Article in Nature reviews bioengineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
11 authors.
Favour NerriseDepartment of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.ORCID 0000-0002-1959-5302
Narayan SchützDepartment of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
Qingyu ZhaoWeill Cornell Medicine, Cornell University, New York, NY, USA.
Christine GouldDepartment of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
Arnold MilsteinClinical Excellence Research Center (CERC), Stanford University, Stanford, CA, USA.
Kevin SchulmanClinical Excellence Research Center (CERC), Stanford University, Stanford, CA, USA.
Victor W HendersonDepartment of Epidemiology and Population Health, Stanford University, Stanford, CA, USA.ORCID 0000-0003-1198-9240
James LandayDepartment of Computer Science, Stanford University, Stanford, CA, USA.
Li Fei-FeiDepartment of Computer Science, Stanford University, Stanford, CA, USA.
Feng Vankee LinDepartment of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.ORCID 0000-0003-1945-0362
Ehsan AdeliDepartment of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
Funding
Validating novel sleep sensors and devices in older adults with Alzheimer's diseaseP30AG073107 · NIA · UNIVERSITY OF MASSACHUSETTS AMHERST · PI Benjamin M. Marlin · 2021 to 2026
$32.0M
Stanford Alzheimer's Disease Research CenterAdmin Supp: Developing iPSC models for AD and PDP30AG066515 · NIA · STANFORD UNIVERSITY · PI Lisa Goldman Rosas · 2020 to 2026
$29.0M
Working Memory in Parkinson Disease: A Cognitive & Systems Neuroscience ApproachP50AG047366 · NIA · STANFORD UNIVERSITY · PI HENDERSON, VICTOR · 2015 to 2019
$7.9M
Neural mechanisms of gait disturbances as individualized digital biomarker trajectories in preclinical dementiaR01AG089169 · NIA · STANFORD UNIVERSITY · PI Ehsan Adeli · 2024 to 2026
$4.5M
U24 NEW Brain Aging Diversity SupplementU24AG072701 · NIA · UNIVERSITY OF ROCHESTER · PI CONWELL, YEATES, LIN, FENG VANKEE · 2021 to 2024
$2.6M
A facial expression-based personalization engine (FPE) for monitoring and modulating real-time effective engagement in cognitive training in older adults at risk for AD/ADRDR33AG084471 · NIA · STANFORD UNIVERSITY · PI Ehsan Adeli, Feng Vankee Lin · 2025 to 2026
$1.1M
A facial expression-based personalization engine (FPE) for monitoring and modulating real-time effective engagement in cognitive training in older adults at risk for AD/ADRDR61AG084471 · NIA · STANFORD UNIVERSITY · PI ADELI, EHSAN, LIN, FENG VANKEE · 2023 to 2024
Digital biomarkers (DBMs) are a new class of health indicators derived from digital technologies - including smartphones, wearable devices and ambient sensors - that enable continuous, real-time monitoring of signals in everyday settings. By providing richer and more dynamic data than conventional, point-in-time measurements, DBMs offer fresh opportunities for remote patient assessment, personalized care and large-scale biomedical research. Importantly, DBMs function as powerful complementary tools to traditional biomarkers that can screen candidates for more invasive tests and provide contextual data between clinical visits. This Review provides a standardized classification of DBMs focused on neurodegenerative diseases, including Alzheimer disease, Parkinson disease, mild cognitive impairment, Huntington disease, multiple sclerosis, frontotemporal dementia, spinocerebellar ataxia and dementia with Lewy bodies, centred around three questions: what is being measured (the concept of interest), how it is measured (the sensing technologies) and why it is measured (the application areas). By examining these dimensions, we highlight the potential of DBMs to transform clinical monitoring, early detection and therapeutic interventions in these disorders.
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
A framework of digital biomarkers for neurodegenerative diseases. · full record | OpenQuestion