Evidence map›Paper›PMID 28085085›Full record

ReviewSensors (Basel, Switzerland)2017

Wearable Sensors for Remote Health Monitoring.

Sumit Majumder, Tapas Mondal, M Jamal Deen

3 registry-linked trialsOpen access · goldAbstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 3 registered trials, which are not on this map. Cited by 316 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
316citing papers in PubMed, 9 pooled it
37.1field-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.

NCT04703166 recruitingnot on this mapstarted 2025, after this paper: background citation

Tulane iPredict, Prevent (TiPP) Study

TypeobservationalSponsorTulane University School of MedicineRan2025 to 2027Enrolled360ConditionsAtrial FibrillationArmsSamsung Galaxy Watch Active2
NCT05456386 naunknown statusnot on this mapstarted 2022, after this paper: background citation

BioRhythm Health Coaching and Sleep Monitoring Program for Weight Loss

TypeinterventionalSponsorStephen HutchisonRan2022 to 2023Enrolled65ConditionsObesityArmsThe Circadian Rhythm Approach to Weight Loss (CRAWL)
NCT07349901 recruitingnot on this mapstarted 2026, after this paper: background citation

Predicting Hospital Readmission for Surgical Patients Using Deep Learning Models With Smart Watch and Smart Ring Sensors Data

TypeobservationalSponsorGetúlio Vargas University HospitalRan2026 to 2028Enrolled300ConditionsPatient Readmission
3 · Its place in the literature

Who cites it

316 citing papers in PubMed, 9 syntheses or guidelines pooled it, 1,299 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Pooled it
  8. Pooled it
  9. Pooled it
  10. Trial
  11. Trial
  12. Article
  13. Article
  14. Review
  15. Review
  16. Article
  17. Observational
  18. Article
  19. Article
  20. Article

256 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

3 authors at 1 institution in 1 country.

Sumit MajumderDepartment of Electrical and Computer Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada. majums3@mcmaster.ca.
Tapas MondalDepartment of Pediatrics, McMaster University, Hamilton, ON L8S 4L8, Canada. mondalt@mcmaster.ca.
M Jamal DeenDepartment of Electrical and Computer Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada. jamal@mcmaster.ca.
McMaster University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Life expectancy in most countries has been increasing continually over the several few decades thanks to significant improvements in medicine, public health, as well as personal and environmental hygiene. However, increased life expectancy combined with falling birth rates are expected to engender a large aging demographic in the near future that would impose significant  burdens on the socio-economic structure of these countries. Therefore, it is essential to develop cost-effective, easy-to-use systems for the sake of elderly healthcare and well-being. Remote health monitoring, based on non-invasive and wearable sensors, actuators and modern communication and information technologies offers an efficient and cost-effective solution that allows the elderly to continue to live in their comfortable home environment instead of expensive healthcare facilities. These systems will also allow healthcare personnel to monitor important physiological signs of their patients in real time, assess health conditions and provide feedback from distant facilities. In this paper, we have presented and compared several low-cost and non-invasive health and activity monitoring systems that were reported in recent years. A survey on textile-based sensors that can potentially be used in wearable systems is also presented. Finally, compatibility of several communication technologies as well as future perspectives and research challenges in remote monitoring systems will be discussed.

Indexed as

Monitoring, PhysiologicDelivery of Health CareHumansTelemedicineTextilesWearable Electronic Devicesambulatory monitoringbody sensor networkremote health monitoringsmart textilevital sign monitoringwearable sensors

Identifiers

PMID28085085
PMCPMC5298703
OpenAlexW2573003069

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.