| Literature DB >> 30735147 |
Yiyu Zhang1,2,3, Xia Li1,2,3, Shuoming Luo1,2,3, Chaoyuan Liu4, Yuting Xie1,2,3, Jia Guo5, Fang Liu1, Zhiguang Zhou1,2,3.
Abstract
BACKGROUND: The diabetes disease burden in China is heavy, and mobile apps have a great potential for diabetes management. However, there is a lack of investigation of diabetes app use among Chinese diabetes patients and diabetologists. The perspectives and attitudes of diabetes patients and diabetologists regarding diabetes apps are also unclear.Entities:
Keywords: diabetes mellitus; mobile applications; surveys and questionnaires
Mesh:
Year: 2019 PMID: 30735147 PMCID: PMC6384538 DOI: 10.2196/12658
Source DB: PubMed Journal: JMIR Mhealth Uhealth ISSN: 2291-5222 Impact factor: 4.773
Figure 1Distribution of the diabetic patient sample in China by province.
Characteristics of patients with diabetes.
| Characteristics | T1DMa (n=473) | T2DMb (n=733) | Totalc (N=1276) | ||
| Adults (n=295) | Juveniles (n=178) | ||||
| Male | 115 (39.0) | 81 (45.5) | 426 (58.1) | 642 (50.31) | |
| Age (years), mean (SD) | 33.5 (11.9) | 10.3 (4.2) | 52.2 (12.0) | 41.3 (18.5) | |
| Disease duration (years), mean (SD) | 7.9 (8.0) | 3.0 (3.2) | 7.7(6.8) | 6.8(6.9) | |
| Urban | 206 (69.8) | 110 (61.8) | 572 (78.0) | 933 (73.12) | |
| Rural | 89 (30.2) | 68 (38.2) | 161 (22.0) | 343 (26.88) | |
| Junior middle school or below | 55 (18.6) | —d | 193 (26.3) | 422 (33.07) | |
| High school | 93 (31.5) | — | 244 (33.3) | 373 (29.23) | |
| University or above | 147 (49.8) | — | 296 (40.4) | 481 (37.70) | |
| Oral medicine | 14 (4.7) | 1 (0.6) | 379 (51.7) | 416 (32.60) | |
| Insulin injection | 196 (66.4) | 128 (71.9) | 281 (38.3) | 636 (49.84) | |
| Insulin pump | 83 (28.1) | 49 (27.5) | 9 (1.2) | 144 (11.29) | |
| Untreated | 2 (0.7) | 0 | 64 (8.7) | 80 (6.27) | |
| Student | 44 (14.9) | — | 3 (0.4) | 206 (16.14) | |
| Institutional staff | 45 (15.3) | — | 115 (15.7) | 171 (13.40) | |
| Employee of state-owned enterprise | 19 (6.4) | — | 83 (11.3) | 109 (8.54) | |
| Employee of foreign or private company | 43 (14.6) | — | 69 (9.4) | 121 (9.48) | |
| Private enterprise owner or self-employed | 26 (8.8) | — | 77 (10.5) | 113 (8.86) | |
| Retired | 19 (6.4) | — | 206 (28.1) | 231 (18.10) | |
| Farmer | 19 (6.4) | — | 75 (10.2) | 103 (8.07) | |
| Unemployed | 47 (15.9) | — | 70 (9.5) | 131 (10.27) | |
| Others | 33 (11.2) | — | 35 (4.8) | 9 (7.05) | |
aT1DM: type 1 diabetes mellitus.
bT2DM: type 2 diabetes mellitus.
cTotal including patients with T1DM, patients with T2DM, 27 patients with gestational diabetes, and 43 patients with an unknown type of diabetes.
dIndicates that there is no value.
Factors associated with app use by multivariate logistic regression analysis (N=1008).
| Characteristics | App usage rate, n (%) | Adjusted odds ratio (95% CI) | ||
| 18-39a | 97 (27.2) | —b | — | |
| 40-59 | 47 (10.7) | 0.42 (0.27-0.65) | <.001 | |
| ≥60 | 19 (9.0) | 0.40 (0.22-0.72) | .002 | |
| ¥<50,000a | 34 (10.9) | — | — | |
| ¥50,000-100,000 | 50 (16.1) | 1.38 (0.84-2.28) | .20 | |
| ¥>100,000 | 79 (20.5) | 1.73 (1.07-2.81) | .03 | |
| T2DMa,d | 78 (11.2) | — | — | |
| T1DMe | 76 (29.7) | 2.0 (1.30-3.05) | .001 | |
| Gestational diabetes | 3 (11.1) | 0.47 (0.13-1.67) | .25 | |
| Unknown type | 6 (16.2) | 0.15 (0.45-2.93) | .77 | |
| The other 20 provincesa | 96 (14.1) | — | — | |
| 10 provinces of the top GDPf per capita | 67 (20.5) | 1.5 (1.04-2.17) | .03 | |
| Junior middle school or belowa | 18 (7.6) | — | — | |
| High school | 49 (15.0) | 1.85 (1.02-3.37) | .04 | |
| University or above | 96 (21.7) | 2.40 (1.34-4.25) | .003 | |
aReference group.
bNot applicable.
c90 samples with missing data on family income were excluded from the logistic regression analysis.
dT2DM: type 2 diabetes mellitus.
eT1DM: type 1 diabetes mellitus.
fGDP: gross domestic product.
App functions considered to be most important by patients with both T1DM and T2DM.
| Features | T1DMa (N=473), n (%) | T2DMb (N=733), n (%) | |
| Diabetes diaries | 109 (23.0) | 192 (26.2) | .22 |
| Doctor-patient communication | 151 (31.9) | 299 (40.8) | .002 |
| Diabetes education knowledge | 54 (11.2) | 86 (11.7) | .87 |
| Peer support | 13 (2.7) | 10 (1.4) | .09 |
| Insulin dose calculator | 51 (10.8) | 11 (1.5) | <.001 |
| Abnormal blood sugar reminder | 60 (12.7) | 85 (11.6) | .57 |
| Blood sugar test reminder | 8 (1.7) | 15 (2.0) | .66 |
| Others | 27 (5.7) | 35 (4.8) | .47 |
aT1DM: type 1 diabetes mellitus.
bT2DM: type 2 diabetes mellitus.
cA Chi-square test was used to calculate P values.
Figure 2Importance of different app functions reported by patients with diabetes (N=1276).
Differences in the selection of diabetes apps between patients with type 1 diabetes mellitus (T1DM) and patients with type 2 diabetes mellitus (T2DM).
| App name | T1DM (N=108), n (%) | T2DM (N=79), n (%) | |
| Welltang (Shanghai Geping Information Technology Co, Ltd) | 24 (22.2) | 5 (6.3) | .003 |
| Diabetes Circle (Aibaowei Biotechnology Co, Ltd) | 30 (27.8) | 3 (3.8) | <.001 |
| Control Diabetes (Fuzhou Kangwei Network Technology Co, Ltd) | 1 (0.9) | 5 (6.3) | .08 |
| Diabetes Doctor (Shanghai Huima Medical Technology Co, Ltd) | 6 (5.6) | 14 (17.7) | .008 |
| Diabetes Nurse (Beijing Dnurse Technology Co,Ltd) | 28 (25.9) | 34 (43.0) | .01 |
| Others | 19 (17.6) | 18 (22.8) | .38 |
aA Chi-square test was used to calculate the P values.
Characteristics of the surveyed diabetologists (N=608).
| Characteristics | Statistics | |
| Male | 197 (32.4) | |
| Female | 411 (67.6) | |
| ≤30 | 99 (16.3) | |
| 30-39 | 274 (45.1) | |
| 40-49 | 162 (26.6) | |
| 50-59 | 67 (11.0) | |
| ≥60 | 6 (1.0) | |
| Resident | 141 (23.2) | |
| Attending specialist | 239 (39.3) | |
| Associate chief doctor | 125 (20.6) | |
| Chief doctor | 103 (16.9) | |
| Tertiary hospital | 419 (68.9) | |
| Secondary hospital or lower | 189 (31.0) | |
Figure 3App awareness rate, recommendation rate, and usage among different age groups of diabetologists (N=608).
Figure 4Diabetes app functions considered to be most important by diabetologists (N=608).