Evidence map›Paper›PMID 32369031›Full record

ArticleJournal of medical Internet research2020

Early Detection of Mild Cognitive Impairment With In-Home Sensors to Monitor Behavior Patterns in Community-Dwelling Senior Citizens in Singapore: Cross-Sectional Feasibility Study.

Iris Rawtaer, Rathi Mahendran, Ee Heok Kua, Hwee Pink Tan, Hwee Xian Tan, Tih-Shih Lee, Tze Pin Ng

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 9 pooled it
5.9field-weighted citation impact, top 3% 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

36 citing papers in PubMed, 9 syntheses or guidelines pooled it, 78 citations in OpenAlex.

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  9. Can Smart Home Technologies Help Older Adults Manage Their Chronic Condition? A Systematic Literature Review.International journal of environmental research and public health · 2023
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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 3 institutions in 1 country.

Iris RawtaerDepartment of Psychiatry, Sengkang General Hospital, Singhealth Duke NUS Academic Medical Centre, Singapore, Singapore.ORCID 0000-0002-8214-6454
Rathi MahendranDepartment of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.ORCID 0000-0003-4213-7163
Ee Heok KuaDepartment of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.ORCID 0000-0001-5574-0530
Hwee Pink TanSchool of Information Systems, Singapore Management University, Singapore, Singapore.ORCID 0000-0002-8279-1429
Hwee Xian TanSchool of Information Systems, Singapore Management University, Singapore, Singapore.ORCID 0000-0003-3838-8197
Tih-Shih LeeDuke-NUS Graduate Medical School, Singapore, Singapore.ORCID 0000-0002-0788-422X
Tze Pin NgDepartment of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.ORCID 0000-0001-9585-855X
Duke-NUS Medical School · SGNational University of Singapore · SGSingapore Management University · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDementia is a global epidemic and incurs substantial burden on the affected families and the health care system. A window of opportunity for intervention is the predementia stage known as mild cognitive impairment (MCI). Individuals often present to services late in the course of their disease and more needs to be done for early detection; sensor technology is a potential method for detection.

objectiveThe aim of this cross-sectional study was to establish the feasibility and acceptability of utilizing sensors in the homes of senior citizens to detect changes in behaviors unobtrusively.

methodsWe recruited 59 community-dwelling seniors (aged >65 years who live alone) with and without MCI and observed them over the course of 2 months. The frequency of forgetfulness was monitored by tagging personal items and tracking missed doses of medication. Activities such as step count, time spent away from home, television use, sleep duration, and quality were tracked with passive infrared motion sensors, smart plugs, bed sensors, and a wearable activity band. Measures of cognition, depression, sleep, and social connectedness were also administered.

resultsOf the 49 participants who completed the study, 28 had MCI and 21 had healthy cognition (HC). Frequencies of various sensor-derived behavior metrics were computed and compared between MCI and HC groups. MCI participants were less active than their HC counterparts and had more sleep interruptions per night. MCI participants had forgotten their medications more times per month compared with HC participants. The sensor system was acceptable to over 80% (40/49) of study participants, with many requesting for permanent installation of the system.

conclusionsWe demonstrated that it was both feasible and acceptable to set up these sensors in the community and unobtrusively collect data. Further studies evaluating such digital biomarkers in the homes in the community are needed to improve the ecological validity of sensor technology. We need to refine the system to yield more clinically impactful information.

Indexed as

AgedCognitive DysfunctionCross-Sectional StudiesEarly DiagnosisFeasibility StudiesFemaleHumansIndependent LivingMaleSingaporedementiaearly diagnosisinternet of thingsneurocognitive disorderpattern recognition, automated/methods

Identifiers

PMID32369031
PMCPMC7238076
OpenAlexW3023534352

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.