Literature DB >> 23844570

Active assistance technology reduces glycosylated hemoglobin and weight in individuals with type 2 diabetes: results of a theory-based randomized trial.

Anna-Leena Orsama1, Jaakko Lähteenmäki, Kari Harno, Minna Kulju, Eva Wintergerst, Holly Schachner, Pat Stenger, Juha Leppänen, Hannu Kaijanranta, Ville Salaspuro, William A Fisher.   

Abstract

BACKGROUND: Type 2 diabetes is an individual health challenge requiring ongoing self-management. Remote patient reporting of relevant health parameters and linked automated feedback via mobile telephone have potential to strengthen self-management and improve outcomes. This research involved development and evaluation of a mobile telephone-based remote patient reporting and automated telephone feedback system, guided by health behavior change theory, aimed at improving self-management and health status in individuals with type 2 diabetes. SUBJECTS AND METHODS: This research comprised a randomized controlled trial. Inclusion criteria were diagnosis of type 2 diabetes, elevated glycosylated hemoglobin (HbA1c) levels (range, 6.5-11%) or use of oral diabetes medication, and 30-70 years of age. Intervention subjects (n=24) participated in remote patient reporting of health status parameters and linked health behavior change feedback. Control participants (n=24) received standard of care including diabetes education and healthcare provider counseling. Patients were followed for approximately 10 months.
RESULTS: Intervention participants achieved, compared with controls and controlling for baseline, a significantly greater mean reduction in HbA1c of -0.40% (95% confidence interval [CI] -0.67% to -0.14%) versus 0.036% (95% CI -0.23% to 0.30%) (P<0.03) and significantly greater weight reduction of -2.1 kg (95% CI -3.6 to -0.6 kg) versus 0.4 kg (95% CI -1.1 to 1.9 kg). Nonsignificant trends for greater intervention compared with control improvement in systolic and diastolic blood pressure were observed.
CONCLUSIONS: Sophisticated information technology platforms for remote patient reporting linked with theory-based health behavior change automated feedback have potential to improve patient outcomes in type 2 diabetes and merit scaled-up research efforts.

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Year:  2013        PMID: 23844570     DOI: 10.1089/dia.2013.0056

Source DB:  PubMed          Journal:  Diabetes Technol Ther        ISSN: 1520-9156            Impact factor:   6.118


  38 in total

Review 1.  Digital health technology and mobile devices for the management of diabetes mellitus: state of the art.

Authors:  Rongzi Shan; Sudipa Sarkar; Seth S Martin
Journal:  Diabetologia       Date:  2019-04-08       Impact factor: 10.122

Review 2.  Current Science on Consumer Use of Mobile Health for Cardiovascular Disease Prevention: A Scientific Statement From the American Heart Association.

Authors:  Lora E Burke; Jun Ma; Kristen M J Azar; Gary G Bennett; Eric D Peterson; Yaguang Zheng; William Riley; Janna Stephens; Svati H Shah; Brian Suffoletto; Tanya N Turan; Bonnie Spring; Julia Steinberger; Charlene C Quinn
Journal:  Circulation       Date:  2015-08-13       Impact factor: 29.690

3.  Mobile phone intervention and weight loss among overweight and obese adults: a meta-analysis of randomized controlled trials.

Authors:  Fangchao Liu; Xiaomu Kong; Jie Cao; Shufeng Chen; Changwei Li; Jianfeng Huang; Dongfeng Gu; Tanika N Kelly
Journal:  Am J Epidemiol       Date:  2015-02-10       Impact factor: 4.897

4.  Automated Feedback Messages With Shichifukujin Characters Using IoT System-Improved Glycemic Control in People With Diabetes: A Prospective, Multicenter Randomized Controlled Trial.

Authors:  Tomoko Kobayashi; Kazuyo Tsushita; Eri Nomura; Akiko Muramoto; Ayako Kato; Yukari Eguchi; Takeshi Onoue; Motomitsu Goto; Shigeki Muto; Hiroshi Yatsuya; Hiroshi Arima
Journal:  J Diabetes Sci Technol       Date:  2019-05-20

Review 5.  Effect of telemedicine on glycated hemoglobin in diabetes: a systematic review and meta-analysis of randomized trials.

Authors:  Labib Imran Faruque; Natasha Wiebe; Arash Ehteshami-Afshar; Yuanchen Liu; Neda Dianati-Maleki; Brenda R Hemmelgarn; Braden J Manns; Marcello Tonelli
Journal:  CMAJ       Date:  2016-10-31       Impact factor: 8.262

6.  Exploring app features with outcomes in mHealth studies involving chronic respiratory diseases, diabetes, and hypertension: a targeted exploration of the literature.

Authors:  Sara Belle Donevant; Robin Dawson Estrada; Joan Marie Culley; Brian Habing; Swann Arp Adams
Journal:  J Am Med Inform Assoc       Date:  2018-10-01       Impact factor: 4.497

Review 7.  A Systematic Review of Application and Effectiveness of mHealth Interventions for Obesity and Diabetes Treatment and Self-Management.

Authors:  Youfa Wang; Hong Xue; Yaqi Huang; Lili Huang; Dongsong Zhang
Journal:  Adv Nutr       Date:  2017-05-15       Impact factor: 8.701

8.  An Analysis of Diabetes Mobile Applications Features Compared to AADE7™: Addressing Self-Management Behaviors in People With Diabetes.

Authors:  Qing Ye; Uzma Khan; Suzanne A Boren; Eduardo J Simoes; Min Soon Kim
Journal:  J Diabetes Sci Technol       Date:  2018-02-01

9.  Personalized Type 2 Diabetes Management Using a Mobile Application Integrated with Electronic Medical Records: An Ongoing Randomized Controlled Trial.

Authors:  Eun-Young Lee; Jae-Seung Yun; Seon-Ah Cha; Sun-Young Lim; Jin-Hee Lee; Yu-Bae Ahn; Kun-Ho Yoon; Seung-Hyun Ko
Journal:  Int J Environ Res Public Health       Date:  2021-05-16       Impact factor: 3.390

10.  Usability Evaluation of Four Top-Rated Commercially Available Diabetes Apps for Adults With Type 2 Diabetes.

Authors:  Helen N C Fu; Rubina F Rizvi; Jean F Wyman; Terrence J Adam
Journal:  Comput Inform Nurs       Date:  2020-06       Impact factor: 2.146

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