Evidence map›Paper›PMID 38811633›Full record

ArticleNPJ Parkinson's disease2024

Digital outcome measures from smartwatch data relate to non-motor features of Parkinson's disease.

Ann-Kathrin Schalkamp, Neil A Harrison, Kathryn J Peall, Cynthia Sandor

Abstract read
In one paragraph

Article in NPJ Parkinson's disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 2 pooled it
–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.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  10. Levodopa treatment: impacts and mechanisms throughout Parkinson's disease progression.Journal of neural transmission (Vienna, Austria : 1996) · 2025
    Review
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4 · The record

Corrections and comments

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

4 authors.

Ann-Kathrin SchalkampDivision of Psychological Medicine and Clinical Neuroscience, School of Medicine, Cardiff University, Cardiff, United Kingdom.ORCID http://orcid.org/0000-0002-8609-9165
Neil A HarrisonDivision of Psychological Medicine and Clinical Neurosciences, Neuroscience and Mental Health Innovation Institute, Cardiff, United Kingdom.
Kathryn J PeallDivision of Psychological Medicine and Clinical Neurosciences, Neuroscience and Mental Health Innovation Institute, Cardiff, United Kingdom. PeallKJ@cardiff.ac.uk.
Cynthia SandorDivision of Psychological Medicine and Clinical Neuroscience, School of Medicine, Cardiff University, Cardiff, United Kingdom. c.sandor@imperial.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Monitoring of Parkinson's disease (PD) has seen substantial improvement over recent years as digital sensors enable a passive and continuous collection of information in the home environment. However, the primary focus of this work has been motor symptoms, with little focus on the non-motor aspects of the disease. To address this, we combined longitudinal clinical non-motor assessment data and digital multi-sensor data from the Verily Study Watch for 149 participants from the Parkinson's Progression Monitoring Initiative (PPMI) cohort with a diagnosis of PD. We show that digitally collected physical activity and sleep measures significantly relate to clinical non-motor assessments of cognitive, autonomic, and daily living impairment. However, the poor predictive performance we observed, highlights the need for better targeted digital outcome measures to enable monitoring of non-motor symptoms.

Identifiers

PMID38811633
PMCPMC11137004

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.