Wrist-worn sensor-based measurements for drug effect detection with small samples in people with Lewy Body Dementia

Chen, Chen; Kowahl, Nathan; Rainaldi, Erin; Burq, Maximilien; Munsie, Leanne; Battioui, Chakib; Wang, Jian; Biglan, Kevin; Marks, William J.; Kapur, Ritu

doi:10.1016/j.parkreldis.2023.105355

Cited by 10 publications

(8 citation statements)

References 17 publications

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“…However, the growing acceptance of DHTs as secondary endpoints in ADRD research[33, 34] is slowly moving the field towards this possibility[35, 36]. Studies employ DHTs for monitoring response to treatment in a variety of ways, such as using actigraphy to discern impacts of Mevidalen on activity and sleep within Lewy Body dementia cohorts[37, 38] or using smartwatches to detect suvorexant-induced changes in sleeping patterns for persons with Alzheimer’s experiencing insomnia[39].…”

Section: Discussionmentioning

confidence: 99%

Digital Health Technologies for Alzheimer’s Disease and Related Dementias: Initial Results from a Landscape Analysis and Community Collaborative Effort

Lott,

Streel,

Bachman

et al. 2024

Preprint

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Digital health technologies offer valuable advantages to dementia researchers and clinicians as screening tools, diagnostic aids, and monitoring instruments. To support the use and advancement of these resources, a comprehensive overview of the current technological landscape is essential. A multi-stakeholder working group, convened by the Digital Medicine Society (DiMe), conducted a landscape review to identify digital health technologies for Alzheimers disease and related dementia populations. We searched studies indexed in PubMed, Embase, and APA PsycInfo to identify manuscripts published between May 2003 to May 2023 reporting analytical validation, clinical validation, or usability/feasibility results for relevant digital health technologies. Additional technologies were identified through community outreach. We collated 172 peer-reviewed manuscripts, poster presentations, or regulatory documents for 106 different technologies for Alzheimers disease and related dementia assessment covering diverse populations such as Lewy Body, vascular dementias, frontotemporal dementias, and all severities of Alzheimers disease. Wearable sensors represent 32% of included technologies, non-wearables 61%, and technologies with components of both account for the remaining 7%. Neurocognition is the most prevalent concept of interest, followed by physical activity and sleep. Clinical validation is reported in 69% of evidence, analytical validation in 34%, and usability/feasibility in 20% (not mutually exclusive). These findings provide a landscape overview for clinicians and researchers to appraise the clinical utility and relative maturity of technologies for assessing Alzheimers disease and related dementias.

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Section: Discussionmentioning

confidence: 99%

Digital Health Technologies for Alzheimer’s Disease and Related Dementias: Initial Results from a Landscape Analysis and Community Collaborative Effort

Lott,

Streel,

Bachman

et al. 2024

Preprint

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“…One of the goals of our research is to build accurate tools to objectively quantify aspects of walking behavior and extract clinically meaningful information in discrete populations of interest. In our prior work, we have developed algorithms whose outputs (measures for step counts and ambulatory time) demonstrated sensitivity to treatment effects in patients with Lewy Body Dementia [31]. We have also characterized the accuracy of an iteration of that algorithm in a cohort of diverse individuals in the real world [30].…”

Section: Discussionmentioning

confidence: 99%

“…In previous work, we developed an algorithm that accurately classifies ambulatory status from data collected from a wrist-worn device, characterizing its performance across diverse demographic groups in a real-world setting [30]. Further, results from a substudy of an interventional randomized phase-2 trial demonstrated that digital measures of physical activity (step count and ambulatory time) could be sensitive to treatment effect in patients with Lewy Body dementia [31].…”

Section: Introductionmentioning

confidence: 99%

Accuracy and Reliability of a Suite of Digital Measures of Walking Generated Using a Wrist-worn Sensor: Performance Characterization Study

Kowahl

Shin

Barman

et al. 2023

Preprint

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Background/Objectives: Mobility is a meaningful aspect of individual health whose quantification can provide clinical insights. Wearable sensor technology can quantify walking behaviors (a key aspect of mobility) through continuous passive monitoring. Our objective was to characterize the accuracy and reliability performance of a suite of digital measures of walking behaviors, as critical aspects in the practical implementation of digital measures into clinical studies. Methods: We collected data from a wrist-worn device (the Verily Study Watch) worn by a cohort of volunteer participants for multiple days (1-10 days) in a real world setting. Based on step measurements computed in 10-second epochs from sensor data, we generated individual daily aggregates (participant-days) to derive a suite of measures of walking: step count, daily ambulatory time, walking bout duration, number of total walking bouts, number of long walking bouts, number of short walking bouts, peak 30-minute walking cadence, peak 30-minute walking pace. To characterize accuracy of the measures, we examined agreement with truth labels generated by a concurrent, ankle-worn, reference device (Modus StepWatch 4) with known low error, calculating the following metrics: Intraclass Correlation Coefficient (ICC), Pearson R, Mean Error (ME), Mean Absolute Error (MAE). To characterize the reliability, we developed a novel approach to identify the time to reach a reliable readout (time-to-reliability) for each measure. This was accomplished by computing mean values over aggregation scopes ranging from 1-30 days, and analyzing test-retest reliability based on ICCs between adjacent (non-overlapping) time windows for each measure. Results: In the accuracy characterization, we collected data for a total of 162 participant-days from a testing cohort (N=35 participants; median observation time, 5 days). Agreement with the reference device-based readouts in the testing subcohort (n=35) for the eight measurements under evaluation, as reflected by ICCs, ranged between 0.7-0.9; Pearson R values were all greater than 0.75. For the time-to-reliability characterization, we collected data for a total of 15,120 participant days (overall cohort N=234; median observation time, 119 days). Here, all digital measures achieved an ICC between adjacent readouts > 0.75 by 16 days of wear time. Conclusions: We characterized accuracy and reliability of a suite of digital measures that provides comprehensive information about walking behaviors in real-world settings. These results, which report the level of agreement with high-accuracy reference labels and the time duration required to establish reliable measure readouts, can guide practical implementation of these measures into clinical studies. Well-characterized tools to quantify walking behaviors in research contexts can provide valuable clinical information about general population cohorts and patients with specific conditions.

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“…The PRESENCE study [10] leveraged digital endpoints to detect treatment effect in patients with mild to moderate Lewy Body Dementia in a phase 2 trial. The results of the PRESENCE study showed that digital measures can detect treatment effects in a smaller cohort over a shorter period than conventional clinical assessments.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Net Financial Benefits of Employing Digital Endpoints in Clinical Trials

DiMasi,

Dirks,

Smith

et al. 2024

Preprint

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BackgroundIn the last few decades developers of new drugs, biologics, and devices have increasingly leveraged digital health technologies (DHTs) to assess clinical trial digital endpoints. To our knowledge, a comprehensive assessment of the financial net benefits of digital endpoints in clinical trials has not been conducted.Data and MethodsWe obtained data from the Digital Medicine Society (DiMe) Library of Digital Endpoints and the U.S. clinical trials registry, ClinicalTrials.gov. The benefit metrics are changes in trial phase duration and enrollment associated with the use of digital endpoints. The cost metric was obtained from an industry survey of the costs of including digital endpoints in clinical trials. We developed an expected net present value (eNPV) model of the cash flows for new drug development and commercialization to assess financial value. The value measure is the increment in eNPV that occurs when digital endpoints are employed. We also calculated a return on investment (ROI) as the ratio of the estimated increment in eNPV to the mean digital endpoint implementation cost.ResultsFor phase 2 trials, the increase in eNPV varied from $2.2 million to $3.3 million, with ROIs between 32% to 48% per indication. The net benefits were substantially higher for phase 3 trials, with the increase in eNPV varying from $27 million to $48 million, with ROIs that were four to seven times the investment.ConclusionsThe use of digital endpoints in clinical trials can provide substantial extra value to sponsors developing new drugs, with high ROIs.

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Wrist-worn sensor-based measurements for drug effect detection with small samples in people with Lewy Body Dementia

Cited by 10 publications

References 17 publications

Digital Health Technologies for Alzheimer’s Disease and Related Dementias: Initial Results from a Landscape Analysis and Community Collaborative Effort

Digital Health Technologies for Alzheimer’s Disease and Related Dementias: Initial Results from a Landscape Analysis and Community Collaborative Effort

Accuracy and Reliability of a Suite of Digital Measures of Walking Generated Using a Wrist-worn Sensor: Performance Characterization Study

Assessing the Net Financial Benefits of Employing Digital Endpoints in Clinical Trials

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