Evidence map›Paper›PMID 35210486›Full record

ArticleScientific reports2022

Machine learning detects altered spatial navigation features in outdoor behaviour of Alzheimer's disease patients.

Abhirup Ghosh, Vaisakh Puthusseryppady, Dennis Chan, Cecilia Mascolo, Michael Hornberger

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 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
8.6field-weighted citation impact, top 2% 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, 34 citations in OpenAlex.

  1. Pooled it
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  11. GPS Suitability for Physical Frailty Assessment.Sensors (Basel, Switzerland) · 2024
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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

5 authors at 3 institutions in 2 countries.

Abhirup GhoshDepartment of Computer Science and Technology, University of Cambridge, Cambridge, UK.
Vaisakh PuthusseryppadyNorwich Medical School, 2.04 Bob Champion Research and Education Building, University of East Anglia, Norwich, NR4 7TJ, UK.
Dennis ChanInstitute of Cognitive Neuroscience, University College London, London, UK.
Cecilia MascoloDepartment of Computer Science and Technology, University of Cambridge, Cambridge, UK.
Michael HornbergerNorwich Medical School, 2.04 Bob Champion Research and Education Building, University of East Anglia, Norwich, NR4 7TJ, UK. m.hornberger@uea.ac.uk.
University of Cambridge · GBUniversity of East Anglia · GBUniversity College London · GB

Funding

Wellcome TrustWellcome Trust 213939
6 · The paper itself

Abstract

Impairment of navigation is one of the earliest symptoms of Alzheimer's disease (AD), but to date studies have involved proxy tests of navigation rather than studies of real life behaviour. Here we use GPS tracking to measure ecological outdoor behaviour in AD. The aim was to use data-driven machine learning approaches to explore spatial metrics within real life navigational traces that discriminate AD patients from controls. 15 AD patients and 18 controls underwent tracking of their outdoor navigation over two weeks. Three kinds of spatiotemporal features of segments were extracted, characterising the mobility domain (entropy, segment similarity, distance from home), spatial shape (total turning angle, segment complexity), and temporal characteristics (stop duration). Patients significantly differed from controls on entropy (p-value 0.008), segment similarity (p-value [Formula: see text]), and distance from home (p-value [Formula: see text]). Graph-based analyses yielded preliminary data indicating that topological features assessing the connectivity of visited locations may also differentiate patients from controls. In conclusion, our results show that specific outdoor navigation features discriminate AD patients from controls, which has significant implication for future AD diagnostics, outcome measures and interventions. Furthermore, this work illustrates how wearables-based sensing of everyday behaviour may be used to deliver ecologically-valid digital biomarkers of AD pathophysiology.

Indexed as

Machine LearningSpatial BehaviorSpatial NavigationAgedAged, 80 and overAlzheimer DiseaseFemaleHumansMaleMiddle AgedNeuropsychological TestsSpatio-Temporal Analysis

Identifiers

PMID35210486
PMCPMC8873255
OpenAlexW4213459059

What OpenQuestion holds

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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.