Evidence map›Paper›PMID 33620323›Full record

ArticleJMIR aging2021

Using Consumer-Grade Physical Activity Trackers to Measure Frailty Transitions in Older Critical Care Survivors: Exploratory Observational Study.

Ben Kim, Miranda Hunt, John Muscedere, David M Maslove, Joon Lee

Open access · goldAbstract read
In one paragraph

Article in JMIR aging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.6field-weighted citation impact, top 33% 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

5 citing papers in PubMed, 13 citations in OpenAlex.

  1. Observational
  2. Article
  3. Article
  4. Article
  5. Review
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

5 authors at 3 institutions in 1 country.

Ben KimSchool of Public Health and Health Systems, University of Waterloo, Waterloo, ON, Canada.ORCID https://orcid.org/0000-0002-6793-5551
Miranda HuntDepartment of Critical Care Medicine, Queen's University, Kingston, ON, Canada.ORCID https://orcid.org/0000-0001-5086-3291
John MuscedereDepartment of Critical Care Medicine, Queen's University, Kingston, ON, Canada.ORCID https://orcid.org/0000-0002-9027-7198
David M MasloveDepartment of Critical Care Medicine, Queen's University, Kingston, ON, Canada.ORCID https://orcid.org/0000-0002-0765-7158
Joon LeeData Intelligence for Health Lab, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.ORCID https://orcid.org/0000-0001-8593-9321
Queen's University · CAUniversity of Calgary · CAUniversity of Waterloo · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCritical illness has been suggested as a sentinel event for frailty development in at-risk older adults. Frail critical illness survivors are affected by increased adverse health outcomes, but monitoring the recovery after intensive care unit (ICU) discharge is challenging. Clinicians and funders of health care systems envision an increased role of wearable devices in monitoring clinically relevant measures, as sensor technology is advancing rapidly. The use of wearable devices has also generated great interest among older patients, and they are the fastest growing group of consumer-grade wearable device users. Recent research studies indicate that consumer-grade wearable devices offer the possibility of measuring frailty.

objectiveThis study aims to examine the data collected from wearable devices for the progression of frailty among critical illness survivors.

methodsAn observational study was conducted with 12 older survivors of critical illness from Kingston General Hospital in Canada. Frailty was measured using the Clinical Frailty Scale (CFS) at ICU admission, hospital discharge, and 4-week follow-up. A wearable device was worn between hospital discharge and 4-week follow-up. The wearable device collected data on step count, physical activity, sleep, and heart rate (HR). Patient assessments were reviewed, including the severity of illness, cognition level, delirium, activities of daily living, and comorbidity.

resultsThe CFS scores increased significantly following critical illness compared with the pre-ICU frailty level (P=.02; d=-0.53). Survivors who were frail over the 4-week follow-up period had significantly lower daily step counts than survivors who were not frail (P=.02; d=1.81). There was no difference in sleep and HR measures. Daily step count was strongly correlated with the CFS at 4-week follow-up (r=-0.72; P=.04). The average HR was strongly correlated with the CFS at hospital discharge (r=-0.72; P=.046). The HR SD was strongly correlated (r=0.78; P=.02) with the change in CFS from ICU admission to 4-week follow-up. No association was found between the CFS and sleep measures. The pattern of increasing step count over the 4-week follow-up period was correlated with worsening of frailty (r=.62; P=.03).

conclusionsThis study demonstrated an association between frailty and data generated from a consumer-grade wearable device. Daily step count and HR showed a strong association with the frailty progression of the survivors of critical illness over time. Understanding this association could unlock a new avenue for clinicians to monitor and identify a vulnerable subset of the older adult population that might benefit from an early intervention.

Indexed as

activity trackerscritical care outcomesfitness trackersfrail elderlyfrailtyheart ratesleep monitoringwearable electronic devices

Identifiers

PMID33620323
PMCPMC8081159
OpenAlexW3118181319

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.