Crowd Profile: Research and Review on Diabetes Mellitus Health Management

DOI: https://doi.org/10.62517/jmhs.202405416

Author(s)

Longfeng Weng, Zhongyan Lin*

Affiliation(s)

International Digital Economy College, Minjiang University, Fuzhou, Fujian, China *Corresponding Author.

Abstract

Diabetes Mellitus is a paradigmatic case of long-term care, being one of the most prevalent chronic diseases worldwide, with millions of patients with no cure. Health management is pivotal in addressing diabetes; however, the lack of personalized and tailored diabetes intervention and self-care management strategies has prevented patients from maximizing health outcomes. The crowd profile technique, an effective tool for user analysis, combines artificial intelligence and big data analytics to provide diabetes risk prediction, personalized health management, and digital consultation services for patients with diabetes. This study reviews the current research on the application of the crowd profile in diabetes health management, highlighting the potential benefits and challenges associated with crowd profile in diabetes management. The findings underscore the critical need for integrating crowd profile into healthcare systems to enhance the quality and effectiveness of diabetes health management.

Keywords

Crowd Profile; Health Management; Diabetes Mellitus; Risk Prediction; Digital Consultation

References

[1] Mingzhen Li, Zhang Bing, Guo Lichuan, et al. Portrait for Type 2 Diabetes with Goal-Achieved HbA1c Using Digital Diabetes Care Model: A Real-World 12-Month Study from China. Patient preference and adherence, 2023, 172227-2235. [2] Jinli Liu, Liu Min, Chai Zhonglin, et al. Projected Rapid Growth in Diabetes Disease Burden and Economic Burden in China: A Spatio-temporal Study from 2020 to 2030. The Lancet Regional Health - Western Pacific, 2023, 33(100700). [3] George Moschonis, Siopis George, Jung Jenny, et al. Effectiveness, Reach, Uptake, and Feasibility of Digital Health Interventions for Adults with Type 2 Diabetes: A Systematic Review and Meta-analysis of Randomised Controlled Trials. The Lancet Digital Health, 2023, 5(3): e125-e143. [4] Sebastian Stevens, Gallagher Susan, Andrews Tim, et al. The Effectiveness of Digital Health Technologies for Patients with Diabetes Mellitus: A Systematic Review. Frontiers in Clinical Diabetes and Healthcare, 2022, 3. [5] Iris-Ten Klooster, Image Author-Orcid, Wentzel Jobke, et al. Personas for Better Targeted eHealth Technologies: User-Centered Design Approach. JMIR Hum Factors, 2022, 9(1): E24172. [6] Sara-Laureen Bartels, Taygar Afra-S, Johnsson Sophie-I, et al. Using Personas in the Development of eHealth Interventions for Chronic Pain: A Scoping Review and Narrative Synthesis. Internet Interventions, 2023, 32100619. [7] Z Guan, Li H, Liu R, et al. Artificial Intelligence in Diabetes Management: Advancements, Opportunities, and Challenges. Cell Rep Med, 2023, 4(10): 101213. [8] Shaogang Gong, Loy Chen-Change, Chen Ke, et al. Crowd Counting and Profiling: Methodology and Evaluation. New York, NY: Springer New York, 2013: 347-382. [9] Muhammad-Exell Febrian, Ferdinan Fransiskus-Xaverius, Sendani Gustian-Paul, et al. Diabetes Prediction Using Supervised Machine Learning. Procedia Computer Science, 2023, 21621-30. [10] Rashi Rastogi, Bansal Mamta. Diabetes prediction Model Using Data Mining Techniques. Measurement: Sensors, 2023, 25100605. [11] G Fico, Martinez-Millana A, Leuteritz J-P, et al. User Centered Design to Improve Information Exchange in Diabetes Care Through eHealth: Results from a Small Scale Exploratory Study. J Med Syst, 2019, 44(1): 2. [12] Mi-Kyoung Cho, Kim Mi-Young. Self-Management Nursing Intervention for Controlling Glucose among Diabetes: A Systematic Review and Meta-Analysis. International Journal of Environmental Research and Public Health, 2021, 18(23): 12750. [13] Chollette-C Olisah, Smith Lyndon, Smith Melvyn. Diabetes Mellitus Prediction and Diagnosis from a Data Preprocessing and Machine Learning Perspective. Computer Methods and Programs in Biomedicine, 2022, 220106773. [14] Bin Sheng, Guan Zhouyu, Lim Lee-Ling, et al. Large Language Models for Diabetes Care: Potentials and Prospects. Science Bulletin, 2024, 69(5): 583-588. [15] Rosanna O Ciani, Petracca Francesco, Svae Liv, et al. Which Behaviour Change Techniques Work Best for Diabetes Self-management Mobile Apps? Results from a Systematic Review and Meta-analysis of Randomised Controlled Trials. EBioMedicine, 2024, 103105091. [16] N Galliford, Yin K, Blandford A, et al. Patient Work Personas of Type 2 Diabetes-A Data-Driven Approach to Persona Development and Validation. Front Digit Health, 2022, 4838651. [17] Michelle-L Litchman, Walker Heather-R, Fitzgerald Caroline, et al. Patient-Driven Diabetes Technologies: Sentiment and Personas of the #WeAreNotWaiting and #OpenAPS Movements. Journal of Diabetes Science and Technology, 2020, 14(6): 990-999. [18] A-K Bohm, Jensen M-L, Sorensen M-R, et al. Real-World Evidence of User Engagement with Mobile Health for Diabetes Management: Longitudinal Observational Study. JMIR Mhealth Uhealth, 2020, 8(11): e22212. [19] Stephen-R Ali, Dobbs Thomas-D, Hutchings Hayley-A, et al. Using ChatGPT to Write Patient Clinic Letters. The Lancet Digital Health, 2023, 5(4): e179-e181. [20] S Amagai, Pila S, Kaat A-J, et al. Challenges in Participant Engagement and Retention Using Mobile Health Apps: Literature Review. J Med Internet Res, 2022, 24(4): e35120. [21] Signe-Marie Cleveland, Haddara Moutaz. Internet of Things for Diabetics: Identifying Adoption Issues. Internet of Things, 2023, 22100798.