1. Study participants and recruitment methods

The recruitment of workplaces to implement the intervention involved explaining the purpose and content of the study to persons in charge of public institutions and small and medium-sized enterprises in Seoul, Gyeonggi, and Chungcheong. Eight workplaces from seven companies with more than 100 but less than 500 employees were recruited, and each workplace recruited more than 30 employees. The researchers provided posters, leaflets, and the applications to each business site to promote the purpose and procedure of the intervention. Interested employees were instructed to write down their height and weight on the application form and submit it to the person in charge of their workplace. Of the 293 applicants, 266 with a BMI of 23 kg/m² or above participated in the pre-intervention survey and the 12-week intervention. The intervention was conducted from July to October 2019, and the data of a total of 235 employees were analyzed, excluding 7 who left the company, 3 who voluntarily withdrew, and 21 who did not participate in the post-intervention survey.

2. Research methods

2) Mobile Apps

The features of the apps used in the intervention are as presented in Fig. 1.

Fig. 1
The features of the apps used in the intervention

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(1) Dietary coaching app

A dietary coaching app, specifically the NOOM (Noom Korea, Korea), was purchased and provided to the participants. The NOOM offers evaluation results of nutrient intake based on the type and amount of food recorded by the participant, 1:1 coaching service, nutrition information, and step-by-step exercise and stretching videos. The participants were required to record their meals at least 2 days a week using the app to check nutrient intake and receive 1:1 coaching service.

(2) Wearable device that can monitor physical activity and body composition

A smartband, the InBody band 2 (InBody, Korea), was purchased and provided to the participants for the monitoring of their physical activity and body composition. This device is worn on the wrist and measures body composition effortlessly, providing information on the amount of activity, such as walking and exercise, in addition to percentage of body fat and muscle mass. The participants were required to wear the band daily, link it with the app once a day to check their physical activity, and measure their body composition at least once a week.

(3) Messenger app

KakaoTalk (Kakao, Korea) was used for communication and support for the participants in the intervention. The manager could simultaneously send messages to multiple people from a single account for necessary notices during the intervention. Furthermore, the participants could contact the manager at any time through 1:1 chat.

3. Statistical analysis

The categorical variables were expressed as frequency and percentage, and the continuous variables as mean and standard deviation. To compare pre- and post-intervention data, the McNemar test and paired t-test were used for categorical and continuous variables, respectively. To determine the differences in anthropometric data, dietary behavior, lifestyle habits, and nutrient intake among the UPG, MDG, and LG before and after the intervention, categorical variables were analyzed using the χ²-test, whereas continuous variables were analyzed using analysis of variance, followed by Duncan’s multiple range test. After the intervention, the differences among the UPG, MDG, and LG were also analyzed using analysis of covariance corrected for pre-intervention results for continuous variables. Data analysis was conducted using SAS version 9.4 (SAS Institute Inc. Cary, NC, USA), and the significance level for all analyses was set at P < 0.05.

1. General characteristics

Table 1 presents the general characteristics of the participants. The majority (78.3%) of the participants were men, with 42.1% being in their 30s and 24.7% in their 40s. The average age of the participants was 36.9 years. Furthermore, 58.7% of the participants were married, and 80.9% had a college degree or higher. Approximately 38.3% of the participants had a monthly income of between 3 and 4.99 million won, and 32.8% had an income of at least 5 million won. Regarding occupation, 66.8% of the participants were office workers, 33.2% were production workers, and 11.9% worked shifts. No significant differences were observed in the general characteristics of the participants across the three different engagement groups.

Table 1
General characteristics of the participants according to engagement level¹⁾

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2. Anthropometric changes pre- and post-intervention

Table 2 demonstrates significant reductions in weight (P < 0.001), BMI (P < 0.001), percentage of body fat (P < 0.05), body fat mass (P < 0.001), skeletal muscle mass (P < 0.001), and waist circumference (P < 0.001) after the intervention. When the participants were categorized into three groups according to their engagement level, the UPG showed significant reductions in all six indicators after the intervention (P < 0.001). The MDG showed significant reductions in weight (P < 0.001), BMI (P < 0.01), skeletal muscle mass (P < 0.001), and waist circumference (P < 0.001) but not in body fat mass or percentage of body fat. The LG only showed significant reductions in skeletal muscle mass (P < 0.01) and waist circumference (P < 0.001).

Table 2
Anthropometric changes pre- and post-intervention, stratified by engagement level¹⁾

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No significant differences in anthropometric variables were observed between the UPG, MDG, and LG pre- and post-intervention. However, after adjusting for pre-intervention anthropometric data, differences were observed between the three groups in weight (P < 0.05), BMI (P < 0.001), body fat mass (P < 0.01), skeletal muscle mass (P < 0.01), and waist circumference (P < 0.05). Specifically, the UPG showed a greater decrease in weight, BMI, body fat mass, skeletal muscle mass, and waist circumference than the LG after the intervention.

3. Dietary and lifestyle habit changes pre- and post-intervention

The dietary and lifestyle habit changes pre- and post-intervention are presented in Table 3. After the intervention, significant changes in breakfast frequency (P < 0.01), mealtime regularity (P < 0.05), meal duration (P < 0.01), amount of meals (P < 0.001), physical activity level (P < 0.001), and frequency of exercise for at least 30 min (P < 0.001) were observed. Specifically, breakfast frequency increased from 25.5% to 34.1% for “almost every day”, and mealtime regularity increased from 11.9% to 19.2% for “very regular”. Meal duration “less than 10 min” decreased from 21.3% to 12.3% , and the rate of eating “extremely full” and “slightly full” decreased from 81.3% to 65.5%. The percentage of physical activity being “active” and exercising “≥3/week” for at least 30 min increased from 10.2% to 18.3% and from 23.8% to 38.2%, respectively.

Table 3
Dietary and lifestyle habit changes pre- and post-intervention, stratified by engagement level¹⁾

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When comparing pre- and post-intervention in the UPG, there were significant differences in breakfast frequency (P < 0.01), meal duration (P < 0.05), amount of meals (P < 0.001), physical activity level (P < 0.001), and frequency of exercise for at least 30 min (P < 0.01). In the MDG, there was a significant difference in the frequency of eating three times a day (P < 0.05), and in the LG, there was a significant difference in snack frequency (P < 0. 05).

Prior to the intervention, no significant differences were observed between the UPG, MDG, and LG in all categories of dietary and lifestyle habits. However, significant differences were observed between the three groups in the frequency of eating three times a day (P < 0.01), breakfast frequency (P < 0.01), amount of meals (P < 0.05), physical activity level (P < 0.001), and frequency of exercise for at least 30 min (P < 0.05) after the intervention. In terms of the frequency of eating three times a day, the UPG and MDG had a higher frequency of eating “6-7 days/week” (27.6% and 27.1%, respectively) compared with the LG (11.3%). The UPG also had a higher proportion of eating “almost every day” for breakfast (44.8%) compared with the MDG (33.9%) and LG (18.3%). Furthermore, the UPG had a higher proportion of eating “moderately” (39.0%) compared with the MDG (23.7%) and LG (21.1%). In terms of physical activity level, the UPG had a higher proportion of “active” (24.8%) compared with the MDG (10.2%) and LG (15.5%). Finally, the proportion of participants exercising “≥3/week” for at least 30 min was higher in the UPG (49.6%) than in the MDG (28.8%) and LG (31.0%).

4. Changes in food group consumption frequencies pre- and post-intervention

Table 4 presents the changes in food group consumption frequencies pre- and post-intervention. After the intervention, the consumption frequencies of multigrain rice (P < 0.001) and legumes (P < 0.01) significantly increased, whereas those of pork belly (P < 0.01), instant noodles (P < 0.01), processed meat (P < 0.05), carbonated beverages (P < 0.001), and fast food (P < 0.001) significantly decreased. Specifically, the proportion of participants who reported eating multigrain rice “almost every day” and legumes “twice a week or more” significantly increased from 36.6% to 46.8% and from 42.6% to 54.9%, respectively. Conversely, the proportion of participants who reported eating pork belly “rarely” increased from 6.0% to 12.8%. In addition, the proportion of participants who reported “rarely” eating instant noodles and processed meat increased from 23.4% to 32.8% and from 24.7% to 31.5%, respectively. Furthermore, the proportion of participants who reported “rarely” drinking carbonated beverages significantly increased from 26.0% to 45.5%, and the proportion of participants who reported eating fast food “less than once a week” significantly increased from 67.2% to 78.3%.

Table 4
Changes in food group consumption frequencies pre- and post-intervention, stratified by engagement level¹⁾

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After the intervention, the UPG showed significant improvement in the consumption frequencies of all food groups, except for milk and dairy products, and fish, whereas the MDG showed an increase in the consumption frequency of milk and dairy products (P < 0.05) and a decrease in the consumption frequencies of carbonated beverages (P < 0.001) and fast food (P < 0.05). Contrarily, the LG only showed significant decrease in the consumption frequency of carbonated beverages (P < 0.05).

No differences were observed in the consumption frequency of the food group among the three groups before the intervention; however, af ter the intervention, significant differences were observed in the consumption frequencies of fruit (P < 0.01), multigrain rice (P < 0.05), legumes (P < 0.01), pork belly (P < 0.05), and instant noodles (P < 0.05). The proportion of the UPG who ate fruit “at least once a day” was higher at 17.2% compared with those of the MDG at 10.2% and the LG at 0.0%. The proportion of the UPG who ate multigrain rice “almost every day” was 54.3%, whereas those of the MDG and LG were 49.2% and 33.8%, respectively. For legumes, the proportion of the UPG who ate “twice a week or more” was the highest at 60.0%, followed by the MDG at 55.9% and LG at 46.5%. The proportion of the UPG who ate pork belly “rarely” was 20.0%, whereas those of the MDG and LG were 5.1% and 8.4%, respectively. Even for instant noodles, the proportion of the UPG who ate “rarely” was the highest at 41.9%, followed by the MDG at 27.1% and LG at 23.9%.

5. Changes in nutrient intake and INQ pre- and post-intervention

The changes in the average daily intake of energy and nutrients pre- and post-intervention are presented in Table 5. After the intervention, there was a significant decrease in energy (P < 0.001) and all nutrient intake (P < 0.05), except vitamin C. When comparing pre- and post-intervention in the UPG, a significant decrease in energy and all nutrient intake, except vitamin A and vitamin C was observed. In addition, intakes of energy, carbohydrates, protein, niacin, phosphorus, and iron were significantly decreased in the MDG, and intakes of energy, carbohydrates, protein, fat, vitamin A, riboflavin, niacin, phosphorus, iron, and sodium were significantly decreased in the LG.

Table 5
Changes in dietary intake of energy and nutrients pre- and post-intervention, stratified by engagement level¹⁾

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Before the intervention, no differences were observed in energy and nutrient intake among the three groups; however, after the intervention, significant differences were observed in energy (P < 0.01), fat (P < 0.05), and vitamin C (P < 0.05) intakes. The UPG had lower average daily energy intake of 1,544 kcal/day than the MDG and LG, which had 1,892 and 1,868 kcal/day, respectively. Fat intake was also lower in the UPG with 45.7 g/day than in the LG with 55.9 g/day. However, vitamin C intake was higher in the UPG with 64.7 mg/day than in the LG with 47.3 mg/day. After adjusting for baseline intake, significant differences were observed in the intakes of energy (P < 0.001), carbohydrates (P < 0.001), protein (P < 0.05), fat (P < 0.01 ), thiamin (P < 0.01), vitamin C (P < 0.05), phosphorus (P < 0.05), and iron (P < 0.05) among the three groups.

Table 6 presents the changes in INQ pre- and post-intervention. In the overall participants, the INQ of vitamin C (P < 0.01 ), calcium (P < 0.05), and phosphorus (P < 0.01) significantly increased after the intervention. When compared pre- and post-intervention in the UPG, there was a significant increase in the INQ of protein, vitamin A, niacin, vitamin C, calcium, phosphorus, and iron. In the MDG, the INQ of calcium significantly increased, whereas there were no differences in the INQ of any nutrients in the LG. Before the intervention, there were no differences in the INQ among the three groups; however, after adjusting for baseline INQ, significant differences were observed in the intake of all nutrients, except for vitamin A and thiamin, with the UPG having higher INQ than the LG.

Table 6
Changes in INQ pre- and post-intervention, stratified by engagement level¹⁾

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