ArticleScientific reports2017
Cognitive impairment categorized in community-dwelling older adults with and without dementia using in-home sensors that recognise activities of daily living.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04793555 (Validation of an Apartment to Monitor Human Behaviour During Day and Night), which is not on this map. Cited by 49 papers, 3 of them syntheses that pooled it.
What it found
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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.
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.
Validation of an Apartment to Monitor Human Behaviour During Day and Night
Who cites it
49 citing papers in PubMed, 3 syntheses or guidelines pooled it, 122 citations in OpenAlex.
- Digital Endpoints for Assessing Instrumental Activities of Daily Living in Mild Cognitive Impairment: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Can Smart Home Technologies Help Older Adults Manage Their Chronic Condition? A Systematic Literature Review.International journal of environmental research and public health · 2023Pooled it
- Current State of Digital Biomarker Technologies for Real-Life, Home-Based Monitoring of Cognitive Function for Mild Cognitive Impairment to Mild Alzheimer Disease and Implications for Clinical Care: Systematic Review.Journal of medical Internet research · 2019Pooled it
- Indoor Mobility Patterns Measured by PIR Sensors for Classifying Health-Related Quality of Life in Older Adults Using Machine Learning.Sensors (Basel, Switzerland) · 2026Article
- Predicting daily cognition and lifestyle behaviors for older adults using smart home data and ecological momentary assessment.The Clinical neuropsychologist · 2026Article
- Development and Validation of Machine Learning Models for Predicting Early Cognitive Decline Using Home Sensor-Derived Behavioral Data: Sensors in-Home for Elder Wellbeing (SINEW) Cohort Study.JMIR research protocols · 2026Article
- Digital Measurement of Subjective Experiences in Alzheimer Disease and Related Dementias (AD/ADRD).JMIR aging · 2025Article
- Changes in sensor recorded activity patterns and neuropsychiatric symptoms after deep brain stimulation for Parkinson's disease: 5 case reports.BMC neurology · 2025Article
- Gait-Based AI Models for Detecting Sarcopenia and Cognitive Decline Using Sensor Fusion.Diagnostics (Basel, Switzerland) · 2024Article
- Remote Digital Technologies for the Early Detection and Monitoring of Cognitive Decline in Patients With Type 2 Diabetes: Insights From Studies of Neurodegenerative Diseases.Journal of diabetes science and technology · 2024Review
- Cognition and Activity of Daily Living Function in people with Parkinson's disease.Journal of neural transmission (Vienna, Austria : 1996) · 2024Review
- A Systematic Review of the Application of Artificial Intelligence in Nursing Care: Where are We, and What's Next?Journal of multidisciplinary healthcare · 2024Review
- Assessing the cognitive decline of people in the spectrum of AD by monitoring their activities of daily living in an IoT-enabled smart home environment: a cross-sectional pilot study.Frontiers in aging neuroscience · 2024Article
- Artificial intelligence for dementia-Applied models and digital health.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Review
- Using home monitoring technology to study the effects of traumatic brain injury in older multimorbid adults.Annals of clinical and translational neurology · 2023Article
- Article
- Leading mediators of sex differences in the incidence of dementia in community-dwelling adults in the UK Biobank: a retrospective cohort study.Alzheimer's research & therapy · 2023Article
- Unobtrusive Sensing Technology Detects Ecologically Valid Spatiotemporal Patterns of Daily Routines Distinctive to Persons With Mild Cognitive Impairment.The journals of gerontology. Series A, Biological sciences and medical sciences · 2022Article
- Predicting Activity Duration in Smart Sensing Environments Using Synthetic Data and Partial Least Squares Regression: The Case of Dementia Patients.Sensors (Basel, Switzerland) · 2022Article
- Are Smart Homes Adequate for Older Adults with Dementia?Sensors (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cognitive impairment due to dementia decreases functionality in Activities of Daily Living (ADL). Its assessment is useful to identify care needs, risks and monitor disease progression. This study investigates differences in ADL pattern-performance between dementia patients and healthy controls using unobtrusive sensors. Around 9,600 person-hours of activity data were collected from the home of ten dementia patients and ten healthy controls using a wireless-unobtrusive sensors and analysed to detect ADL. Recognised ADL were visualized using activity maps, the heterogeneity and accuracy to discriminate patients from healthy were analysed. Activity maps of dementia patients reveal unorganised behaviour patterns and heterogeneity differed significantly between the healthy and diseased. The discriminating accuracy increases with observation duration (0.95 for 20 days). Unobtrusive sensors quantify ADL-relevant behaviour, useful to uncover the effect of cognitive impairment, to quantify ADL-relevant changes in the course of dementia and to measure outcomes of anti-dementia treatments.
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Registered trials
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.