklin, L. Omberg, M. Schwarzschild, E. Dorsey
Background: Smartphones can generate objective measures of Parkinson’s disease (PD) and supplement traditional in-person rating scales. However, smartphone use in clinical trials has been limited. Objective: This study aimed to determine the feasibility of introducing a smartphone research application into a PD clinical trial and to evaluate the resulting measures. Methods: A smartphone application was introduced part-way into a phase 3 randomized clinical trial of inosine. The application included finger tapping, gait, and cognition tests, and participants were asked to complete an assessment battery at home and in clinic alongside the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). Results: Of 236 eligible participants in the parent study, 88 (37%) consented to participate, and 59 (27 randomized to inosine and 32 to placebo) completed a baseline smartphone assessment. These 59 participants collectively completed 1,292 batteries of assessments. The proportion of participants who completed at least one smartphone assessment was 61% at 3, 54% at 6, and 35% at 12 months. Finger tapping speed correlated weakly with the part III motor portion (r = −0.16, left hand; r = −0.04, right hand) and total (r = −0.14) MDS-UPDRS. Gait speed correlated better with the same measures (r = −0.25, part III motor; r = −0.34, total). Over 6 months, finger tapping speed, gait speed, and memory scores did not differ between those randomized to active drug or placebo. Conclusions: Introducing a smartphone application midway into a phase 3 clinical trial was challenging. Measures of bradykinesia and gait speed correlated modestly with traditional outcomes and were consistent with the study’s overall findings, which found no benefit of the active drug.
背景:智能手机可以生成帕金森病(PD)的客观指标,并补充传统的面对面评分量表。然而,智能手机在临床试验中的使用受到了限制。目的:本研究旨在确定将智能手机研究应用引入帕金森病临床试验的可行性,并评估由此产生的措施。方法:将智能手机应用程序部分引入肌苷的3期随机临床试验。该应用程序包括手指敲击、步态和认知测试,参与者被要求在家中和诊所完成一组评估,并使用运动障碍协会统一帕金森病评定量表(MDS-UPDRS)。结果:在236名符合条件的父母研究参与者中,88人(37%)同意参与,59人(27人随机接受肌苷治疗,32人接受安慰剂治疗)完成了智能手机基线评估。这59名参与者共完成了1292组评估。参与者在3个月时完成了至少一次智能手机评估的比例为61%,在6个月时为54%,在12个月时则为35%。手指敲击速度与第三部分电机部分(r=−0.16,左手;r=−0.04,右手)和总MDS-UPDRS(r=–0.14)的相关性较弱。步态速度与相同测量值的相关性更好(r=−0.25,第三部分运动;r=−0.34,总计)。在6个月的时间里,随机接受活性药物或安慰剂治疗的患者的手指敲击速度、步态速度和记忆得分没有差异。结论:在3期临床试验的中途引入智能手机应用程序具有挑战性。运动迟缓和步态速度的测量与传统结果适度相关,并与该研究的总体结果一致,该研究没有发现活性药物的益处。
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引用次数: 4
Digital Endpoints: Definition, Benefits, and Current Barriers in Accelerating Development and Adoption. 数字端点:加速发展和采用的定义、好处和当前障碍。
Q1 Computer Science Pub Date : 2021-09-13 eCollection Date: 2021-09-01 DOI: 10.1159/000517885
Matthew Landers, Ray Dorsey, Suchi Saria

The assessment of health and disease requires a set of criteria to define health status and progression. These health measures are referred to as "endpoints." A "digital endpoint" is defined by its use of sensor-generated data often collected outside of a clinical setting such as in a patient's free-living environment. Applicable sensors exist in an array of devices and can be applied in a diverse set of contexts. For example, a smartphone's microphone might be used to diagnose or predict mild cognitive impairment due to Alzheimer's disease or a wrist-worn activity monitor (such as those found in smartwatches) may be used to measure a drug's effect on the nocturnal activity of patients with sickle cell disease. Digital endpoints are generating considerable excitement because they permit a more authentic assessment of the patient's experience, reveal formerly untold realities of disease burden, and can cut drug discovery costs in half. However, before these benefits can be realized, effort must be applied not only to the technical creation of digital endpoints but also to the environment that allows for their development and application. The future of digital endpoints rests on meaningful interdisciplinary collaboration, sufficient evidence that digital endpoints can realize their promise, and the development of an ecosystem in which the vast quantities of data that digital endpoints generate can be analyzed. The fundamental nature of health care is changing. With coronavirus disease 2019 serving as a catalyst, there has been a rapid expansion of home care models, telehealth, and remote patient monitoring. The increasing adoption of these health-care innovations will expedite the requirement for a digital characterization of clinical status as current assessment tools often rely upon direct interaction with patients and thus are not fit for purpose to be administered remotely. With the ubiquity of relatively inexpensive sensors, digital endpoints are positioned to drive this consequential change. It is therefore not surprising that regulators, physicians, researchers, and consultants have each offered their assessment of these novel tools. However, as we further describe later, the broad adoption of digital endpoints will require a cooperative effort. In this article, we present an analysis of the current state of digital endpoints. We also attempt to unify the perspectives of the parties involved in the development and deployment of these tools. We conclude with an interdependent list of challenges that must be collaboratively addressed before these endpoints are widely adopted.

对健康和疾病的评估需要一套标准来确定健康状况和进展。这些健康措施被称为“端点”。“数字端点”是通过使用传感器生成的数据来定义的,这些数据通常是在临床环境之外收集的,例如在患者的自由生活环境中。适用的传感器存在于一系列设备中,可以应用于不同的环境中。例如,智能手机的麦克风可以用来诊断或预测由阿尔茨海默病引起的轻度认知障碍,手腕上佩戴的活动监测器(比如智能手表上的那些)可以用来测量药物对镰状细胞病患者夜间活动的影响。数字终端正在引起相当大的兴奋,因为它们允许对患者的经历进行更真实的评估,揭示以前不为人知的疾病负担的现实,并且可以将药物发现成本降低一半。然而,在实现这些好处之前,不仅必须在数字端点的技术创建上付出努力,还必须在允许其开发和应用的环境上付出努力。数字端点的未来取决于有意义的跨学科合作,数字端点能够实现其承诺的充分证据,以及数字端点产生的大量数据可以分析的生态系统的发展。医疗保健的基本性质正在发生变化。以2019年冠状病毒病为催化剂,家庭护理模式、远程医疗和远程患者监测迅速扩大。越来越多地采用这些医疗保健创新将加快对临床状态数字化特征的需求,因为目前的评估工具往往依赖于与患者的直接互动,因此不适合远程管理。随着相对便宜的传感器无处不在,数字端点被定位为推动这一重大变化。因此,监管机构、医生、研究人员和顾问都对这些新工具提出了自己的评估,这并不奇怪。然而,正如我们后面进一步描述的那样,数字端点的广泛采用将需要合作的努力。在本文中,我们对数字端点的现状进行了分析。我们还试图统一参与开发和部署这些工具的各方的观点。最后,我们列出了一系列相互依存的挑战,在这些端点被广泛采用之前,必须协作解决这些挑战。
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引用次数: 9
Real-World Evidence for a Smartwatch-Based Parkinson's Motor Assessment App for Patients Undergoing Therapy Changes. 基于智能手表的帕金森运动评估应用程序的真实世界证据,用于接受治疗改变的患者。
Q1 Computer Science Pub Date : 2021-09-08 eCollection Date: 2021-09-01 DOI: 10.1159/000518571
Aaron J Hadley, David E Riley, Dustin A Heldman

Introduction: Parkinson's disease (PD) is poorly quantified by patients outside the clinic, and paper diaries have problems with subjective descriptions and bias. Wearable sensor platforms; however, can accurately quantify symptoms such as tremor, dyskinesia, and bradykinesia. Commercially available smartwatches are equipped with accelerometers and gyroscopes that can measure motion for objective evaluation. We sought to evaluate the clinical utility of a prescription smartwatch-based monitoring system for PD utilizing periodic task-based motor assessment.

Methods: Sixteen patients with PD used a smartphone- and smartwatch-based monitoring system to objectively assess motor symptoms for 1 week prior to instituting a doctor recommended change in therapy and for 4 weeks after the change. After 5 weeks the participants returned to the clinic to discuss their results with their doctor, who made therapy recommendations based on the reports and his clinical judgment. Symptom scores were synchronized with the medication diary and the temporal effects of therapy on weekly and hourly timescales were calculated.

Results: Thirteen participants successfully completed the study and averaged 4.9 assessments per day for 3 days per week during the study. The doctor instructed 8 participants to continue their new regimens and 5 to revert to their previous regimens. The smartwatch-based assessments successfully captured intraday fluctuations and short- and long-term responses to therapies, including detecting significant improvements (p < 0.05) in at least one symptom in 7 participants.

Conclusions: The smartwatch-based app successfully captured temporal trends in symptom scores following application of new therapy on hourly, daily, and weekly timescales. These results suggest that validated smartwatch-based PD monitoring can provide clinically relevant information and may reduce the need for traditional office visits for therapy adjustment.

简介:帕金森病(PD)在临床之外的量化程度较差,纸质日记存在主观描述和偏见的问题。可穿戴传感器平台;然而,可以准确地量化症状,如震颤、运动障碍和运动迟缓。市售的智能手表配备了加速度计和陀螺仪,可以测量运动以进行客观评估。我们试图评估基于处方智能手表的PD监测系统的临床应用,该系统利用周期性的基于任务的运动评估。方法:16例PD患者使用基于智能手机和智能手表的监测系统,在医生建议改变治疗方案前1周和改变治疗方案后4周客观评估运动症状。5周后,参与者回到诊所与医生讨论他们的结果,医生根据报告和他的临床判断提出治疗建议。症状评分与用药日记同步,并计算治疗在每周和每小时时间尺度上的时间效应。结果:13名参与者成功完成了研究,平均每天4.9次评估,每周3天。医生指示8名参与者继续他们的新方案,5名恢复他们以前的方案。基于智能手表的评估成功地捕获了日内波动以及对治疗的短期和长期反应,包括在7名参与者中检测到至少一种症状的显着改善(p < 0.05)。结论:基于智能手表的应用程序成功捕获了每小时、每天和每周时间尺度上应用新疗法后症状评分的时间趋势。这些结果表明,经过验证的基于智能手表的PD监测可以提供临床相关信息,并可能减少传统办公室就诊以调整治疗的需要。
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引用次数: 8
Developing a Novel Measurement of Sleep in Rheumatoid Arthritis: Study Proposal for Approach and Considerations. 开发一种新的类风湿关节炎睡眠测量方法:方法和考虑的研究建议。
Q1 Computer Science Pub Date : 2021-09-02 eCollection Date: 2021-09-01 DOI: 10.1159/000518024
Michelle Crouthamel, Robert J Mather, Suraj Ramachandran, Kai Bode, Godhuli Chatterjee, Luis Garcia-Gancedo, Joseph Kim, Rinol Alaj, Matthew F Wipperman, Lada Leyens, Henrik Sillen, Tina Murphy, Michael Benecky, Brandon Maggio, Thomas Switzer

The development of novel digital endpoints (NDEs) using digital health technologies (DHTs) may provide opportunities to transform drug development. It requires a multidisciplinary, multi-study approach with strategic planning and a regulatory-guided pathway to achieve regulatory and clinical acceptance. Many NDEs have been explored; however, success has been limited. To advance industry use of NDEs to support drug development, we outline a theoretical, methodological study as a use-case proposal to describe the process and considerations when developing and obtaining regulatory acceptance for an NDE to assess sleep in patients with rheumatoid arthritis (RA). RA patients often suffer joint pain, fatigue, and sleep disturbances (SDs). Although many researchers have investigated the mobility of joint functions using wearable technologies, the research of SD in RA has been limited due to the availability of suitable technologies. We proposed measuring the improvement of sleep as the novel endpoint for an anti-TNF therapy and described the meaningfulness of the measure, considerations of tool selection, and the design of clinical validation. The recommendations from the FDA patient-focused drug development guidance, the Clinical Trials Transformation Initiative (CTTI) pathway for developing novel endpoints from DHTs, and the V3 framework developed by the Digital Medicine Society (DiMe) have been incorporated in the proposal. Regulatory strategy and engagement pathways are also discussed.

使用数字健康技术(dht)开发新型数字端点(NDEs)可能为改变药物开发提供机会。它需要一个多学科、多研究的方法,具有战略规划和监管指导的途径,以实现监管和临床接受。许多濒死体验已经被探索过;然而,成功是有限的。为了促进濒死体验的工业应用以支持药物开发,我们概述了一项理论、方法学研究作为用例提案,以描述在开发和获得监管机构认可的濒死体验评估类风湿性关节炎(RA)患者睡眠时的过程和考虑因素。RA患者经常遭受关节疼痛、疲劳和睡眠障碍(SDs)。尽管许多研究人员已经使用可穿戴技术研究了关节功能的移动性,但由于合适技术的可用性,SD在RA中的研究受到限制。我们提出测量睡眠改善作为抗肿瘤坏死因子治疗的新终点,并描述了测量的意义、工具选择的考虑和临床验证的设计。FDA以患者为中心的药物开发指南、用于从dht开发新终点的临床试验转化倡议(CTTI)途径以及数字医学协会(DiMe)开发的V3框架的建议已被纳入提案。还讨论了监管策略和参与途径。
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引用次数: 3
First Regulatory Qualification of a Novel Digital Endpoint in Duchenne Muscular Dystrophy: A Multi-Stakeholder Perspective on the Impact for Patients and for Drug Development in Neuromuscular Diseases. 杜氏肌萎缩症新型数字终点的首次监管资格:对神经肌肉疾病患者和药物开发影响的多方利益相关者视角
Q1 Computer Science Pub Date : 2021-08-05 eCollection Date: 2021-05-01 DOI: 10.1159/000517411
Laurent Servais, Eric Camino, Aude Clement, Craig M McDonald, Jacek Lukawy, Linda P Lowes, Damien Eggenspieler, Francesca Cerreta, Paul Strijbos

Background: Functional outcome measures used to assess efficacy in clinical trials of investigational treatments for rare neuromuscular diseases like Duchenne muscular dystrophy (DMD) are performance-based tasks completed by the patient during hospital visits. These are prone to bias and may not reflect motor abilities in real-world settings. Digital tools, such as wearable devices and other remote sensors, provide the opportunity for continuous, objective, and sensitive measurements of functional ability during daily life. Maintaining ambulation is of key importance to individuals with DMD. Stride velocity 95th centile (SV95C) is the first wearable acquired digital endpoint to receive qualification from the European Medicines Agency (EMA) to quantify the ambulation ability of ambulant DMD patients aged ≥5 years in drug therapeutic studies; it is also currently under review for the US Food and Drug Administration (FDA) qualification.

Summary: Focusing on SV95C as a key example, we describe perspectives of multiple stakeholders on the promise of novel digital endpoints in neuromuscular disease drug development.

背景:在罕见神经肌肉疾病(如杜氏肌营养不良症(DMD))研究性治疗的临床试验中,用于评估疗效的功能结局指标是患者在医院就诊期间完成的基于性能的任务。这些容易产生偏差,可能不能反映现实环境中的运动能力。数字工具,如可穿戴设备和其他远程传感器,为日常生活中功能能力的连续、客观和敏感测量提供了机会。保持活动对DMD患者至关重要。Stride velocity 95 centile (SV95C)是第一个获得欧洲药品管理局(EMA)认证的可穿戴获得式数字终端,用于量化药物治疗研究中5岁以上的动态DMD患者的行走能力;它目前也正在接受美国食品和药物管理局(FDA)资格的审查。摘要:以SV95C为例,我们描述了多个利益相关者对神经肌肉疾病药物开发中新型数字终点前景的看法。
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引用次数: 21
Beyond the Therapist's Office: Merging Measurement-Based Care and Digital Medicine in the Real World. 超越治疗师办公室:在现实世界中融合基于测量的护理和数字医学。
Q1 Computer Science Pub Date : 2021-07-29 eCollection Date: 2021-05-01 DOI: 10.1159/000517748
Emil Chiauzzi, Paul Wicks

This viewpoint focuses on the ways in which digital medicine and measurement-based care can be utilized in tandem to promote better assessment, patient engagement, and an improved quality of psychiatric care. To date, there has been an underutilization of digital measurement in psychiatry, and there is little discussion of the feedback and patient engagement process in digital medicine. Measurement-based care is a recognized evidence-based strategy that engages patients in an understanding of their outcome data. When implemented as designed, providers review the scores and trends in outcome immediately and then provide feedback to their patients. However, the process is typically confined to office visits, which does not provide a complete picture of a patient's progress and functioning. The process is labor intensive, even with digital feedback systems, but the integration of passive metrics obtained through wearables and apps can supplement office-based observations. This enhanced measurement-based care process can provide a picture of real-world patient functioning through passive metrics (activity, sleep, etc.). This can potentially engage patients more in their health data and involve a critically needed therapeutic alliance component in digital medicine.

这一观点侧重于数字医学和基于测量的护理可以协同使用的方式,以促进更好的评估,患者参与和提高精神病学护理的质量。迄今为止,精神病学对数字测量的利用不足,对数字医学中的反馈和患者参与过程的讨论很少。基于测量的护理是一种公认的基于证据的策略,它使患者了解其结果数据。当按照设计实施时,提供者会立即审查结果的得分和趋势,然后向患者提供反馈。然而,这一过程通常仅限于办公室访问,这并不能提供患者进展和功能的完整图景。这个过程是劳动密集型的,即使有数字反馈系统,但通过可穿戴设备和应用程序获得的被动指标的整合可以补充办公室的观察结果。这种增强的基于测量的护理过程可以通过被动指标(活动、睡眠等)提供真实世界患者功能的图片。这可能会让患者更多地参与到他们的健康数据中,并涉及到数字医学中急需的治疗联盟组件。
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引用次数: 5
Advanced Machine Learning Tools to Monitor Biomarkers of Dysphagia: A Wearable Sensor Proof-of-Concept Study. 先进的机器学习工具监测吞咽困难的生物标志物:可穿戴传感器的概念验证研究。
Q1 Computer Science Pub Date : 2021-07-27 eCollection Date: 2021-05-01 DOI: 10.1159/000517144
Megan K O'Brien, Olivia K Botonis, Elissa Larkin, Julia Carpenter, Bonnie Martin-Harris, Rachel Maronati, KunHyuck Lee, Leora R Cherney, Brianna Hutchison, Shuai Xu, John A Rogers, Arun Jayaraman

Introduction: Difficulty swallowing (dysphagia) occurs frequently in patients with neurological disorders and can lead to aspiration, choking, and malnutrition. Dysphagia is typically diagnosed using costly, invasive imaging procedures or subjective, qualitative bedside examinations. Wearable sensors are a promising alternative to noninvasively and objectively measure physiological signals relevant to swallowing. An ongoing challenge with this approach is consolidating these complex signals into sensitive, clinically meaningful metrics of swallowing performance. To address this gap, we propose 2 novel, digital monitoring tools to evaluate swallows using wearable sensor data and machine learning.

Methods: Biometric swallowing and respiration signals from wearable, mechano-acoustic sensors were compared between patients with poststroke dysphagia and nondysphagic controls while swallowing foods and liquids of different consistencies, in accordance with the Mann Assessment of Swallowing Ability (MASA). Two machine learning approaches were developed to (1) classify the severity of impairment for each swallow, with model confidence ratings for transparent clinical decision support, and (2) compute a similarity measure of each swallow to nondysphagic performance. Task-specific models were trained using swallow kinematics and respiratory features from 505 swallows (321 from patients and 184 from controls).

Results: These models provide sensitive metrics to gauge impairment on a per-swallow basis. Both approaches demonstrate intrasubject swallow variability and patient-specific changes which were not captured by the MASA alone. Sensor measures encoding respiratory-swallow coordination were important features relating to dysphagia presence and severity. Puree swallows exhibited greater differences from controls than saliva swallows or liquid sips (p < 0.037).

Discussion: Developing interpretable tools is critical to optimize the clinical utility of novel, sensor-based measurement techniques. The proof-of-concept models proposed here provide concrete, communicable evidence to track dysphagia recovery over time. With refined training schemes and real-world validation, these tools can be deployed to automatically measure and monitor swallowing in the clinic and community for patients across the impairment spectrum.

吞咽困难(吞咽困难)常见于神经系统疾病患者,可导致误吸、窒息和营养不良。吞咽困难的诊断通常使用昂贵的侵入性成像程序或主观的定性床边检查。可穿戴传感器是非侵入性的、客观测量与吞咽相关的生理信号的一种很有前途的替代方法。该方法面临的一个持续挑战是将这些复杂的信号整合成敏感的、临床有意义的吞咽表现指标。为了解决这一差距,我们提出了两种新颖的数字监测工具,利用可穿戴传感器数据和机器学习来评估燕子。方法:根据Mann吞咽能力评估(MASA),比较脑卒中后吞咽困难患者和非吞咽困难对照组患者在吞咽不同浓度的食物和液体时的生物特征吞咽和呼吸信号。开发了两种机器学习方法来(1)对每次吞咽损伤的严重程度进行分类,并使用透明临床决策支持的模型可信度评级,以及(2)计算每次吞咽与非吞咽困难表现的相似性度量。使用505只燕子(321只来自患者,184只来自对照组)的运动学和呼吸特征来训练特定任务模型。结果:这些模型提供了敏感的指标来衡量每次吞咽的损害。这两种方法都证明了受试者吞咽变异性和患者特异性变化,而这些变化并不是单独由MASA捕获的。传感器测量编码呼吸-吞咽协调是与吞咽困难存在和严重程度相关的重要特征。吞下果泥比吞下唾液或小口液体表现出更大的差异(p < 0.037)。讨论:开发可解释的工具对于优化基于传感器的新型测量技术的临床应用至关重要。这里提出的概念验证模型提供了具体的、可传递的证据来跟踪吞咽困难随时间的恢复。通过完善的训练方案和真实世界的验证,这些工具可以在诊所和社区中自动测量和监测吞咽障碍患者。
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引用次数: 7
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Digital Biomarkers
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