Diabetes Management Through Remote Patient Monitoring: The Importance of Patient Activation and Engagement with the Technology

Su, Dejun; Michaud, Tzeyu L.; Estabrooks, Paul A.; Schwab, Robert; Eiland, Leslie; Hansen, Geri; DeVany, Mary; Zhang, Donglan; Li, Yan; Pagán, José A.; Siahpush, Mohammad

doi:10.1089/tmj.2018.0205

Cited by 69 publications

(52 citation statements)

References 23 publications

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“…Patient experience was captured in some of the articles we reviewed [ 8 , 26 ] but the analysis was limited. An analysis of patient experience and engagement is important as the literature on the use of remote patient monitoring for other conditions has demonstrated that higher levels of patient engagement with remote patient monitoring technology are associated with better patient outcomes [29] .…”

Section: Discussionmentioning

confidence: 99%

Remote home monitoring (virtual wards) for confirmed or suspected COVID-19 patients: a rapid systematic review

Vindrola‐Padros¹,

Singh²,

Sidhu³

et al. 2021

eClinicalMedicine

105

121

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Background the aim of this review was to analyze the implementation and impact of remote home monitoring models (virtual wards) for confirmed or suspected COVID-19 patients, identifying their main components, processes of implementation, target patient populations, impact on outcomes, costs and lessons learnt. Methods we carried out a rapid systematic review on models led by primary and secondary care across seven countries (US, Australia, Canada, The Netherlands, Ireland, China, UK). The main outcomes included in the review were: impact of remote home monitoring on virtual length of stay, escalation, emergency department attendance/reattendance, admission/readmission and mortality. The search was updated on February 2021. We used the PRISMA statement and the review was registered on PROSPERO (CRD: 42020202888). Findings the review included 27 articles. The aim of the models was to maintain patients safe in the appropriate setting. Most models were led by secondary care and confirmation of COVID-19 was not required (in most cases). Monitoring was carried via online platforms, paper-based systems with telephone calls or (less frequently) through wearable sensors. Models based on phone calls were considered more inclusive. Patient/career training was identified as a determining factor of success. We could not reach substantive conclusions regarding patient safety and the identification of early deterioration due to lack of standardized reporting and missing data. Economic analysis was not reported for most of the models and did not go beyond reporting resources used and the amount spent per patient monitored. Interpretation future research should focus on staff and patient experiences of care and inequalities in patients’ access to care. Attention needs to be paid to the cost-effectiveness of the models and their sustainability, evaluation of their impact on patient outcomes by using comparators, and the use of risk-stratification tools.

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Section: Discussionmentioning

confidence: 99%

Remote home monitoring (virtual wards) for confirmed or suspected COVID-19 patients: a rapid systematic review

Vindrola‐Padros¹,

Singh²,

Sidhu³

et al. 2021

eClinicalMedicine

105

121

View full text Add to dashboard Cite

show abstract

“…Patient experience was captured in some of the examples we reviewed 8 21 but the analysis of patient experience was limited. An analysis of patient experience and engagement is important as the literature on the use of remote patient monitoring for other conditions has demonstrated that higher levels of patient engagement with remote patient monitoring technology are associated with better patient outcomes 23 .…”

Section: Discussionmentioning

confidence: 99%

Remote home monitoring (virtual wards) during the COVID-19 pandemic: a systematic review

Vindrola‐Padros

et al. 2020

Preprint

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Objectives: The aim of this review was to analyse the implementation and impact of remote home monitoring models (virtual wards) during COVID-19, identifying their main components, processes of implementation, target patient populations, impact on outcomes, costs and lessons learnt. Design: The review was designed as a rapid systematic review to capture an evolving evidence base. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. Setting: The review included models led by primary and secondary care across seven countries. Participants: 17 examples of models were included in the review. Main outcome measures: Impact of remote home monitoring on virtual length of stay, escalation, emergency department attendance/reattendance, admission/readmission and mortality. Results: The aim of the models was to maintain patients safe in the right setting. Most models were led by secondary care and confirmation of COVID-19 was not required (in most cases). Monitoring was carried via online platforms, paper-based systems with telephone calls or (less frequently) through wearable sensors. Models based on phone calls were considered more inclusive. Patient/carer training was identified as a determining factor of success. We could not reach conclusions regarding patient safety and the identification of early deterioration due to lack of standardised reporting and missing data. Economic analysis was not reported beyond how the resources were used. Conclusions: Future research should focus on staff and patient experiences of care and inequalities in patients access to care. Attention needs to be paid to the cost-effectiveness of the models and their sustainability, evaluation of their impact on patient outcomes by using comparators and the use of risk-stratification tools. Protocol registration: The review protocol was published on PROSPERO (CRD: 42020202888).

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“…Social inequality and unequal access to health-promoting resources have previously been shown to lower patient activation and engagement, negatively affecting diabetes outcomes. 6,11 The relationship between inequality and patient activation and engagement underscores the importance of addressing socioeconomic disadvantages among black patients in improving their activation and engagement with telemedicine programs, which would, in turn, facilitate their diabetes management and reduction of related disparities.…”

Section: Discussionmentioning

confidence: 99%

“…5 Moreover, prior research indicates that patient activation and engagement with telemedicine can impact diabetes management outcomes, although little is known about how this has been linked to racial disparities in diabetes outcomes. 6 Although patient activation denotes the extent to which a patient understands his or her role in the care process and has the knowledge, skill, and confidence to manage his or her own health and health care, patient engagement in the context of telemedicine represents ''an effective way to foster patients'' self-management of their diseases by supporting the use of technological devices that can facilitate selfmonitoring skills. 7,8 Lower levels of patient activation (e.g., self-management skills) and engagement may result from stressful social environments (e.g., discrimination, low levels of social support) and hazardous physical conditions (e.g., poor housing conditions).…”

Section: Introductionmentioning

confidence: 99%

Racial Disparities in Diabetes Management Outcomes: Evidence from a Remote Patient Monitoring Program for Type 2 Diabetic Patients

Andersen

Scoggins

Michaud

et al. 2021

Telemedicine and e-Health

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Introduction: Despite growing documentation of the efficacy of telemedicine in diabetes management, racial disparities in telemedicine-facilitated diabetes management remain underexplored. This study examined disparities in diabetes management outcomes between black and white patients with type 2 diabetes (T2D) in a remote monitoring program. Methods: The analysis sample included 914 white T2D patients and 365 black T2D patients in Nebraska who completed a 3-month remote patient monitoring and coaching after hospital discharge from 2014 to 2017. Ordinary least squares regression was estimated to examine racial differences in hemoglobin A1c (HbA1c), and logistic regression was used to determine the odds of HbA1c > 9% at the end of the program, controlling for demographics, baseline health conditions, and patient activation and engagement with the program. Results: The proportion of white patients with HbA1c > 9% was reduced from 16% at the baseline to 7% at program completion, and the corresponding reduction among black patients was from 30% to 18%. After adjusting for the effects of baseline HbA1c and other covariates, the average HbA1c among black patients at the end of the program was 0.23 points higher than that among white patients (p < 0.01), and the adjusted odds of black patients having HbA1c > 9% was 1.68 times that of white patients (95% confidence interval [1.07-2.63]). Discussion: The remote patient monitoring and coaching program reduced the absolute gap between black and white T2D patients. However, substantial racial disparities in HbA1c still remained at the end of the program and warranted further research.

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Diabetes Management Through Remote Patient Monitoring: The Importance of Patient Activation and Engagement with the Technology

Cited by 69 publications

References 23 publications

Remote home monitoring (virtual wards) for confirmed or suspected COVID-19 patients: a rapid systematic review

Remote home monitoring (virtual wards) for confirmed or suspected COVID-19 patients: a rapid systematic review

Remote home monitoring (virtual wards) during the COVID-19 pandemic: a systematic review

Racial Disparities in Diabetes Management Outcomes: Evidence from a Remote Patient Monitoring Program for Type 2 Diabetic Patients

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