Evidence map›Paper›PMID 35974156›Full record

ArticleNPJ digital medicine2022

A systems approach towards remote health-monitoring in older adults: Introducing a zero-interaction digital exhaust.

Narayan Schütz, Samuel E J Knobel, Angela Botros, Michael Single, Bruno Pais, Valérie Santschi, Daniel Gatica-Perez, Philipp Buluschek, Prabitha Urwyler, Stephan M Gerber and 4 more

Open access · goldAbstract read
In one paragraph

Article in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
3.1field-weighted citation impact, top 7% of its field
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

19 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.

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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

14 authors at 3 institutions in 1 country.

Narayan SchützARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland. narayan.schuetz@unibe.ch.ORCID http://orcid.org/0000-0001-5069-407X
Samuel E J KnobelARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
Angela BotrosARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
Michael SingleARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
Bruno PaisLaSource School of Nursing Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Lausanne, Switzerland.ORCID http://orcid.org/0000-0002-4457-0983
Valérie SantschiLaSource School of Nursing Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Lausanne, Switzerland.
Daniel Gatica-PerezIdiap Research Institute, Martigny, Switzerland.
Philipp BuluschekDomoHealth SA, Lausanne, Switzerland.ORCID http://orcid.org/0000-0001-6577-200X
Prabitha UrwylerARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
Stephan M GerberARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
René M MüriARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
Urs P MosimannARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
Hugo Saner *ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
Tobias Nef *ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
University of Bern · CHHES-SO University of Applied Sciences and Arts Western Switzerland · CHÉcole Polytechnique Fédérale de Lausanne · CH

Funding

Hasler Stiftung (Hasler Foundation) 16072
6 · The paper itself

Abstract

Using connected sensing devices to remotely monitor health is a promising way to help transition healthcare from a rather reactive to a more precision medicine oriented proactive approach, which could be particularly relevant in the face of rapid population ageing and the challenges it poses to healthcare systems. Sensor derived digital measures of health, such as digital biomarkers or digital clinical outcome assessments, may be used to monitor health status or the risk of adverse events like falls. Current research around such digital measures has largely focused on exploring the use of few individual measures obtained through mobile devices. However, especially for long-term applications in older adults, this choice of technology may not be ideal and could further add to the digital divide. Moreover, large-scale systems biology approaches, like genomics, have already proven beneficial in precision medicine, making it plausible that the same could also hold for remote-health monitoring. In this context, we introduce and describe a zero-interaction digital exhaust: a set of 1268 digital measures that cover large parts of a person's activity, behavior and physiology. Making this approach more inclusive of older adults, we base this set entirely on contactless, zero-interaction sensing technologies. Applying the resulting digital exhaust to real-world data, we then demonstrate the possibility to create multiple ageing relevant digital clinical outcome assessments. Paired with modern machine learning, we find these assessments to be surprisingly powerful and often on-par with mobile approaches. Lastly, we highlight the possibility to discover novel digital biomarkers based on this large-scale approach.

Identifiers

PMID35974156
PMCPMC9381599
OpenAlexW4292062181

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.