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Mobile health (mHealth) tools may be useful platforms for dietary monitoring and assessment.
This study aims to evaluate the effectiveness of a mobile dietary self-monitoring app for weight loss versus a paper-based diary among adults with a BMI of 23 kg/m2 or above.
A total of 33 men and 17 women aged 18-39 years participated in a 6-week randomized controlled trial. We randomly assigned participants to one of two groups: (1) a smartphone app group (n=25) or (2) a paper-based diary group (n=25). The smartphone app group recorded foods and dietary supplements that they consumed and received immediate dietary feedback using Well-D, a dietary self-monitoring app developed by our team. The paper-based diary group was instructed to record foods or supplements that they consumed using a self-recorded diary. The primary outcomes were weight, BMI, waist circumference, body fat mass, and skeletal muscle mass. We also examined changes in nutrient intake, including energy, carbohydrate, protein, fat, dietary fiber, vitamins, and minerals, using 3-day 24-hour recalls. Differences in changes between the two groups were analyzed using independent t tests or Wilcoxon Mann-Whitney tests. All of the data were analyzed using intent-to-treat analysis.
The mean number of days recorded was 18.5 (SD 14.1) for the app group and 15.5 (SD 10.1) for the paper-based diary group. The differences in changes in weight, BMI, and waist circumference were not significantly different between the app group and paper-based diary group (
There were no differences in changes in anthropometric measures and nutrient intake between the app group and the paper-based diary group. Both mobile dietary self-monitoring app and paper-based diary may be useful for improving anthropometric measures.
Clinical Research Information Service KCT0003170; https://cris.nih.go.kr/cris/search/search_result_st01_en.jsp?seq=11642<ype=&rtype=
Noncommunicable diseases (NCDs) were responsible for 71% of all deaths globally in 2016, and obesity is a risk factor for NCDs like diabetes, coronary heart disease, stroke, and cancer [
Dietary modification approaches for obesity management often involve multiple strategies from governments, businesses, communities, individuals, and families. Use of mobile devices, including smartphones, tablets, laptops, wearable devices, and barcode scanners, is thought to increase accessibility at a lower cost by reducing face-to-face, in-person education, clinic visits, and phone calls. Although several studies have shown that mobile interventions led to weight loss [
The third global survey on eHealth conducted by the WHO Global Observatory for eHealth defines mHealth as “the use of mobile devices, such as mobile phones, patient monitoring devices, Personal Digital Assistants (PDAs), and wireless devices, for medical and public health practice” [
Mobile technology may be feasible, sustainable, and cost-effective for weight loss. A systematic review of the literature on self-monitoring in weight loss showed that self-monitoring tools (eg, the paper diary, web tools, PDAs, and electronic digital scales) helped individuals lose weight [
We have developed a mobile dietary self-monitoring app, Well-D, the features of which have been described elsewhere [
South Korea had the highest rate of smartphone ownership and internet usage worldwide in 2018, followed by Israel and the Netherlands [
We recruited participants between February 6, 2018, and April 12, 2018, via poster advertisement at Seoul National University and web-based announcements. The inclusion criteria were as follows: (1) 18-40 years of age, (2) BMI ≥23 kg/m2, (3) willingness for weight loss, and (4) smartphone ownership. We excluded participants if they were pregnant or lactating. This study was approved by the Seoul National University Institutional Review Board (IRB #1710/003-007). The trial was registered with the Clinical Research Information Service (KCT0003170).
A two-arm, parallel RCT was conducted. Potential participants contacted author JSA via phone to show their willingness to participate in the intervention study. Potential participants were invited to attend a baseline session held at Seoul National University (30-45 minutes). Before starting the baseline session, potential participants reported their age, and their height and weight were measured using a stadiometer to confirm eligibility. All eligible participants returned a written informed consent prior to enrollment. Participants received 20,000 KRW (approximately $17 USD) for attending each of the three visits.
Participants were randomly assigned with a 1:1 allocation to the Well-D app group and the paper-based diary group using a random number table generated by PROC PLAN in SAS version 9.4 (SAS Institute). The allocation sequence was concealed to both JSA and each participant, and the intervention arm allocation was sealed after the participant signed the consent form at the visit site. The participant was informed whether he or she was assigned to the intervention (smartphone group) or the control group (paper-based diary group).
Flow diagram of participants.
Both groups were instructed to record foods or supplements that they consumed during the 6-week intervention period. The energy goal was reducing 500 kcal/day from the the estimated energy requirement (EER) in both groups. Participants in the app group received a link to download Well-D, which was developed by our multidisciplinary team (eg, dietitians, nutrition professionals, and software engineers) [
In the intervention arm, participants were instructed to use Well-D for at least 14 days. Contact was minimal except when technical questions were raised. Since Well-D was implemented as a hybrid app, users who had either iOS or Android smartphones could freely access the app in a network environment. To facilitate app usage, dietitian staff sat with users and helped them register and log into the app. The users also reviewed all the menus in the app with the dietitian staff and were provided a Well-D manual. Users typed in their age, sex, weight, height, and physical activity level during registration. The app provides a database of more than 20,000 foods and recipe items. For foods and dietary supplements that are not available in the database, users could add new food data by typing in the item name and describing the item or adding a photo. The users could also create new recipe data by typing in the ingredients from a food list. Dietitians checked the items that users created and updated the recipe and the food and nutrient database. Based on users’ age, sex, BMI, physical activity level, and foods and supplements that they recorded, users received real-time feedback about daily total energy, carbohydrates, protein, total fat, sodium, saturated fat, fiber, sugar, calcium, vitamin C, riboflavin, and food groups on the diabetic exchange list. All of the data on the intake of foods, supplements, and nutrients were collected. We reviewed and downloaded the data from the admin page of the website.
In the paper-based diary group, participants were provided paper-based diaries and pamphlets. The paper-based diary was designed to record the date, time, name and amount of food and ingredients consumed, and the energy intake that participants roughly calculated. Each participant in the paper-based diary was provided a pamphlet that had tips about weight loss strategies and information on the website and URLs available for calculating the energy content of food items. We also provided instructions on how participants could set a proper energy intake goal via the paper-based diary and pamphlet, and participants wrote their energy intake goal for weight loss in the paper-based diary.
Our study design and results were presented according to the CONSORT-eHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and Online Telehealth) guidelines [
Weight, BMI, waist circumference, body fat mass, and skeletal muscle mass were measured as primary outcomes at baseline and after 6 weeks of intervention. Height was measured only at baseline. On the day before measurements, we sent text messages to inform participants to avoid large meals before the visit and to wear light clothing for the measurements. Height was measured twice to the nearest 0.1 cm without shoes using a digital stadiometer (Biospace Korea). Body weight was measured to the nearest 0.1 kg on the Inbody 720 (Biospace Korea) with participants wearing light clothing [
The participants’ diets were assessed using scheduled 24-hour recalls (24HRs) for 3 days including 1 day during a weekend. A dietitian conducted the 24HRs using the automated multiple-pass method (AMPM). AMPM uses five steps, including listing foods consumed the previous day, probing for forgotten foods, collecting the time of consumption, collecting descriptions about and amounts of each food, and final questions [
Participants recorded self-reported physical activity using a South Korean version of the Global Physical Activity Questionnaire (GPAQ) [
We estimated the sample size to detect statistically significant differences in weight change between the app group and paper-based diary group using a similar previous 6-week trial [
All data were analyzed with intent-to-treat analysis. The differences in changes in anthropometric measures and nutrient intake between the app group and the paper-based diary group were analyzed by independent
There was one missing value for weight and two for other primary outcomes. The missing outcomes were carried forward from the baseline assessments. We also conducted a sensitivity analysis where we used per-protocol analysis by excluding those with missing outcomes.
After randomization, one lactating participant was excluded (
We found no statistically significant difference in change in body weight between the app group and the paper-based diary group (mean –0.4, SD 1.6 kg vs mean –1.4, SD 2.7 kg
When we compared the preintervention anthropometric measures with the postintervention measures, significant decreases in body weight and BMI were observed in the paper-based diary group (
Baseline characteristics of participants by intervention arms.
Characteristics | Total | App group | Paper-based diary group | ||
|
|
|
|
.77 | |
|
Male | 33 (66) | 16 (64) | 17 (68) |
|
|
Female | 17 (34) | 9 (36) | 8 (32) |
|
Age (year), mean (SD) | 26.0 (4.8) | 26.5 (5.3) | 25.6 (4.3) | .50 | |
Weight (kg), mean (SD) | 77.1 (11.5) | 77.9 (12.9) | 76.3 (10.2) | .62 | |
BMI (kg/m2), mean (SD) | 26.7 (2.7) | 27.1 (3.0) | 26.4 (2.5) | .54 | |
Waist circumference (cm), mean (SD) | 91.7 (9.3) | 93.1 (9.6) | 90.3 (8.9) | .25 | |
Body fat mass (kg), mean (SD) | 23.3 (6.3) | 24.2 (5.6) | 22.3 (6.9) | .29 | |
Skeletal muscle mass (kg), mean (SD) | 30.3 (6.1) | 30.2 (6.5) | 30.4 (5.7) | .91 | |
Total physical activity (METb-hours/week), mean (SD) | 26.4 (25.6) | 25.3 (23.4) | 27.6 (28.1) | .87 | |
|
2166.1 (546.5) | 2270.3 (522.1) | 2061.9 (560.8) | .18 | |
|
Carbohydrate (% energy/day) | 50.5 (6.7) | 48.8 (7.8) | 52.1 (5.1) | .11 |
|
Protein (% energy/day) | 18.6 (4.0) | 19.3 (4.2) | 17.8 (3.6) | .34 |
|
Fat (% energy/day) | 30.9 (5.7) | 31.7 (6.7) | 30.0 (4.4) | .34 |
Total dietary fiber (g/day), mean (SD) | 17.0 (6.1) | 16.7 (5.9) | 17.4 (6.4) | .69 | |
Calcium (mg/day), mean (SD) | 530.8 (237.9) | 512.8 (185.3) | 548.8 (283.8) | .74 | |
Sodium (mg/day), mean (SD) | 4021.7 (1157.2) | 4110.6 (1124.0) | 3932.7 (1205.8) | >.99 |
aChi-square tests were used for categorical variables, and independent
bMET: metabolic equivalent task.
Differences in change between the app group and the paper-based diary group in terms of (A) body weight, (B) BMI, (C) waist circumference, (D) body fat mass, and (E) skeletal muscle mass. The asterisk denotes
Differences in anthropometric measures between the app group and the paper-based diary group (intent-to-treat analysis).
Measure | App group (n=25), mean (SD) | Paper-based diary group (n=25), mean (SD) | ||||||||
|
Baseline | 6 weeks | Changeb | Baseline | 6 weeks | Changeb |
|
|||
Weight (kg) | 78.0 (12.9) | 77.6 (13.0) | –0.4 (1.6) | .25 | 76.3 (10.2) | 75.0 (9.3) | –1.4 (2.7) | .02 | .33 | |
BMI (kg/m2) | 27.1 (3.0) | 26.9 (3.0) | –0.1 (0.6) | .26 | 26.4 (2.5) | 25.9 (2.2) | –0.5 (0.9) | .01 | .34 | |
Waist circumference (cm) | 93.1 (9.6) | 90.9 (9.2) | –2.2 (2.8) | <.001 | 90.3 (9.0) | 88.1 (7.1) | –2.3 (3.8) | .004 | .70 | |
Body fat mass (kg) | 24.2 (5.6) | 23.0 (6.1) | –1.2 (1.8) | .004 | 22.3 (6.9) | 21.0 (5.9) | –1.3 (2.4) | .01 | .71 | |
Skeletal muscle mass (kg) | 30.2 (6.5) | 30.6 (6.6) | 0.4 (1.0) | .048 | 30.4 (5.7) | 30.4 (5.8) | –0.01 (0.7) | .48 | .054 |
aIndependent
bChanges were calculated as postintervention anthropometric measures minus preintervention anthropometric measures.
cPaired
Differences in changes in nutrient intake between the app group and the paper-based diary group were not statistically significant (
Over the 6-week intervention period, differences in the number of days recorded was not significant between the app group and the paper-based diary group (mean 18.5, SD 14.1 vs mean 15.5, SD 10.1, respectively;
When we counted the number of participants who recorded food items in each week, we found higher proportions of recording in week 1 and week 2 in both groups than in later weeks and a higher proportion of recording, in general, in the app group than in the paper-based diary group. In all, 51.43% (12.86 on average per day for 7 days) of participants recorded food items in the app group and 38.10% (8 on average per day) of participants recorded food items in the paper-based diary group in week 1, but the proportions decreased to 36.00% (9 on average per day) in week 5 in the app group and 32.65% (6.86 on average per day) in the paper-based dairy group (
Differences in changes in nutrient intake assessed from 24-hour recalls between the app group and the paper-based diary group (intent-to-treat analysis).
Characteristic | App group (n=25), mean (SD) | Paper-based diary group (n=25), mean (SD) | |||||||
|
Baseline | 6 weeks | Baseline | 6 weeks |
|
||||
Energy (kcal/day) | 2269.7 (522.8) | 1983.5 (365.3) | .04 | 2061.9 (560.8 | 1780.6 (571.0) | .06 | .98 | ||
Carbohydrate |
48.8 (7.8) | 48.9 (8.5) | .95 | 52.2 (5.1) | 49.8 (8.3) | .20 | .34 | ||
Protein (% energy/day) | 19.3 (4.2) | 19.7 (4.4) | .68 | 17.8 (3.6) | 17.4 (3.5) | .63 | .60 | ||
Fat (% energy/day) | 31.9 (7.0) | 31.4 (7.4) | .70 | 30.0 (4.4) | 32.8 (7.6) | .13 | .18 | ||
Saturated fat |
12.1 (6.2) | 12.2 (6.5) | .83 | 10.4 (2.3) | 10.1 (2.4) | .50 | .76 | ||
Total dietary fiber (g/day) | 16.7 (5.9) | 15.3 (5.1) | .42 | 17.4 (6.4) | 15.0 (5.7) | .10 | .64 | ||
Cholesterol (mg/day) | 371.9 (122.7) | 363.8 (160.3) | .70 | 363.0 (146.2) | 289.0 (100.7) | .04 | .35 | ||
Calcium (mg/day) | 512.8 (185.3) | 525.2 (312.4) | .57 | 548.8 (283.8) | 438.9 (241.9) | .01 | .23 | ||
Phosphorus (mg/day) | 1076.5 (300.2) | 1041.9 (297.1) | .63 | 1067.6 (281.1) | 878.1 (268.6) | .01 | .14 | ||
Iron (mg/day) | 16.8 (12.3) | 14.5 (7.9) | .43 | 19.7 (32.3) | 17.9 (32.4) | .05 | .30 | ||
Sodium (mg/day) | 4110.6 (1124) | 3833.4 (1176.3) | .23 | 3932.7 (1205.8) | 3531.9 (1596.0) | .21 | .77 | ||
Potassium (mg/day) | 2254.4 (562.3) | 2318.1 (743.5) | .77 | 2325.9 (658.3) | 2015.7 (587.8) | .01 | .07 | ||
Vitamin A (μg REc/day) | 742.0 (937.1) | 785.2 (1087.6) | .83 | 588.4 (716.9) | 574.1 (580.0) | .84 | .55 | ||
Thiamine (mg/day) | 2.7 (5.1) | 2.9 (5.9) | .22 | 6.3 (21.5) | 1.6 (1.8) | .05 | .42 | ||
Riboflavin (mg/day) | 2.9 (5.3) | 2.9 (5.8) | .78 | 5.9 (21.1) | 1.5 (1.3) | .09 | .35 | ||
Niacin (mg/day) | 25.7 (28.7) | 24.3 (30.8) | .76 | 25.2 (28.7) | 18 (10.2) | .26 | .54 | ||
Vitamin C (mg/day) | 276.4 (338.0) | 201.7 (314.2) | .07 | 127.3 (120.0) | 154.7 (281.8) | .42 | .46 |
aIndependent
bPaired
cRE: retinol equivalents.
A comparison of nutrient intake levels from the Well-D app with those from the 24-hour recalls (n=25).
Nutrienta | Correlation coefficient | |
Energy (kcal/day) | 0.68 | <.01 |
Carbohydrate (% energy/day) | 0.55 | <.01 |
Protein (% energy/day) | 0.60 | <.01 |
Fat (% energy/day) | 0.57 | <.01 |
Saturated fat (% energy/day) | 0.66 | <.001 |
Total dietary fiber (g/day) | 0.51 | .01 |
Cholesterol (mg/day) | 0.47 | .02 |
Calcium (mg/day) | 0.49 | .01 |
Phosphorus (mg/day) | 0.47 | .02 |
Iron (mg/day) | 0.71 | <.001 |
Sodium (mg/day) | 0.56 | <.01 |
Potassium (mg/day) | 0.56 | <.01 |
Vitamin A (μg RE/day) | 0.54 | .01 |
Thiamine (mg/day) | 0.43 | .03 |
Riboflavin (mg/day) | 0.50 | .01 |
Niacin (mg/day) | 0.70 | <.001 |
Vitamin C (mg/day) | 0.66 | <.001 |
aThe residual method was used to adjust for nutrient intake.
bEither Pearson correlation or Spearman correlation was used.
Weight loss by the number of days of dietary recording in the app group (left) and the paper-based diary group (right).
Mean number (percentage) of days participants spent recording food items for each week in the app group and in the paper-based diary group.
Week | App group (n=25), mean (%) | Paper-diary group (n=21)a, mean (%) |
Week 1 | 12.86 (51.43) | 8.00 (38.10) |
Week 2 | 11.43 (45.71) | 8.14 (38.78) |
Week 3 | 9.57 (38.29) | 8.57 (40.82) |
Week 4 | 9.00 (36.00) | 6.86 (32.65) |
Week 5 | 9.00 (36.00) | 6.86 (32.65) |
Week 6 | 10.00 (40.00) | 4.29 (20.41) |
aOut of 25, one was lost to follow-up. Three did not provide information on dates recorded, but provided the number of days recorded.
We conducted an RCT on weight loss in young adults with a BMI ≥23 kg/m2 to compare the effectiveness of a mobile dietary self-monitoring app versus a paper-based diary. In our study, 66% of participants were men, which reflects the sex ratio of overweight individuals in Korea [
In summary, we did not find significant differences in changes in body weight, BMI, waist circumference, body fat mass, or skeletal muscle mass between the app group and the paper-based diary group. Additionally, the changes in nutrient intake were not different between the two groups. However, when we compared anthropometric changes from pre- to postintervention, we found reductions in weight and BMI in the paper-based group and reductions in waist circumference and body fat mass in both groups. Skeletal muscle mass increased slightly in the app group. We also found decreases in total energy intake in both groups and as well as decreases in intake of cholesterol, calcium, phosphorus, and potassium in the paper-based diary group, but not in the app group. When we compared nutrient intake from the Well-D app with 24HRs, we found modest-to-high correlations, suggesting potential use of Well-D for dietary assessment.
Previous studies found that the effectiveness of mHealth technologies was similar to the paper-based diary [
There have been only a few studies that have supported the effectiveness of an app to improve users’ diet compared to a paper-based method [
Several studies conducted in South Korea reported potential weight change when using mHealth tools. A longitudinal study with a median follow-up of 275 days investigated the effectiveness of the Noom Coach app on weight reduction among 35,921 South Korean adults with a BMI ≥23 kg/m2, who recorded dietary data two or more times a month for 6 consecutive months [
Additionally, a randomized trial that compared weight loss according to the frequency of usage of the My Meal Mate app found that participants in the highest frequency-of-use category lost an average of 6.4 kg more than those in the lowest frequency-of-use category during the 6-month intervention period [
Our study was a randomized, parallel trial with a high follow-up rate. Because we developed the app, we had full access to the data. We also assessed participants’ dietary information using two 3-day 24HRs, and therefore we were able to compare nutrient intake between the two groups. However, our study had several limitations. Because the population was composed of young adults, the results may not be generalizable to children or older people. Since we included participants with a BMI ≥23 kg/m2, the magnitude of weight change during the 6-week intervention period may not be large enough to see differences. Furthermore, the sample size was small and the study period was relatively short. Well-D did not have an exercise tracking function, and we were not able to track participants’ exercise levels. However, we asked participants to maintain their usual physical activity levels. When we examined their usual exercise levels at baseline and at postintervention, we found no significant differences.
We conducted an RCT to evaluate the effectiveness of a mobile dietary self-monitoring app for weight loss versus a paper-based diary. We found that participants reduced their energy intake, waist circumference, and body fat mass in both groups. However, we found no difference in changes between the app group and paper-based diary group. Our study suggests that both the smartphone app and the paper-based diary method may exhibit similar effectiveness for short-term body fat loss. Our findings may contribute to understanding and implementing mHealth interventions in health care services. Further prospective or intervention studies with larger sample sizes and long-term follow-up are warranted to explore the effectiveness of mHealth tools for the management of common chronic diseases in Korea.
Differences in anthropometric measures between the app group and the paper-based diary group (per-protocol analysis).
CONSORT eHEALTH (V1.6.1).
24-hour recall
automated multiple-pass method
Cell Phone Intervention For You
Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and Online Telehealth
Global Physical Activity Questionnaire
information and communications technology
metabolic equivalent of task
mobile health
noncommunicable disease
Personal Digital Assistant
randomized controlled trial
smartphone-based behavioral obesity treatment
World Health Organization
This research was supported by the Ministry of Science and ICT, Korea, under the Information Technology Research Center support program (IITP-2019-2014-1-00720) supervised by the Institute for Information & Communications Technology Promotion.
JSA and JEL conceptualized the study design and drafted the manuscript. JSA, HL, and JEL conducted data collection and statistical analysis. All authors contributed to data acquisition and design of the Well-D app. JK and HP devised the app. All authors reviewed and approved the final version of the manuscript.
JK and HP were employed by Bluecore Co, Ltd. The remaining authors declare no conflicts of interest.