Digital health technologies for diabetes-centered five-condition co-management in China: Theoretical foundations, practical experience, and technical challenges

Table 1 Evidence map of digital interventions in the management of diabetes complicated with multiple metabolic abnormalities in China and internationally

Research region	Study design	Sample size	Population characteristics	Intervention form	Follow-up duration	Primary outcome	Risk of bias/Limitations	Ref.
China	Quasi-experiment (pre-post control design)	189	T2DM patients complicated with hypertension	AI-powered remote monitoring system + personalized medical guidance	9 months	HbA1c decreased by 0.6% on average, blood pressure control rate increased by 36%, fasting plasma glucose reduced by 43.8 mg/dL	(1) No randomization (potential selection bias); (2) Loss to follow-up (15% attrition rate); and (3) Unblinded design (performance bias)	[118]
China	Quasi-experiment (single-arm intervention with historical control)	156	Primary T2DM patients (with DR risk)	Multimodal AI system (fundus images + clinical data) + referral management	6 months	DR screening accuracy reached the level of professional ophthalmologists, referral compliance was significantly improved, HbA1c decreased by 0.8% on average	(1) Historical control may introduce confounding bias; (2) Limited to primary care settings; and (3) No assessment of DR progression (only screening accuracy)	[119]
International	Parallel-group randomized clinical trial	246	T2DM combined with metabolic syndrome patients	APP-based self-management (blood glucose/diet/exercise recording) + healthy behavior rewards	12 months	HbA1c decreased by 0.4% (mean difference vs usual care), body weight decreased by 3.0 kg, LDL-C decreased by 0.38 mmol/L, intervention compliance increased by 32%	(1) Selection bias (strict inclusion/exclusion criteria); (2) Reward mechanism may introduce performance bias; and (3) Lack of subgroup analysis by age/gender	[120]
International	Quasi-experiment (non-randomized controlled trial)	112	T2DM patients complicated with proatherogenic dyslipidemia	Multimodal remote monitoring (blood glucose + lipid + postprandial glucose) + telehealth consultation	10 months	HbA1c decreased by 0.7% on average, LDL-C reduced by 0.45 mmol/L, postprandial glucose variability decreased by 52.6 mg/dL	(1) Small sample size (limited statistical power); (2) Single-center design (geographic bias); and (3) No blinding of outcome assessors	[121]
International	Real-world observational study	418	T2DM patients with multiple metabolic abnormalities	AI-integrated management platform (medication + diet + exercise + metabolic prediction)	15 months	HbA1c decreased by 1.3% on average, body weight reduced by 5.1 kg, metabolic index compliance rate increased by 48%, insulin sensitivity improved	(1) Selection bias (voluntary participation); (2) Lack of control group (cannot rule out temporal trends); and (3) Technical threshold for platform use (excludes elderly/illiterate patients)	[122]

T2DM: Type 2 diabetes mellitus; AI: Artificial intelligence; HbA1c: Glycated hemoglobin; DR: Diabetic retinopathy; LDL-C: Low-density lipoprotein cholesterol.

Table 2 Digital health monitorable indicators

Core monitoring indicators	Trigger thresholds (refer to 2024 CDS guidelines)	Intervention actions	Responsible subjects
HbA1c	≥ 7.0% or increase ≥ 0.5% within 3 months	(1) Adjust hypoglycemic regimen (prioritize GLP-1RA); (2) Strengthen diet/exercise intervention; and (3) Increase monitoring frequency to ≥ 3 times/week	Endocrinologist + dietitian
Systolic blood pressure/diastolic blood pressure	≥ 130/80 mmHg	(1) Initiate or adjust antihypertensive drugs (synergistic with hypoglycemic drugs); (2) Restrict sodium intake (< 5 g/day); and (3) Recommend aerobic exercise	Physician + health management nurse
LDL-C	High risk ≥ 1.8 mmol/L; medium-low risk ≥ 2.6 mmol/L	(1) Initiate statins; (2) Adjust diet structure (reduce saturated fat); and (3) Monitor liver function	Physician + dietitian
Uric acid	Male ≥ 420 μmol/L; female ≥ 360 μmol/L	(1) Restrict high-purine foods in diet; (2) Increase water intake (≥ 2000 mL/day); and (3) Initiate uric acid-lowering drugs if necessary	Physician + nurse
Body weight/BMI	BMI ≥ 28 kg/m² or increase ≥ 5% within 3 months	(1) Use weight loss-oriented hypoglycemic drugs (GLP-1RA); (2) Formulate individualized exercise prescription (150 minutes moderate-intensity exercise/week); and (3) Diet calorie control	Physician + exercise therapist + dietitian
Waist circumference	Male ≥ 90 cm; female ≥ 85 cm	(1) Core strength training; and (2) Reduce risk factors related to abdominal obesity (sedentary/high-sugar diet)	Exercise therapist + dietitian
Urinary microalbumin/creatinine ratio	≥ 30 mg/g	(1) Prefer SGLT2i/GLP-1RA; (2) Control blood pressure < 130/80 mmHg; and (3) Recheck every 3 months	Physician + nurse

Trigger thresholds strictly follow the requirements of the “China Guidelines for the Prevention and Treatment of Diabetes (2024 Edition)” and the Healthy China Action Plan. The responsible subjects clarify the division of labor within the diabetes center to ensure the intervention closed loop. HbA1c: Glycated hemoglobin; GLP-1RA: Glucagon-like peptide-1 receptor agonist; LDL-C: Low-density lipoprotein cholesterol; BMI: Body mass index; SGLT2i: Sodium-glucose cotransporter-2 inhibitor; CDS: Chinese Diabetes Society.