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

activity trackers critical care outcomes fitness trackers frail elderly frailty heart rate sleep monitoring wearable electronic devices

Journal

JMIR aging
ISSN: 2561-7605
Titre abrégé: JMIR Aging
Pays: Canada
ID NLM: 101740387

Informations de publication

Date de publication:
23 Feb 2021
Historique:
received: 04 05 2020
accepted: 19 12 2020
revised: 30 11 2020
entrez: 23 2 2021
pubmed: 24 2 2021
medline: 24 2 2021
Statut: epublish

Résumé

Critical 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. This study aims to examine the data collected from wearable devices for the progression of frailty among critical illness survivors. An 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. The 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). This 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.

Sections du résumé

BACKGROUND BACKGROUND
Critical 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.
OBJECTIVE OBJECTIVE
This study aims to examine the data collected from wearable devices for the progression of frailty among critical illness survivors.
METHODS METHODS
An 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.
RESULTS RESULTS
The 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).
CONCLUSIONS CONCLUSIONS
This 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.

Identifiants

pubmed: 33620323
pii: v4i1e19859
doi: 10.2196/19859
pmc: PMC8081159
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e19859

Informations de copyright

©Ben Kim, Miranda Hunt, John Muscedere, David M Maslove, Joon Lee. Originally published in JMIR Aging (http://aging.jmir.org), 23.02.2021.

Références

Can J Aging. 2016 Sep;35(3):281-97
pubmed: 27211065
CMAJ. 2014 Feb 4;186(2):E95-102
pubmed: 24277703
J Gerontol A Biol Sci Med Sci. 2015 Dec;70(12):1586-94
pubmed: 26400736
Clin Pediatr (Phila). 2017 Jan;56(1):26-32
pubmed: 27317609
J Nutr Health Aging. 2015 Dec;19(10):1043-8
pubmed: 26624218
Bone Joint J. 2016 Jun;98-B(6):799-805
pubmed: 27235523
CMAJ. 2015 Oct 20;187(15):E442-E449
pubmed: 26323697
Lancet Neurol. 2020 May;19(5):378-379
pubmed: 32059810
Eur Heart J. 2004 Mar;25(5):363-70
pubmed: 15033247
J Hosp Med. 2008 Nov-Dec;3(6):473-82
pubmed: 19084897
J Intensive Care. 2017 Nov 21;5:64
pubmed: 29201377
Physiol Behav. 2016 May 1;158:143-9
pubmed: 26969518
Am J Respir Crit Care Med. 2016 Aug 1;194(3):299-307
pubmed: 26840348
J Surg Res. 2018 Oct;230:13-19
pubmed: 30100028
JAMA. 1963 Sep 21;185:914-9
pubmed: 14044222
Intensive Care Med. 2018 Sep;44(9):1512-1520
pubmed: 30105600
PLoS Med. 2016 Feb 02;13(2):e1001953
pubmed: 26836780
CMAJ. 2005 Aug 30;173(5):489-95
pubmed: 16129869
Sensors (Basel). 2016 May 05;16(5):
pubmed: 27164110
J Gerontol A Biol Sci Med Sci. 2009 Jun;64(6):682-7
pubmed: 19223607
JAMA. 2010 Feb 24;303(8):763-70
pubmed: 20179286
J Gerontol A Biol Sci Med Sci. 2004 Mar;59(3):255-63
pubmed: 15031310
Crit Care Med. 2016 Jun;44(6):e362-9
pubmed: 26974547
J Med Syst. 2019 Jun 15;43(8):233
pubmed: 31203472
Crit Care Med. 2015 May;43(5):973-82
pubmed: 25668751
J Am Geriatr Soc. 2009 Nov;57(11):2094-100
pubmed: 19793356
Crit Care. 2011;15(1):301
pubmed: 21345259
Intensive Care Med. 2015 Nov;41(11):1911-20
pubmed: 26306719
BMC Geriatr. 2015 Apr 02;15:36
pubmed: 25887474
J Chronic Dis. 1987;40(5):373-83
pubmed: 3558716
Ann Thorac Surg. 2013 Sep;96(3):1057-61
pubmed: 23992697
Crit Care Med. 1985 Oct;13(10):818-29
pubmed: 3928249
Sleep Med. 2012 Dec;13(10):1217-25
pubmed: 22705247
J Am Geriatr Soc. 2008 Sep;56(9):1698-703
pubmed: 19166446
J Pers Med. 2017 May 24;7(2):
pubmed: 28538708
Age Ageing. 2017 May 1;46(3):383-392
pubmed: 28064173
Crit Care Med. 2001 Jul;29(7):1370-9
pubmed: 11445689
Int J Environ Res Public Health. 2009 Feb;6(2):492-525
pubmed: 19440396
J Gerontol A Biol Sci Med Sci. 2009 Jan;64(1):61-8
pubmed: 19164276
CMAJ. 1994 Feb 15;150(4):489-95
pubmed: 8313261
Physiother Can. 2018;70(1):57-63
pubmed: 29434419

Auteurs

Ben Kim (B)

School of Public Health and Health Systems, University of Waterloo, Waterloo, ON, Canada.

Miranda Hunt (M)

Department of Critical Care Medicine, Queen's University, Kingston, ON, Canada.

John Muscedere (J)

Department of Critical Care Medicine, Queen's University, Kingston, ON, Canada.

David M Maslove (DM)

Department of Critical Care Medicine, Queen's University, Kingston, ON, Canada.

Joon Lee (J)

Data Intelligence for Health Lab, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
Department of Cardiac Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.

Classifications MeSH