Published on in Vol 7, No 4 (2019): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13204, first published .
Usage Patterns of GlucoNote, a Self-Management Smartphone App, Based on ResearchKit for Patients With Type 2 Diabetes and Prediabetes

Usage Patterns of GlucoNote, a Self-Management Smartphone App, Based on ResearchKit for Patients With Type 2 Diabetes and Prediabetes

Usage Patterns of GlucoNote, a Self-Management Smartphone App, Based on ResearchKit for Patients With Type 2 Diabetes and Prediabetes

Journals

  1. Inomata T, Sung J, Nakamura M, Fujisawa K, Muto K, Ebihara N, Iwagami M, Fujio K, Okumura Y, Okano M, Murakami A. New medical big data for P4 medicine on allergic conjunctivitis. Allergology International 2020;69(4):510 View
  2. Waki K, Sankoda A, Amano S, Ogawa M, Ohe K. Responding to COVID-19: Agile Use of Information Technology to Serve Patients With Diabetes. Journal of Diabetes Science and Technology 2020;14(4):807 View
  3. Harada N, Harada S, Ito J, Atsuta R, Hori S, Takahashi K. Mobile Health App for Japanese Adult Patients With Asthma: Clinical Observational Study. Journal of Medical Internet Research 2020;22(8):e19006 View
  4. Lalloo C, Pham Q, Cafazzo J, Stephenson E, Stinson J. A ResearchKit app to deliver paediatric electronic consent: Protocol of an observational study in adolescents with arthritis. Contemporary Clinical Trials Communications 2020;17:100525 View
  5. Hirano R, Yamaguchi S, Waki K, Kimura Y, Chin K, Nannya Y, Nangaku M, Kadowaki T, Ohe K. Willingness of Patients Prescribed Medications for Lifestyle-Related Diseases to Use Personal Health Records: Questionnaire Study. Journal of Medical Internet Research 2020;22(5):e13866 View
  6. Rhee S, Kim C, Shin D, Steinhubl S. Present and Future of Digital Health in Diabetes and Metabolic Disease. Diabetes & Metabolism Journal 2020;44(6):819 View
  7. Inomata T, Sung J, Nakamura M, Iwagami M, Okumura Y, Iwata N, Midorikawa-Inomata A, Fujimoto K, Eguchi A, Nagino K, Fujio K, Miura M, Shokirova H, Murakami A. Using Medical Big Data to Develop Personalized Medicine for Dry Eye Disease. Cornea 2020;39(1):S39 View
  8. Wyner Z, Dublin S, Chambers C, Deval S, Herzig-Marx C, Rao S, Rauch A, Reynolds J, Brown J, Martin D. The FDA MyStudies app: a reusable platform for distributed clinical trials and real-world evidence studies. JAMIA Open 2021;3(4):500 View
  9. Li J, Yuan P, Hu X, Huang J, Cui L, Cui J, Ma X, Jiang T, Yao X, Li J, Shi Y, Bi Z, Wang Y, Fu H, Wang J, Lin Y, Pai C, Guo X, Zhou C, Tu L, Xu J. A tongue features fusion approach to predicting prediabetes and diabetes with machine learning. Journal of Biomedical Informatics 2021;115:103693 View
  10. Pelle T, van der Palen J, de Graaf F, van den Hoogen F, Bevers K, van den Ende C. Use and usability of the dr. Bart app and its relation with health care utilisation and clinical outcomes in people with knee and/or hip osteoarthritis. BMC Health Services Research 2021;21(1) View
  11. Xie Q, Hu X, Wang Y, Peng J, Cheng L. Exploration of the health needs of patients with poorly controlled type 2 diabetes using a user-centred co-production approach in the area of mHealth: an exploratory sequential mixed-method protocol. BMJ Open 2022;12(12):e063814 View
  12. Senoo K, Miki T, Ohkura T, Iwakoshi H, Nishimura T, Shiraishi H, Teramukai S, Matoba S. A Smartphone App to Improve Oral Anticoagulation Adherence in Patients With Atrial Fibrillation: Prospective Observational Study. JMIR mHealth and uHealth 2022;10(1):e30807 View
  13. Senoo K, Yukawa A, Ohkura T, Iwakoshi H, Nishimura T, Shimoo S, Inoue K, Sakatani T, Kakita K, Hattori T, Kitajima H, Nakai K, Nishiuchi S, Nakata M, Teramukai S, Shiraishi H, Matoba S. The impact of home electrocardiograph measurement rate on the detection of atrial fibrillation recurrence after ablation: A prospective multicenter observational study. IJC Heart & Vasculature 2023;44:101177 View
  14. Huang H, Aschettino S, Lari N, Lee T, Rosenberg S, Ng X, Muthuri S, Bakshi A, Bishop K, Ezzeldin H. A Versatile and Scalable Platform That Streamlines Data Collection for Patient-Centered Studies: Usability and Feasibility Study. JMIR Formative Research 2022;6(9):e38579 View
  15. Kondo M, Okitsu T, Waki K, Yamauchi T, Nangaku M, Ohe K. Effect of Information and Communication Technology–Based Self-management System DialBeticsLite on Treating Abdominal Obesity in the Specific Health Guidance in Japan: Randomized Controlled Trial. JMIR Formative Research 2022;6(3):e33852 View
  16. Bardram J, Cramer-Petersen C, Maxhuni A, Christensen M, Bækgaard P, Persson D, Lind N, Christensen M, Nørgaard K, Khakurel J, Skinner T, Kownatka D, Jones A. DiaFocus: A Personal Health Technology for Adaptive Assessment in Long-Term Management of Type 2 Diabetes. ACM Transactions on Computing for Healthcare 2023;4(2):1 View
  17. Bührmann L, Van Daele T, Rinn A, De Witte N, Lehr D, Aardoom J, Loheide-Niesmann L, Smit J, Riper H. The feasibility of using Apple's ResearchKit for recruitment and data collection: Considerations for mental health research. Frontiers in Digital Health 2022;4 View
  18. Ferreira Gomes M, da Silva Fernandes C, Aguiar de Sousa M, Bráz da Silva R, da Costa Silva I, Moreira Barros L. Aplicativos móveis direcionados aos idosos para autogerenciamento do cuidado: revisão de escopo. Revista Cuidarte 2023;14(1) View
  19. Kawai Y, Waki K, Yamaguchi S, Shibuta T, Miyake K, Kimura S, Toyooka T, Nakajima R, Uneda K, Wakui H, Tamura K, Nangaku M, Ohe K. The Use of Information and Communication Technology–Based Self-management System DialBeticsLite in Treating Abdominal Obesity in Japanese Office Workers: Prospective Single-Arm Pilot Intervention Study. JMIR Diabetes 2022;7(4):e40366 View
  20. Gosak L, Pajnkihar M, Stiglic G. The Impact of Mobile Health Use on the Self-care of Patients With Type 2 Diabetes: Protocol for a Randomized Controlled Trial. JMIR Research Protocols 2022;11(6):e31652 View
  21. König L, Van Emmenis M, Nurmi J, Kassavou A, Sutton S. Characteristics of smartphone-based dietary assessment tools: a systematic review. Health Psychology Review 2022;16(4):526 View
  22. Jungnickel T, von Jan U, Albrecht U. Implementation of Mobile Psychological Testing on Smart Devices: Evaluation of a ResearchKit-Based Design Approach for the Implicit Association Test. Frontiers in Digital Health 2022;4 View
  23. Shibata S, Hoshide S. Current situation of telemedicine research for cardiovascular risk in Japan. Hypertension Research 2023;46(5):1171 View
  24. Barker M, Chauhan R, Davies M, Brough C, Northern A, Stribling B, Schreder S, Khunti K, Hadjiconstantinou M. User Retention and Engagement in the Digital-Based Diabetes Education and Self-Management for Ongoing and Newly Diagnosed (myDESMOND) Program: Descriptive Longitudinal Study. JMIR Diabetes 2023;8:e44943 View
  25. Sze W, Waki K, Enomoto S, Nagata Y, Nangaku M, Yamauchi T, Ohe K. StepAdd: A personalized mHealth intervention based on social cognitive theory to increase physical activity among type 2 diabetes patients. Journal of Biomedical Informatics 2023;145:104481 View
  26. Hani S, Saleh M. Using Real-Time, Partially Automated Interactive System to Interpret Patient’s Data; Helping The Patient To Achieve Diabetic Self-Management: A Rapid Literature Review. Current Diabetes Reviews 2023;19(5) View
  27. Waki K, Tsurutani Y, Waki H, Enomoto S, Kashiwabara K, Fujiwara A, Orime K, Kinguchi S, Yamauchi T, Hirawa N, Tamura K, Terauchi Y, Nangaku M, Ohe K. Efficacy of StepAdd, a Personalized mHealth Intervention Based on Social Cognitive Theory to Increase Physical Activity Among Patients With Type 2 Diabetes Mellitus: Protocol for a Randomized Controlled Trial. JMIR Research Protocols 2024;13:e53514 View
  28. Takahashi A, Ishii M, Kino Y, Sasaki K, Matsui T, Arakawa K, Kunisaki M. Effectiveness of a Lifestyle Improvement Support App in Combination with a Wearable Device in Japanese People with Type 2 Diabetes Mellitus: STEP-DM Study. Diabetes Therapy 2024 View

Books/Policy Documents

  1. Niwa M, Hara Y. Mobile Health (mHealth). View