Use of wearable devices for post-discharge monitoring of ICU patients: a feasibility study

Kroll, Ryan R.; McKenzie, Erin; Boyd, J. Gordon; Sheth, Prameet M.; Howes, Daniel; Wood, Michael D.; Maslove, David

doi:10.1186/s40560-017-0261-9

Cited by 53 publications

(47 citation statements)

References 29 publications

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“…Indeed, our results were similar to results found in ICU patients (R = 0.33, P = 0.03) with self-reported sleep. 11 Since the RCSQ has been found to correlate well with polysomnography in ICU patients 14,25 , the discrepancy between our ndings and the previous results with Fitbits may suggest that sleep detection algorithms of Fitbits may be less accurate for hospitalized patients. Fitbit sleep detection algorithms using actigraphy have not been derived from hospitalized patients, and the algorithms are known to be inaccurate with disrupted or abnormal sleep, which is highly prevalent in hospital.…”

Section: Discussioncontrasting

confidence: 78%

“…A convenience sample size of 50 participants was chosen for this pilot study similar to a recent feasibility wearable study. 12 The Research Ethics Board of University Health Network approved the study (ID# 18-5621). All participants provided written informed consent.…”

Section: Methodsmentioning

confidence: 99%

“…9 The Fitbit wearable device (Fitbit, Inc) has been studied in the critical care setting where heart rate has been found to have moderate accuracy in patients who are in sinus rhythm, and Fitbit sleep data correlated moderately with self-reported sleep. 10,11 To date, there have been limited studies on the accuracy of wearables in hospitalized medical patients.…”

Section: Introductionmentioning

confidence: 99%

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Fitbit data show poor correlation with measures of activity and sleep among hospitalized general medicine patients

Patel

Moosa

et al. 2020

Preprint

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BackgroundWearable devices such as Fitbits may provide important insights about hospitalized patients that include data on low activity and poor sleep. Monitoring this information could spur interventions to improve mobility and sleep which may reduce the adverse effects associated with hospitalization. However, there is a lack of studies assessing the accuracy of wearables in hospitalized medical patients. The purpose of our study was to determine the accuracy of Fitbit heart rate, sleep and physical activity in hospitalized medical patients.MethodsWe conducted a prospective cohort feasibility study enrolling 50 medical inpatients at two hospitals providing them with a Fitbit Charge. Our main measures were Fitbit heart rate, sleep and activity data as well as nurse recorded heart rates, patient reported sleep, and nurse assessments of activity.ResultsOf the 50 patients who consented to the study, 47 patients wore the devices. Comparing pairs of heart rate data from Fitbit and nurse recorded vital signs for the same minute, there were 261 pairs available for comparison. The mean difference was 0.45 bpm (SD: 13.0, Pearson correlation: 0.68 P<0.001) and the 95% limits of agreement were -25 to 26 bpm. The association between the patient-reported sleep score and Fitbit total sleep duration was 0.19 (P=0.24) and between the self-reported hours of sleep and Fitbit total sleep duration was 0.21 (P=0.21). The correlation between nurse-recorded activity and Fitbit daily steps was 0.06 (P=0.52). ConclusionsFitbit heart rates correlated well with nurse-recorded heart rate but did not correlate well with nurse assessments of activity nor with patient self-assessment of sleep. This study highlights limitations of the accuracy of current wearable wrist-worn device algorithms in activity and sleep detection in patients in hospital. The findings call into question the validity of Fitbits for assessment of patient activity and sleep in the hospital setting and suggest that they should not be routinely used without further validation.Trial RegistrationClinicalTrials.gov NCT03646435

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Section: Discussioncontrasting

confidence: 78%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fitbit data show poor correlation with measures of activity and sleep among hospitalized general medicine patients

Patel

Moosa

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…11 The Fitbit wearable device (Fitbit, Inc) has been studied in the critical care setting where heart rate has been found to have moderate accuracy in patients who are in sinus rhythm, and Fitbit sleep data correlated moderately with self-reported sleep. 12,13 To date, there have been limited studies on the accuracy of wearables in hospitalized medical patients.…”

Section: Introductionmentioning

confidence: 99%

Fitbit data show poor correlation with measures of activity and sleep among hospitalized general medicine patients

Patel

Moosa

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Wearable devices such as Fitbits may provide important insights about hospitalized patients that include data on low activity and poor sleep. Monitoring this information could spur interventions to improve mobility and sleep which may reduce the adverse effects associated with hospitalization. However, there is a lack of studies assessing the accuracy of wearables in hospitalized medical patients. The purpose of our study was to determine the accuracy of Fitbit heart rate, sleep and physical activity in hospitalized medical patients.Methods: We conducted a prospective cohort feasibility study enrolling 50 medical inpatients at two hospitals providing them with a wrist-worn Fitbit Charge. Our main measures were Fitbit heart rate, sleep and activity data as well as nurse recorded heart rates, patient reported sleep, and nurse assessments of activity.Results: Of the 50 patients who consented to the study, 47 patients wore the devices. Comparing pairs of heart rate data from Fitbit and nurse recorded vital signs for the same minute, there were 261 pairs available for comparison. The mean difference was 0.45 bpm (SD: 13.0, Pearson correlation: 0.68 P<0.001) and the 95% limits of agreement were -25 to 26 bpm. The association between the patient-reported sleep score and Fitbit total sleep duration was 0.19 (P=0.24) and between the self-reported hours of sleep and Fitbit total sleep duration was 0.21 (P=0.21). The correlation between nurse-recorded activity and Fitbit daily steps was 0.06 (P=0.52). Conclusions: Fitbit heart rates correlated well with nurse-recorded heart rate but did not correlate well with nurse assessments of activity nor with patient self-assessment of sleep. This study highlights limitations of the accuracy of current wearable wrist-worn device algorithms in activity and sleep detection in patients in hospital. The findings call into question the validity of Fitbits for assessment of patient activity and sleep in the hospital setting and suggest that they should not be routinely used without further validation.Trial Registration: ClinicalTrials.gov NCT03646435

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“…Such medical devices are increasing in use, particularly for conditions that require frequent monitoring. For example, wearable trackers have been shown effective in providing real-time data on the condition of ICU patients postdischarge 14 . Applications have also been developed for behavioral health.…”

Section: Introductionmentioning

confidence: 99%

A novel mobile biobehavioral regulation system for personalized trauma recovery support

Matto¹,

Seshaiyer²,

Newcomb³

et al. 2019

Patient Experience Journal

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Even years after experiencing a physical trauma, individuals describe trauma-specific distress, as trauma triggers present as sensory reminders that initiate physiological reactions at time of exposure. Mobile technologies offer tremendous potential in helping individuals who have experienced trauma manage symptoms as they transition out of hospital care and move back into their communities where they are at risk of trauma trigger exposure. A personalized wearable device, tailored to a patient-specific diagnosis (e.g., PTSD) with programmable neurophysiological behavioral risk set-points, could be a useful tool in helping individuals monitor symptomology. When this type of monitoring device is also connected to a personalized recovery cue intervention on a smartwatch or phone, and activated when the wearable sensor detects heightened risk, there is the opportunity for in-the-moment symptom management. In this study we sought to understand the value for trauma survivors of using this type of personalized mobile recovery support system. Study participants were all trauma survivors or family members of survivors who were involved in the Trauma Survivors Network. A semi-structured interview was conducted with participants to understand perceptions on the utility, sensory experiences, and innovation insights of a mobile recovery sensory support system overall, and about the recovery cue intervention most specifically. Results from participant interviews inform the further development of our mobile recovery support system model in significant ways, suggesting that three components must be included: 1) Recovery cues; 2) Relationships (connecting to supportive network); and 3) Regulation (neurophysiological regulation and behavioral risk reduction).

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Use of wearable devices for post-discharge monitoring of ICU patients: a feasibility study

Cited by 53 publications

References 29 publications

Fitbit data show poor correlation with measures of activity and sleep among hospitalized general medicine patients

Fitbit data show poor correlation with measures of activity and sleep among hospitalized general medicine patients

Fitbit data show poor correlation with measures of activity and sleep among hospitalized general medicine patients

A novel mobile biobehavioral regulation system for personalized trauma recovery support

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