Evidence map›Paper›PMID 30568582›Full record

ArticleFrontiers in behavioral neuroscience2018

Time to be "smart"-Opportunities Arising From Smartphone-Based Behavioral Analysis in Daily Patient Care.

Kevin Akeret, Flavio Vasella, Olivia Geisseler, Noemi Dannecker, Arko Ghosh, Peter Brugger, Luca Regli, Martin N Stienen

Open access · goldAbstract read
In one paragraph

Article in Frontiers in behavioral neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
3.4field-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

7 citing papers in PubMed, 12 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

8 authors at 3 institutions in 2 countries.

Kevin AkeretDepartment of Neurosurgery, University Hospital Zurich & Clinical Neuroscience Center, University of Zurich, Zurich, Switzerland.
Flavio VasellaDepartment of Neurosurgery, University Hospital Zurich & Clinical Neuroscience Center, University of Zurich, Zurich, Switzerland.
Olivia GeisselerNeuropsychology Unit, Department of Neurology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Noemi DanneckerNeuropsychology Unit, Department of Neurology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Arko GhoshCognitive Psychology Unit, Institute of Psychology, Leiden University, Leiden, Netherlands.
Peter BruggerNeuropsychology Unit, Department of Neurology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Luca RegliDepartment of Neurosurgery, University Hospital Zurich & Clinical Neuroscience Center, University of Zurich, Zurich, Switzerland.
Martin N StienenDepartment of Neurosurgery, University Hospital Zurich & Clinical Neuroscience Center, University of Zurich, Zurich, Switzerland.
University Hospital of Zurich · CHUniversity of Zurich · CHLeiden University · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

While pathologies of the central nervous system (CNS) are often associated with neuropsychological deficits, adequate quantification and monitoring of such deficits remains challenging. Due to their complex nature, comprehensive neuropsychological evaluations are needed, which are time-consuming, resource-intensive and do not adequately account for daily or hourly fluctuations of a patient's condition. Innovative approaches are required to improve the diagnostics and continuous monitoring of brain function, ideally in the form of a simple, objective, time-saving and inexpensive tool that overcomes the aforementioned weaknesses of conventional assessments. As smartphones are widely used and integrated in virtually every aspect of our lives, their potential regarding the acquisition of data representing an individual's behavior and health is enormous. Alterations in a patient's physical or mental health state may be recognized as behavioral deviation from the physiological range of the normal population, but also in comparison to the patient's individual baseline assessment. As smartphone-based assessment allows for continuous monitoring and therefore accounts for possible fluctuations or transiently occurring abnormalities in a patient's neurologic state, it may serve as a surveillance tool in the acute setting for early recognition of complications, or in the long-term outpatient setting to quantify rehabilitation or disease progress. This may be particularly interesting for regions of the world where healthcare resources for comprehensive clinical/neuropsychological examinations are insufficient or distances to healthcare providers are long. Here, we highlight the potential of smartphone-based behavioral monitoring in healthcare.

Indexed as

behavioral analysisdigital behaviormachine learningneuropsychologysmartphonesmartphone-based monitoringsmart surgery

Identifiers

PMID30568582
PMCPMC6290758
OpenAlexW2902040159

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.