Digital Biomarkers最新文献

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.

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.

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.

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.

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.

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.

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.

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.