This clinical trial followed a one-group pre- and posttest design. The study period was June-October 2020, during which a 3-month intervention was applied to all participants. The initial study population included 215 individuals who volunteered to participate and who had taken part in the 2020 National Health Insurance Service (NHIS) health examination. The inclusion criteria were as follows: Korean adults aged ≥20 years, individuals who received NHIS health examinations at Ilsan Hospital in 2020, individuals able to use smart devices, and individuals without cancer or severe rare diseases. After the preliminary eligibility assessment, 45 individuals were excluded. Then, the following individuals were excluded from the final study population: 61 individuals who did not attend the postintervention evaluation (posttest), 40 individuals who did not access the application during the intervention period, and 6 individuals who withdrew their consent to participate. Ultimately, the final analysis was performed using data from 63 participants (30 males and 33 females; Figure 1).

This study was approved by the institutional review board of NHIS Ilsan Hospital (NHIMC 2020-02-002). All participants received verbal explanations regarding the study and submitted their prior written consent. The study was conducted using only data without personal identification information. Participants in the study were not rewarded for their participation.

All items were measured at NHIS Ilsan Hospital before the use of the CDHCS and 3 months thereafter. The step count variable was measured in real time by a mobile phone app, and the analysis was performed using the average of the values from the first and last month.

The variables collected for data analysis were as follows: height, weight, WC, SBP, and diastolic blood pressure (DBP). Blood analysis was performed using samples collected in the morning after at least 8 hours of fasting. The analyzed markers were FBS, TG, and HDL-C [30].

The groups were divided by evaluating how many of the following 5 criteria were applied: (1) obesity (WC): more than 90 cm for males and more than 85 cm for females; (2) low HDL-C: less than 40 mg/dL for males and less than 50 mg/dL for females or taking medication for this condition; (3) hypertriglyceridemia (TG): more than 150 mg/dL or taking medication for this condition; (4) hypertension (SBP/DBP): more than 130/85 mm/Hg or taking medication for this condition; and (5) hyperglycemia (FBS): more than 100 mg/dL or taking medication for this condition. Individuals who meet 3 or more of the above 5 criteria were assigned to the MS group. Individuals who met 1 or 2 were placed in the risk group, and individuals who met none of the criteria were placed in the normal group [30]. We grouped the participants to determine whether the factors related to MS would change and confirmed the distribution of these groups.

MSA refers to the age calculated through biomarkers related to MS and indicates the severity of MS. The application used the following equation to automatically calculate MSA based on the biomarkers of the user and the results were displayed on the screen [31].

This was used to classify each type of MSA to provide differentiated feedback based on the severity of the MS. In addition, the stage was classified using the difference value between the chronological age (CA) and the metabolic syndrome age–chronological age (MSA-CA): more than +1=“good,” between 1 and +1=“average,” less than 1=“poor.”

The following lifestyle variables were measured using a questionnaire survey: frequency of exercise, smoking history, weekly alcohol consumption, sleep duration, stress levels, and dietary habits. Smoking history was investigated by classifying the participants as nonsmokers and current smokers. Alcohol consumption was investigated based on the number of drinks per week and the amount of alcohol consumed per day (glasses) among participants who were identified as drinkers, and the findings were used to calculate the weekly alcohol consumption using the equation “number of drinks in a week (frequency) × amount of alcohol (ml) × alcohol content × 0.785 (specific gravity of alcohol)/100” (Suk, unpublished data, 2018). Sleep duration was categorized into 3 groups: <7, 7-8, and >8 hours of sleep. For stress levels, subjective stress levels were graded on a 5-point Likert scale (1: very low stress; 2: low stress; 3: moderate stress; 4: high stress; 5: very high stress). Dietary habits were arbitrarily categorized as good or bad based on habits related to obesity, hypertension, hyperlipidemia, and diabetes.

The CDHCS used in this study, called Dr Healthing, was developed by the Mediage Research Institute. Dr Healthing provides information regarding the health status of individuals by analyzing the data linked to health checkups from medical institutions and average daily step count data from a Samsung Health app. With respect to the mobile health care contents used, health checkup results from the same sex and age groups were compared and the poorest factors were selected. Then, subjects were assigned to 5 management groups: obesity, blood pressure, blood sugar, lipid, and normal group. Participants were assigned to these groups based on the poorest value of the clinical indicator, meaning participants assigned to the obesity group had WC as their poorest indicator, those in the blood pressure group had the poorest SBP or DBP, those in the blood sugar group has the poorest FBS, and those in the lipid group had the poorest TG or HDL-C. Individuals with no risk factors were assigned to the normal group. According to the customized groups assigned in this manner, health coaching contents were provided 3 times a week: day 1 (Tuesday 2 PM)—nutrition/exercise/mental coaching; day 2 (Thursday 2 PM)—general medical knowledge; and day 3 (Sunday 9 AM)—individualized weekly health report. It was possible to set the target number of steps according to the MSA, and by providing weekly/monthly summary reports, it was possible to provide feedback on health care/nutrition components necessary for each individual on a daily basis, while also being able to check on their own the changes in MSA (Figure 2).

Participants’ characteristics were presented as mean (SD) for continuous variables and frequency and percentage for nominal variables. For comparison between before and after CDHCS use (pre-post comparison), paired t test was used for continuous variables, and a chi-square test was used for nominal variables. The analysis was performed using Python version 3.8.3 software (Python Software Foundation) with statistical significance set to 0.05.

Changes in clinical indicators before and after 3 months were presented as mean (SD). The results showed a decrease in weight, WC, TG, and HDL-C and increases in SBP and DBP after the intervention. All indicators tended to show improvement, although the differences were not statistically significant (Table 1).

The distribution of the risk group increased from 32 (50.8%) to 34 (54%) and that of the MS group decreased from 18 (28.6%) to 16 (25.4%). The mean MSA-CA before CDHCS was 2.20 (6.47) years, which decreased to 1.72 (5.80) years after CDHCS, showing a decrease of 0.48 years in the mean MSA-CA after the intervention. With respect to classification by MSA, the number of participants increased from 6 (9.5%) to 8 (12.7%) in the “good” MSA group and from 30 (47.6%) to 39 (61.9%) in the “average” MSA group, whereas the number of participants decreased from 27 (42.9%) to 16 (25.4%) in the “poor” MSA group (Table 2).

The mean frequency of exercise per week was 4.11 times before the intervention, which increased by 0.27 times to 4.38 times after the intervention. For smoking and drinking status, the number of nonsmokers increased from 58 (92.1%) to 59 (53.7%) and smokers decreased from 5 (7.9%) to 4 (6.3%). The number of nondrinkers decreased from 38 (60.3%) to 32 (50.8%) and drinkers increased from 25 (39.7%) to 31 (49.2%). The mean frequency of alcohol consumption per week decreased from 1.75 to 1.15 times. The number of participants who slept < 7 or > 8 hours decreased from 47 (74.6%) to 37 (58.7%), whereas the number of participants who slept for 7-8 hours increased from 16 (25.4%) to 26 (41.3%). The stress score changed from 3.04 to 3.03 points. The mean step count was 6425 steps in the first month of using CDHCS, which increased by 203 steps to 6628 steps in the third month. Finally, the number of participants with good dietary habits increased by 3 after the intervention (Table 3). While all lifestyle variables, except alcohol consumption, showed a tendency towards improvement, the differences were not statistically significant.

The objective of this pilot study was to verify the effects of using a CDHCS for 3 months on MS risk factors and lifestyle improvement among 63 participants. The results showed a tendency toward improvement in all parameters, except alcohol consumption, but the differences were not statistically significant. In the distribution of MS types, 2 out of 18 participants (11.1%) in the MS group were downgraded by one level to the risk group after the intervention. Of 27 participants, 9 (56.3%) in the “poor” group were upgraded to the “average” or “good” groups, which can be interpreted as a partial improvement in factors affecting MS.

According to previous studies that evaluated the effectiveness of digital health care on patients who are diabetic, patients who received 12 months of untact monitoring and health care coaching showed improvement in test parameters (FBS, hemoglobin A_1c, glycated hemoglobin, total cholesterol, HDL-C, LDL-C, and TG). The results also confirmed significant improvement in clinical indicators in the group that received the service, compared to the control group that did not [19,22]. A previous study on hypertension reported improvement in BP levels after receiving digital health care intervention [21]. Similarly, another study on the effects of 12 weeks of digital health care use on the MS indicators and physical characteristics of MS reported statistically significant improvement in weight, BMI, WC, FBS, cholesterol, and TG, indicating that encouraging physical activity promotion through digital health care had a positive effect on clinical indicators related to MS [32]. Moreover, other MS studies concluded that the severity of MS was improved more effectively by digital health care use [33].

Data for this study were collected in 2020 during the COVID-19 pandemic. At the time, the social distancing policy including restrictions on outdoor activities, visiting sports facilities, and private gatherings was being implemented. These restrictions may amount to a factor affecting the lifestyle and may have influenced the results of this study. Restrictions on outdoor activities naturally caused a decrease in the frequency of exercise and the number of steps. Our results showed a slight increase in the number of steps and frequency of exercise after using the CDHCS, but we expect that if there were no restrictions owing to COVID-19, the results would have changed. Meanwhile, prolongation of such a lifestyle pattern can act as a factor that causes significant levels of stress, depression, and helplessness among modern people facing tremendous changes in their daily lives, a phenomenon referred to as “Corona Blue” [34].

It was difficult to find statistical significance in this pilot study because of several factors. However, this study was significant in that it was the first to analyze the severity of MS and provide the overall health status, and aging to the users in an easy-to-understand manner. While many previous studies on digital health care applied health care coaching using SMS or telephone calls, this study used a mobile application that was developed based on big-data analysis research [31].

MS has many aspects that require improvements in daily life, and thus, long-term health care services rather than short-term medical services are required [35]. In this study, the normal group maintained its normal status without any change even after 3 months. It is important for the risk or MS group to improve and to help the normal group maintain its condition. Therefore, health care systems must focus on continuous care such as disease prevention and lifestyle improvement to increase the healthy life expectancy, while it is also important to provide adequate and effective health care services without visiting hospitals [28]. Effective digital health care can help individuals identify the risk factors for diseases to prevent them while also helping to prevent the onset of complications and symptom exacerbation [26-29].

The services help by providing continuous feedback, motivation, and encouragement for lifestyle and behavioral changes, and continuous interactions between health care coaching (apps) and users can lead to positive results by improving user compliance [22,36]. Activation of such interaction has some requirements; it needs to be cost-effective and customizable, and provide timely notifications.

Lastly, this study had some limitations. Relatively short-term effects were investigated. This was a pilot study; thus, an additional study will consider and supplement these points to demonstrate the effectiveness of a CDHCS with respect to significant improvement in MS and lifestyle. Moreover, the additional study will have a larger sample size and will extend the intervention period to increase the statistical power.

The CDHCS used in this study identifies and analyzes lifestyle, exercise, habits, and current step count through a mobile phone, and based on this, provided customized real-time feedback and health guidance. These services served to encourage, remind, and motivate study subjects about health care. However, although no statistically significant variable was found in this study, it is thought that it may have been influenced by several external factors. Currently, the world is rapidly advancing to the era of the fourth industrial revolution owing to COVID-19, which renders the development of non–face-to-face medical and health care field as indispensable. Therefore, research in this field will make a great contribution to the development of digital health care by providing a foothold to deliver a high-quality personalized health care program with scientific evidence to healthy people and subjects with disease risk factors.