Evidence map›Paper›PMID 32429331›Full record

ReviewSensors (Basel, Switzerland)2020

IoT Wearable Sensors and Devices in Elderly Care: A Literature Review.

Thanos G Stavropoulos, Asterios Papastergiou, Lampros Mpaltadoros, Spiros Nikolopoulos, Ioannis Kompatsiaris

Open access · goldAbstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
110citing papers in PubMed, 8 pooled it
17.2field-weighted citation impact, top 1% 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

110 citing papers in PubMed, 8 syntheses or guidelines pooled it, 332 citations in OpenAlex.

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50 more citing papers are in PubMed but not listed here.

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 1 institution in 1 country.

Thanos G StavropoulosCentre for Research & Technology Hellas, Information Technologies Institute, 6th Km Charilaou-Thermi, 57001 Thessaloniki, Greece.ORCID 0000-0003-2389-4329
Asterios PapastergiouCentre for Research & Technology Hellas, Information Technologies Institute, 6th Km Charilaou-Thermi, 57001 Thessaloniki, Greece.
Lampros MpaltadorosCentre for Research & Technology Hellas, Information Technologies Institute, 6th Km Charilaou-Thermi, 57001 Thessaloniki, Greece.
Spiros NikolopoulosCentre for Research & Technology Hellas, Information Technologies Institute, 6th Km Charilaou-Thermi, 57001 Thessaloniki, Greece.ORCID 0000-0002-1367-5133
Ioannis KompatsiarisCentre for Research & Technology Hellas, Information Technologies Institute, 6th Km Charilaou-Thermi, 57001 Thessaloniki, Greece.ORCID 0000-0001-6447-9020
Information Technologies Institute · GR

Funding

European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH-CREATE-INNOVATE T1EDK-02668Innovative Medicines Initiative 806999
6 · The paper itself

Abstract

The increasing ageing global population is causing an upsurge in ailments related to old age, primarily dementia and Alzheimer's disease, frailty, Parkinson's, and cardiovascular disease, but also a general need for general eldercare as well as active and healthy ageing. In turn, there is a need for constant monitoring and assistance, intervention, and support, causing a considerable financial and human burden on individuals and their caregivers. Interconnected sensing technology, such as IoT wearables and devices, present a promising solution for objective, reliable, and remote monitoring, assessment, and support through ambient assisted living. This paper presents a review of such solutions including both earlier review studies and individual case studies, rapidly evolving in the last decade. In doing so, it examines and categorizes them according to common aspects of interest such as health focus, from specific ailments to general eldercare; IoT technologies, from wearables to smart home sensors; aims, from assessment to fall detection and indoor positioning to intervention; and experimental evaluation participants duration and outcome measures, from acceptability to accuracy. Statistics drawn from this categorization aim to outline the current state-of-the-art, as well as trends and effective practices for the future of effective, accessible, and acceptable eldercare with technology.

Indexed as

Alzheimer DiseaseWearable Electronic DevicesAccidental FallsAgedAgingHumansTechnologyAALAlzheimer’sdementiadeviceseldersIoTold agesensorswearables

Identifiers

PMID32429331
PMCPMC7288187
OpenAlexW3025469716

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.