Efficacy of Emerging Technologies to Manage Childhood Obesity

Figures & data

Table 1 Characteristics of the Included Literature

Study Number	Author, Year	Study Design	Study Purpose	Participant Characteristics
				Number of Participants 1) Intervention 2) Control	Gender	Age in Years	Characteristics
1	Lindberg et al., 2016^Citation35	RCT	Education and Prevention	1)32 2)29	M + F	10	Healthy 3rd grade elementary children
2	Yang et al., 2017^Citation36	Non-RCT	Prevention	1) 558 2) 288	M + F	10–12	Healthy school students
3	Argarini et al., 2020^Citation37	Experimental study	Treatment	17	M + F	6–12	BMI >85th percentile
4	Adamo et al., 2010^Citation38	Experimental study	Activity	30	M + F	12–17	Overweight children
5	Staiano et al., 2018^Citation39	RCT	Treatment	1)23 2)23	M + F	10–12	BMI z-score
6	Ruggiero et al., 2020^Citation40	Comparative study	Experiment	48	M + F	7–13	–
7	Trost et al., 2014^Citation41	Group RCT	Activity	1)34 41	M + F	8–12	BMI >85th percentile
8	Staiano et al., 2017^Citation42	RCT	Activity	1)21 2)19	F	14–18	Overweight and obese girls
9	Maddison et al., 2011^Citation43	RCT	Activity	1)160 162	M + F	10–14	Overweight and obese children
10	Duman et al., 2016^Citation44	Experimental study	Activity	50	M + F	12–16	Slightly overweight and obese
11	Wagener et al., 2012^Citation45	Experimental study	Activity	1)21 20	M + F	12–18	BMI >95th percentile
12	Coknaz et al. 2019^Citation46	RCT	Prevention	106	M+F	9.6 (mean)	Healthy children
13	Gao et al. 2019^Citation47	RCT	Prevention	32	M+F	4–6 yrs	Healthy children
14	Curiel et al., 2020^Citation48	Experimental study	Training and Prevention	60	M + F	9 (mean)	Healthy children
15	Nawi et al., 2015^Citation49	RCT	Education	1)47 2)50	M + F	16	BMI >25 kg/m^2
16	Rio et al., 2019^Citation50	Quasi-experimental control trial	Activity and Education	1)13 2)10	M + F	6–12	Obese children
17	Chen et al., 2019^Citation51	RCT	Treatment and Prevention	2)54	M + F	12–15	BMI ≥85thpercentile
18	Rerksuppaphol et al. 2017^Citation52	RCT	Prevention	3)217	M+F	10.6 (mean)	Healthy children
19	Nystrom et al. 2017^Citation53	RCT	Prevention	4)263	M+F	4.5 (mean	Healthy children
20	Hammersley et al. 2019^Citation13	RCT	Prevention	5)86	M+F	2–5 yrs	Healthy children
21	Fiechtner et al., 2016^Citation54	Experimental study	Monitoring	498	M = F	6–12	BMI >95th percentile
22	Lingren et al., 2016^Citation55	ML-based study	Detection	428	M + F	1–6	–
23	Rios-Julian et al., 2017^Citation56	ML-based study	Screening	221	M + F	6–13	–
24	Fergus et al., 2015^Citation57	ML-based study	Screening	28	M + F	10–11	–
25	Dugan et al., 2015^Citation58	ML-based study	Screening	7519	M + F	0–2	–
26	Balbir et al., 2020^Citation59	ML-based study	Screening	18,818	M + F	9 months–14 years	–

Abbreviations: BMI, body mass index; F, female; M, male; ML, machine learning; RCT, randomized controlled trial.

Table 2 Key Findings of the Studies

Study Number	Author, Year	Intervention	Intervention Duration and Intensity	Intervention Strategy	Key Findings
1	Lindberg et al., 2016^Citation35	Exergame RO2 with smartphone and wrist band using South Korean PE curriculum	1 week, 15 min per student	The intervention group studied the curriculum by playing RO2, while the control group learned the curriculum using handouts. Questionnaires were developed to be answered by game groups to rate RO2. A quiz was held at the end of the intervention to calculate learning and retention in both groups.	Positive outcomes were observed; exergames motivated students to play irrespective of their physique. Learning via RO2 was more effective, kept students involved, and increased their heart rates. Learning outcomes showed significant variations between both groups.
2	Yang et al., 2017^Citation36	HAPPYME platform using quests involving physical activities and healthy dietary habits	12-week test and 6-month follow-up	HAPPYME platform helps teachers and parents to monitor and provide encouragement to participating children. The application asks students to complete quests involving physical activities and healthy eating habits that help prevent obesity. The child acquires points after completing each quest, which adds a motivational aspect. The platform sends feedback to children and parents for monitoring their daily and weekly performance.	Demonstrated the efficacy of a mobile service with a comprehensive intervention program by measuring anthropometric parameters, such as body weight, height, BMI, and percentiles.
3	Argarini et al., 2020^Citation37	Moderate-intensity exergaming	12 exercises (30–40 min per session) 3 sessions per week for 4 weeks	All measurements were made three days before the first exercise and after exercise, except energy expenditure. Exergaming was played using Microsoft Xbox 360 and Kinect Consoles. Game types: Kinect Dance and Kinect Sports.	Normal distribution with no significant difference between boys and girls. Regular moderate exergaming for 4 weeks in obese and overweight children can reduce BMI and improve fundamental movement skills.
4	Adamo et al., 2010^Citation38	Interactive video game with stationary cycling	10 weeks	Divided into two groups: (1) interactive video game and stationary cycling (GameBike) and (2) stationary cycling and music.	Similar improvements were observed in the body composition. Cholesterol profiles and fitness in both music and cycling as well as stationary cycling while playing video games in overweight and obese teenagers resulted in improved attendance and a robust concentration of physical activity.
5	Staiano et al., 2018^Citation39	Exergaming using a gaming console	1 h per session, 3 sessions a week for 24 weeks	Randomized group: Four exergames using Kinect and Xbox 360 gaming console. A FitBit Zip was worn during the 24-week period. Regular meetings with a fitness coach to keep the child motivated.Control group: Maintained their regular physical activity. Xbox given at final clinical visit.	BMI z-score, cardiometabolic health, and physical activity levels all improved when performed at home regularly.
6	Ruggiero et al., 2020^Citation40	Exergaming and educational exercises	Not available	Physical activity and nutrition education combined into technological game created to encourage youth for healthier behavioral change to combat obesity. The educational exergame “MyPlatePicks” promoted movement, delivered knowledge, improved motivation, and changed behavior associated with healthy eating and physical activity.	Initial evaluation showed positive results, changes in physical activity, and healthy eating behavior.
7	Trost et al., 2014^Citation41	Active gaming and educational program	8–16-week study	A family-based pediatric weight management program (JOIN for ME) was provided to the participants. Gaming console and motion capture device with two activity games were given to the activity group. Console was given to the program group only after program completion.	Adding active video gaming for pediatric weight management curriculum showed promising effects on the activity and weight.
8	Staiano et al., 2017^Citation42	Dance-based exergaming	1 h per session, 3 sessions per week for 4 weeks	Kinect for Xbox 360 used for dance-based exergaming using Just Dance and Dance Central. Attendance was encouraged, and motivation to exercise was performed by providing incentives. Control group maintained their normal level of activity.	Bone mineral density increased and body fat reduced.
9	Maddison et al., 2011^Citation43	Active video games	24 weeks	Intervention group used the following hardware: EyeToy camera, Dancemat, and active video games. Control group used their usual video game. Outcome measure was BMI.	A small but considerable impact on BMI and body composition was observed with active video game intervention.
10	Duman et al., 2016^Citation44	Active video games	3 days per week for 8 weeks	Aerobic exercises with music and active video games were used, and BMI was measured after 8 weeks. Data collected were then analyzed using SPSS 18.0 program.	Exercise program along with active video games showed promising effects as well as a constructive impact on self-respect and psychological wellbeing.
11	Wagener et al., 2012^Citation45	Dance-based exergaming	3 sessions (40 min per session) per week for 10 weeks	Participants (2–3) stood on dance pads with colorful arrows placed out in a cross shape. Feet were used to hit arrows corresponding to musical and visual cues displayed on the screen.	Increased competence to exercise regularly.
12	Coknaz et al. 2019^Citation46	A parallel RCT to evaluate the effect of physical fitness, reaction times, self-perception and enjoyment levels by active video games in inactive and technologically preoccupied children	12 weeks	Active games was used as intervention for children from 3 schools and 1 school was a control group. Primary outcomes were weight, body mass and fat ratios.	Active video games led to a reduction in weight gain. They are also beneficial tools in diverting children from inactivity thus preventing obesity.
13	Gao et al. 2019^Citation47	Home-based educational exergaming intervention effects on preschoolers.	12 weeks	The participants were divided into intervention and control groups. The intervention group did exergaming on Leap TV gaming console control group continued with their regular physical activities,	Home-based educational exergaming may positively impact cognitive flexibility in preschoolers.
14	Curiel et al., 2020^Citation48	Video game FoodRateMaster to improve knowledge regarding food and encourage healthy eating behavior	12 sessions in 6 weeks	Gaming was used to improve nutritional knowledge and apply techniques to bring about behavioral change, increase awareness of unhealthy and healthy foods, and improve consumption of healthy food. This active game increases physical activity.	Positive outcome was observed as children showed improved food knowledge before and after the game. Parents gave positive feedback and reported that they observed changes in eating habits in their children.
15	Nawi et al., 2015^Citation49	Educational information given via internet and pamphlets	12 weeks	Internet-based intervention ObeseGo! was used in a randomized group (healthy lifestyle and diet information were provided via the internet). Control group was given pamphlets containing health education material.	ObeseGo! showed minimal effect in the reduction of BMI, waist circumference, and percentage body fat.
16	Rio et al., 2019^Citation50	Physical activity, vocation projects, and training sessions	3 years	Along with training, weekly group sessions on eating healthy as well as health education were conducted using active video games. Vocal projects were created to identify healthy activities using virtual platforms.	Improvements in understanding and obedience to the Mediterranean diet were observed in the experimental group.
17	Chen et al., 2019^Citation51	Web-based behavior program	8 months	Web-based program to improve self-efficiency for enhancing the understanding and use of problem-solving skills in relation to nutrition and physical activity. An interactive dietary training software program (The Wok) was custom-made according to common Chinese food and was used by participants to check the nutritional information. Internet information included voice overs, graphics, and comics.	Decreased waist-to-hip ratio, reduced blood pressure, increased the intake of vegetables and fruits, increased physical activity, and increased knowledge about nutrition.
18	Rerksuppaphol et al. 2017^Citation52	Internet based obesity program for obesity prevention in Thai school children	Four months, monthly	Healthy children were randomized to intervention (internet based) program or the control group. Anthropometric characteristics were screened at baseline and for four months in monthly intervals. Changes in the percentage of overweight/obese children and changes in BMI at the end of study were the primary and secondary outcome, respectively	Internet the based obesity prevention program was effective and helped in addressing the rising obesity in children.
19	Nystrom et al. 2017^Citation53	Mobile health (mHealth) obesity prevention program on body fat, dietary habits, and physical activity in healthy children	6 months	p. 6-month mHealth program. Where the primary outcome was fat mass index (FMI), whereas the secondary outcomes were intakes healthy and unhealthy foods and time spent being non-active and in moderate-to-vigorous physical activity	No difference between the intervention and control group but post intervention the intervention group showed higher composite score than the control group, especially in children with a higher FMI
20	Hammersley et al. 2019^Citation13	Internet and email based intervention lifestyle program	11 weeks, 6 months follow up	11-week internet-based healthy lifestyle program, by fortnightly emails for 3 months for intervention group. Comparison participants received email communication only. BMI was the primary outcome.	eHealth childhood obesity prevention improved dietary-related practices and self-efficacy but did not reduce BMI.
21	Fiechtner et al., 2016^Citation54	Monitoring behavior	1 year	CDS system used by clinicians. Family self-guided changes in behavior and coaching for health. Results were recorded as a change in BMI z-score as well as in the intake of sugar-based drinks, fruits, and vegetables.	Distance to a supermarket <1 mile and intervention improved fruit and vegetable consumption by 0.29 portions per day and reduced BMI z-score by 0.04 units compared with controls.
22	Lingren et al., 2016^Citation55	ML-based algorithms	Not available	ML- and rule-based algorithms from structured and unstructured data from two electronic health record databases were developed to detect severe early childhood obesity and high long-term risk of developing obesity-related comorbidities.	Precision was stressed in the high-fidelity group. The rule-based algorithm achieved the best overall results.
23	Rios-Julian et al., 2017^Citation56	ML-based algorithms	Not available	The study evaluated the practicability of an automated screening tool for diagnosing obesity using anthropometric variables and an ML approach.	High capacity shown by classifiers for assessing whether or not the participant was overweight.
24	Fergus et al., 2015^Citation57	ML-based algorithms	Not available	Activities were evaluated using data recorded from wearable accelerometer sensors in free-living environments. Physical activity and assessment was performed using a multilayer perceptron neural network for the classification of physical activities by the type of activity.	Overall accuracy, 96%; sensitivity, 95%; specificity, 99%.
25	Dugan et al., 2015^Citation58	ML-based algorithms	Data collected over 9 years	An algorithm was used to predict obesity in children aged >2 years with data accumulated before the second birthday using CHICA. Six ML techniques were used: RandomTree, RandomForest, J48, ID3, Naïve Bayes, and Bayes.	Accurate model was created.
26	Balbir et al., 2020^Citation59	ML-based algorithms	Not available	Overweight or obesity prediction for young people using ML techniques.	Approximately 90% accuracy in prediction for the target class was attained.

Abbreviations: BMI, body mass index; CDS, computer decision system; CHICA, Child Health Improvement through Computer Automation; ML, machine learning; PE, physical education; RO2, Running Othello.

Table 3 Quality Assessment of Included Studies

Sno	Author, Year	Q1	Q2	Q3	Q4	Q5
1	Lindberg et al., 2016^Citation35	Y	Y	Y	N	N
2	Yang et al., 2017^Citation36	Y	Y	Y	Y	N
4	Argarini et al., 2020^Citation37	N	Y	Y	Y	N
5	Adamo et al., 2010^Citation38	Y	Y	Y	Y	N
6	Staiano et al., 2018^Citation39	Y	Y	Y	Y	N
7	Ruggiero et al., 2020^Citation40	Y	Y	Y	?	N
9	Trost et al., 2014^Citation41	Y	Y	Y	Y	N
10	Staiano et al., 2017^Citation42	Y	Y	Y	Y	N
19	Maddison et al., 2011^Citation43	Y	Y	Y	Y	N
20	Duman et al., 2016^Citation44	Y	Y	Y	Y	N
12	Wagener et al., 2012^Citation45	Y	Y	Y	Y	N
24	Coknaz et al. 2019^Citation46	Y	Y	Y	Y	N
26	Gao et al. 2019^Citation47	Y	Y	Y	Y	N
3	Curiel et al., 2020^Citation48	Y	Y	Y	Y	N
8	Nawi et al., 2015^Citation49	Y	Y	Y	Y	N
11	Rio et al., 2019^Citation50	Y	Y	Y	Y	N
21	Chen et al., 2019^Citation51	Y	Y	Y	Y	N
22	Rerksuppaphol et al. 2017^Citation52	Y	Y	Y	Y	N
23	Nystrom et al. 2017^Citation53	Y	Y	Y	Y	N
25	Hammersley et al. 2019^Citation13	Y	Y	Y	Y	N
13	Fiechtner et al., 2016^Citation54	Y	Y	Y	Y	N
14	Lingren et al., 2016^Citation55	Y	Y	N	Y	Y
15	Rios-Julian et al., 2017^Citation56	Y	Y	N	?	Y
16	Fergus et al., 2015^Citation57	Y	Y	N	?	Y
17	Dugan et al., 2015^Citation58	Y	Y	N	?	Y
18	Balbir et al., 2020^Citation59	Y	Y	N	?	Y

Notes: Q1, studies with sample size >20; Q2, studies with age details; Q3, studies with the sample characteristic; Q4, studies with intervention duration >3 weeks; Q5, studies with machine learning /artificial intelligence/robots.

Figure 1 Search strategy used for selection of studies.

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