A Personalized Mobile Health Program for Type 2 Diabetes During the COVID-19 Pandemic: Single-Group Pre–Post Study

Abstract

Background With increasing type 2 diabetes prevalence, there is a need for effective programs that support diabetes management and improve type 2 diabetes outcomes. Mobile health (mHealth) interventions have shown promising results. With advances in wearable sensors and improved integration, mHealth programs could become more accessible and personalized. Objective The study aimed to evaluate the feasibility, acceptability, and effectiveness of a personalized mHealth-anchored intervention program in improving glycemic control and enhancing care experience in diabetes management. The program was coincidentally implemented during the national-level lockdown for COVID-19 in Singapore, allowing for a timely study of the use of mHealth for chronic disease management. Methods Patients with type 2 diabetes or prediabetes were enrolled from the Singapore Armed Forces and offered a 3-month intervention program in addition to the usual care they received. The program was standardized to include (1) in-person initial consultation with a clinical dietitian; (2) in-person review with a diabetes specialist doctor; (3) 1 continuous glucose monitoring device; (4) access to the mobile app for dietary intake and physical activity tracking, and communication via messaging with the dietitian and doctor; and (5) context-sensitive digital health coaching over the mobile app. Medical support was rendered to the patients on an as-needed basis when they required advice on adjustment of medications. Measurements of weight, height, and glycated hemoglobin A1c (HbA1c) were conducted at 2 in-person visits at the start and end of the program. At the end of the program, patients were asked to complete a short acceptability feedback survey to understand the motivation for joining the program, their satisfaction, and suggestions for improvement. Results Over a 4-week recruitment period, 130 individuals were screened, the enrollment target of 30 patients was met, and 21 patients completed the program and were included in the final analyses; 9 patients were lost to follow-up (full data were not available for the final analyses). There were no differences in the baseline characteristics between patients who were included and excluded from the final analyses (age category: P=.23; gender: P=.21; ethnicity: P>.99; diabetes status category: P=.52, medication adjustment category: P=.65; HbA1c category: P=.69; BMI: P>.99). The 21 patients who completed the study rated a mean of 9.0 out of 10 on the Likert scale for both satisfaction questions. For the Yes-No question on benefit of the program, all of the patients selected “Yes.” Mean HbA1c decreased from 7.6% to 7.0% (P=.004). There were no severe hypoglycemia events (glucose level <3.0 mmol/L) reported. Mean weight decreased from 76.8 kg to 73.9 kg (P<.001), a mean decrease of 3.5% from baseline weight. Mean BMI decreased from 27.8 kg/m2 to 26.7 kg/m2 (P<.001). Conclusions The personalized mHealth program was feasible, acceptable, and produced significant reductions in HbA1c (P=.004) and body weight (P<.001) in individuals with type 2 diabetes. Such mHealth programs could overcome challenges posed to chronic disease management by COVID-19, including disruptions to in-person health care access.

“Wearables on vogue”: a scoping review on wearables on physical activity and sedentary behavior during COVID-19 pandemic

“…Heterogeneity in the study types was observed as follows: cross-sectional (n = 9; 53%), longitudinal (n = 4; 24%), retrospective (n = 1; 6%), narrative review (n = 1; 6%) and pilot non-randomized studies (n = 2; 12%). Similar heterogeneity was found in participants too in the included studies: pregnant women [27], elderly [24,26], office workers [21], diabetes [18] and children [25]. All the studies were from high-income countries.…”

“…From the evidence included, we found a significant increase in the use of wearables to improve physical activity during the confinement or lockdown periods [21,24]. There is a convincing evidence to show that there is a significant decrease in physical activity and increased sedentary time in people around the world when judged objectively using wearables [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. Figure 2 represents the summary of our findings.…”

“…Our review findings concur with the results of the above review, which concluded that physical activity and active time could be improved significantly with wearable devices. In our review, most of the included studies involved healthy adults and four studies investigated physical activity changes in pregnant women [27], asthmatic children [25] and diabetes population [18,24]. We could also find a trend of studies exploring the use of wearable technology in the elderly for improving self-efficacy and behavioral change for physical activity promotion [24,26].…”

“…The mean follow-up period of longitudinal studies was 21 weeks [18,20,21,24,26,27]. The average reduction in the daily step count after the pandemic compared to before the pandemic was 2812 steps/day.…”

“…Cloud computing and artificial intelligence have leveraged healthcare through wearable sensors, increasing health monitoring and medical automation for speedy diagnosis, including COVID-19 [33]. The above facts are reciprocated with double-fold increase in utilization of wearable devices to self-monitor physical behavior during this pandemic [16][17][18][19][20][21][22][23][24][25][26][27][28]34]. Although found to be less valid and reliable compared to research-based accelerometers, these wearables provide an opportunity for its end users to self-monitor their physical activity levels, energy expenditure, sleep, sitting time and create a framework for personalized prevention [34].…”