Evidence map›Paper›PMID 35542758›Full record

ReviewContrast media & molecular imaging2022

IoT-Based Wearable Devices for Patients Suffering from Alzheimer Disease.

Waleed Salehi, Gaurav Gupta, Surbhi Bhatia, Deepika Koundal, Arwa Mashat, Assaye Belay

Abstract readReview
In one paragraph

Review in Contrast media & molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 3 pooled it
–field-weighted citation impact
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

12 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Using Apple Watches to Monitor Health and Behaviors of Individuals With Cognitive Impairment: A Case Series Study.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025
    Article
  11. Review
  12. Article
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

6 authors.

Waleed SalehiYogananda School of Artificial Intelligence Computer and Data Sciences, Shoolini University, Bajhol Solan 173229, HP, India.ORCID 0000-0002-5248-9720
Gaurav GuptaYogananda School of Artificial Intelligence Computer and Data Sciences, Shoolini University, Bajhol Solan 173229, HP, India.ORCID 0000-0002-5192-4428
Surbhi BhatiaDepartment of Information Systems, College of Computer Science and Information Technology, King Faisal University, Al Hasa, Saudi Arabia.ORCID 0000-0003-3097-6568
Deepika KoundalDepartment of Systemics, School of Computer Science, University of Petroleum & Energy Studies, Dehradun, Uttrakhand, India.
Arwa MashatFaculty of Computing and Information Technology, King Abdulaziz University, Rabigh, Saudi Arabia.
Assaye BelayDepartment of Statistics, Mizan-Tepi University, Ethiopia.ORCID 0000-0001-7315-9141

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The disorder of Alzheimer's (AD) is defined as a gradual deterioration of cognitive functions, such as the failure of spatial cognition and short-term memory. Besides difficulties in memory, a person with this disease encounters visual processing difficulties and even awareness and identifying of their beloved ones. Nowadays, recent technologies made this possible to connect everything that exists around us on Earth through the Internet, this is what the Internet of Things (IoT) made possible which can capture and save a massive amount of data that are considered very important and useful information which then can be valuable in training of the various state-of-the-art machine and deep learning algorithms. Assistive mobile health applications and IoT-based wearable devices are helping and supporting the ongoing health screening of a patient with AD. In the early stages of AD, the wearable devices and IoT approach aim to keep AD patients mentally active in all of life's daily activities, independent from their caregivers or any family member of the patient. These technological solutions have great potential in improving the quality of life of an AD patient as this helps to reduce pressure on healthcare and to minimize the operational cost. The purpose of this study is to explore the State-of-the-Art wearable technologies for people with AD. Significance, challenges, and limitations that arise and what will be the future of these technological solutions and their acceptance. Therefore, this study also provides the challenges and gaps in the current literature review and future directions for other researchers working in the area of developing wearable devices.

Indexed as

Alzheimer DiseaseInternet of ThingsWearable Electronic DevicesDelivery of Health CareHumansQuality of Life

Identifiers

PMID35542758
PMCPMC9054450

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.