Digital health technologies (DHTs) enable us to measure human physiology and behavior remotely, objectively and continuously. With the accelerated adoption of DHTs in clinical trials, there is an unmet need to identify statistical approaches to address missing data to ensure that the derived endpoints are valid, accurate, and reliable. It is not obvious how commonly used statistical methods to handle missing data in clinical trials can be directly applied to the complex data collected by DHTs.

Patients with atopic dermatitis experience increased nocturnal pruritus which leads to scratching and sleep disturbances that significantly contribute to poor quality of life. Objective measurements of nighttime scratching and sleep quantity can help assess the efficacy of an intervention. Wearable sensors can provide novel, objective measures of nighttime scratching and sleep; however, many current approaches were not designed for passive, unsupervised monitoring during daily life. In this work, we present the development and analytical validation of a method that sequentially processes epochs of sample-level accelerometer data from a wrist-worn device to provide continuous digital measures of nighttime scratching and sleep quantity.

This study aimed to investigate the impact of reducing the number of wearable devices on the ability to assess gait impairments in patients with PD. Comparable performance was observed for predicting MDS-UPDRS gait score with features derived using 1, 3, and 6 wearable devices. Furthermore, predicted MDS-UPDRS gait scores from all 3 models differed significantly between On and Off motor states. Our results suggest a marginal benefit in using multiple devices for assessing gait impairments in patients with PD when compared with gait features derived using a single lumbar-mounted accelerometer.

We investigated the use of digital devices during traditional clinical assessments and in real-world environments in a group of healthy younger (n = 33, 18–40 years) and older (n = 32, 65–85 years) adults. Gait speed estimated in-lab, with or without digital devices, failed to differentiate between age groups, whereas gait speed derived during at-home monitoring was able to distinguish between age groups. Gait speed estimated in-lab was weakly correlated with both median and 95th percentile at-home gait speed. Furthermore, three days of at-home monitoring was sufficient to reliably estimate gait speed in our population, and still capture age-related group differences. Our results suggest that gait speed derived from activities during daily life using data from wearable devices may have the potential to transform clinical trials by non-invasively and unobtrusively providing a more objective and naturalistic measure of functional ability.

This paper presents a novel wavelet-based, orientation independent, algorithm for detecting STS transfers from accelerometer data from lower back. The algorithm was validated in healthy adults and patients with Parkinson's disease, and then deployed during free-living conditions in healthy adults. Results show comparable or better performance than commercially available systems (precision: 0.999 vs 0.868 in healthy adults) and a previously published algorithm (precision: 0.988 vs 0.643 in persons with PD). STS transfer performance metrics from at-home additionally showed significantly better ability to differentiate between older and younger healthy subjects than in-lab. Simulated results showed a minimum monitoring duration of 3 days was sufficient to achieve good reliability for measurement of STS features.

The use of digital health products has gained considerable interest as a new way to improve therapeutic research and development. Although these products are being adopted by various industries and stakeholders, their incorporation in clinical trials has been slow due to a disconnect between the promises of digital products and potential risks in using these new technologies in the absence of regulatory support. The Foundation for the National Institutes of Health (FNIH) Biomarkers Consortium hosted a public workshop to address challenges and opportunities in this field. Important characteristics of tool development were addressed in a series of presentations, case studies, and open panel sessions. The workshop participants endorsed the usefulness of an evidentiary criteria framework, highlighted the importance of early patient engagement, and emphasized the potential impact of digital monitoring tools and precompetitive collaborations. Concerns were expressed about the lack of real-life validation examples and the limitations of legacy standards used as a benchmark for novel tool development and validation. Participants recognized the need for novel analytical and statistical approaches to accommodate analyses of these novel data types. Future directions are to harmonize definitions to build common methodologies and foster multidisciplinary collaborations; to develop approaches toward integrating digital monitoring data with the totality of the data in clinical trials, and to continue an open dialog in the community. There was a consensus that all these efforts combined may create a paradigm shift of how clinical trials are planned, conducted, and results brought to regulatory reviews.

Investigating (i) the utility and reliability of self-reports for describing motor fluctuations; (ii) the agreement between participants and clinical raters on the presence of motor complications; (iii) the ability of video raters to accurately assess motor symptoms, and (iv) the dynamics of tremor, dyskinesia, and bradykinesia as they evolve over the medication cycle.

Development of methods for continuous detection and assessment of resting tremor and bradykinesia using a single wrist-worn wearable sensor.

SleepPy is an open-source python package incorporating several published algorithms in a modular framework and providing a suite of measures for the assessment of sleep quantity and quality. 

GaitPy is an open-source Python package that implements several published algorithms in a modular framework for extracting clinical features of gait from a single accelerometer device mounted on the lower back.