Abstract
Introduction
Obesity in childhood and adolescence represents one of the most challenging public health problems of our century and is associated with significant morbidity and mortality, as well as increased public health costs. To address the obesity epidemic more effectively, the World Health Organization suggests the development and implementation of reliable e-health systems (digital technologies, such as electronic health records, clinical decision support systems, and mobile health tools) that would monitor the daily behavior objectively. Our objective was to determine the effectiveness of BigO system in the prevention and management of childhood obesity.
Methods
Our study was part of the 4-year European BigO project (http://bigoprogram.eu, Horizon2020, No. 727688). Overall, 1,727 (n = 1,727) children and adolescents (mean age ± SD: 12.6 ± 2.4; 898 males, 829 females) were studied prospectively following approval by the local Ethics Human Research Committee. The data collection system included the BigO technology platform, which interfaces with a Smartphone and Smartwatch and records data objectively (using inertial sensors and GPS) for each patient. Data were transmitted to BigO servers to extract behavioral indicators. Participants used the BigO system for at least 4 weeks. Subsequently, they entered a personalized lifestyle intervention program of diet, physical exercise, and sleep for 6 months and used the system again for 4 weeks.
Results
Subjects were classified as having obesity (n = 1,277, 73.9%), overweight (n = 413, 23.9%), or normal BMI (n = 37, 2.1%) according to WHO cutoff points. At the end of the study, the proportion of subjects with obesity decreased, while the proportion of subjects with overweight and normal BMI increased. The BigO system monitored the daily behavior of all subjects objectively and effectively and provided detailed information on their diet, physical activity, and sleep habits, as well as the availability of exercise facilities in their communities and their living conditions.
Conclusion
These novel e-health applications and digital technologies were effective at collecting and analyzing objective data about the daily behavior of children and adolescents with overweight and obesity. Therefore, they may be useful to use in clinical practice and to design public health policies to address the epidemic of childhood obesity.
Keywords: Childhood and adolescent obesity, Big data, E-health, Prevention and management
Introduction
Obesity in childhood and adolescence represents one of the most challenging public health problems of our century and is associated with significant morbidity and mortality, as well as increased public health costs. According to WHO, overweight and obesity affect more than 38.2 million children younger than 5 years and more than 340 million children and adolescents aged 5–19 years worldwide [1–5]. In Greece, the prevalence of overweight and obesity ranges from 21% in preschool aged children to 41% in school-aged children and adolescents and is significantly higher than the rest of the European countries, where the corresponding prevalence is 15% and 21%, respectively. These data indicate that Greece has the highest rates of overweight and obesity in childhood and adolescence in Europe [6].
Children and adolescents with overweight and obesity are more likely to have obesity in adulthood and to develop complications, such as hypertension, dyslipidemia, insulin resistance, diabetes mellitus type 2, atherosclerotic cardiovascular disease, orthopedic problems, fatty liver disease, social stigmatization, and increased incidence of malignancies, at a younger age [7–13]. Obesity accounts for approximately 5% of all deaths worldwide [1, 14]. It is likely that the “obesity epidemic” may reverse the current trend of the declining rate of mortality from cardiovascular causes, leading to a shorter lifespan for today’s children.
In addition to the increased morbidity and mortality, overweight and obesity account for a significant increase in public health costs. The global economic impact from obesity is approximately 2.0 trillion USD or 2.8% of the global gross domestic product, which is almost equivalent to the global impact from smoking or armed violence, war, and terrorism [14, 15]. In Greece, the estimated annual cost associated with obesity and its complications exceeds 4 billion Euros [16].
The factors contributing to obesity in childhood and adolescence are several, including the socio-economic status, family habits, and conditions at school (e.g., duration of school lunches). These factors are associated with individual obesogenic behavioral patterns, such as consumption of fast food, unhealthy food choices, lack of physical exercise, and disturbed sleep. While the exact interactions among these factors have not been fully elucidated, unhealthy eating and physical inactivity are observed early in life, leading to increased BMI that usually persists throughout adulthood [6–13]. The above patterns are difficult to quantify in real life, thereby limiting their use in context-based preventive programs.
Several scientific and health organizations have suggested the development of appropriate e-health1 [17] and m-health2 [18] applications, and more specifically mobile and wearable sensors combined with deep learning3 [19] and big data analytics4 [20], to address the obesity epidemic [21–25]. During the last 15 years, the widespread use of personal digital technologies, such as smartphones and accelerometers, offer new possibilities for objective collection of behavioral data [21, 26, 27]. Furthermore, the use of large-scale data (big data) in modern healthcare settings has been proven extremely valuable [21, 27]. The development of such methodologies enables scientists and public health authorities to collect and analyze objective data on the daily behavior of children at high risk for developing overweight and/or obesity and to modify public health policy strategies at the local level accordingly.
The Program “BigO: Big Data against Childhood Obesity” (http://bigoprogram.eu, Horizon2020, No. 727688) was based on these principles. This program brought together 13 European partners from Greece, Sweden, Ireland, Spain, and the Netherlands. BigO studied students and age-matched children and adolescents with obesity across different European countries and developed innovative technological/scientific tools that delineate the associative dependencies among local living-environmental conditions and individually recorded behavioral patterns that result in increased BMI [28, 29]. A range of novel technologies for collecting objective measurements of obesogenic behaviors of children and adolescents during their day-to-day life was designed and implemented for this purpose. The overall aim was to collect and analyze big data on the behavior and living environments of children and adolescents with overweight and obesity in order for public health authorities to plan and execute effective programs to address the obesity epidemic.
The primary aim of our study was to evaluate the usefulness of the BigO system in terms of altering the perceptions of patients in relation to diet, physical activity, and sleep habits, the perceived general health of the individual, and information on the available exercise facilities in their communities, along with their living conditions and their mood. Secondary aims included the evaluation of the BigO system in a clinical setting by determining the BMI trajectory of subjects with obesity, overweight, and normal BMI after using the BigO system at the beginning and the end of a 6-month period and during the COVID-19 pandemic. The secondary aims were evaluated in a subpopulation of our initial cohort.
Methods
Data Collection System and Deployment Strategy
The data collection system included the BigO technology platform, which interfaces with a Smartphone and Smartwatch, and records data objectively (using inertial sensors and GPS) for each patient. Data were transmitted to BigO servers to extract behavioral indicators, including (1) physical activity/exercise, (2) dietetic habits, and (3) environmental conditions (urban, socioeconomic, nutritional) [21]. Collected data were anonymized, encrypted, and stored exclusively in European data centers, which comply with the EU Directive 95/46/EC and the newer EU General Data Protection Regulation (GDPR).
Children and adolescents with overweight or obesity or at risk of having overweight/obesity aged 9–18 years followed up at the Center for the Prevention and Management of Overweight and Obesity, Division of Endocrinology, Metabolism and Diabetes, First Department of Pediatrics, School of Medicine, National and Kapodistrian University of Athens, “Aghia Sophia” Children’s Hospital, Athens, Greece, were recruited prospectively to participate in the study. The study was approved by the local Committee on the Ethics of Human Research (Approval No. EB-PASCH-MoM: 29/11/2017, Re: 26660-16/11/2017). Written informed consent was obtained by parents/guardians in all cases and assent was given by children older than 7 years.
The study included three phases. Phase 0 was a pilot study to test the BigO system (May 2018–November 2018). The main study occurred during phase 1 (December 2018–November 2019) and phase 2 (December 2019–February 2021; extended due to COVID-19). A 4-week continuous deployment strategy proposed in order to accommodate the easier inclusion of the significant number of patients expected to be recruited into the BigO data collection. Participants used the BigO system for 4 weeks in order to take photographs of the food they consumed, as well as food advertisements, and wore the watch for specific periods during the week (at least 2 weekdays, 1 weekend, and 3 nights). Subsequently, they entered a personalized lifestyle intervention program designed to promote healthy behavioral changes in both children and their families for 6 months.
The intervention consisted of personalized guidance on nutritional habits, duration and quality of sleep, and physical activity. A multidisciplinary pediatric team – comprising a pediatrician, pediatric endocrinologist, and dietitian – conducted a comprehensive initial assessment. Dietary intake was evaluated using the 24-h dietary recall method to identify existing nutritional patterns. Based on these findings, families received personalized counseling aimed at improving dietary behaviors, with a focus on reducing the consumption of processed foods and encouraging the intake of vegetables, seasonal fruits, whole grains high in fiber, lean protein sources, and healthy fats, in alignment with USDA dietary guidelines [30–33]. Personalized meal plans were developed, incorporating three main meals and two snacks daily to support balanced nutrition. In addition to nutritional support, the intervention included structured recommendations on sleep duration and quality, following the American Academy of Sleep Medicine guidelines, which advise 9–12 h of sleep per night for children aged 10–12 years and 8–10 h for adolescents [30–32, 34]. Educational components emphasized the metabolic and weight-related consequences of inadequate sleep and the importance of minimizing screen time, particularly before bedtime. To promote physical activity, a certified personal trainer conducted regular weekly assessments and designed customized exercise regimens for each participant. These programs aimed to foster consistent engagement in physical activity and establish long-term healthy habits. Participants were encouraged to engage in daily physical activity lasting 30–45 min, selecting from a range of preferred activities such as brisk walking, jogging, hiking, swimming, cycling, or dancing.
Participants were also asked to fill in a series of self-rating questions regarding their diet, sleep, and physical activity habits, in comparison to their peers. Additional questions collected information about the perceived general health of the individual, the exercise facilities available in their communities, their living conditions, as well as their mood. They also had the ability to take and upload pictures of different types of meals eaten during the day (divided into breakfast, lunch, dinner, snacks, and drinks), as well as food advertisements that they encountered or any other photograph in relation to their diet and/or physical activity. Participants used the system again for 4 weeks. At the end of the study, participants were asked to complete an evaluation questionnaire regarding their experience of using the BigO system (application and smartwatch).
Statistical Analysis
Categorical variables are presented with absolute and relative frequencies (%), while continuous variables with mean and standard deviation (SD) or median and interquartile range for skewed variables. Normality was performed with Kolmogorov-Smirnov test and graphically with histograms. Homogeneity of variance was tested with the Levene’s test. Differences in the distribution of continuous variables between the three categories of BMI (obesity, overweight, and normal BMI) were assessed using the ANOVA F test. The Bonferroni rule was applied to each of the multiple comparisons for adjustment for a significance level of 5%. The comparison of pre- and post-intervention results within each category of BMI was calculated by a paired-samples t test for normally distributed variables. The associations between skewed variables and groups of participants were evaluated by the Mann-Whitney U test, the Kruskal-Wallis H test, or Wilcoxon’s signed-rank test. The associations between categorical variables were analyzed using Pearson’s χ2 test, Fisher’s exact χ2 test, or Monte Carlo test. McNemar’s Bowker test was also performed to detect changes in qualitative measurements. A series of two-sample z tests of proportions was conducted to determine whether there were significant differences between groups. The reliability of participants’ responses in terms of the assessment of the BigO system itself (both in the mobile and the smartwatch) was tested with the coefficient Cronbach’s a. Correlations between continuous variables were performed with Spearman’s rho coefficient. For all analyses, results were considered significant at p < 0.05. All analyses were performed with the SPSS statistical package version 25.0 (SPSS Inc., Chicago, IL, USA).
Results
Clinical Population at Baseline
Clinical Characteristics of All Subjects
Overall, 1,727 (n = 1,727) children and adolescents (mean age ± SD: 12.6 ± 2.4; 898 males, 829 females) were studied prospectively. Subjects were classified as having obesity (n = 1,277, 73.9%), overweight (n = 413, 23.9%), or normal BMI (n = 37, 2.1%) according to WHO cutoff points. The clinical characteristics of all subjects according to gender and BMI are shown in Table 1A, B, respectively.
Table 1.
Clinical characteristics of all subjects: (A) according to gender; (B) according to BMI
| | Males | Females | Total | p value |
|---|---|---|---|---|
| n = 898 (52.0%) | n = 829 (48.0%) | n = 1,727 (100%) | ||
| (A)1 | ||||
| Age, years | 12.7 (10.9–14.4) | 12.4 (10.9–14.5) | 12.6 (10.7–14.5) | 0.276 |
| Weight, kg | 70.7 (57.1–87.7) | 67.5 (56.0–78.6) | 71.1 (57.7–85.1) | <0.001 |
| Weight SDS | 2.1 (0.7) | 1.9 (0.6) | 2.0 (0.6) | <0.001 |
| Height, cm | 160.0 (148.6–168.9) | 156.4 (148.3–163.1) | 157.8 (148.7–165.5) | <0.001 |
| Height SDS | 0.8 (1.0) | 0.6 (1.1) | 0.7 (1.1) | <0.001 |
| Pubertal category, n (%) | | | | <0.001 |
| Prepubertal | 288 (32.1) | 82 (9.9) | 370 (21.4) | |
| Pubertal | 608 (67.9) | 747 (90.1) | 1.355 (78.6) | |
| BMI, kg/m2 | 27.7 (24.7–31.5) | 27.2 (25.0–30.5) | 28.2 (25.4–31.8) | 0.464 |
| Z-score class, n (%) | | | | 0.020 |
| Obesity | 688 (76.6) | 589 (71.0) | 1,277 (73.9) | |
| Overweight | 190 (21.2) | 223 (26.9) | 413 (23.9) | |
| Normal BMI | 20 (2.2) | 17 (2.1) | 37 (2.1) | |
| | Obesity | Overweight | Normal BMI | p value |
|---|---|---|---|---|
| (B)2 | ||||
| Gender, n (%) | | | | 0.020 |
| Male | 688 (53.9) | 190 (46.0) | 20 (54.1) | |
| Female | 589 (46.1) | 223 (54.0) | 17 (45.9) | |
| Age, years | 12.3 (10.5–14.2) | 13.5 (11.5–15.0) | 14.5 (12.1–15.6) | <0.001 |
| Weight, kg | 72.9 (58.7–87.9) | 64.3 (51.4–71.5) | 56.0 (44.7–64.5) | <0.001 |
| Weight SDS | 2.2 (0.5) | 1.4 (0.4) | 0.6 (0.4) | <0.001 |
| Height, cm | 157.0 (148.0–165.6) | 159.4 (151.0–166.8) | 160.6 (147.5–167.9) | 0.059 |
| Height SDS | 0.8 (1.0) | 0.4 (1.0) | 0.1 (0.8) | <0.001 |
| Pubertal category, n (%) | | | | <0.001 |
| Prepubertal | 312 (24.4) | 53 (12.9) | 5 (13.5) | |
| Pubertal | 965 (75.6) | 358 (87.1) | 32 (86.5) | |
| BMI, kg/m2 | 29.0 (26.4–32.2) | 24.7 (22.6–26.3) | 21.7 (19.4–22.9) | <0.001 |
1Continuous variables are presented as means (SD) or medians (interquartile range) and categorical as frequencies (percentages); p values were derived by comparisons between the categories of gender using T test for normal distributed variables or Mann-Whitney U test for skewed variables and Pearson’s chi-square test for categorical variables; statistically significant associations are shown in bold.
2Continuous variables are presented as means (SD) or medians (interquartile range) and categorical as frequencies (percentages); p values were derived by comparisons between the three categories of BMI using ANOVA test for normal distributed variables or Kruskal-Wallis H test for skewed variables and Pearson’s chi-square test for categorical variables; statistically significant associations are shown in bold.
At baseline, the percentage of subjects with obesity was 73.9%, overweight 23.9%, and normal BMI 2.1% (shown in Fig. 1a). A significantly higher number of boys had obesity compared with girls (76.6% vs. 71.0%), while a higher number of girls had overweight compared with boys (26.9% vs. 21.2%, p = 0.020) (shown in Fig. 1b).
Fig. 1.
Distribution of BMI. a In all subjects (n = 1,727). b According to gender.
Perceptions of All Participants regarding Their Diet, Sleep, and Physical Activity Habits, Their General Health, the Exercise Facilities Available in Their Communities, as well as Their Living Conditions
The perceptions of all participants regarding their diet, sleep, and physical activity habits, their general health, the exercise facilities available in their communities, as well as their living conditions were determined according to gender (Table 2A) and BMI (Table 2B). A significantly higher number of boys said that they eat quite much or very much (39.6% or 8.6%) and quite quickly or very quickly (29.3% or 12.5%) compared with girls (34.0% or 6.8%, p = 0.044, and 21.4% or 5.7%, p < 0.001, respectively) (Table 2A). Compared with their overweight or normal-BMI counterparts, a significantly higher number of subjects with obesity said that they eat quite much or very much (p < 0.001) and quite quickly or very quickly (p = 0.025). Furthermore, a significantly higher number of subjects with obesity think that their health is bad or fair (p < 0.001), they mostly lie/sit or mostly sit (p = 0.001), and they feel very unsafe or unsafe to play/exercise in places/facilities near their home compared with their overweight or normal-BMI counterparts (p = 0.042) (Table 2B).
Table 2.
Perceptions of all participants regarding their diet, sleep, and physical activity habits, their perceived general health, the exercise facilities available in their communities, as well as their living conditions: (A) according to gender; (B) according to BMI
| | Males | Females | Total | p value | |
|---|---|---|---|---|---|
| 898 (52.0%) | 829 (48.0%) | 1,727 (100%) | |||
| (A)1 | |||||
| 1 | How much do you think you eat compared to others of your age?, n (%) | | | | 0.044 |
| | Very much | 75 (8.6) | 55 (6.8) | 130 (7.7) | |
| Quite much | 345 (39.6) | 275 (34.0) | 620 (36.9) | ||
| Average | 389 (44.7) | 410 (50.7) | 799 (47.6) | ||
| Not very much | 43 (4.9) | 50 (6.2) | 93 (5.5) | ||
| Not much at all | 19 (2.2) | 19 (2.3) | 38 (2.3) | ||
| 2 | How quickly do you think you eat compared to others of your age?, n (%) | | | | <0.001 |
| | Very quickly | 109 (12.5) | 46 (5.7) | 155 (9.2) | |
| Quite quickly | 255 (29.3) | 172 (21.4) | 427 (25.5) | ||
| Average | 318 (36.5) | 313 (38.9) | 631 (37.6) | ||
| Not very quickly | 119 (13.7) | 173 (21.5) | 292 (17.4) | ||
| Not quickly at all | 70 (8.0) | 101 (12.5) | 171 (10.2) | ||
| 3 | How active do you think you usually are compared to others of your age?, n (%) | | | 0.659 | |
| | Mostly lying/sitting | 45 (5.2) | 46 (5.7) | 91 (5.4) | |
| Mostly sitting | 49 (5.6) | 58 (7.2) | 107 (6.4) | | |
| Sitting/standing/walking | 265 (30.5) | 252 (31.2) | 517 (30.9) | ||
| Standing/walking most of the time | 201 (23.2) | 181 (22.4) | 382 (22.8) | ||
| Exercising a lot | 308 (35.5) | 270 (33.5) | 578 (34.5) | ||
| 4 | How well do you sleep at night?, n (%) | | | | 0.816 |
| | Very well | 323 (37.2) | 288 (35.6) | 611 (36.4) | |
| Well | 282 (32.5) | 277 (34.2) | 559 (33.3) | ||
| Average | 208 (23.9) | 184 (22.7) | 392 (23.4) | ||
| Bad | 41 (4.7) | 45 (5.6) | 86 (5.1) | ||
| Exercising a lot | 15 (1.7) | 15 (1.9) | 30 (1.8) | ||
| 5 | What time do you usually sleep on weekdays? | −/− | −/− | −/− | |
| What time do you usually wake up on weekdays? | |||||
| What time do you usually sleep on weekends? | |||||
| What time do you usually wake up on weekdays? | |||||
| 6 | How is your health in general compared to others of your age?, n (%) | | | | 0.075 |
| | Very good | 287 (33.1) | 313 (38.8) | 600 (35.8) | |
| Good | 341 (39.3) | 311 (38.6) | 652 (38.9) | ||
| Fair | 194 (22.4) | 151 (18.7) | 345 (20.6) | ||
| Bad | 36 (4.1) | 24 (3.0) | 60 (3.6) | ||
| Very bad | 10 (1.2) | 7 (0.9) | 17 (1.0) | ||
| 7 | Do you have your own bedroom for yourself?, n (%) | | | | 0.004 |
| | Yes | 579 (66.4) | 481 (59.7) | 1,060 (63.2) | |
| No | 293 (33.6) | 325 (40.3) | 618 (36.8) | ||
| 8a | Are there places/facilities near your home where you can play/exercise?, n (%) | | | 0.388 | |
| | Very many | 101 (11.7) | 102 (12.7) | 203 (12.2) | |
| Quite a lot | 237 (27.5) | 204 (25.3) | 441 (26.5) | ||
| Some | 230 (26.7) | 197 (24.4) | 427 (25.6) | ||
| Few | 167 (19.4) | 162 (20.1) | 329 (19.7) | ||
| Very few | 126 (14.6) | 141 (17.5) | 267 (16.0) | ||
| 8b | How safe do you feel to play/exercise in these places?, n (%) | | | | 0.257 |
| | Very safe | 290 (35.2) | 236 (30.5) | 526 (32.9) | |
| Safe | 350 (42.5) | 337 (43.6) | 687 (43.0) | ||
| Average | 144 (17.5) | 162 (21.0) | 306 (19.2) | ||
| Unsafe | 24 (2.9) | 22 (2.8) | 46 (2.9) | ||
| Very unsafe | 16 (1.9) | 16 (2.1) | 32 (2.0) | ||
| | Obesity | Overweight | Normal BMI | p value | |
|---|---|---|---|---|---|
| 1,277 (73.9%) | 413 (23.9%) | 37 (2.1%) | |||
| 1 | How much do you think you eat compared to others of your age?, n (%) | | | | <0.001 |
| | Very much | 116 (9.4) | 14 (3.5) | – | |
| Quite much | 495 (39.9) | 115 (28.4) | 10 (28.6) | ||
| Average | 533 (43.0) | 246 (60.7) | 20 (57.1) | ||
| Not very much | 66 (5.3) | 23 (5.7) | 4 (11.4) | ||
| Not much at all | 30 (2.4) | 7 (1.7) | 1 (2.9) | ||
| 2 | How quickly do you think you eat compared to others of your age?, n (%) | | | | 0.025 |
| | Very quickly | 126 (10.2) | 25 (6.2) | 4 (11.4) | |
| Quite quickly | 326 (26.4) | 94 (23.2) | 7 (20.0) | ||
| Average | 460 (37.2) | 154 (38.0) | 17 (48.6) | ||
| Not very quickly | 196 (15.9) | 90 (22.2) | 6 (17.1) | ||
| Not quickly at all | 128 (10.4) | 42 (10.4) | 1 (2.9) | ||
| 3 | How active do you think you usually are compared to others of your age?, n (%) | | | 0.001 | |
| | Mostly lying/sitting | 82 (6.6) | 8 (2.0) | 1 (2.9) | |
| Mostly sitting | 84 (6.8) | 21 (5.2) | 2 (5.7) | ||
| Sitting/standing/walking | 394 (31.9) | 116 (28.6) | 7 (20.0) | ||
| Standing/walking most of the time | 271 (21.9) | 103 (25.4) | 8 (22.9) | ||
| Exercising a lot | 404 (32.7) | 157 (38.8) | 17 (48.6) | ||
| 4 | How well do you sleep atnight?, n (%) | | | | 0.554 |
| | Very well | 463 (37.4) | 140 (34.6) | 8 (22.9) | |
| Well | 403 (32.6) | 141 (34.8) | 15 (42.9) | ||
| Average | 286 (23.1) | 97 (24.0) | 9 (25.7) | ||
| Bad | 61 (4.9) | 22 (5.4) | 3 (8.6) | ||
| Very bad | 25 (2.0) | 5 (1.2) | – | ||
| 5 | What time do you usually sleep on weekdays? | −/− | −/− | −/− | |
| What time do you usually wake up on weekdays? | |||||
| What time do you usually sleep on weekends? | |||||
| What time do you usually wake up on weekdays? | |||||
| 6 | How is your health in general compared to others of your age?, n (%) | | | | <0.001 |
| | Very good | 420 (34.0) | 164 (40.5) | 16 (45.7) | |
| Good | 460 (37.3) | 176 (43.5) | 16 (45.7) | ||
| Fair | 283 (22.9) | 59 (14.6) | 3 (8.6) | ||
| Bad | 54 (4.4) | 6 (1.5) | – | ||
| Very bad | 17 (1.4) | – | – | ||
| 7 | Do you have your own bedroom for yourself?, n (%) | | | | 0.592 |
| | Yes | 779 (63.0) | 256 (63.1) | 25 (71.4) | |
| No | 458 (37.0) | 150 (36.9) | 10 (28.6) | ||
| 8a | Are there places/facilities near your home where you can play/exercise?, n (%) | | | 0.154 | |
| | Very many | 150 (12.2) | 51 (12.6) | 2 (5.7) | |
| Quite a lot | 314 (25.6) | 115 (28.5) | 12 (34.3) | ||
| Some | 314 (25.6) | 99 (24.5) | 14 (40.0) | ||
| Few | 258 (21.0) | 67 (16.6) | 4 (11.4) | ||
| Very few | 192 (15.6) | 72 (17.8) | 3 (8.6) | ||
| 8b | How safe do you feel to play/exercise in these places?, n (%) | | | | 0.042 |
| | Very safe | 394 (33.7) | 123 (31.4) | 9 (25.7) | |
| Safe | 492 (42.1) | 171 (43.6) | 24 (68.6) | ||
| Average | 222 (19.0) | 83 (21.2) | 1 (2.9) | ||
| Unsafe | 38 (3.2) | 7 (1.8) | 1 (2.9) | ||
| Very unsafe | 24 (2.1) | 8 (2.0) | – | ||
1Variables are presented as frequencies (percentages); p values were derived by comparisons between the gender using Pearson’s chi-square test; statistically significant associations are shown in bold.
2Variables are presented as frequencies (percentages); p values were derived by comparisons between the three categories of BMI using Pearson’s chi-square test or Monte Carlo test; statistically significant associations are shown in bold.
Perceptions of All Participants about Their Mood
The perceptions of all participants regarding their mood were determined according to gender (Table 3A) and BMI (Table 3B). No statistically significant relation was noted between gender or BMI and mood responses.
Table 3.
Perceptions of all participants about their mood: (A) according to gender; (B) according to BMI
| | Males | Females | Total | p value |
|---|---|---|---|---|
| 898 (52.0%) | 829 (48.0%) | 1,727 (100%) | ||
| (A)1 | ||||
| Mood perceptions, n (%) | | | | 0.481 |
| Very happy | 197 (28.3) | 203 (31.1) | 400 (29.7) | |
| Happy | 374 (53.8) | 337 (51.6) | 711 (52.7) | |
| Neutral | 96 (13.8) | 80 (12.3) | 176 (13.1) | |
| Unhappy | 21 (3.0) | 21 (3.2) | 42 (3.1) | |
| Very unhappy | 7 (1.0) | 12 (1.8) | 19 (1.4) | |
| | Obesity | Overweight | Normal BMI | p value |
|---|---|---|---|---|
| 1,277 (73.9%) | 413 (23.9%) | 37 (2.1%) | ||
| (B)2 | ||||
| Mood perceptions, n (%) | | | | 0.600 |
| Very happy | 309 (30.8) | 84 (26.2) | 7 (28.0) | |
| Happy | 513 (51.2) | 184 (57.3) | 14 (56.0) | |
| Neutral | 130 (13.0) | 42 (13.1) | 4 (16.0) | |
| Unhappy | 33 (3.3) | 9 (2.8) | – | |
| Very unhappy | 17 (1.7) | 2 (0.6) | – | |
1Variables are presented as frequencies (percentages); p values were derived by comparisons between the gender using Pearson’s chi-square test; statistically significant associations are shown in bold.
2Variables are presented as frequencies (percentages); p values were derived by comparisons between the three categories of BMI using Pearson’s chi-square test or Monte Carlo test; statistically significant associations are shown in bold.
Dietary Habits
Figure 2a illustrates the distribution of photos annotated according to gender. The proportion of boys eating breakfast is similar to that of girls (21.3% vs. 21.4%). A slight increase is observed for boys at lunch (33.9% vs. 32.7%), while the proportion of eating dinner remains similar (18.3% vs. 18.9%). Boys tend to snack slightly more often than girls (17.0% vs. 16.1%) but drink significantly less (7.6% vs. 9.0%). Figure 2b illustrates the distribution of photos annotated according to BMI. There is no notable difference in the proportion of subjects with obesity and those with overweight regarding the consumption of breakfast (21.4% vs. 21.3%) or lunch (33.3% vs. 33.1%). A significantly higher proportion of subjects with obesity eat dinner compared to those with overweight (19.1% vs. 17.0%). However, subjects with obesity use snacks slightly less often (16.4% vs. 17.4%) and drink significantly less often (7.9% vs. 9.7%) compared to their overweight counterparts.
Fig. 2.
Different types of meals eaten during the day (number of photos annotated). a According to gender. b According to BMI.
Evaluation of BMI Trajectory after Using the BigO System
In order to evaluate the BMI trajectory, we analyzed our data: (i) after using the BigO system twice, at the beginning and the end of a 6-month period, and (ii) after using the BigO system during the COVID-19 pandemic (March 2020–February 2021).
Evaluation of the BMI Trajectory after Using the BigO System at the Beginning and the End of a 6-Month Period
The study sample consisted of 430 children and adolescents (median [IQR]: 12.6 [10.7–14.4] years; 221 males, 209 females) (Table 4A). At initial evaluation, the percentage of subjects with obesity was 77.2%, overweight 21.9%, and normal BMI 0.9% (shown in Fig. 3a). A higher number of boys had obesity compared with girls (53.6% vs. 46.4%), while a higher number of girls had overweight compared with boys (56.4% vs. 43.6%). After using the BigO system twice, at the beginning and the end of a 6-month period, the proportion of subjects with obesity decreased by 4.2% (77.2% vs. 74.0%, p = 0.014), while the proportion of subjects with overweight and normal BMI increased by 10.5% and 111%, respectively (21.9% vs. 24.2%, 0.9% vs. 1.9%, p = 0.014) (shown in Fig. 3a). Similar changes were observed in both boys (shown in Fig. 3b) and girls (shown in Fig. 3c).
Table 4.
Clinical characteristics of subjects at initial assessment and assessment at 6 months: (A) after using the BigO system twice, at the beginning and the end of a 6-month period; (B) after using the BigO system twice during COVID-19 period
| | Initial assessment | Assessment at 6 months | p value |
|---|---|---|---|
| (A)1 | |||
| Gender, n (%) | | | |
| Male | 221 (51.4) | | |
| Female | 209 (48.6) | | |
| Age, years | 12.6 (10.7–14.4) | 13.0 (11.1–14.8) | <0.001 |
| Weight, kg | 69.4 (56.0–83.4) | 71.1 (58.3–85.0) | <0.001 |
| Height, cm | 157.1±12.2 | 159.1±11.9 | <0.001 |
| BMI, kg/m2 | 27.6 (25.0–30.9) | 27.8 (25.0–30.9) | 0.239 |
| z-score class, n (%) | | | 0.014 |
| Obese | 332 (77.2) | 318 (74.0) | |
| Overweight | 94 (21.9) | 104 (24.2) | |
| Normal BMI | 4 (0.9) | 8 (1.9) | |
| (B)2 | |||
| Gender, n (%) | | | <0.001 |
| Male | 137 (54.6%) | | |
| Female | 114 (45.4%) | | |
| Age, years | 12.8±2.6 | 13.0±2.6 | |
| Weight, kg | 71.8 (57.7–86.7) | 71.1 (58.2–86.0) | 0.927 |
| Height, cm | 157.8±12.3 | 158.4±12.1 | <0.001 |
| BMI, kg/m2 | 28.1 (25.6–31.6) | 27.6 (25.6–31.5) | <0.001 |
| Z-score class, n (%) | | | 0.267 |
| Obese | 193 (76.9) | 187 (74.5) | |
| Overweight | 58 (23.1) | 63 (25.1) | |
| Normal BMI | – | 1 (0.4) | |
1Continuous variables are presented as means ± SD or medians (interquartile range) and categorical as frequencies (percentages); p values were derived by comparisons between the two assessments using paired T test or Wilcoxon’s signed rank test for skewed variables and McNemar’s Bowker test for categorical variables; statistically significant associations are shown in bold.
2Continuous variables are presented as means ± SD or medians (interquartile range) and categorical as frequencies (percentages); p values were derived by comparisons between the two assessments using paired T test or Wilcoxon’s signed rank test for skewed variables and McNemar’s Bowker test for categorical variables; statistically significant associations are shown in bold.
Fig. 3.
Alteration in BMI after using the BigO system before and after a 6-month period of interventions. a In all subjects (n = 430). b In boys. c In girls.
Evaluation of the BMI Trajectory after Using the BigO System during the COVID-19 Pandemic (March 2020–February 2021)
The SARS-CoV-2 coronavirus (COVID-19) pandemic has led to lifestyle changes as a result of Public Health Regulations and Guidelines introduced by governments worldwide. We determined the BMI trajectory in children and adolescents with overweight and obesity with respect to the COVID-19 outbreak in Greece, while using the BigO system. For our analysis, we took into consideration patients that participated in the study from March 2020 until February 2021, who contributed data twice, at the beginning and the end of a 6-month period.
Overall, 251 (n = 251) children and adolescents (mean age ± SD: 12.8 ± 2.6; 137 males, 114 females) were studied prospectively during the COVID-19 period (Table 4B). At initial evaluation, the percentage of subjects with obesity was 76.9% and overweight 23.1% (shown in Fig. 4a). A higher number of boys had obesity compared with girls (54.9% vs. 45.1%). During the COVID-19 pandemic, following the use of the BigO system twice, at the beginning and the end of a 6-month period, the proportion of subjects with obesity decreased by 3.1% (76.9% vs. 74.5%), while the proportion of subjects with overweight and normal BMI subjects increased by 8.7% and 40%, respectively (23.1% vs. 25.1%, 0% vs. 0.4%) (shown in Fig. 4a). Similar changes were observed in both boys (shown in Fig. 4b) and girls (shown in Fig. 4c).
Fig. 4.
Alteration in BMI after using the BigO system during COVID-19 period for 6 months. a In all subjects (n = 251). b In boys. c In girls.
Evaluation of the Perceptions of Patients after Use of the BigO System Twice over a Period of 6 Months
The alterations in the perceptions of all participants regarding their diet, sleep, and physical activity habits, their general health, the exercise facilities available in their communities, their living conditions, and their mood after using the BigO system twice, at the beginning and the end of the study, are shown in Table 5. Following the use of the BigO system twice, the proportion of patients who thought they ate very much decreased significantly by 55.1% (p = 0.046), while the proportion of patients who thought that they ate average and not much at all compared to the others of their age increased significantly by 16.0% and 23.3%, respectively. Similar changes were observed in relation to their perception of their health in general. The proportion of patients who thought that their health was very bad, bad, or fair decreased significantly by 77.7%, 40.5%, and 23.1%, respectively, while the proportion of patients who thought that their health was good compared to their peers increased significantly by 24.5%.
Table 5.
Alterations in the perceptions of all participants regarding their diet, sleep, and physical activity habits, their general health, the exercise facilities available in their communities, their living conditions, and their mood after using the BigO system twice, at the beginning and the end of a 6-month period
| | Initial assessment | Assessment at 6 months | p value | |
|---|---|---|---|---|
| 1 | How much do you think you eat compared to others of your age?, n (%) | | | 0.046 |
| | Very much | 42 (9.8) | 18 (4.4) | |
| Quite much | 144 (33.6) | 134 (32.9) | ||
| Average | 198 (46.2) | 218 (53.6) | ||
| Not very much | 32 (7.5) | 22 (5.4) | ||
| Not much at all | 13 (3.0) | 15 (3.7) | ||
| 2 | How quickly do you think you eat compared to others of your age?, n (%) | | | 0.193 |
| | Very quickly | 34 (7.9) | 30 (7.4) | |
| Quite quickly | 101 (23.6) | 97 (23.8) | ||
| Average | 174 (40.7) | 143 (35.1) | ||
| Not very quickly | 78 (18.2) | 76 (18.7) | ||
| Not quickly at all | 41 (9.6) | 61 (15.0) | ||
| 3 | How active do you think you usually are compared to others of your age?, n (%) | | 0.402 | |
| | Mostly lying/sitting | 19 (4.4) | 16 (3.9) | |
| Mostly sitting | 18 (4.2) | 21 (5.2) | ||
| Sitting/standing/walking | 132 (30.9) | 120 (29.6) | ||
| Standing/walking most of the time | 103 (24.1) | 106 (26.1) | ||
| Exercising a lot | 155 (36.3) | 143 (35.2) | ||
| 4 | How well do you sleep atnight?, n (%) | | | 0.574 |
| | Very well | 152 (35.4) | 131 (32.3) | |
| Well | 149 (34.7) | 149 (36.7) | ||
| Average | 99 (23.1) | 97 (23.9) | ||
| Bad | 21 (4.9) | 22 (5.4) | ||
| Very bad | 8 (1.9) | 7 (1.7) | ||
| 5 | How is your health in general compared to others of your age?, n (%) | | | 0.038 |
| | Very good | 156 (36.4) | 145 (35.8) | |
| Good | 148 (34.6) | 175 (43.2) | ||
| Fair | 102 (23.8) | 74 (18.3) | ||
| Bad | 18 (4.2) | 10 (2.5) | ||
| Very bad | 4 (0.9) | 1 (0.2) | ||
| 6 | Do you have your own bedroom for yourself?, n (%) | | | 0.001 |
| | Yes | 251 (58.6) | 265 (65.3) | |
| No | 177 (41.4) | 141 (34.7) | ||
| 7a | Are there places/facilities near your home where you can play/exercise?, n (%) | | 0.625 | |
| | Very many | 52 (12.1) | 50 (12.4) | |
| Quite a lot | 97 (22.6) | 100 (24.9) | ||
| Some | 121 (28.2) | 101 (25.1) | ||
| Few | 87 (20.3) | 86 (21.4) | ||
| Very few | 72 (16.8) | 65 (16.2) | ||
| 7b | How safe do you feel to play/exercise in these places?, n (%) | | | 0.156 |
| | Very safe | 128 (31.5) | 136 (35.1) | |
| Safe | 175 (43.1) | 168 (43.3) | ||
| Average | 87 (21.4) | 65 (16.8) | ||
| Unsafe | 11 (2.7) | 11 (2.8) | ||
| Very unsafe | 5 (1.2) | 8 (2.1) | ||
| Mood perception, n (%) | | | 0.559 | |
| Very happy | 95 (27.4) | 105 (34.1) | | |
| Happy | 198 (57.1) | 160 (51.9) | ||
| Neutral | 38 (11.0) | 28 (9.1) | ||
| Unhappy | 12 (3.5) | 10 (3.2) | ||
| Very unhappy | 4 (1.2) | 5 (1.6) | ||
Variables are presented as frequencies (percentages); p values were derived by comparisons between the two assessments using McNemar’s Bowker test for categorical variables; statistically significant associations are shown in bold.
Assessment of BigO System in a Clinical Setting
The use of the BigO system in a clinical setting was assessed as a useful behavioral monitoring toolset for the monitoring of (i) the progress of site-specific clinical interventions (display of BMI trajectory) (shown in Fig. 5a), (ii) food and beverages consumed during the day (annotation of photographs) (shown in Fig. 5b), (iii) the physical activity (display of average physical activity per day per week), and (iv) the alteration in the perceptions of patients regarding their diet, sleep, and physical activity habits, their general health, the exercise facilities available in their communities, as well as their living conditions (display of responses).
Fig. 5.
a Monitoring of the body mass index (BMI) trajectory while using the BigO system. b Monitoring of the food and beverages consumed during the day (annotation of photographs) while using the BigO system.
Discussion
Overweight and obesity are chronic conditions associated with significant morbidity and mortality. The rising rates of overweight and obesity among children and adolescents suggest that current health policies are insufficiently effective, highlighting the need for more advanced and targeted interventions to combat the obesity epidemic [14–16, 35]. To be successful, these interventions must be evidence based and address the multiple environmental factors that contribute to obesity [36–39]. However, most existing data on children’s obesogenic behaviors are based on self-reports or reports from parents and guardians regarding diet and physical activity. These reports may lack accuracy [40, 41] and often fail to capture how children interact with their environment – for example, access to physical activity opportunities or exposure to various types of food retailers. Advancements in e-health and m-health technologies, particularly the convergence of mobile and wearable sensor systems with deep learning algorithms and big data analytics, have significantly transformed healthcare by promoting the implementation of precision and personalized medicine [42].
Although several scientific and health organizations have suggested the development of appropriate e-health and m-health applications to address the obesity epidemic, only a few studies have investigated their effectiveness in weight loss [21–25]. In our study, we evaluated the effectiveness of the BigO system in monitoring the obesogenic behavior of children and adolescents objectively, as well as the perception of participants about their diet, sleep and physical activity habits, their general health, their living conditions, and their mood. We demonstrated that the use of BigO system resulted in a significant reduction in the obesity rates, even during the COVID-19 pandemic. These findings indicate that the use of e-health applications that provide objective monitoring of lifestyle behaviors is effective in the management of obesity. We studied a large cohort of children and adolescents with obesity, overweight, and normal BMI. After using the BigO system for 1 month twice, at the beginning and the end of a 6-month period, the proportion of subjects with obesity decreased by 4.2%, while the proportion of subjects with overweight and normal BMI increased by 10.5% and 111%, respectively. In addition, the proportion of patients who thought they eat very much decreased significantly by 55.1%, while the proportion of patients who thought they eat average and not much at all compared to others of their age increased significantly by 16.0% and 23.3%, respectively. Similar changes were observed in relation to their perception regarding their health in general. The proportion of patients who thought their health was very bad decreased significantly by 77.7%, while the proportion of patients who thought their health was good compared to others of their age increased significantly by 24.5%.
Further to the above, we estimated the BMI trajectory during the COVID-19 pandemic (from March 2020 until February 2021) in subjects who also contributed data twice, at the beginning and the end of a 6-month period. The proportion of subjects with obesity decreased by 3.1%, while the proportion of subjects with overweight and normal-BMI subjects increased by 8.7% and 40%, respectively. Future studies will analyze other available data in the system to determine changes in obesogenic behaviors during the COVID-19 lockdown period(s) [43].
The main limitation of our study is the lack of a control group. However, we could not recruit a similar number of subjects to whom we would apply no intervention because the population in our study was recruited from our Center for the Prevention and Management of Overweight and Obesity in Childhood and Adolescence, where all patients expected to enter a management program during their follow-up period. All subjects were self-referred or referred by their pediatricians or general practitioners; they were motivated to reduce their BMI and were interested in entering the multidisciplinary lifestyle intervention program provided at our Center for the Prevention and Management of Overweight and Obesity in Childhood and Adolescence [30].
In conclusion, our findings suggest that the use of BigO system that provides objective monitoring of lifestyle obesogenic behaviors is effective in the management of overweight and obesity in childhood and adolescence. These results were evident even during the COVID-19 pandemic, which was associated with lifestyle changes promoting overweight and obesity. The BigO system was also effective in monitoring the perceptions of patients about their diet, sleep, and physical activity habits, their general health, their living conditions, and their mood. Future studies in children and adolescents with overweight and obesity are required to provide further evidence on the role of personal digital technologies, such as smart mobiles and accelerometers, create new possibilities for collecting behavioral data objectively, and investigate their effectiveness in preventing childhood obesity.
Acknowledgments
We are most grateful to the children and adolescents and their families for participating in our studies, as well as all the staff of the Center for the Prevention and Management of Overweight and Obesity.
Statement of Ethics
The study was conducted in accordance with the Declaration of Helsinki and was approved by the Committee on the Ethics of Human Research of “Aghia Sophia” Children’s Hospital (Approval No. EB-PASCH-MoM: 29/11/2017, Re: 26660-16/11/2017). Written informed consent was obtained from all subjects involved in the study by a parent/guardian, and assent was given by all children and adolescents.
Conflict of Interest Statement
Prof. Evangelia Charmandari was a member of the journal’s Editorial Board at the time of submission. The authors have no conflicts of interest to declare.
Funding Sources
The work leading to these results received funding from the European Commission’s Horizon 2020 project under Grant Agreement No.: 727688 (BigO, http://bigoprogram.eu).
Author Contributions
Conceptualization, resources, writing – original draft preparation, supervision, and project administration: P.K. and E.C.; methodology, validation, and writing – review and editing: P.K., M.M., and E.C.; software, investigation, and data curation, and visualization: P.K. and M.M.; formal analysis: M.M.; and funding acquisition: E.C. All authors have read and agreed to the published version of the manuscript.
Funding Statement
The work leading to these results received funding from the European Commission’s Horizon 2020 project under Grant Agreement No.: 727688 (BigO, http://bigoprogram.eu).
Footnotes
E-health in clinical settings refers to the application of digital technologies – such as electronic health records (EHRs), telemedicine, clinical decision support systems (CDSS), and mobile health tools – to enhance the quality, efficiency, and accessibility of healthcare delivery. It enables healthcare professionals to diagnose, treat, and monitor patients more effectively, while also improving care coordination and patient engagement [17].
M-health (mobile health) refers to the use of mobile and wireless technologies – such as smartphones, tablets, wearable devices, and mobile apps – to support the delivery of healthcare and health-related services. It is a subcategory of e-health and is widely used for health monitoring, disease management, health education, remote diagnostics, and communication between patients and healthcare providers [18].
Deep learning is a subset of machine learning in artificial intelligence (AI) that uses neural networks with many layers (hence “deep”) to model and understand complex patterns in data. It is particularly effective in tasks such as image and speech recognition, natural language processing, and predictive analytics and is widely applied in fields like healthcare, finance, robotics, and autonomous systems [19].
Big data analytics refers to the process of examining large and complex data sets – often from multiple sources – to uncover hidden patterns, correlations, trends, and insights that can support decision-making. It uses advanced techniques such as machine learning, statistical analysis, and data mining to extract meaningful information from data characterized by high volume, velocity, variety, and veracity (the “4Vs”) [20].
Data Availability Statement
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.
