Personalized Digital Health Information to Substantiate Human-Delivered Exercise Support for Adults With Type 1 Diabetes.
Journal
Clinical journal of sport medicine : official journal of the Canadian Academy of Sport Medicine
ISSN: 1536-3724
Titre abrégé: Clin J Sport Med
Pays: United States
ID NLM: 9103300
Informations de publication
Date de publication:
01 09 2023
01 09 2023
Historique:
medline:
4
9
2023
pubmed:
31
1
2023
entrez:
30
1
2023
Statut:
ppublish
Résumé
Pilot-test personalized digital health information to substantiate human-delivered exercise support for adults with type 1 diabetes (T1D). Single-group, 2-week baseline observation, then 10-week intervention with follow-up observation. Community-based sample participating remotely with physician oversight. Volunteers aged 18 to 65 years with T1D screened for medical readiness for exercise intervention offerings. N = 20 enrolled, and N = 17 completed all outcomes with 88% to 91% biosensor adherence. Feedback on personalized data from continuous glucose monitoring (CGM), its intersection with other ecological data sets (exercise, mood, and sleep), and other informational and motivational elements (exercise videos, text-based exercise coach, and self-monitoring diary). Feasibility (use metrics and assessment completion), safety (mild and severe hypoglycemia, and diabetic ketoacidosis), acceptability (system usability scale, single items, and interview themes), and standard clinical and psychosocial assessments. Participants increased exercise from a median of 0 (Interquartile range, 0-21) to 64 (20-129) minutes per week ( P = 0.001, d = 0.71) with no severe hypoglycemia or ketoacidosis. Body mass index increased (29.5 ± 5.1 to 29.8 ± 5.4 kg/m 2 , P = 0.02, d = 0.57). Highest satisfaction ratings were for CGM use (89%) and data on exercise and its intersection with CGM and sleep (94%). Satisfaction was primarily because of improved exercise management behavioral skills, although derived motivation was transient. The intervention was feasible, safe, and acceptable. However, there is a need for more intensive, sustained support. Future interventions should perform analytics upon the digital health information and molecular biomarkers (eg, genomics) to make exercise support tools that are more personalized, automated, and intensive than our present offerings.
Identifiants
pubmed: 36715983
doi: 10.1097/JSM.0000000000001078
pii: 00042752-990000000-00091
doi:
Substances chimiques
Blood Glucose
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
512-520Informations de copyright
Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.
Déclaration de conflit d'intérêts
The authors report no conflicts of interest.
Références
Centers for Disease Control and Prevention. National Diabetes Statistics Report; 2020. Available at: https://www.cdc.gov/diabetes/data/statistics-report/index.html . Accessed February 27, 2021.
American Diabetes Association. Standards of medical care in diabetes-2021. Diabetes Care. 2021;44(suppl 1):S1–S232.
Brazeau AS, Rabasa-Lhoret R, Strychar I, et al. Barriers to physical activity among patients with type 1 diabetes. Diabetes Care. 2008;31:2108–2109.
Martyn-Nemeth P, Quinn L, Penckofer S, et al. Fear of hypoglycemia: influence on glycemic variability and self-management behavior in young adults with type 1 diabetes. J Diabetes Complications. 2017;31:735–741.
Nathan DM; DCCT/EDIC Research Group. The diabetes control and complications trial/epidemiology of diabetes interventions and complications study at 30 years: overview. Diabetes Care. 2014;37:9–16.
Orchard TJ, Forrest KY, Kuller LH, et al.; Pittsburgh Epidemiology of Diabetes Complications Study. Lipid and blood pressure treatment goals for type 1 diabetes: 10-year incidence data from the Pittsburgh Epidemiology of Diabetes Complications Study. Diabetes Care. 2001;24:1053–1059.
Amsberg S, Anderbro T, Wredling R, et al. A cognitive behavior therapy-based intervention among poorly controlled adult type 1 diabetes patients—a randomized controlled trial. Patient Educ Couns. 2009;77:72–80.
Pyatak EA, Carandang K, Vigen CLP, et al. Occupational therapy intervention improves glycemic control and quality of life among young adults with diabetes: the resilient, empowered, active living with diabetes (REAL diabetes) randomized controlled trial. Diabetes Care. 2018;41:696–704.
Foster NC, Beck RW, Miller KM, et al. State of type 1 diabetes management and outcomes from the T1D exchange in 2016-2018. Diabetes Technol Ther. 2019;21:66–72.
Bonoto BC, de Araujo VE, Godoi IP, et al. Efficacy of mobile apps to support the care of patients with diabetes mellitus: a systematic review and meta-analysis of randomized controlled trials. JMIR Mhealth Uhealth. 2017;5:e4.
Wu N, Bredin SSD, Guan Y, et al. Cardiovascular health benefits of exercise training in persons living with type 1 diabetes: a systematic review and meta-analysis. J Clin Med. 2019;8:E253.
McCarthy MM, Funk M, Grey M. Cardiovascular health in adults with type 1 diabetes. Prev Med. 2016;91:138–143.
Bohn B, Herbst A, Pfeifer M, et al.; DPV Initiative. Impact of physical activity on glycemic control and prevalence of cardiovascular risk factors in adults with type 1 diabetes: a cross-sectional multicenter study of 18, 028 patients. Diabetes Care. 2015;38:1536–1543.
Kennedy A, Narendran P, Andrews RC, et al.; EXTOD Group. Attitudes and barriers to exercise in adults with a recent diagnosis of type 1 diabetes: a qualitative study of participants in the exercise for type 1 diabetes (EXTOD) study. BMJ Open. 2018;8:e017813.
Riddell MC, Gallen IW, Smart CE, et al. Exercise management in type 1 diabetes: a consensus statement. Lancet Diabetes Endocrinol. 2017;5:377–390.
Alvarez-Romero J, Voisin S, Eynon N, et al. Mapping robust genetic variants associated with exercise responses. Int J Sports Med. 2021;42:3–18.
Fagherazzi G. Deep digital phenotyping and digital twins for precision health: time to dig deeper. J Med Internet Res. 2020;22:e16770.
Houlder SK, Yardley JE. Continuous glucose monitoring and exercise in type 1 diabetes: past, present and future. Biosensors (Basel). 2018;8:E73.
Dyck RA, Kleinman NJ, Funk DR, et al. We can work (it) out together: type 1 diabetes boot camp for adult patients and providers improves exercise self-efficacy. Can J Diabetes. 2018;42:619–625.
Pickup JC, Ford Holloway M, Samsi K. Real-time continuous glucose monitoring in type 1 diabetes: a qualitative framework analysis of patient narratives. Diabetes Care. 2015;38:544–550.
Ehrhardt N, Al Zaghal E. Behavior modification in prediabetes and diabetes: potential use of real-time continuous glucose monitoring. J Diabetes Sci Technol. 2019;13:271–275.
Fisher WA, Kohut T, Schachner H, et al. Understanding self-monitoring of blood glucose among individuals with type 1 and type 2 diabetes: an information-motivation-behavioral skills analysis. Diabetes Educ. 2011;37:85–94.
Ash GI, Griggs S, Nally LM, et al. Evaluation of web-based and in-person methods to recruit adults with type 1 diabetes for a technology-based exercise intervention: prospective observational study. JMIR Preprints. 2021;6:e28309.
Tonstad S, Herring P, Lee J, et al. Two physical activity measures: paffenbarger physical activity questionnaire versus aerobics center longitudinal study as predictors of adult-onset type 2 diabetes in a follow-up study. Am J Health Promot. 2018;32:1070–1077.
Haddad M, Stylianides G, Djaoui L, et al. Session-RPE method for training load monitoring: validity, ecological usefulness, and influencing factors. Front Neurosci. 2017;11:612.
Monk TH, Reynolds CF III, Kupfer DJ, et al. The pittsburgh sleep diary. J Sleep Res. 1994;3:111–120.
Curran SL, Andrykowski MA, Studts JL. Short form of the profile of mood states (POMS-SF): psychometric information. Psychol Assess. 1995;7:80–83.
Fuller D, Colwell E, Low J, et al. Reliability and validity of commercially available wearable devices for measuring steps, energy expenditure, and heart rate: systematic review. JMIR Mhealth Uhealth. 2020;8:e18694.
Miller WR, Zweben A, Carlo DW, et al. Motivational Enhancement Therapy Manual: A Clinical Research Guide for Therapists Treating Individuals with Alcohol Abuse and Dependence. Rockville, MD: U.S. Department of Health and Human Services; 1999.
Ainsworth BE, Haskell WL, Herrmann SD, et al. 2011 compendium of physical activities: a second update of codes and MET values. Med Sci Sports Exerc. 2011;43:1575–1581.
Panza GA, Weinstock J, Ash GI, et al. Psychometric evaluation of the timeline followback for exercise among college students. Psychol Sport Exerc. 2012;13:779–788.
Troiano RP, Berrigan D, Dodd KW, et al. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40:181–188.
Bangor A, Kortum P. Determining what individual SUS scores mean: adding an adjective rating scale. J Usability Stud. 2009;4:114–123.
Sandelowski M. What's in a name? qualitative description revisited. Res Nurs Health. 2010;33:77–84.
Huizenga NA, Koper JW, De Lange P, et al. A polymorphism in the glucocorticoid receptor gene may be associated with and increased sensitivity to glucocorticoids in vivo. J Clin Endocrinol Metab. 1998;83:144–151.
Jacobson NC, Summers B, Wilhelm S. Digital biomarkers of social anxiety severity: digital phenotyping using passive smartphone sensors. J Med Internet Res. 2020;22:e16875.
Nahum-Shani I, Smith SN, Spring BJ, et al. Just-in-time adaptive interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Ann Behav Med. 2018;52:446–462.
Lightfoot JT, DE Geus EJC, Booth FW, et al. Biological/genetic regulation of physical activity level: consensus from GenBioPAC. Med Sci Sports Exerc. 2018;50:863–873.
Driscoll KA, Corbin KD, Maahs DM, et al.; Advancing Care for Type 1 Diabetes and Obesity Network ACT1ON. Biopsychosocial aspects of weight management in type 1 diabetes: a review and next steps. Curr Diab Rep. 2017;17:58.
Agarwal S, Schechter C, Gonzalez J, et al. Racial-ethnic disparities in diabetes technology use among young adults with type 1 diabetes. Diabetes Technol Ther. 2021;23:306–313.