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CC BY-NC-ND 4.0 · Appl Clin Inform 2023; 14(04): 644-653
DOI: 10.1055/a-2096-0326
CIC 2022

The ACTIVATE Digital Health Pilot Program for Diabetes and Hypertension in an Underserved and Rural Community

Katherine K. Kim
1   MITRE Corporation, Health Innovation Center, McLean, Virginia, United States
2   Department of Public Health Sciences, Division of Health Informatics, University of California Davis, School of Medicine, Sacramento, California, United States
,
Scott P. McGrath
3   CITRIS and the Banatao Institute, University of California Berkeley, Berkeley, California, United States
,
Juan L. Solorza
4   Livingston Community Health, Livingston, California, United States
,
David Lindeman
3   CITRIS and the Banatao Institute, University of California Berkeley, Berkeley, California, United States
› Institutsangaben Funding This study was funded by a gift grant from an anonymous, private donor who had no role in the study design or analysis.
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Abstract

Background Community health centers and patients in rural and agricultural communities struggle to address diabetes and hypertension in the face of health disparities and technology barriers. The stark reality of these digital health disparities were highlighted during the coronavirus disease 2019 pandemic.

Objectives The objective of the ACTIVATE (Accountability, Coordination, and Telehealth in the Valley to Achieve Transformation and Equity) project was to codesign a platform for remote patient monitoring and program for chronic illness management that would address these disparities and offer a solution that fit the needs and context of the community.

Methods ACTIVATE was a digital health intervention implemented in three phases: community codesign, feasibility assessment, and a pilot phase. Pre- and postintervention outcomes included regularly collected hemoglobin A1c (A1c) for participants with diabetes and blood pressure for those with hypertension.

Results Participants were adult patients with uncontrolled diabetes and/or hypertension (n = 50). Most were White and Hispanic or Latino (84%) with Spanish as a primary language (69%), and the mean age was 55. There was substantial adoption and use of the technology: over 10,000 glucose and blood pressure measures were transmitted using connected remote monitoring devices over a 6-month period. Participants with diabetes achieved a mean reduction in A1c of 3.28 percentage points (standard deviation [SD]: 2.81) at 3 months and 4.19 percentage points (SD: 2.69) at 6 months. The vast majority of patients achieved an A1c in the target range for control (7.0–8.0%). Participants with hypertension achieved reductions in systolic blood pressure of 14.81 mm Hg (SD: 21.40) at 3 months and 13.55 mm Hg (SD: 23.31) at 6 months, with smaller reductions in diastolic blood pressure. The majority of participants also reached target blood pressure (less than 130/80).

Conclusion The ACTIVATE pilot demonstrated that a codesigned solution for remote patient monitoring and chronic illness management delivered by community health centers can overcome digital divide barriers and show positive health outcomes for rural and agricultural residents.

Keywords

diabetes mellitus - hypertension - telemedicine - codesign - patient participation - digital divide - consumer health informatics

Protection of Human and Animal Subjects

ACTIVATE was a quality improvement project conducted at the health center to demonstrate how RPM might improve chronic illness care and be expanded throughout the clinic. It was not deemed human subjects research. The ACTIVATE team had no contact with patients.




Publikationsverlauf

Eingereicht: 27. Januar 2023

Angenommen: 16. Mai 2023

Accepted Manuscript online:
18. Mai 2023

Artikel online veröffentlicht:
16. August 2023

© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Xie B, He D, Mercer T. et al. Global health crises are also information crises: a call to action. J Assoc Inf Sci Technol 2020; 71 (12) 1419-1423
    CrossrefPubMedSuche in Google Scholar
  • 2 Egan AM, Dinneen SF. What is diabetes?. Medicine (Baltimore) 2019; 47 (01) 1-4
    CrossrefPubMedSuche in Google Scholar
  • 3 Sun H, Saeedi P, Karuranga S. et al. IDF Diabetes Atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract 2022; 183: 109119
    Suche in Google Scholar
  • 4 Carretero OA, Oparil S. Essential hypertension. Part I: definition and etiology. Circulation 2000; 101 (03) 329-335
    CrossrefPubMedSuche in Google Scholar
  • 5 Mills KT, Stefanescu A, He J. The global epidemiology of hypertension. Nat Rev Nephrol 2020; 16 (04) 223-237
    CrossrefPubMedSuche in Google Scholar
  • 6 Baker L, Bundorf K, Rosen-Slater M. Racial and ethnic disparities in primary care use and the Covid-19 pandemic. December 4, 2020. Report No.: f145649.slides.pdf (nber.org)
    PubMed
  • 7 Cohen S. Pandemic weight gain is real. connect.uclahealth.org. Accessed April 2, 2021 at: http://archive.today/7ZwD4
    PubMed
  • 8 American Diabetes Association, Thrivable. . Effects of the COVID-19 pandemic on people with diabetes. diabetes.org; December 20, 2020
    PubMed
  • 9 Patel SY, McCoy RG, Barnett ML, Shah ND, Mehrotra A. Diabetes care and glycemic control during the COVID-19 pandemic in the United States. JAMA Intern Med 2021; 181 (10) 1412-1414
    CrossrefPubMedSuche in Google Scholar
  • 10 Frank SR. Digital health care–the convergence of health care and the Internet. J Ambul Care Manage 2000; 23 (02) 8-17
    CrossrefPubMedSuche in Google Scholar
  • 11 Koonin LM, Hoots B, Tsang CA. et al. Trends in the use of telehealth during the emergence of the COVID-19 pandemic—United States, January–March 2020. MMWR Morb Mortal Wkly Rep 2020; 69 (43) 1595-1599
    CrossrefPubMedSuche in Google Scholar
  • 12 Vegesna A, Tran M, Angelaccio M, Arcona S. Remote patient monitoring via non-invasive digital technologies: a systematic review. Telemed J E Health 2017; 23 (01) 3-17
    CrossrefPubMedSuche in Google Scholar
  • 13 Farias FAC, Dagostini CM, Bicca YA, Falavigna VF, Falavigna A. Remote patient monitoring: a systematic review. Telemed J E Health 2020; 26 (05) 576-583
    CrossrefPubMedSuche in Google Scholar
  • 14 Salehi S, Olyaeemanesh A, Mobinizadeh M, Nasli-Esfahani E, Riazi H. Assessment of remote patient monitoring (RPM) systems for patients with type 2 diabetes: a systematic review and meta-analysis. J Diabetes Metab Disord 2020; 19 (01) 115-127
    CrossrefPubMedSuche in Google Scholar
  • 15 Lee PA, Greenfield G, Pappas Y. The impact of telehealth remote patient monitoring on glycemic control in type 2 diabetes: a systematic review and meta-analysis of systematic reviews of randomised controlled trials. BMC Health Serv Res 2018; 18 (01) 495
    CrossrefPubMedSuche in Google Scholar
  • 16 Al-Khafajiy M, Baker T, Chalmers C. et al. Remote health monitoring of elderly through wearable sensors. Multimedia Tools Appl 2019; 78 (17) 24681-24706
    CrossrefPubMedSuche in Google Scholar
  • 17 Gordon WJ, Blood AJ, Chaney K. et al. Workflow automation for a virtual hypertension management program. Appl Clin Inform 2021; 12 (05) 1041-1048
    Thieme ConnectPubMedSuche in Google Scholar
  • 18 Wiley KK, Mendonca E, Blackburn J, Menachemi N, Groot M, Vest JR. Quantifying electronic health record data quality in telehealth and office-based diabetes care. Appl Clin Inform 2022; 13 (05) 1172-1180
    Thieme ConnectPubMedSuche in Google Scholar
  • 19 Harrington L, Parker C, Ulanday K, Harrington C. Heuristic evaluation of a top-rated diabetes self-management app. Appl Clin Inform 2021; 12 (05) 1014-1020
    Thieme ConnectPubMedSuche in Google Scholar
  • 20 Hayes CJ, Dawson L, McCoy H. et al. Utilization of remote patient monitoring within the United States health care system: a scoping review. Telemed J E Health 2023; 29 (03) 384-394
    CrossrefPubMedSuche in Google Scholar
  • 21 Noah B, Keller MS, Mosadeghi S. et al. Impact of remote patient monitoring on clinical outcomes: an updated meta-analysis of randomized controlled trials. NPJ Digit Med 2018; 1 (01) 1-12
    CrossrefPubMedSuche in Google Scholar
  • 22 Nana-Sinkam P, Kraschnewski J, Sacco R. et al. Health disparities and equity in the era of COVID-19. J Clin Transl Sci 2021; 5 (01) e99
    CrossrefPubMedSuche in Google Scholar
  • 23 Chunara R, Zhao Y, Chen J. et al. Telemedicine and healthcare disparities: a cohort study in a large healthcare system in New York City during COVID-19. J Am Med Inform Assoc 2021; 28 (01) 33-41
    CrossrefPubMedSuche in Google Scholar
  • 24 Franciosi EB, Tan AJ, Kassamali B. et al. The impact of telehealth implementation on underserved populations and no-show rates by medical specialty during the COVID-19 pandemic. Telemed J E Health 2021; 27 (08) 874-880
    CrossrefPubMedSuche in Google Scholar
  • 25 Chen J, Amaize A, Barath D. Evaluating telehealth adoption and related barriers among hospitals located in rural and urban areas. J Rural Health 2021; 37 (04) 801-811
    CrossrefPubMedSuche in Google Scholar
  • 26 Ramsetty A, Adams C. Impact of the digital divide in the age of COVID-19. J Am Med Inform Assoc 2020; 27 (07) 1147-1148
    CrossrefPubMedSuche in Google Scholar
  • 27 Andersen JA, Scoggins D, Michaud T, Wan N, Wen M, Su D. Racial disparities in diabetes management outcomes: evidence from a remote patient monitoring program for type 2 diabetic patients. Telemed J E Health 2021; 27 (01) 55-61
    CrossrefPubMedSuche in Google Scholar
  • 28 Vrany EA, Hill-Briggs F, Ephraim PL, Myers AK, Garnica P, Fitzpatrick SL. Continuous glucose monitors and virtual care in high-risk, racial and ethnic minority populations: toward promoting health equity. Front Endocrinol (Lausanne) 2023; 14: 1083145
    CrossrefPubMedSuche in Google Scholar
  • 29 Truong M, Yeganeh L, Cook O, Crawford K, Wong P, Allen J. Using telehealth consultations for healthcare provision to patients from non-Indigenous racial/ethnic minorities: a systematic review. J Am Med Inform Assoc 2022; 29 (05) 970-982
    CrossrefPubMedSuche in Google Scholar
  • 30 Ariel-Donges AH, Gordon EL, Dixon BN. et al. Rural/urban disparities in access to the National Diabetes Prevention Program. Transl Behav Med 2020; 10 (06) 1554-1558
    PubMedSuche in Google Scholar
  • 31 Ford ES. Prevalence of the metabolic syndrome defined by the International Diabetes Federation among adults in the U.S. Diabetes Care 2005; 28 (11) 2745-2749
    CrossrefPubMedSuche in Google Scholar
  • 32 Cheng YJ, Kanaya AM, Araneta MRG. et al. Prevalence of diabetes by race and ethnicity in the United States, 2011-2016. JAMA 2019; 322 (24) 2389-2398
    CrossrefPubMedSuche in Google Scholar
  • 33 Dugani SB, Mielke MM, Vella A. Burden and management of type 2 diabetes in rural United States. Diabetes Metab Res Rev 2021; 37 (05) e3410
    CrossrefPubMedSuche in Google Scholar
  • 34 Gaskin DJ, Thorpe Jr RJ, McGinty EE. et al. Disparities in diabetes: the nexus of race, poverty, and place. Am J Public Health 2014; 104 (11) 2147-2155
    CrossrefPubMedSuche in Google Scholar
  • 35 Lusk JL, Chandra R. Farmer and farm worker illnesses and deaths from COVID-19 and impacts on agricultural output. PLoS One 2021; 16 (04) e0250621
    CrossrefPubMedSuche in Google Scholar
  • 36 Harrington RA, Califf RM, Balamurugan A. et al. Call to action: rural health: a presidential advisory from the American Heart Association and American Stroke Association. Circulation 2020; 141 (10) e615-e644
    CrossrefPubMedSuche in Google Scholar
  • 37 Detz A, Mangione CM, Nunez de Jaimes F. et al. Language concordance, interpersonal care, and diabetes self-care in rural Latino patients. J Gen Intern Med 2014; 29 (12) 1650-1656
    CrossrefPubMedSuche in Google Scholar
  • 38 Parker MM, Fernández A, Moffet HH, Grant RW, Torreblanca A, Karter AJ. Association of patient-physician language concordance and glycemic control for limited–English proficiency Latinos with type 2 diabetes. JAMA Intern Med 2017; 177 (03) 380-387
    CrossrefPubMedSuche in Google Scholar
  • 39 Vidal TM, Williams CA, Ramoutar UD, Haffizulla F. Type 2 diabetes mellitus in latinx populations in the United States: a culturally relevant literature review. Cureus 2022; 14 (03) e23173
    PubMedSuche in Google Scholar
  • 40 Chandler J, Sox L, Kellam K, Feder L, Nemeth L, Treiber F. Impact of a culturally tailored mHealth medication regimen self-management program upon blood pressure among hypertensive Hispanic adults. Int J Environ Res Public Health 2019; 16 (07) 1226
    CrossrefPubMedSuche in Google Scholar
  • 41 Campos CL, Rodriguez CJ. High blood pressure in Hispanics in the United States: a review. Curr Opin Cardiol 2019; 34 (04) 350-358
    CrossrefPubMedSuche in Google Scholar
  • 42 Wosik J, Fudim M, Cameron B. et al. Telehealth transformation: COVID-19 and the rise of virtual care. J Am Med Inform Assoc 2020; 27 (06) 957-962
    CrossrefPubMedSuche in Google Scholar
  • 43 Clemensen J, Rothmann MJ, Smith AC, Caffery LJ, Danbjorg DB. Participatory design methods in telemedicine research. J Telemed Telecare 2017; 23 (09) 780-785
    CrossrefPubMedSuche in Google Scholar
  • 44 Moll S, Wyndham-West M, Mulvale G. et al. Are you really doing ‘codesign’? Critical reflections when working with vulnerable populations. BMJ Open 2020; 10 (11) e038339
    CrossrefPubMedSuche in Google Scholar
  • 45 Noorbergen TJ, Adam MT, Roxburgh M, Teubner T. Co-design in mHealth systems development: insights from a systematic literature review. AIS Trans Hum-Comput Interact 2021; 13 (02) 175-205
    CrossrefPubMedSuche in Google Scholar
  • 46 Vandekerckhove P, de Mul M, Bramer WM, de Bont AA. Generative participatory design methodology to develop electronic health interventions: systematic literature review. J Med Internet Res 2020; 22 (04) e13780
    CrossrefPubMedSuche in Google Scholar
  • 47 American Diabetes Association. . eAG/A1C conversion calculator; 2008. Accessed January 1, 2023 at: https://professional.diabetes.org/diapro/glucose_calc
    PubMed
  • 48 Kim Y-N, Shin DG, Park S, Lee CH. Randomized clinical trial to assess the effectiveness of remote patient monitoring and physician care in reducing office blood pressure. Hypertens Res 2015; 38 (07) 491-497
    CrossrefPubMedSuche in Google Scholar
  • 49 Patil SJ, Wareg NK, Hodges KL, Smith JB, Kaiser MS, LeFevre ML. Home blood pressure monitoring in cases of clinical uncertainty to differentiate appropriate inaction from therapeutic inertia. Ann Fam Med 2020; 18 (01) 50-58
    CrossrefPubMedSuche in Google Scholar
  • 50 Persell SD, Peprah YA, Lipiszko D. et al. Effect of home blood pressure monitoring via a smartphone hypertension coaching application or tracking application on adults with uncontrolled hypertension: a randomized clinical trial. JAMA Netw Open 2020; 3 (03) e200255
    CrossrefPubMedSuche in Google Scholar