Evidence map›Paper›PMID 33379319›Full record

ReviewInternational journal of environmental research and public health2020

Technology Used to Recognize Activities of Daily Living in Community-Dwelling Older Adults.

Nicola Camp, Martin Lewis, Kirsty Hunter, Julie Johnston, Massimiliano Zecca, Alessandro Di Nuovo, Daniele Magistro

Open access · goldAbstract readReview
In one paragraph

Review in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 2 pooled it
11.3field-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

26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 76 citations in OpenAlex.

  1. Pooled it
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  3. Frailty, polypharmacy, deprescribing, and 23-hour activity: insights from a mouse model.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026
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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

7 authors at 4 institutions in 1 country.

Nicola CampDepartment of Sport Science, School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK.
Martin LewisDepartment of Sport and Exercise Science, University of Derby, Derby DE22 1GB, UK.
Kirsty HunterDepartment of Sport Science, School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK.
Julie JohnstonDepartment of Sport Science, School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK.
Massimiliano ZeccaWolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough LE11 3TU, UK.ORCID 0000-0003-4741-4334
Alessandro Di NuovoDepartment of Computing, Sheffield Hallam University, Sheffield S1 1WB, UK.ORCID 0000-0003-2677-2650
Daniele MagistroDepartment of Sport Science, School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK.ORCID 0000-0002-2554-3701
Nottingham Trent University · GBLoughborough University · GBSheffield Hallam University · GBUniversity of Derby · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The use of technology has been suggested as a means of allowing continued autonomous living for older adults, while reducing the burden on caregivers and aiding decision-making relating to healthcare. However, more clarity is needed relating to the Activities of Daily Living (ADL) recognised, and the types of technology included within current monitoring approaches. This review aims to identify these differences and highlight the current gaps in these systems. A scoping review was conducted in accordance with PRISMA-ScR, drawing on PubMed, Scopus, and Google Scholar. Articles and commercially available systems were selected if they focused on ADL recognition of older adults within their home environment. Thirty-nine ADL recognition systems were identified, nine of which were commercially available. One system incorporated environmental and wearable technology, two used only wearable technology, and 34 used only environmental technologies. Overall, 14 ADL were identified but there was variation in the specific ADL recognised by each system. Although the use of technology to monitor ADL of older adults is becoming more prevalent, there is a large variation in the ADL recognised, how ADL are defined, and the types of technology used within monitoring systems. Key stakeholders, such as older adults and healthcare workers, should be consulted in future work to ensure that future developments are functional and useable.

Indexed as

Activities of Daily LivingWearable Electronic DevicesAgedHumansIndependent LivingTechnologyautonomous livingenvironmental sensorswearable technology

Identifiers

PMID33379319
PMCPMC7795436
OpenAlexW3114326620

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.