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Observational Study
. 2023 Apr 5:25:e43134.
doi: 10.2196/43134.

Compliance Challenges in a Longitudinal COVID-19 Cohort Using Wearables for Continuous Monitoring: Observational Study

Mario Mekhael #  1 , Chan Ho  1 , Charbel Noujaim  1 , Ala Assaf  1 , Hadi Younes  1 , Abdel Hadi El Hajjar  2 , Humza A Chaudhry  1 , Brennan Lanier  1 , Nour Chouman  1 , Noor Makan  1 , Botao Shan  1 , Yichi Zhang  1 , Lilas Dagher  3 , Omar Kreidieh  1 , Nassir Marrouche  1 , Eoin Donnellan  1
Affiliations
Observational Study

Compliance Challenges in a Longitudinal COVID-19 Cohort Using Wearables for Continuous Monitoring: Observational Study

Mario Mekhael et al. J Med Internet Res. .

Abstract

Background: The WEAICOR (Wearables to Investigate the Long Term Cardiovascular and Behavioral Impacts of COVID-19) study was a prospective observational study that used continuous monitoring to detect and analyze biometrics. Compliance to wearables was a major challenge when conducting the study and was crucial for the results.

Objective: The aim of this study was to evaluate patients' compliance to wearable wristbands and determinants of compliance in a prospective COVID-19 cohort.

Methods: The Biostrap (Biostrap USA LLC) wearable device was used to monitor participants' biometric data. Compliance was calculated by dividing the total number of days in which transmissions were sent by the total number of days spent in the WEAICOR study. Univariate correlation analyses were performed, with compliance and days spent in the study as dependent variables and age, BMI, sex, symptom severity, and the number of complications or comorbidities as independent variables. Multivariate linear regression was then performed, with days spent in the study as a dependent variable, to assess the power of different parameters in determining the number of days patients spent in the study.

Results: A total of 122 patients were included in this study. Patients were on average aged 41.32 years, and 46 (38%) were female. Age was found to correlate with compliance (r=0.23; P=.01). In addition, age (r=0.30; P=.001), BMI (r=0.19; P=.03), and the severity of symptoms (r=0.19; P=.03) were found to correlate with days spent in the WEAICOR study. Per our multivariate analysis, in which days spent in the study was a dependent variable, only increased age was a significant determinant of compliance with wearables (adjusted R2=0.1; β=1.6; P=.01).

Conclusions: Compliance is a major obstacle in remote monitoring studies, and the reasons for a lack of compliance are multifactorial. Patient factors such as age, in addition to environmental factors, can affect compliance to wearables.

Keywords: COVID-19; biometric; cardiovascular health; compliance; digital health; heart disease; remote monitoring; wearable device; wearables.

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Conflict of interest statement

Conflicts of Interest: N Marrouche reports receiving grant support from Abbott, Medtronic, Biosense Webster, and Boston Scientific and consulting fees from Preventice, Biosense Webster, and Atricure (lectures: Biotronik, Bristol Myers Squibb, and Biosense Webster). All other authors have no conflicts of interest associated with the content of this manuscript.

Figures

Figure 1
Figure 1
Average compliance (%) and days spent in the study for the study population.
Figure 2
Figure 2
Expected and actual compliance evolution during the study. The difference between expected and actual compliance represents noncompliant patients.
Figure 3
Figure 3
Univariate regressions with compliance and days in the study as dependent variables and age, BMI, sex, sx, and nComp as independent variables. nComp: number of comorbidities; sx: severity of symptoms.
See this image and copyright information in PMC

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References

    1. Free C, Knight R, Robertson S, Whittaker R, Edwards P, Zhou W, Rodgers A, Cairns J, Kenward MG, Roberts I. Smoking cessation support delivered via mobile phone text messaging (txt2stop): a single-blind, randomised trial. Lancet. 2011 Jul 02;378(9785):49–55. doi: 10.1016/S0140-6736(11)60701-0. https://linkinghub.elsevier.com/retrieve/pii/S0140-6736(11)60701-0 S0140-6736(11)60701-0 - DOI - PMC - PubMed
    1. Zhuo X, Zhang P, Barker L, Albright A, Thompson TJ, Gregg E. The lifetime cost of diabetes and its implications for diabetes prevention. Diabetes Care. 2014 Sep;37(9):2557–2564. doi: 10.2337/dc13-2484.dc13-2484 - DOI - PubMed
    1. Peyrot M, Rubin RR. Access to diabetes self-management education. Diabetes Educ. 2008;34(1):90–97. doi: 10.1177/0145721707312399.34/1/90 - DOI - PubMed
    1. Khadjesari Z, Murray E, Hewitt C, Hartley S, Godfrey C. Can stand-alone computer-based interventions reduce alcohol consumption? A systematic review. Addiction. 2011 Feb;106(2):267–282. doi: 10.1111/j.1360-0443.2010.03214.x. - DOI - PubMed
    1. Mizuno A, Changolkar S, Patel MS. Wearable devices to monitor and reduce the risk of cardiovascular disease: Evidence and opportunities. Annu Rev Med. 2021 Jan 27;72:459–471. doi: 10.1146/annurev-med-050919-031534. - DOI - PubMed

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