Smart App and Wearable Device-Based Approaches for Contactless Public Healthcare for Adolescents in Korea

Abstract

In Korea, the Public Health Center Mobile Healthcare Project was implemented in 2016. This project utilizes Information and Communication Technology (ICT) and big data to establish a health-related service foundation and a healthcare service operation system. Equipment and methods: This study recruited 1261 adolescents (660 males (13.40 ± 1.14 years, 156.12 ± 10.59 cm) and 601 females (13.51 ± 1.23 years, 154.45 ± 7.48 cm)) from 22 public health centers nationwide. Smart bands were provided, and the ‘Future Health’ application (APP) was installed on personal smartphones to assess body composition, physical fitness, and physical activity. Results: A paired sample t-test revealed height, 20 m shuttle run, grip strength, and long jump scores significantly differed after 24 weeks in males. Females exhibited significant height, 20 m shuttle run, grip strength, sit-ups, and long jump differences. Moderate physical activity (MPA, p < 0.001), vigorous physical activity (VPA, p < 0.001), and moderate-to-vigorous physical activity (MVPA, p < 0.001) were significantly different after 24 weeks in adolescents. These results establish that an ICT-based health promotion service can provide adolescent students with individual information from a centralized organization to monitor health behaviors and receive feedback regardless of location in South Korea.

1. Introduction

The global prevalence of overweight and obesity has surged dramatically. From 1975 to 2016, the prevalence of overweight/obesity among children and adolescents aged 5–19 years escalated from 4% to 18% [1]. According to the National Korean School Health Examination Survey, in which participants self-reported their height and weight, childhood (6–18 years) obesity in South Korea increased from 8.7% in 2007 to 15.0% in 2017 [2]. Males exhibited a higher overweight and obesity prevalence, adolescents aged 13–15 years had the highest severe obesity prevalence, and positive linear trends were significant for both sexes and all ages for overweight, class 1 obesity, and class 2 obesity [3].

Obesogenic behaviors often begin at a young age and may have a lasting impact on adult weight status and health [4]. Obesity prevention efforts help patients and families establish healthy home habits and model, teach, and reinforce healthy habits in youth [5]. In 40 Korean adolescent girls with obesity (BMI ≥ 30 kg/m²), prehypertension, hyperinsulinemia, and abdominal obesity, 12 weeks of resistance and aerobic exercise training significantly improved body composition and other hemodynamic parameters [6]. These findings suggest that combined resistance and aerobic exercise may be a useful treatment for obesity comorbidities [6]. A positive association between moderate-to-vigorous physical activity (MVPA) and fitness was observed in males (β = 0.013, 95% CI: 0.005; 0.021) and females (β = 0.014, 95% CI: 0.006; 0.022), indicating that public health recommendations to promote physical fitness in youth should target MVPA and should also consider reducing sedentary time [7]. Various studies have corroborated the influence of physical activity or exercise on weight-to-height, bone, and muscle tissue growth in children and adolescents [8].

Household and individual physical activity habits have become even more crucial since the COVID-19 pandemic. Physical activity decreased significantly during the COVID-19 pandemic, ranging from −10.8 to −91 min/day. These statistics urge governments to consider the negative impact of restrictive measures on physical activity among children and adolescents and ensure high levels of physical activity [9]. Physical activity is essential for a healthy childhood and adolescence, and the WHO recommends 60 min of MVPA a day, such as walking, biking, recreational sports, and active play, for children and adolescents aged 5–17 [10].

In Korea, the Public Health Center Mobile Healthcare Project was implemented in 2016. This project employed Information and Communications Technologies (ICT) and big data to establish a foundation for health-related services and a healthcare service operation system (this project allows intelligence agencies to monitor participants’ physical activity information using wireless wearable devices). Previous studies reported that females in urban areas were significantly taller and had lower BMIs than females in rural areas, and males in urban areas also had lower BMIs than males in rural areas [11]. Obesity is more common in rural children compared with urban children [12]. These technologies break down the boundaries between urban and rural areas and ensure that all Korean adolescents have equal opportunities.

In 2021, the Korea Health Promotion Institute began providing mobile-based healthcare services using ICT to overcome in-person healthcare service limitations from the COVID-19 pandemic [13]. Therefore, this study aimed to assess physical fitness and activity health promotion through an ICT-based health promotion service approach for public health surveillance among adolescents in South Korea.

2. Methods

2.1. Participation

This study was conducted in 22 public health centers nationwide and included 1261 (660 males and 601 females) elementary (grades 5 and 6) and middle school students (grades 1–3). The students owned smartphones and had not engaged in regular exercise or physical activity for at least three months. The study was conducted in four metropolitan cities with a population of more than 10 million (Seoul, Gyeonggi-do, Gwangju, and Daegu) and seven rural areas with a population less than a metropolitan city (Gangwon-do, Chungcheongnam-do, Chungcheongbuk-do, Gyeongsangnam-do, Gyeongsangbuk-do, Jeollanam-do, and Jeju) to reflect characteristics of participants living in urban and rural areas (Figure 1).

Before participation, the purpose and contents of the study were fully explained to parents and participating students in writing, and consent to participate in the study was obtained. Smart bands (PWB-600, Partron, Hwaseong, Republic of Korea) were provided, and the ‘Future Health’ application (APP) for elementary and middle school students was installed on personal smartphones and synchronized through smart band pairing. Training on how to use the APP was provided.

The data set was drawn from a retrospective cohort based on the Republic of Korea Health Promotion Institute Informatics Data (KHPIID), and separate participant recruitment procedures were not carried out. As the data were de-identified, the informed consent of the participant was not applicable. In the KHPIID, de-identified join keys replacing personal identifiers are used to secure ethical clearance. The study complied with the ethical standards of the Declaration of Helsinki.

Table 1. Participant characteristics.

Variable	Total (n = 1261)	Male (n = 660)	Female (n = 601)
Age (years)	13.45 ± 1.18	13.40 ± 1.14	13.51 ± 1.23
Height (cm)	155.33 ± 9.27	156.12 ± 10.59	154.45 ± 7.48
Weight (kg)	53.75 ± 18.41	56.34 ± 17.13	50.90 ± 19.35
BMI (kg/m2)	21.97 ± 6.42	22.72 ± 4.90	21.14 ± 7.66

Values are presented as mean ± standard deviation. BMI; body mass index.

lists the physical characteristics of participants.

2.2. Procedures

Participants completed body composition, physical fitness, and physical activity surveys before the start of the study and after 24 weeks. All measurements were taken twice, using the same items simultaneously. Participants wore a smart band for 24 weeks, and a specialized application was installed on the participants’ smartphones to store their records and provide information.

2.3. Body Composition

The participant’s body composition was measured using an automated height and weight (SECA 284; Seca, Hamburg, Germany). Height and weight data were used to calculate the body mass index (BMI; weight (kg)/height (m²)).

2.4. Physical Fitness

The physical fitness measurement method was based on the physical activity promotion system (PAPS) manual within the Ministry of Education’s School Sports Promotion Act [14]. A round-trip long run with a certain distance was continuously repeated with music to evaluate cardiorespiratory endurance. To evaluate muscle strength, we used a digital dynamometer (TKK 5401 GRIP-D, Takei, Japan) to measure right and left handgrips in an upright position with both arms abducted to 30 degrees twice for each side. If participants twisted during the measurement, or the dynamometer touched the body or bent the arm, the test was repeated at the end, and results were recorded to the nearest 0.1 kg. Flexibility was measured with the sit-and-reach test. Participants were asked to remove their sneakers and sit with their knees extended so that the soles of both feet were in full contact with the vertical surface of the measuring device. When instructed to start, participants extended their hands under the measuring device’s scale with their chest fully forward and their upper body flexed. The point where their fingertips stopped was recorded. The best of the two measurements was recorded to the nearest 0.1 cm. The standing long jump (K-108, KL Sports, Seoul, Republic of Korea) was measured next. Without stepping on the white line above the starting point, participants jumped as far as possible. The participants were asked to jump on all fours, making sure not to cross the starting line with either foot. Participants repeated the test twice to reflect their best performance. For the sit-up test, participants bent their knees with their feet flat about 45 cm from the buttocks; scores were measured using testing equipment (K-111, KL Sports, Republic of Korea). Participants touched their elbow to the sensor with each sit-up, performing as many sit-ups in 1 min as possible. Lastly, the 50 m run was measured. Participants stood at a starting line and waited for the start signal. Once the participants started running, they ran as fast as possible for 50 m straight.

2.5. Smartphone-Based Healthcare APP; Future Health

‘Future Health’ is a smartphone mobile-based healthcare APP for adolescents used in this study to reorganize the output information of the “web-based personal exercise prescription system” developed by Kim et al. [15]. This APP includes an easy-to-navigate mobile environment for elementary and middle school students. Personal physical activity information was automatically linked from the smart band to the APP to check activity information in real time. The physical activities performed separately were detailed in a physical activity diary to provide cumulative activity information as statistical data.

Exercise counseling for active participation was categorized by grade level and gender based on cardiorespiratory fitness test results (levels 1 to 5). Activity levels were predicated on the American College of Sports Medicine (ACSM) daily exercise calorie-burning guidelines [16]. The calories per minute (Kcal/min) from the “Physical Activity Classification for Energy Expenditure in the Human Body” presented by Ainsworth et al. were used to describe the exercise time and goals for each activity [17]. In addition, we created exercise videos and embedded them into the community so students could watch and perform the exercises themselves, making them more accessible.

The programming language based on Java/React was provided as a mobile APP-based service. The ‘Administrator Web System’ was configured to provide self-health management, monthly reports, and counseling services, and the database file comprised tables in five fields: user, counseling, health content, service, and body measurement information. The user was guided through the automation module service for points, health content, and monthly reports. Health content was provided through card news and videos every Monday for six months. This content included health-related information besides physical activity and the five daily missions (walking 6000 steps, strength training, eating vegetables, not drinking sugary drinks, and not eating fast food).

2.6. The International Physical Activity Questionnaire (IPAQ)

For this study, we used the mobile-based Korean version of the IPAQ, which is based on the once-weekly long-term self-administered version found in the IPAQ Operations Manual. The IPAQ, which consists of seven items, captures total time spent on moderate to vigorous physical activity (MVPA), walking physical activity, and inactivity over the past seven days. It was specifically designed to collect information about walking, moderate-intensity physical activity (MPA), and vigorous-intensity physical activity (VPA) (i.e., number of times and average times per session) on weekdays and weekends. The questions about participation in moderate to vigorous physical activity (MVPA) were supplemented with examples of activities that are typically performed. Data obtained from the questionnaire were summed for each item (e.g., vigorous-intensity, moderate-intensity, walking) to estimate the total time spent in physical activity during the week [18].

2.7. Statistical Analysis

All results were reported as the mean ± standard deviation and analyzed using SPSS version 25.0 (SPSS Inc., Chicago, IL, USA). Means and standard deviations were computed for all variables. The participant’s characteristics (height, weight, and BMI), physical fitness (20 m shuttle run, sit-and-reach, grip strength, sit-ups, long jump, and 50 m run), and physical activity (MPA, VPA, and MVPA) were analyzed for significant differences before and after the assessment using a paired sample t-test. The participant group with individuals who lived in urban and rural regions was further analyzed for significant differences using a one-way ANOVA. A post hoc analysis (Bonferroni) was used to compare significant differences (p = 0.05).

3. Results

3.1. Changes Before and After 24 Weeks Wearing the Smart Band

Table 2. (a) Differences before and after 24 weeks of wearing the smart band (male = 660). (b) Differences before and after 24 weeks of wearing the smart band (female = 601).

(a)
Variable	Pre	Post	p-Value
Height (cm)	156.12 ± 10.59	158.76 ± 9.97	<0.001
Weight (kg)	56.34 ± 17.13	58.61 ± 17.50	0.060
BMI (kg/m2)	22.72 ± 4.90	22.89 ± 5.00	0.640
20m shuttle run (rep)	53.45 ± 30.96	58.81 ± 30.72	0.023
Sit-and-reach (cm)	6.07 ± 9.40	5.56 ± 8.03	0.351
Grip strength (kg)	23.39 ± 8.69	26.91 ± 10.26	<0.001
Sit-ups (rep)	49.08 ± 44.24	52.88 ± 42.75	0.596
Long jump (cm)	149.41 ± 40.92	150.16 ± 29.35	<0.001
50m run (sec)	10.31 ± 4.00	9.39 ± 1.30	0.230
(b)
Variable	Pre	Post	p-Value
Height (cm)	154.45 ± 7.48	156.55 ± 6.93	<0.001
Weight (kg)	50.90 ± 19.35	52.06 ± 12.32	0.241
BMI (kg/m2)	21.14 ± 7.66	21.11 ± 4.14	0.984
20m shuttle run (rep)	35.67 ± 17.36	58.69 ± 26.04	<0.001
Sit-and-reach (cm)	11.98 ± 9.01	12.14 ± 9.24	0.776
Grip strength (kg)	22.41 ± 6.70	24.03 ± 7.25	0.003
Sit-ups (rep)	27.74 ± 16.74	40.47 ± 38.30	0.022
Long jump (cm)	142.00 ± 31.60	146.15 ± 27.19	0.033
50m run (sec)	9.87 ± 1.43	9.62 ± 1.40	0.076

Values are presented as mean ± standard deviation. BMI; body mass index.

presents height, weight, BMI, and physical fitness changes for males (Table 2a) and females (Table 2b). A paired sample t-test revealed that height (p < 0.001), 20 m shuttle run (p = 0.023), grip strength (p < 0.001), and long jump (p < 0.001) scores were significantly different after 24 weeks in males. Concerning females, there were significant differences in height (p < 0.001), 20 m shuttle run (p < 0.001), grip strength (p = 0.003), sit-ups (p = 0.022), and long jump (p = 0.033) scores.

Figure 2 presents physical activity changes. A paired sample t-test indicated that MPA (p < 0.001), VPA (p < 0.001), and MVPA (p < 0.001) were significantly different after 24 weeks in males and females.

3.2. Changes in Adolescents Relative to Urban or Rural Regions

Table 3. Changes in male adolescents relative to urban or rural Regions.

Variables	Urban (n = 384)		Rural (n = 276)		p-Value	Post-Hoc
	Pre (a)	Post (b)	Pre (c)	Post (d)
Height (cm)	153.14 ± 9.31	157.66 ± 10.40	158.25 ± 13.70	159.53 ± 9.60	<0.001	a < b, c, d
Weight (kg)	51.87 ± 15.11	56.94 ± 18.00	60.10 ± 18.18	59.77 ± 17.07	<0.001	a < b, c, d
BMI (kg/m2)	21.81 ± 4.73	22.51 ± 5.09	23.48 ± 5.12	23.16 ± 4.92	<0.001	a < c, d
20m shuttle run (rep)	58.99 ± 32.46	62.09 ± 31.07	48.27 ± 27.15	55.87 ± 30.80	<0.001	c < a, b, d
Sit-and-reach (cm)	3.87 ± 9.00	5.91 ± 7.83	7.44 ± 9.41	5.50 ± 8.19	<0.001	c > a, b, d
Grip strength (kg)	19.78 ± 6.44	26.10 ± 10.26	26.72 ± 9.29	26.45 ± 9.73	<0.001	a < b, c, d
Sit-ups (rep)	25.58 ± 16.41	55.19 ± 47.37	26.72 ± 47.37	51.73 ± 42.40	<0.001	a < b, d b > a, c
Long jump (cm)	139.68 ± 49.01	162.33 ± 29.79	161.78 ± 26.74	158.49 ± 27.90	<0.001	a < b, c, d
50m run (sec)	9.69 ± 0.80	10.21 ± 4.91	10.09 ± 3.97	9.90 ± 3.00	0.962	-
MPA (min/week)	185.14 ± 226.7	256.80 ± 369.5	181.81 ± 210.52	252.72 ± 349.0	0.008	c < d
VPA (min/week)	152.22 ± 214.5	198.95 ± 359.8	141.92 ± 192.9	202.63 ± 320.6	0.001	a < b, d b > a, c
MVPA (min/week)	337.35 ± 391.9	455.75 ± 650.7	323.73 ± 355.7	455.35 ± 610.4	0.001	a < d b > c

Values are presented as mean ± standard deviation. BMI; body mass index, MPA; moderate physical activity, VPA; vigorous physical activity, MVPA; moderate-to-vigorous physical activity.

presents height, weight, BMI, physical fitness, and physical activity changes for males before and after 24 weeks, based on urban or rural regions. A one-way ANOVA with post hoc Bonferroni test determined that height (p < 0.001), weight (p < 0.001), BMI (p < 0.001), 20 m shuttle run (p < 0.001), sit-and-reach (p < 0.001), grip strength (p < 0.001), sit-ups (p < 0.001), long jump (p < 0.001), MPA (p = 0.008), VPA (p = 0.001), and MVPA (p = 0.001) scores were significantly different among groups.

3.3. Changes in Female Adolescents Relative to Urban or Rural Regions

Table 4. Changes in female adolescents relative to urban or rural regions.

Variables	Urban (n = 264)		Rural (n = 337)		p-Value	Post-Hoc
	Pre (a)	Post (b)	Pre (c)	Post (d)
Height (cm)	152.41 ± 7.26	156.95 ± 7.14	156.13 ± 7.27	156.24 ± 6.75	<0.001	a < b, c, d
Weight (kg)	49.95 ± 25.97	52.51 ± 13.00	52.57 ± 13.03	51.70 ± 11.77	0.016	a < c
BMI (kg/m2)	20.93 ± 10.69	21.15 ± 4.24	21.34 ± 4.57	21.07 ± 4.07	0.719	-
20m shuttle run (rep)	43.97 ± 24.16	60.40 ± 25.61	36.90 ± 17.97	56.47 ± 24.84	<0.001	c < a, b, d
Sit-and-reach (cm)	10.65 ± 8.49	13.19 ± 9.03	13.11 ± 9.32	11.33 ± 9.33	0.001	a < b, c b < c
Grip strength (kg)	19.68 ± 5.04	23.66 ± 7.02	24.18 ± 7.14	24.19 ± 7.72	<0.001	a < b, c, d
Sit-ups (rep)	23.94 ± 13.33	31.82 ± 30.89	35.21 ± 21.38	32.54 ± 26.45	0.001	a < c, d
Long jump (cm)	133.67 ± 33.15	147.95 ± 27.82	149.68 ± 28.25	143.23 ± 28.37	<0.001	a < b, c, d
50m run (sec)	10.21 ± 1.08	9.99 ± 1.06	10.34 ± 1.38	9.88 ± 1.40	<0.001	a > b, c, d
MPA (min/week)	147.34 ± 173.1	176.66 ± 258.6	143.60 ± 177.5	201.0 ± 325.4	0.019	d > a, c
VPA (min/week)	101.06 ± 151.9	129.93 ± 241.1	79.63 ± 127.3	106.04 ± 227.4	0.008	b > c
MVPA (min/week)	248.40 ± 280.9	305.60 ± 454.5	223.22 ± 259.3	307.03 ± 480.9	0.009	d > c

Values are presented as mean ± standard deviation. BMI; body mass index, MPA; moderate physical activity, VPA; vigorous physical activity, MVPA; moderate-to-vigorous physical activity.

presents height, weight, BMI, physical fitness, and physical activity changes for females before and after 24 weeks, based on urban or rural regions. A one-way ANOVA with post hoc Bonferroni test indicated that height (p < 0.001), weight (p = 0.016), 20 m shuttle run (p < 0.001), sit-and-reach (p = 0.001), grip strength (p < 0.001), sit-ups (p = 0.001), long jump (p < 0.001), 50 m run (p < 0.001), MPA (p = 0.019), VPA (p = 0.008), and MVPA (p = 0.009) scores were significantly different among groups.

4. Discussion

This study found that participation in an ICT-based health promotion service for 24 weeks significantly improved physical fitness in adolescents, such as the 20 m shuttle run, grip strength, long jump, and sit-ups. Notably, MPA, VPA, and MVPA physical activity significantly increased after 24 weeks. Furthermore, there were body composition, physical fitness, and physical activity differences among adolescents living in urban or rural areas.

The rapidly developing ICT-enabled healthcare is a promising healthcare delivery system with unprecedented usage [19]. In chronic diseases, various self-management devices allow patients to monitor their health status in real-time outside specialized clinics [20]. Moreover, digital health interventions are essential in the global fight against infectious diseases caused by COVID-19 [21]. For example, 313 children and their parents participated in a 6-month mobile health technology program designed to help parents achieve a healthy weight and body fat for their children and improve their eating habits and physical activity. There were significant intervention effects on mean composite scores comprised of diet and physical activity variables, and these effects were more pronounced in children with a high-fat mass index [22]. Similarly, a 12-month randomized controlled trial study of digital health interventions found significant and sustained improvements in physical capacities and body composition. This adolescent-appealing mobile health intervention offered a compelling approach for obese adolescents with limited access to health services [23]. Homecare users (receiving a tablet with a fitness app and an activity tracker [treatment group]) strongly agreed that after four months, they increased their regular physical activity by 28 percentage points (p < 0.001; 95% CI: 0.20, 0.36) and reported exercising at least once a week by 45 percentage points (p < 0.001; 95% CI: 0.32, 0.57) [24]. In this study, elementary and middle school students participated in a 24-week ICT-based health promotion service, achieving significant increases in physical fitness, such as the 20 m shuttle run, grip strength, long jump, and sit-ups. The increase in height is characteristic of growing adolescents, and the unchanged weight is a positive effect of increased physical activity. MPA, VPA, and MVPA elevated significantly after 24 weeks for male and female adolescents (Figure 3). Previous studies evaluating ICT programs have reported higher participant utilization and retention rates with improvements in body composition and other health-related clinical indicators [25]. Moreover, the proportion of patients achieving 12.5 METs or more was significantly higher in the group receiving information via ICT than in the control group [26]. This improved effectiveness is due to the “push” approach, where automatic and specific materials (such as web hyperlinks and personalized feedback) are delivered directly to the participant through personal emails or short message services. This approach makes ICT-based interventions more accessible to participants’ existing lifestyles, as they only need to check their email or message services on their cell phones [27]. This study was also designed to collect feedback from adolescents based on ICT to improve their health. These technologies eliminate space and time constraints and can provide and share information with anyone.

Our study found that urban and rural students were given equal opportunities. A preliminary study of health behaviors among urban and rural students in the Republic of Korea revealed that urban students reported higher participation in VPA than rural students [28]. In this study, we examined the body composition, physical fitness, and physical activity of male and female adolescents from urban and rural locations. For males and females, physical activity was higher among those in urban regions before the ICT intervention. After 24 weeks, male and female adolescents exhibited an increase in MPA, VPA, and MVPA, with a significant increase in MPA for rural adolescents and a significant increase in MVPA for rural female adolescents. PA availability was positively associated with physical fitness for urban and rural adolescents. In contrast, screen time was negatively associated with PA and physical fitness, especially for rural students. In addition, factors promoting PA frequency included the availability of PA time and parental education; parental PA habits positively influenced PA for urban students [29]. This study also found that urban students performed more physical activity than rural students before the intervention, but rural students displayed a higher increase. This result is a signature feature of ICT, as it can transcend space and time to provide the same information to urban and rural adolescents. In addition, the body composition and physical fitness of urban and rural students were unexpectedly different. Previous studies report that rural students have lower physical fitness and body composition levels than urban students [30,31,32]. In this study, we found that urban male and female adolescents were significantly smaller than their rural counterparts before the intervention but grew similarly after 24 weeks. Regarding physical fitness, urban male and female adolescents outperformed rural adolescents in the 20 m shuttle run and 50 m run but exhibited lower scores in the sit-and-reach, grip strength, sit-ups, and long jump. Although there were differences between these groups, ICT-based health promotion services for 24 weeks achieved physical improvement in urban and rural adolescents.

The present study acknowledges some limitations. First, the questionnaire used was a standard survey to measure physical activity, but it exhibits low objectivity. Second, urban and rural areas were classified by population. This classification is insufficient for the infrastructure survey, which is an environmental aspect. The group also did not have a control group. This should be considered in future studies. Finally, ICT-based health promotion service wearable devices were used, but the participants’ biometric signal information was not obtained. In future studies, sufficient research should consider these factors. In addition to addressing these limitations in future research, the main advantage of ICT devices is that they allow for immediate feedback. This should be utilized to provide feedback if an adolescent falls short of their physical activity level to ensure that they are on track to achieve their weekly physical activity level.

5. Conclusions

We provided ICT-based health promotion services to elementary and middle school students for 24 weeks, free from space and time constraints. MPA, VPA, and MVPA increased for male and female adolescents regardless of where they lived. There was also an increase in physical activity and, by extension, fitness. These results indicate that an ICT-based health promotion service can provide adolescent students with individual information from a centralized organization (government) to monitor their health behaviors and receive feedback regardless of location. Public health centers in each municipality can provide feedback based on these data to improve the health of adolescent students, so they can know in advance what is lacking and take action. Moreover, our study determined that ICT-based health promotion services can monitor participants around the clock. However, these results may not represent the entire story; therefore, future studies should be multifaceted.