JMIR Publications

ABSTRACT

Background:

Background:

Recent meta-analyses suggest the use of technology-based interventions as a treatment option for obesity in adulthood. Similar meta-analytic approaches for children are scarce. Objectives: This meta-analysis examined the effect of technology-based interventions on overweight/obesity treatment in children and adolescents.

Methods:

A systematic literature search was performed in Medline (PubMed), Scopus and Cochrane Library for randomized clinical trials to identify interventional studies published since Jan 2000 on Feb 2021.

Results:

In total, 9 manuscripts from 8 clinical trials of 582 children/adolescents were considered as eligible. BMI, BMI z-score and other BMI-related baseline metrics, during and post- intervention were considered as primary outcomes. In 7 to 8 studies the technology-based intervention was applied on the top of conventional care. Six studies were conducted in USA, 1 study in Australia and 1 study in northwestern Europe. Five studies included adolescents while the rest addressed 9-12 year-old children. Intervention duration ranged from 3 to 24 months. Five to 8 studies reported significant difference between groups in BMI metrics change. Pooled analysis revealed an overall significantly higher decrease in BMI metrics in intervention group (SMD=-0.61, 95%CI=[-1.10, -0.13]; p=0.014). Subgroup analysis revealed that significance was lost in case of no parental involvement (SMD=-0.36; 95%CI=[-0.83, 0.11]; p=0.135). The small number of clinical trials found, the varying study quality and the study heterogeneity are some limitations.

Conclusions:

Studies reported herein describe functional and acceptable technology-based approaches, on the top of conventional treatments, to enhance weight loss in young populations.

Objective:

The objective of the current meta-analysis was to determine whether such interventions, compared with conventional care or no care, could improve overweight or obese children’s or adolescents’ weight status. The research hypothesis here was that technology-based interventions are effective in weight management and – in case of direct comparison with conventional care – at least equivalent with the conventional care.

Methods:

Search strategy Following the Preferred Reporting Items for Systemic Reviews and Meta-Analyses (PRISMA) 2009 guidelines, a computer-assisted systematic literature search (not registered protocol) was performed by two independent researchers (MK and MK), using Medline (PubMed), Scopus and the Cochrane Library for RCTs examined the effect of technology-based vs. conventional interventions on weight management of children and adolescents with excess weight. The search strategy was mainly based on Medical Subject Headings terms (Supplementary file), limited to publications in English, since Jan 1, 2000 on Feb 1, 2021. Reference lists of retrieved articles were also considered when these were relevant to the issue examined yet not allocated in basic search. The relevance of studies was assessed using a hierarchical approach based on: title, abstract, and full manuscript. Titles and abstracts of identified studies were independently screened by two researchers (MK and MK) and then duplicates were removed. Full-text copies of papers were assessed for eligibility (MK and MK) with any disagreements resolved by a third researcher (EB). Data for each included study were extracted by one researcher (MK) and cleaned and checked by another (MK). The two researchers (MK and MK) extracted data using a standardised extraction form, to ensure that it adequately captured trial data. For papers in which additional information was required, the corresponding authors were contacted via email. Selection criteria Studies were eligible if they met the following inclusion criteria:  Study design: controlled clinical trials with at least one arm with a technology-based intervention controlled by a second arm with a convention care intervention or without any intervention.  Sample: overweight or obese (defined through BMI or validated growth charts) children and adolescents aged ≤18 years old  Intervention: interactive technology-based intervention applied to children or adolescents with or without parents’ or family’s support  Outcome: BMI, BMI z-score and other BMI-related metrics (e.g., BMI-SDS) at baseline, during intervention and post-intervention phase were considered as the primary measurements for the present meta-analysis. The exclusion criteria were: review articles; letters-to-the editors; editorials; articles based on studies with adults; articles providing only feasibility/acceptance level of the applied technology-based interventions or outcomes related only with obesogenic behaviors; articles where the technology-based intervention was applied only to parents; articles where the control group included the use of technology; articles where the technology-based intervention was not interactive with the user e.g., telemedicine or it had only an informative character e.g., a website; articles with inadequate statistical information. Quality assessment of selected studies Two researchers (MK and MK) independently implemented the quality assessment of the selected validation studies using the Consolidated Standards of Reporting Trials statement[12]. Any differences were discussed, and a decision was made by consensus. Effect size measurements The outcome of interest in the present meta- analysis was the difference between the web-based intervention and the control group with regard to the potential changes from CFB in BMI and/ or BMI z-score and/ or BMI-SDS. Studies that reported: (a) BMI-related metrics results as change scores or baseline and final values (b) SD, SE, or CIs and (c) number of participants in each intervention group were included in the meta-analysis. Mean change was calculated where required, and SDs were calculated from SE or 95%CI where SD was not reported[13]. Finally, missing SDs of the changes from baseline were calculated using an imputed correlation coefficient[13]. Data analysis To enable BMI-related metric to be included in the same meta-analysis, SMD was used. Where a study reported more than one BMI-metrics, BMI was used. Pooled values of SMDs between the technology-based intervention and the control group, and 95%CIs, as the recommended summary statistics of the ES, were calculated using either a fixed- or a random-effects model. The fixed-effects model was conducted when sample heterogeneity was <50% and the random-effects model was used when heterogeneity was >50%. Heterogeneity assessed the null hypothesis that all studies evaluated the same effect and was evaluated by the χ2 test. Inconsistency (I2) was calculated to quantify the total variation consistent with interstudy heterogeneity, ranging from 0% to 100%. P-value of < 0.10 for Chi-square test and I2 >50% reflected a significant heterogeneity[14]. Estimates of effect size measures were weighted by the inverse of their variances. The random-effects model (DerSimonian and Laid method) was used in the presence of heterogeneity. On the contrary, fixed-effects models were used to calculate effect size estimates for those studies that were lacking heterogeneity. Subgroup analysis of pre-specified groupings was performed for the following study characteristics: duration of follow- up (3-24 months), parental involvement or not, and the type of the intervention (web-based vs. mobile-based and others). In sub-group analyses only the last follow- up’s values were considered. Possible publication bias was assessed using a contour-enhanced funnel plot of each trial's ES against the standard error. Funnel plot asymmetry was evaluated by means of Begg and Egger tests[15]. Statistical analysis was conducted using the STATA software version 14.0.

Results:

Flow of included studies Literature search flow diagram is illustrated in Figure 1. Initially, 7,245 papers were retrieved and selected for evaluation. The 6,905 manuscripts were removed on the basis of their Title/Abstract, as they were irrelevant to the scope of the present work accompanied by 340 duplicate records from multiple databases and searches that were also excluded. Among the rest (n=192), 9 manuscripts from 8 studies were considered as relevant; 183 manuscripts were excluded as they did not meet the inclusion criteria of the present systematic review. [Figure 1] General characteristics of the selected clinical trials Table 1 outlines the characteristics of the eligible clinical trials for the present meta-analysis. In total, n=582 children and/or adolescents participated in the selected 8 studies with a range of cultural/ethnic groups, including African American, Chinese American, Caucasian and others. Six studies were conducted in United States of America (USA)[16–22], 1 study in Australia[23] and 1 study in northwestern Europe (Netherlands) [24]. Six studies were implemented within the last decade[16–19,23,24] while the rest two studies had been performed earlier[20–22]. Most of the selected studies addressed adolescents[16,17,20–23] while the rest had children at the age of 9-12 years old as target group[18,19,24]. The length of interventions ranged from 3 months[16,19,24] to 4 months[20], 6 months[17,18] and 24 months[21–23]. All studies were two-arm controlled clinical trials, where technology-based interventions were controlled for one conventional care intervention except for two studies where no intervention was implemented in the control group[16,19,24]. Description of the technology-based interventions Four out of 8 studies examined the effect of an mHealth intervention accompanied or not by sensors[16,17,19,24], 3 studies used a web-based intervention[18,20–22] and 1 study a short message service intervention accompanied by telemedicine [23]. Focusing on the mobile-based interventions, those addressed nutrition-related issues and unhealthy dietary behaviors[16,17,19,24] while in three cases physical activity and screen time were also taken into account[16,19,24]. As regards, the web-based interventions, in one study participants were enhanced to increase their physical activity level via a gamification method[18]. The rest two web-based interventions, following a family-oriented approach, provided nutrition education accompanied by physical activity tips and counseling regarding healthy body image[20–22]. Regarding the short message service intervention along with telemedicine and group sessions, this focused on weight loss as well as on weight loss maintenance covering issues from nutrition and physical activity to body image and psychological well-being[23]. The level of parental involvement varied among the selected studies. In 6 out of 8 studies there was participation of parents in the intervention group[18–24] accompanied by a similar participation of parents in control group with the exception of two studies[18,19]. In 7 out of 8 interventions a hybrid approach was followed which means that the technological tools – any kind – were examined as a supportive to the conventional care treatment[17,19–24]. To this issue, in 7 out of 8 studies there was support of health care practitioners, such as dietitians, physicians, pediatricians and psychologists[17–24]. Participants in the intervention group (children/adolescents alone or with their parents) attended weekly, biweekly or monthly face-to-face sessions with health professionals[17,19–24] or video conferences[18]. These sessions included goal setting, motivation techniques, individualized feedback based on the technology-based dietary or physical activity records and enhancement to use the digital tools provided. Primary and secondary outcomes of the selected clinical trials Different measures of weight status and adiposity were used in the selected studies with most of them using multiple measures. Five studies used BMI z-score[17–20,23], three studies used BMI[16,19,21,22], three studies used BMI percentile[17,19,21,22], two studies used body fat[18,21,22], one study used waist-to-hip ratio[23] and one study used BMI-SDS[24]. Other metrics included modifications in obesogenic behaviors, such as dietary habits[16,18–20,22–24], physical activity habits and/or screen time[16,18,19,22,23] and physical examination or biochemical metrics[18,23]. All studies included psychological and self-efficacy metrics related with diet, physical activity, well-being or healthy body image. With the exception of two studies[16,23], the rest studies provided information on participants’ satisfaction and compliance with the technology-based intervention. [Table 1] Risk of bias within selected studies Supplementary file summarizes the results of the risk of bias assessment for all included studies. The selected eligible were of moderate quality, meeting on average around 6 out of the 9 quality criteria. In particular, all studies except for one had a well-documented randomization process[25]. In all studies except for one[22] the baseline characteristics were presented accompanied by statistically non-significant discrepancies among groups. All studies used a valid method to assess the main outcome of interest i.e. BMI while only 4 out of 9 studies reported blinded assessment of the outcome of interest[16,18,20,23]. All studies except for one[23] met the drop-out rate cut-off points (i.e. ≤20% for <6 months and ≤30% for ≥6 months). Regarding the quality of statistical analysis, on average, the selected studies met 2 out of 9 criteria. In specific, all studies but three used intention-to-treat analysis[17,20,24], all studies but two reported adequate statistical power[17,19] while only four studies provided adjusted differences between groups[16–18,23]. Separate outcomes of selected studies The separate outcomes of the eligible clinical trials are summarized in Table 2. Weight and adiposity outcomes Five out of 8 studies reported significant difference between groups in BMI metrics from baseline to the end of intervention[18,20,21,23,24]. Intervention duration of these studies was more than 6 months while all of them addressed not only children or adolescents but also their parents. Significant reductions in body fat[22] as well as waist-to-hip ratio[23] was observed in interventions with a two-year duration. Diet-related outcomes The 7 studies reporting modifications on dietary intake and behaviors revealed a significant difference between groups in regard to improvement in at least one dietary outcome. In particular, decrease in sugar-sweetened beverages[16], lower carbohydrate intake[18], increased fruits consumption[19], decreased meat and fruits juice intake[23], better adherence to a healthier dietary pattern[20,24] as well as lower consumption of food products with high fat content[21,22] were observed. Physical activity-related outcomes Among the five studies that provided input on changes in participants’ physical activity level 1 study revealed significant decrease in screen time[16] while the rest four studies highlighted increases in physical activity level in terms of hours per day or the intensity of exercise20,21,24,25. Physical examination and biochemical metrics In the 1 out of 2 studies with such data, significant reductions in blood pressure and cholesterol levels were observed [18]. Psychological health-related outcomes As already mentioned, all studies provided input on the effect of technology-based intervention over the control group on participants’ psychological health. Seven out of eight studies mentioned that participants in the intervention group increased their self-efficacy in relation to diet[16,19,20] and physical activity[16,18,20], decreased unhealthy eating behaviors related with dieting or weight or body image[22,23] as well as ameliorated their self-esteem[23,24]. Usability and acceptability of the technology-based intervention The 5 out of 6 studies providing information on the level of compliance of participants with the technology-based intervention reported moderate to high level of usability and acceptability of the technology-based intervention[17–20,24]. Nevertheless, the added value of the technology-based intervention over typical care was not clear considering the similar drop-out rates between the two groups (drop-out rate in the intervention group: 12.9% (0%-41%) vs. drop-out rate in the control group: 12.6% (0%-36%), p= 0.964) (excluding studies where the control group had no intervention[18,19]. Among them, one study revealed that children assigned to the technology-based intervention, receiving short message service, were less likely to withdraw from the study than children who did not receive this service[24]. [Table 2] Synthesis of BMI-related outcomes A meta-analysis was conducted on pooled data from 9 manuscripts (8 studies in total), which compared technology-based intervention groups with control groups. The meta-analysis results are displayed in Figures 2-4. As shown in Figure 2, an overall significantly higher decrease of the BMI-related metric was observed (SMD= -0.61, 95% CI= [-1.10, -0.13]; p= 0.014). This was more evident after 6 months in the technology-based intervention group, when compared to the control group (SMD= -0.37; 95% CI= [-0.72, -0.03]; p= 0.032), while a favorable effect of the technology-based interventions was also found after 24 months, yet statistical significance was not reached (SMD= -0.31; 95% CI= [-0.63, 0.02]; p= 0.065). Sensitivity analysis As already mentioned, 2 out of 8 studies had a control group only to eliminate the placebo effect (i.e. without any intervention). Hence, we repeated the aforementioned analysis excluding these two studies. The overall outcome remained significant (SMD=-0.65; 95%CI=[-1.20, -0.10]; p=0.020) while the 6-month outcome remained marginally significant [(SMD=-0.32; 95%CI [-0.71, 0.07]; p=0.100]. [Figure 2] A sub-group analysis was conducted based on the parental involvement and results are illustrated in Figure 3. As depicted in Figure 3, meta-analysis revealed a significantly higher decrease of the BMI-related metric in the technology-based intervention group compared to the control group, only in case of parental involvement (SMD=-0.39; 95%CI=[-0.59, -0.18]; p<0.001). In case of no parental involvement, no significant difference between groups was observed (SMD=-0.36; 95%CI=[-0.83, 0.11]; p=0.135). [Figure 3] Another subgroup analysis performed here was related with the type of technology-based intervention used. Results are depicted in Figure 4. Interventions were grouped as web-based and mobile-based and others. A statistically significantly higher decrease of the BMI-related metric in the intervention group compared with the control group was observed, both in case of the mobile-based and others interventions (SMD=-0.89; 95%CI=[-1.15, -0.64]; p<0.001), as well as in case of the web-based intervention (SMD=-0.45; 95%CI=[-0.72, -0.18]; p=0.001). [Figure 4]

Conclusions:

The present meta-analysis revealed that overall an intervention which combines conventional care with technological facilities could be an effective method in weight management of overweight/obese children and adolescents and probably more effective than the conventional care alone. These observations were more evident in case of interventions of at least 6 months duration. The selected studies included eHealth and mHealth technologies, such as interactive web platforms, mobile apps, gaming, short message services with or without sensors and accompanied or not by other contact forms such as telemedicine, emails and informative websites. The focus of these technologies was more or less related either exclusively or in combination with improvement of dietary habits, enhancement of physical activity or increasing users’ self-monitoring potential. The type of technological means used in each intervention in terms of mobile-based or web-based did not seem to alter the final outcome. Interestingly, the parental involvement was related to greater outcomes of the intervention, mainly in children; however, it was not possible to isolate the separate contribution of parents to the final outcome. Studies reported herein describe functional and acceptable technology-based approaches, on the top of conventional care, to enhance weight loss in overweight or obese children and adolescents through the promotion of healthy lifestyle and improvement of users’ well-being. However, the large heterogeneity in study designs, settings, intervention components, and outcomes probably eliminate the strength of this conclusion. Finally, this field is advancing so quickly that the technology used is often no longer state-of-the art; interventions that employ the full range of novel technologies, such as ubiquitous sensing and real-time feedback are currently being developed and pilot tested. Thereby, similar meta-analytic approaches should be repeated on regular basis. Clinical Trial: not relevant