1,012 articles were obtained from Medline (n=204) and Embase (n=808) and were screened to remove duplicates. Titles and abstracts of the remaining 875 unique articles were screened and 851 studies rejected. Two reviewers independently examined the full text of the 24 remaining articles for inclusion. Eight articles were excluded due to participants being outside the specified age range; two others because they described primary prevention interventions; a further two due to insufficient participant information and one because the technology-based intervention was part of a multicomponent intervention. Therefore, a total of 11 studies were considered suitable for inclusion. No further articles were retrieved from the bibliography search. Figure 2 demonstrates the study selection process. [See Additional file 2]
An overview of study characteristics extracted for this review is presented in Table 1 [Insert Table 1]. Year of publication ranged from 2006 (23) to 2017 (24). Studies were all RCTs, three of which were cluster RCTs, where two randomised schools (25,26) and one randomised a mixture of schools and YMCAs (16). The studies were carried out in a range of countries, with the majority taking place in the USA (n=6) (9,16,23,24,27,28) and others in New Zealand (n=2) (29,30), Canada (n=1) (31), Malaysia (n=1) (26) and the Netherlands (n=1) (25). Study retention rates ranged from 70.2% (23) to 100% (26,30).
The total number of participants analysed varied from 26 (31) to 742 (25), with an age range from 10 years (16) to 16 years (24). The majority of studies included both genders, with a small number of studies conducted among females only (n=2) (23,24). Two studies focused exclusively on participants of one particular ethnicity; one focused on Chinese-American adolescents (27), while the other focused on African-American girls (23). Two studies did not specify ethnicity (25,31), while the remaining seven studies focused on children from a variety of ethnic origins (9,16,24,26,28–30).
All studies focused on technology-based interventions as secondary prevention, since participants were overweight or obese prior to commencement of study. However, two studies also included participants who were normal weight and presented this data separately. In this case, the intervention also served as primary prevention (25,27).
One study included participants who engaged in binge eating or overeating behaviours (9). Two studies had a family component, one of which provided three 15-minute internet sessions for the parents so that they might acquire skills to support their children in leading a healthy lifestyle (27). The other study required the adolescents to participate in the study with a parent (23).
There was some heterogeneity in how weight categories were defined. Seven studies used the CDC definition (9,16,23,24,27,28,31) and two studies used the IOTF definition to explain the terms normal, overweight and obese (25,30). Two studies did not employ an established definition; one required participants to have a BMI > 25 kg/m2 (26), while the other classed its participants as overweight or obese with a BMI z-score between 1.0 and 2.5 (29)
Details of the intervention and control groups, outcomes, follow-up time points and key findings, including the effectiveness of interventions on BMI and other weight-related outcomes, for each included study were noted. The mean difference of weight-related outcomes between intervention and control groups was also calculated.
Technology-based interventions were sub-divided into three main categories; active video games or exergaming, internet or web-based interventions and mobile phone communications. Intervention duration ranged from eight weeks (27) to two years (23,29). Intervention intensity was reported in all but two studies (16,26) and was highly heterogeneous. Follow-up time points varied across studies from between two months (27,29) and 24 months (23,25,29). Four studies compared a technology-based intervention to a nil intervention control group (9,24,25,28); four studies compared a technology-based intervention to a non-technology-based intervention, and these included stationary cycling to music (31), written pamphlets (26) and lifestyle programmes (16,29). Three studies compared the effectiveness between different technology-based interventions, however, the intervention of interest had an interactive or tailored component compared to the passive or sedentary technology-based control intervention (23,27,30). The study outcomes varied depending on the aims of the studies but they all included a BMI outcome, given as either BMI, BMI z-score or BMI percentile. Further common outcomes were other anthropometric, dietary and PA measurements.
Active Video Game Interventions
Five studies explored the effectiveness of active video gaming or exergaming (16,24,28,30,31). [Insert Table 2]
Adamo et al. (31) investigated the effectiveness of an interactive video game which involved stationary cycling, known as ‘GameBike’, compared to stationary cycling to music. The study focused on exercise adherence, energy expenditure, aerobic fitness, body composition, and cardiovascular disease risk markers in overweight and obese adolescents. The author found that the music group had significantly better adherence and expended more energy than the ‘GameBike’ group. There were no other notable differences between the two groups. Within both groups, statistically significant findings included a reduction in peak heart rate (HR) at peak workload, an improvement in peak workload and a reduction in time to exhaustion from pre to post intervention. There were no statistically significant between or within group differences for BMI.
Furthermore, in the second study, Maddison et al. (30) explored the effect of active video games on weight, body composition, PA and physical fitness compared to the effect of sedentary video games. This study established that children in the active video games group had significantly decreased their BMI, BMI z-score, % BF and body weight compared to the children in the sedentary video games group.
The objective of the third study by Staiano et al. (24) was to examine the effect of exergaming on adolescent girls’ body composition and their cardiovascular risk factors compared to a control group who adhered to their normal PA level. No statistically significant differences in body composition or cardiovascular risk factors between the two groups were found at follow-up.
The aim of the fourth study by Trost et al. (16) was to evaluate the effects of active video gaming on PA and weight loss in children participating in a community-based weight management programme. Statistically significant increases in PA were confirmed in the active video gaming group compared to the control group. The control group participated only in the programme and had no access to active video gaming. Both groups had statistically significant reductions in percentage of children overweight and BMI z-scores, however, the active gaming group demonstrated statistically significantly greater reductions.
Finally, Wagner et al. (28) investigated the impact of dance-based exergaming on obese adolescents' perceived ability to exercise, their psychological adjustment and their BMI compared to a wait-list control group. It was noted that there was a statistically significant increase in self-reported perceived ability to exercise compared to the control group. However, no statistically significant differences were found in BMI z-score within or between the two groups.
In conclusion, two out of these five studies demonstrated a statistically significant mean difference for the intervention compared to the control for all or some of their weight-related outcomes; Maddison et al. (30) found beneficial results for four weight-related outcomes. BMI, BMI z score, body weight and % BF, with a statistically significant mean difference of -0.24, -0.06, -0.72 and -0.83 respectively when compared to control group, with P-values of 0.02, 0.03, 0.02 and 0.02. Trost et al. (16) found a statistically significant mean difference of -0.16 for BMI z-score when compared to the control group with a P-value of <0.001. The remaining findings were not statistically significant on between and within group analyses and displayed a degree of heterogeneity in estimates in terms of trend of direction.
Internet-based Interventions
Five studies examined the effect of web-based or internet interventions (9,23,25–27). [Insert Table 3]
Chen et al. (27) aimed to examine the efficacy of a theory-driven, family-based online programme to promote healthy lifestyles and weights in Chinese-American adolescents. This was compared to guidance given on a general health website. Outcomes included BMI, Waist-to-hip ratio (WHtR), diet, PA and knowledge about PA and nutrition. Chen et al. (27) found that adolescents in the intervention group, compared to the control group, had statistically significantly decreased their WHR and their diastolic blood pressure (DBP) while statistically significantly increasing their PA, improving their diet and increasing their knowledge in regard to PA and nutrition. Statistically significant within group changes for the intervention group included WHtR, DBP, PA, fruit and vegetable intake and knowledge related to PA and nutrition. There were no statistically significant within-group changes for any outcomes within the control group.
Similarly, Ezendam et al. (25) conducted a study to evaluate the short- and long-term results of FATaintPHAT, a web-based computer-tailored intervention which aimed to increase PA, decrease sedentary behaviour and promote healthy eating in adolescents. The control group was a nil intervention group. Outcomes included self-reported behaviours in terms of diet, PA and sedentary behaviour, step count, fitness measured by a shuttle run test, as well as anthropometric measures. Analysis for this study was conducted for the total study population and repeated for all at risk students, namely those who did not meet behavioural recommendations at baseline. This study found that, for at risk students, the intervention had no statistically significant effect on anthropometric outcomes or on sedentary behaviour but did show a statistically significant increase in fruit and vegetable consumption at four month follow-up and increased steps at two year follow up. For the total study population, FATaintPHAT had no statistically significant effect on BMI, WC or percentage of overweight or obese students. However, at four month follow-up, the intervention group was less likely to report drinking more than 400ml of sugar sweetened beverages per day compared to the control group. At two year follow-up, this difference was not statistically significant.
Jones et al. (9) examined the effect of an internet-facilitated intervention for weight maintenance and binge eating in adolescents. The control was a wait-list control group, and outcomes included BMI, binge eating behaviours, fat and sugar intake, depression and programme adherence. This study reported no statistically significant differences in outcomes between groups. The only within group statistically significant finding in the intervention group was a reduction in objective and subjective binge episodes from baseline assessment to post-treatment and follow-up assessment.
A further web-based RCT by Nawi and Jamaludin (26) attempted to determine the effectiveness of an internet-based intervention (obeseGO!) to address obesity among adolescents in Kuala Lumpur compared to a control group who were provided with written health education pamphlets. Outcomes measured were anthropometric measures; specifically, BMI, WC and % BF. This study found no statistically significant reduction in any outcomes between groups. However, on within-group analyses, mean BMI, WC and % BF in the obeseGO! group were statistically significantly lower after the intervention.
Finally, Williamson et al. (23) conducted a study to test the efficacy of an internet-based lifestyle behaviour modification programme for African-American girls over a two year period. The control group was a passive internet health education programme. Outcomes measured were anthropometric measures, weight loss behaviours and website use. At the six month follow-up, participants in the intervention group in comparison to the control group had statistically significantly reduced their % BF, however, this difference was not sustained and at the end of the 24 month inter
1,012 articles were obtained from Medline (n=204) and Embase (n=808) and were screened to remove duplicates. Titles and abstracts of the remaining 875 unique articles were screened and 851 studies rejected. Two reviewers independently examined the full text of the 24 remaining articles for inclusion. Eight articles were excluded due to participants being outside the specified age range; two others because they described primary prevention interventions; a further two due to insufficient participant information and one because the technology-based intervention was part of a multicomponent intervention. Therefore, a total of 11 studies were considered suitable for inclusion. No further articles were retrieved from the bibliography search. Figure 2 demonstrates the study selection process. [See Additional file 2]
An overview of study characteristics extracted for this review is presented in Table 1 [Insert Table 1]. Year of publication ranged from 2006 (23) to 2017 (24). Studies were all RCTs, three of which were cluster RCTs, where two randomised schools (25,26) and one randomised a mixture of schools and YMCAs (16). The studies were carried out in a range of countries, with the majority taking place in the USA (n=6) (9,16,23,24,27,28) and others in New Zealand (n=2) (29,30), Canada (n=1) (31), Malaysia (n=1) (26) and the Netherlands (n=1) (25). Study retention rates ranged from 70.2% (23) to 100% (26,30).
The total number of participants analysed varied from 26 (31) to 742 (25), with an age range from 10 years (16) to 16 years (24). The majority of studies included both genders, with a small number of studies conducted among females only (n=2) (23,24). Two studies focused exclusively on participants of one particular ethnicity; one focused on Chinese-American adolescents (27), while the other focused on African-American girls (23). Two studies did not specify ethnicity (25,31), while the remaining seven studies focused on children from a variety of ethnic origins (9,16,24,26,28–30).
All studies focused on technology-based interventions as secondary prevention, since participants were overweight or obese prior to commencement of study. However, two studies also included participants who were normal weight and presented this data separately. In this case, the intervention also served as primary prevention (25,27).
One study included participants who engaged in binge eating or overeating behaviours (9). Two studies had a family component, one of which provided three 15-minute internet sessions for the parents so that they might acquire skills to support their children in leading a healthy lifestyle (27). The other study required the adolescents to participate in the study with a parent (23).
There was some heterogeneity in how weight categories were defined. Seven studies used the CDC definition (9,16,23,24,27,28,31) and two studies used the IOTF definition to explain the terms normal, overweight and obese (25,30). Two studies did not employ an established definition; one required participants to have a BMI > 25 kg/m2 (26), while the other classed its participants as overweight or obese with a BMI z-score between 1.0 and 2.5 (29)
Details of the intervention and control groups, outcomes, follow-up time points and key findings, including the effectiveness of interventions on BMI and other weight-related outcomes, for each included study were noted. The mean difference of weight-related outcomes between intervention and control groups was also calculated.
Technology-based interventions were sub-divided into three main categories; active video games or exergaming, internet or web-based interventions and mobile phone communications. Intervention duration ranged from eight weeks (27) to two years (23,29). Intervention intensity was reported in all but two studies (16,26) and was highly heterogeneous. Follow-up time points varied across studies from between two months (27,29) and 24 months (23,25,29). Four studies compared a technology-based intervention to a nil intervention control group (9,24,25,28); four studies compared a technology-based intervention to a non-technology-based intervention, and these included stationary cycling to music (31), written pamphlets (26) and lifestyle programmes (16,29). Three studies compared the effectiveness between different technology-based interventions, however, the intervention of interest had an interactive or tailored component compared to the passive or sedentary technology-based control intervention (23,27,30). The study outcomes varied depending on the aims of the studies but they all included a BMI outcome, given as either BMI, BMI z-score or BMI percentile. Further common outcomes were other anthropometric, dietary and PA measurements.
Active Video Game Interventions
Five studies explored the effectiveness of active video gaming or exergaming (16,24,28,30,31). [Insert Table 2]
Adamo et al. (31) investigated the effectiveness of an interactive video game which involved stationary cycling, known as ‘GameBike’, compared to stationary cycling to music. The study focused on exercise adherence, energy expenditure, aerobic fitness, body composition, and cardiovascular disease risk markers in overweight and obese adolescents. The author found that the music group had significantly better adherence and expended more energy than the ‘GameBike’ group. There were no other notable differences between the two groups. Within both groups, statistically significant findings included a reduction in peak heart rate (HR) at peak workload, an improvement in peak workload and a reduction in time to exhaustion from pre to post intervention. There were no statistically significant between or within group differences for BMI.
Furthermore, in the second study, Maddison et al. (30) explored the effect of active video games on weight, body composition, PA and physical fitness compared to the effect of sedentary video games. This study established that children in the active video games group had significantly decreased their BMI, BMI z-score, % BF and body weight compared to the children in the sedentary video games group.
The objective of the third study by Staiano et al. (24) was to examine the effect of exergaming on adolescent girls’ body composition and their cardiovascular risk factors compared to a control group who adhered to their normal PA level. No statistically significant differences in body composition or cardiovascular risk factors between the two groups were found at follow-up.
The aim of the fourth study by Trost et al. (16) was to evaluate the effects of active video gaming on PA and weight loss in children participating in a community-based weight management programme. Statistically significant increases in PA were confirmed in the active video gaming group compared to the control group. The control group participated only in the programme and had no access to active video gaming. Both groups had statistically significant reductions in percentage of children overweight and BMI z-scores, however, the active gaming group demonstrated statistically significantly greater reductions.
Finally, Wagner et al. (28) investigated the impact of dance-based exergaming on obese adolescents' perceived ability to exercise, their psychological adjustment and their BMI compared to a wait-list control group. It was noted that there was a statistically significant increase in self-reported perceived ability to exercise compared to the control group. However, no statistically significant differences were found in BMI z-score within or between the two groups.
In conclusion, two out of these five studies demonstrated a statistically significant mean difference for the intervention compared to the control for all or some of their weight-related outcomes; Maddison et al. (30) found beneficial results for four weight-related outcomes. BMI, BMI z score, body weight and % BF, with a statistically significant mean difference of -0.24, -0.06, -0.72 and -0.83 respectively when compared to control group, with P-values of 0.02, 0.03, 0.02 and 0.02. Trost et al. (16) found a statistically significant mean difference of -0.16 for BMI z-score when compared to the control group with a P-value of <0.001. The remaining findings were not statistically significant on between and within group analyses and displayed a degree of heterogeneity in estimates in terms of trend of direction.
Internet-based Interventions
Five studies examined the effect of web-based or internet interventions (9,23,25–27). [Insert Table 3]
Chen et al. (27) aimed to examine the efficacy of a theory-driven, family-based online programme to promote healthy lifestyles and weights in Chinese-American adolescents. This was compared to guidance given on a general health website. Outcomes included BMI, Waist-to-hip ratio (WHtR), diet, PA and knowledge about PA and nutrition. Chen et al. (27) found that adolescents in the intervention group, compared to the control group, had statistically significantly decreased their WHR and their diastolic blood pressure (DBP) while statistically significantly increasing their PA, improving their diet and increasing their knowledge in regard to PA and nutrition. Statistically significant within group changes for the intervention group included WHtR, DBP, PA, fruit and vegetable intake and knowledge related to PA and nutrition. There were no statistically significant within-group changes for any outcomes within the control group.
Similarly, Ezendam et al. (25) conducted a study to evaluate the short- and long-term results of FATaintPHAT, a web-based computer-tailored intervention which aimed to increase PA, decrease sedentary behaviour and promote healthy eating in adolescents. The control group was a nil intervention group. Outcomes included self-reported behaviours in terms of diet, PA and sedentary behaviour, step count, fitness measured by a shuttle run test, as well as anthropometric measures. Analysis for this study was conducted for the total study population and repeated for all at risk students, namely those who did not meet behavioural recommendations at baseline. This study found that, for at risk students, the intervention had no statistically significant effect on anthropometric outcomes or on sedentary behaviour but did show a statistically significant increase in fruit and vegetable consumption at four month follow-up and increased steps at two year follow up. For the total study population, FATaintPHAT had no statistically significant effect on BMI, WC or percentage of overweight or obese students. However, at four month follow-up, the intervention group was less likely to report drinking more than 400ml of sugar sweetened beverages per day compared to the control group. At two year follow-up, this difference was not statistically significant.
Jones et al. (9) examined the effect of an internet-facilitated intervention for weight maintenance and binge eating in adolescents. The control was a wait-list control group, and outcomes included BMI, binge eating behaviours, fat and sugar intake, depression and programme adherence. This study reported no statistically significant differences in outcomes between groups. The only within group statistically significant finding in the intervention group was a reduction in objective and subjective binge episodes from baseline assessment to post-treatment and follow-up assessment.
A further web-based RCT by Nawi and Jamaludin (26) attempted to determine the effectiveness of an internet-based intervention (obeseGO!) to address obesity among adolescents in Kuala Lumpur compared to a control group who were provided with written health education pamphlets. Outcomes measured were anthropometric measures; specifically, BMI, WC and % BF. This study found no statistically significant reduction in any outcomes between groups. However, on within-group analyses, mean BMI, WC and % BF in the obeseGO! group were statistically significantly lower after the intervention.
Finally, Williamson et al. (23) conducted a study to test the efficacy of an internet-based lifestyle behaviour modification programme for African-American girls over a two year period. The control group was a passive internet health education programme. Outcomes measured were anthropometric measures, weight loss behaviours and website use. At the six month follow-up, participants in the intervention group in comparison to the control group had statistically significantly reduced their % BF, however, this difference was not sustained and at the end of the 24 month intervention period, there was no difference in % BF between the two groups. Adolescents in both groups reported a statistically significant improvement in exercise and a reduction in overeating, in comparison with baseline.
To summarise, one study out of the five had a statistically mean difference for the intervention compared to the control for one of its weight-related outcomes; Chen et al. (27) found a small but statistically significant mean decrease in WHtR (0.01) compared to the control group with a P-value of 0.02. The remaining findings were not statistically significant when between group analyses were conducted and, as before, displayed a degree of heterogeneity in estimates in terms of trend of direction.
Mobile Phone Communications
The third category of technology-based interventions, mobile phone communications, was explored in a single study by Nguyen et al. (29), where the effect of supplementary therapeutic contact as an additional support to a community-based weight-management programme for overweight and obese adolescents was examined [Insert Table 4]. Additional therapeutic contact took the form of telephone coaching, SMS or email communications. The control group was the weight-management programme alone. Outcomes measured were anthropometric measures, blood pressure, metabolic profile and self-reported psychosocial and lifestyle changes. Both groups demonstrated statistically significant reductions in BMI z-scores and WHtR as well as improvements in metabolic and psychosocial profiles. The study found that this technology-based intervention had no statistically significant impact on body weight, BMI, BMI z-score, WC and WHtR compared to the control group.
In terms of statistically significant findings, active video gaming was demonstrated to have had a positive impact on weight outcomes in 40% of studies (2/5). However, it is important to note that, within this review, five out of eleven studies focused on the use of active video games, while only one study explored mobile phone-based interventions, and this simple disparity in number of studies conducted to date may partly account for this outcome.
Quality Assessment
When methodological quality was evaluated (22), all studies fell into the category of poor quality. Figure 3 is a pictorial risk of bias summary for each of the studies. [See Additional file 3]. Such low quality, and challenges in judging the presence of selection, performance, attrition and other bias in the included studies renders the evidence less trustworthy and creates a challenge for making reliable recommendations for future practice. The high risk of performance bias found in three studies is concerning particularly in the lack of blinding of participants and personnel.
vention period, there was no difference in % BF between the two groups. Adolescents in both groups reported a statistically significant improvement in exercise and a reduction in overeating, in comparison with baseline.
To summarise, one study out of the five had a statistically mean difference for the intervention compared to the control for one of its weight-related outcomes; Chen et al. (27) found a small but statistically significant mean decrease in WHtR (0.01) compared to the control group with a P-value of 0.02. The remaining findings were not statistically significant when between group analyses were conducted and, as before, displayed a degree of heterogeneity in estimates in terms of trend of direction.
Mobile Phone Communications
The third category of technology-based interventions, mobile phone communications, was explored in a single study by Nguyen et al. (29), where the effect of supplementary therapeutic contact as an additional support to a community-based weight-management programme for overweight and obese adolescents was examined [Insert Table 4]. Additional therapeutic contact took the form of telephone coaching, SMS or email communications. The control group was the weight-management programme alone. Outcomes measured were anthropometric measures, blood pressure, metabolic profile and self-reported psychosocial and lifestyle changes. Both groups demonstrated statistically significant reductions in BMI z-scores and WHtR as well as improvements in metabolic and psychosocial profiles. The study found that this technology-based intervention had no statistically significant impact on body weight, BMI, BMI z-score, WC and WHtR compared to the control group.
In terms of statistically significant findings, active video gaming was demonstrated to have had a positive impact on weight outcomes in 40% of studies (2/5). However, it is important to note that, within this review, five out of eleven studies focused on the use of active video games, while only one study explored mobile phone-based interventions, and this simple disparity in number of studies conducted to date may partly account for this outcome.
Quality Assessment
When methodological quality was evaluated (22), all studies fell into the category of poor quality. Figure 3 is a pictorial risk of bias summary for each of the studies. [See Additional file 3]. Such low quality, and challenges in judging the presence of selection, performance, attrition and other bias in the included studies renders the evidence less trustworthy and creates a challenge for making reliable recommendations for future practice. The high risk of performance bias found in three studies is concerning particularly in the lack of blinding of participants and personnel.