Internet-based self-monitoring interventions for overweight and obese adolescents: A systematic review and meta-analysis

Accepted Manuscript Title: Internet-based self-monitoring interventions for overweight and obese adolescents: A systematic review and meta-analysis Authors: Jian Hui Ho (Tarcisus), Ching Siang Lee (Cindy), Suei Nee Wong, Ying Lau PII: DOI: Reference:

S1386-5056(18)30785-8 https://doi.org/10.1016/j.ijmedinf.2018.09.019 IJB 3755

To appear in:

International Journal of Medical Informatics

Received date: Revised date: Accepted date:

16-7-2018 11-9-2018 24-9-2018

Please cite this article as: Ho (Tarcisus) JH, Lee (Cindy) CS, Wong SN, Lau Y, Internet-based self-monitoring interventions for overweight and obese adolescents: A systematic review and meta-analysis, International Journal of Medical Informatics (2018), https://doi.org/10.1016/j.ijmedinf.2018.09.019 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Internet-based self-monitoring interventions for overweight and obese adolescents: A systematic review and meta-analysis

A running title: Internet-based self-monitoring interventions

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Jian Hui HO, Tarcisus, BSN(Hons), RN

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Staff Nurse I, Health Promotion Board, Singapore

Ching Siang LEE, Cindy, MS, RN

Senior Lecturer, Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine,

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National University of Singapore, Singapore

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Senior Librarian, Medical Resource Team,

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Suei Nee WONG, MSc (Info Studies), BSc (Hons)

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National University of Singapore Libraries, National University of Singapore, Singapore

Ying LAU PhD, MN, BN (Hons), BSc, IBCLC, RM, RN* [email protected] ;

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[email protected]

Assistant Professor, Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine,

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National University of Singapore, Singapore

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*Corresponding

Name:

Address:

author: Ying LAU

Level 2, Clinical Research Centre, Block MD11

10 Medical Drive, Singapore 117597 Telephone:

(65) 66011761 (Office phone)

Fax:

(65) 67767135

Summary points What are already known? 

Adolescents who are overweight and obese show an increased risk of being obese in

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adulthood.

Self-monitoring is a behaviour change technique that is essential to the success of weight

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management.

Internet-based self-monitoring interventions may potentially overcome previous challenges

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and allow considerable compliance.

Meta-analysis revealed that the use of internet-based self-monitoring had small effects on

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reduction of BMI and BMI z-score.

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What this study adds?

Subgroup analyses suggested the use of daily multicomponent self-monitoring, specified

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goal setting, face-to-face counselling and parental involvement. The GRADE system was graded as low based on the methodological limitations and

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Abstract

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imprecision.

Background: Internet-based self-monitoring intervention offers accessible and convenient

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weight management. This review aimed to systematically review the evidence on the effectiveness of internet-based self-monitoring intervention for overweight and obese adolescents. Method: PubMed, CINAHL, Cochrane Library, EMBASE, ProQuest, PsycINFO and SCOPUS were systematically searched for randomised controlled trials (RCTs) from inception until

December 13, 2017. The risk of bias and strength of evidence was assessed using the Cochrane Collaboration Risk of Bias Tool and the Grading of Recommendations, Assessment, Development and Evaluations criteria. Meta-analysis was performed on the RevMan software using a random effects model. The overall effect was assessed using effect size (Cohen’s d) and heterogeneity was evaluated using Cochrane Q and I2 values. PROSPERO database

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#CRD42016050089.

Results: A total of 6,841 records were identified. Six RCTs in 10 articles were selected amongst

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505 adolescents across three countries who were overweight and obese. The meta-analysis

revealed a small effect on the reduction of body mass index (BMI) and BMI z-scores (d = 0.30,

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95% CI: -0.48 to -0.12). Subgroup analyses suggest the use of daily multicomponent self-

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monitoring, specified goal setting, face-to-face counselling and parental involvement. The

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overall quality of evidence was low due to the risk of bias and imprecision.

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Conclusion: Internet-based self-monitoring intervention is a possible approach for overweight and obese adolescents to reduce their BMI. Further well-designed RCTs with follow-up data

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and large sample sizes are needed to ensure the robustness of the evidence.

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KEYWORDS: Meta-analysis; Systematic review; Weight control; Self-Monitoring

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Keywords: Internet-based, self-monitoring, Obesity, Adolescents

1. Introduction

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The worldwide prevalence of obesity among children is 13.9% [1], and adolescents present the highest prevalence (34.5%) amongst different children age groups [2]. Notably, trends reveal that the prevalence of overweight and obese adolescents is increasing [3, 4]. Adolescents who are overweight and obese show an increased risk of being obese in adulthood [5], and they are at risk of developing type 2 diabetes mellitus, cardiometabolic risks, cardiovascular mortality

[6-8], and psychosocial problems [9]. Given the adverse health outcomes and high prevalence rate, effective interventions are needed to target overweight and obese adolescents. Self-monitoring is a behaviour change technique that is essential to the success of weight management [10, 11]. However, the traditional practice of self-monitoring using paper diaries is difficult due to the complexity of recording [12]. Given that almost 90% of

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adolescents are daily internet users [13, 14], the internet may offer a new opportunity to engage

in self-monitoring [15]. Therefore, internet-based self-monitoring interventions may

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potentially overcome previous challenges and allow considerable compliance [12, 16] by offering high usability, accessibility, cost-effectiveness and convenience [17, 18] with

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interactive multimedia, graphical features, tracking systems, sensor-based devices,

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individualised feedback and asynchronous and synchronous interactions [19-21].

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Self-monitoring intervention was recommended as an essential strategy for adult

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weight management in four systematic reviews [10, 11, 22, 23]. A growing number of systematic reviews have supported the use of internet-based interventions in achieving weight

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loss in adults [24-26], but little is known whether it could be generalised to adolescents who are overweight and obese is unclear.

Three previous systematic reviews [27-29] reported internet-based weight management

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interventions to be effective in reducing BMI outcomes for overweight or obese adolescents.

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The components identified to contribute to weight loss were the use of feedback and reminders [28], parental involvement [27-29], and behaviour changing techniques [29]. However, the

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effectiveness of internet-based weight management could not be isolated due to the multiple components involved and evidence was limited to short-term outcomes with only one study with outcomes beyond one year [27-29]. These reviews were limited to a few selected trials (n = 4)[27], some double counting the same trial [28, 29], included trials with heterogeneous age groups with a mixture of normal weight, overweight and obese participants [27] and various

study designs [27]; in addition, none of these reviews used a meta-analytic approach. Metaanalysis obtains the strongest and highest quality of evidence [30]. Thus, further rigorous review is warranted to fill the gaps. This review aims to (1) systematically locate, appraise and synthesise evidence on the effectiveness of internet-based self-monitoring interventions in overweight and obese

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adolescents based on BMI or BMI z-score and (2) identify essential components, formats and approaches in designing interventions according to theory-based intervention, frequency,

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counselling provision, goal setting, different self-monitoring components, professional

support, parental involvement and intervention duration. The results from this review may be

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useful to provide future recommendations in designing effective interventions.

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2. Methods

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This systematic review and meta-analysis is reported in accordance to the Preferred

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Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [31], referred to in the PRISMA Checklist in Table S1. This review is also registered in the PROSPERO database

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(CRD42016050089).

2.1 Eligibility Criteria

Table S2.

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Trials were included in the systematic review if they satisfied the eligibility criteria in

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2.1.1 Population

The population of the trials was composed of adolescents who were overweight or

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obese. The review adopted the adolescent age range of 12 to 18 years based on the Cochrane Review on obesity intervention in children [32]. Overweight and obese were defined using the BMI cut-off points of IOTF [33] or the BMI-for-age percentile that defines overweight and obese based on a reference population, for instance, the WHO 85th and 95th percentiles as cutoff points for overweight and obese, respectively [34, 35].

2.1.2 Intervention Internet-based interventions that used a self-guided programme through a website with interactive components were considered [36]. The interactive components of self-monitoring had to be recorded on a website and had to be meaningful for weight management that includes diet, physical activity and weight.

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2.1.3 Comparison

Standard care, the use of other internet interventions without a self-monitoring

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component and the use of traditional approach for self-monitoring were compared. 2.1.4 Outcomes

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Primary outcomes included BMI and BMI z-score; secondary outcomes included

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quality of life (QoL), psychosocial (i.e., depression and self-esteem), physical activity (i.e.,

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duration of exercise) and diet (i.e., calories consumed and frequency of fried food

2.1.5 Types of study design

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consumption) outcomes.

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Only randomised controlled trials (RCTs) were included. Trials were excluded if participants were diagnosed with medical or psychiatric conditions and were receiving drugs or surgical weight loss intervention. Obesity prevention trials were excluded because they often

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incorporate environmental modifications, such as revision of school curriculum and physical

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activity classes [27].

2.2 Search Strategy

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We searched the Cochrane Databases of Systematic Review, PubMed Clinical Queries,

Centre for Review and Dissemination and the Joanne Briggs Institute to prevent duplication of systematic reviews. We collaborated closely with a senior librarian to develop a three-step extensive search strategy according to the recommendations of the Cochrane Handbook for Systematic Review [37]. First, we searched through seven databases, including PubMed,

CINAHL, the Cochrane Library, EMBASE, ProQuest Dissertations and Thesis, PsycINFO and Scopus. The index terms and keywords are documented in Table S3. We explored and truncated the index terms and key terms according to the different syntax rules of the individual databases. All searches were from inception until December 13, 2017. Second, we searched for ongoing and unpublished trials from various clinical trial registries. Third, we optimised

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potential trials by a hand search of the reference lists of selected trials and relevant systematic reviews and scanned the potential trials in obese-specific and adolescent-related peer-reviewed

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journals (Table S4). 2.3 Study Selection

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The study selection involved four phases using the PRISMA flow diagram [31]. In the

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first phase of identification, all records from the respective databases were collated in

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ENDNOTE software version X8 (Thomson Reuters, New York, USA), and duplicates were

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removed. In the second phase of screening, two reviewers (TH and CL) independently screened the title and abstract to remove irrelevant trials. In the third phase, the reviewers independently

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assessed the full-text articles for eligibility. In the fourth phase, the two reviewers met to compare their findings and verified if any article was overlooked. The two reviewers resolved any disagreement either through discussion or by involving a third reviewer (YL).

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2.4 Risk of Bias and GRADE Assessment

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Cochrane Collaboration’s tool [38] was used to assess the quality of the individual trials. Six domains were assessed for risk of bias. Selection bias was assessed through the use

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of 1) random sequence generation and 2) allocation concealment; performance bias was evaluated through 3) blinding participants and personnel; detection bias was examined through 4) blinding the outcome assessment; attrition bias was assessed through 5) incomplete outcome data; and 6) reporting bias was determined through selective reporting [38]. Each reviewer (TH and CL) independently assessed the risk of bias and graded the domains as low risk or high

risk; however, if the study lacked details, an unclear evaluation was given[38]. The Grading of Recommendations, Assessment, Development and Evaluation (GRADE) system (GRADE pro 3.6) was used to assess the overall strength of the evidence [39]. Each reviewer (TH and CL) rated the quality of the evidence as high, moderate, low or very low based on the five domains of evidence, namely, methodological limitations, inconsistency, indirectness, imprecision and

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publication bias [39]. Any discrepancy encountered during the assessment of the risk of bias or GRADE was resolved through discussion by the two reviewers or, if needed, by consulting

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with the third reviewer (LY). [Table 1]

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2.5 Data extraction

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The two reviewers (TH and CL) extracted data independently from the included trials

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using the standardised data extraction form based on the Cochrane Handbook for Systematic

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Reviews of Intervention [37]. Items extracted from eligible trials included author, year, setting, country, inclusion criteria, sample size, age, intervention, comparator, outcomes, attrition rate,

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ITT analysis, protocol, trial registration and grant support (Table 1). Items extracted for selfmonitoring intervention were number of components, frequency, duration, theory-based intervention, device, goal setting, use of reminders, professional support, parental involvement,

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online forum and follow-up (Table S6). The authors of the studies were contacted if

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information was missing or insufficient. After the extraction of all the data, the two reviewers (TH and CL) met to verify the data outcomes. When inconsistencies existed among the

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extracted data, full-text articles were reviewed for verification by the third reviewer (YL). 2.6 Data Synthesis RevMan software (Review Manager Version 5.3, The Nordic Cochrane Centre, Copenhagen) was used to synthesise the outcomes of the meta-analyses. We compared the changes in BMI and BMI z-score between the intervention and control groups. When studies

reported both outcomes, BMI outcomes were chosen for the analysis. When the studies did not report the change in of BMI and BMI z-score, the mean difference was calculated from the mean and standard deviation (SD) of the baseline and post-intervention scores based on the formula provided by the Cochrane Handbook for Systematic Review [37]. The SD of the mean

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difference was calculated as SD = √ SD2b + SD2𝑝 − 2 × 𝑟 × SD𝑏 × SD𝑃 , where the baseline SD is represented by SDb and the post-intervention SD is represented by SDp. The BMI correlation

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coefficient of 0.83 was used, which represents the correlation of the adolescents’ BMI based

on a one-year follow-up [40]. The standardised mean difference (SMD) with inverse variance method [41] was utilised to allow the synthesis of the BMI and BMI Z-score outcomes, and

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secondary outcomes were analysed in a similar manner [37]. To allow the quantification of the

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effect of internet-based self-monitoring intervention on the metric of BMI, an additional meta-

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analysis using mean difference was conducted for trials that reported outcomes in BMI.

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95% Confidence Interval (CI) was used to evaluate the overall effect of intervention. We compared the changes in BMI and BMI z-scores between the intervention and control

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groups using effect size, which measures the magnitude of the intervention effect, expressed as d or SMD and defined as d (0.1) = very small, d (0.2) = small, d (0.5) = medium, d (0.8) =

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large, d (1.2) = very large, and d (2.0) = huge [42]. Based on the recommendations of the Cochrane Handbook for Systematic Reviews [37] Cochran’s Q (𝜒 2 tests) was used to examine

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heterogeneity, with the statistical significance set at p < 0.10 due to its limitations of detecting true heterogeneity, and 𝐼 2 statistics, used to evaluate inconsistency in the trials results [43], was

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interpreted as unimportant (0%–40%), moderate (30%–60%), substantial (50%–90%) and considerable (75%–100%)[37]. The classification of the inconsistency in areas with overlapping I2 values considered the p-value of the Cochran’s Q (𝜒 2 tests) with the magnitude and direction of the treatment effects.

The meta-analysis adopted the recommendation of Borenstein and colleagues [44], who encouraged reviewers to decide on a model for statistical analysis based on the understanding of whether studies shared a common effect size and discouraged the practice of using a fixedeffect model and subsequently changing to a random-effects model in the presence of statistical heterogeneity. The random effect model was used, which assumes the true effect size to be

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normally distributed with different effect sizes between studies that could result from the

implementation of the intervention and the nature of the participants [45]. We conducted an

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influence analysis for point-estimate changes in outcome with each corresponding trial deleted

from the model once. To examine the cumulation of evidence, a cumulative meta-analysis was

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conducted based on the year of publication to examine the accumulation of results over time

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[46]. When heterogeneity was statistically significant, a sensitivity analysis was conducted to

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identify the heterogeneous trial and remove it to maintain homogeneity [37]. Subgroup

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analyses were performed to identify sources of heterogeneity and understand the essential components, formats and approaches to optimise the effectiveness of intervention [16]. The

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predefined subgroups included the type of statistical analysis conducted, study design, theorybased intervention, frequency, provision of counselling, goal setting, different self-monitoring components, professional support, parental involvement and intervention duration. Based on

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the recommendations by Sterner and colleagues [47], the assessment of small-study effects,

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including publication bias, was conducted if the review included 10 or more trials which was assessed qualitatively using the funnel plot and quantitatively using the Egger’s regression-

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intercept.

3. Results The systematic search process is illustrated in Fig. 1. A total of 6,841 records were generated from the seven databases. We removed 2,091 duplicates using ENDNOTE software. Two reviewers independently screened and excluded 2,521 records by assessing the titles and

2,161 records by evaluating the abstract. We downloaded 68 full-text articles with the eligibility criteria. Fifty-eight articles were excluded due to the reasons given in Fig. 1. Table S5 provides a list of studies with the reasons for exclusion. This systematic review and metaanalysis included six trials selected from 10 articles [48-57]. Eight articles were published as peer-reviewed papers, and two were unpublished theses. Multiple reports from three trials were

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also obtained. [Figure 1]

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3.1 Characteristics of the trials

Table 1 provides a summary of the characteristics of six RCTs amongst 505 adolescents across the United States of America (n = 4), Portugal (n = 1), and Malaysia (n = 1). Overweight

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and obese were defined as BMI values ≥ 25 kg/m2 or BMI percentiles ≥ 85th. The mean age of

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the participants ranged from 13.1 years to 15.2 years. The sample size ranged from 57 to 105

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participants. All trials reported more than one research outcome, and six trials granted support. Three trials reported outcomes in BMI and BMI Z-score, two trials reported in BMI only, and

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one trial reported in BMI Z-score only.

3.2 Risk of Bias and GRADE Assessment Only one trial presented published protocols [50], and four trials were registered in

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various clinical trial registries. Attrition rates ranged from 0% to 30.7% with three trials using

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ITT analysis. The risk of bias summary is presented in Fig. 2. Three trials used random sequence generation, and one trial used opaque envelopes for allocation concealment. None of

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these trials blinded the participants and personnel. One trial reported the blinding of outcome assessment, and five trials were rated with a low risk of attrition bias and selective reporting. The GRADE system [39] was used to evaluate the overall quality of the evidence, which was graded as low based on the methodological limitations and imprecision in Table 2. To assess the domain of methodological limitations, we provided a low rating due to selection

and performance bias. Amongst the six selected trials, only three trials used random sequence generation, one trial concealed allocation, and none of them blinded the participants and study personnel. To assess the domain of imprecision, we examined the sample size and 95% CI between the upper and lower boundaries of the selected trials [39]. Sample sizes ranged from 57 to 101 participants, which were considered small. Three CIs had a wide range, with the

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widest ranging from -1.59 to 0.04 for BMI z-score. Other domains of inconsistency, indirectness and publication bias were rated as insignificant or non-existent. Small study effects

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and publication bias were not assessed as fewer than 10 trials were included in this review [47]. [Figure 2] [Table 2]

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3.3 Internet-based self-monitoring intervention

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Details of the internet-based self-monitoring intervention are presented in Table S6.

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Five trials were delivered on websites, and one trial used e-therapeutic platforms, which are

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websites incorporating an educational computer game design to provide information on weight management [58]. Five trials (83.3%) utilised multicomponent self-monitoring by combining

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physical activity and weight or by combining physical activity, diet and weight. Five trials (87.5%) used theoretical bases for intervention development; these theories included the cognitive behavioural therapy model [48, 49, 51, 52], health promotion model [59], family-

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based theory [60], transtheoretical model [61] and self-determination theory [62]. The

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frequencies of self-monitoring ranged from daily to fortnightly. Four trials involved parents through the provision of information support by mail, website and face-to-face counselling.

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Four trials provided professional support through personalised feedback by e-mail, short message service and face-to-face counselling. Four trials provided online forum for peer support. The duration of the intervention ranged from 3 months to 12 months. Only three trials conducted follow-up sessions, which ranged from 8 months to 24 months.

3.4 Effectiveness of internet-based self-monitoring intervention in reducing BMI and BMI z-scores Six trials assessed the effectiveness of the internet-based self-monitoring interventions amongst 505 participants using changes in BMI and BMI z-score as the outcomes. Figure 3 illustrates the pooled meta-analysis examining BMI changes between intervention and control

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groups. The meta-analysis revealed a small effect (d = 0.30, 95% CI: -0.48 to -0.12) favouring internet-based self-monitoring. No evidence of statistical heterogeneity (I2 = 0%, p = 0.76) was

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observed among the trials. Among these six trials, five of them used the original BMI metric

as the outcome and a second meta-analysis was conducted which revealed a BMI reduction of

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0.75 kg/m2 (95% CI: -1.23 to -0.28) favouring internet based self-monitoring, as shown in

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Table S7.

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[Figure 3]

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Subgroup analyses were conducted to explore intervention characteristics of BMI reduction, as shown in Table 3. Subgroup analyses revealed a significant difference between

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the subgroups on the provision of counselling (I2 = 72%, p = 0.06) with a small effect observed in trials with no face-to-face counselling (d = 0.22, 95% CI: -0.42 to -0.03) and a medium effect observed in trials providing additional face-to-face counselling (d = 0.69, 95% CI: -1.14 to -

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0.25). The frequency of daily self-monitoring (d = 0.36, 95% CI: -0.59 to -0.13), use of

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multicomponent self-monitoring (d = 0.30, 95% CI: -0.53 to -0.08), parental involvement in counselling and internet self-monitoring (d = -0.69, 95% CI: -1.14 to -0.25), face-to-face

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professional support through the internet (d = -0.69, 95% CI: -1.14 to -0.25), and specified goal setting (d = 0.36, 95% CI: -0.59 to -0.13) produced a larger effect size compared to their counterparts. [Table 3] 3.5 Effectiveness of internet-based self-monitoring intervention on secondary outcomes

Four selected trials assessed 2 different secondary outcomes, as shown in Table 4. However, no significant differences were observed between the internet-based self-monitoring intervention group and the control group for the secondary outcomes of QoL subscales of physical functioning (d = 0.45, 95% CI: -0.10 to 0.99), social functioning (d = 0.06, 95% CI: 0.30 to 0.42), and the psychosocial outcome of depression (d = 0.18, 95% CI: -0.47 to 0.11).

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[Table 4]

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4. Discussion

To the best of our knowledge, this review is the first meta-analysis to quantitatively synthesise the evidence of internet-based self-monitoring intervention for overweight and

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obese adolescents, and the results support the use of internet-based self-monitoring for

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reducing BMI and BMI z-score. Subgroup analyses suggested that daily multicomponent self-

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monitoring, goal setting and professional face-to-face counselling with parental involvement

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were effective in reducing BMI. For the secondary outcomes, no effect was observed in QoL

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and outcomes for depression. Nonsignificant results may be explained by the insufficient power of the combined sample size as data were only pooled from two trials for each outcome. Hence, many rigorous trials are necessary to draw substantial conclusions.

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4.1 Internet-based self-monitoring interventions Eligible interventions used websites to allow adolescents who are overweight and obese

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to enter their self-monitoring data on various combinations of physical activities, diet and

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weight. Interventions utilised websites or e-therapeutic platforms, which are websites incorporating an educational computer game design [63], to provide a structural programme of diet, physical activity or weight control. The eligible interventions combined a variety of interventional components which could include individualised e-mail feedback, online forum, face-to-face counselling, use of self-monitoring devices and reminders. The attrition rates of the trials ranged from 11.5% to 14.5% for trials with intervention duration of four months or

less and greater attrition rates were observed, from 13.6% to 30.7%, for interventions of six months to a year. Three trials, in seven publications [48, 49, 51, 52, 55-57], reported outcomes of adolescent’s adherence of the internet-based features but were measured differently making it difficult to synthesise it in a meaningful manner. One trial, in three publications [55-57], analysed the usage of the interactive components and found participants’ submission of the

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weight graph, e-mail communication to professional support and quiz scores to be associated

with reduction in obesity outcomes. In addition, adolescents usage of the website were found

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to decrease in the initial two to three months [51, 52, 55-57], and recommendations were made for essential weight management topics to be conducted in the initial months to maximise

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effectiveness [51, 52].

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To the best of our knowledge, this review did not identify other modalities of internet-

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based self-monitoring besides the use of the computer with the earliest trial publish more than

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a decade ago [55-57]. Moreover, none of the identified trials provided tailored weight management content to individual participants. Considering that adolescents are early adopters

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of social media and communication technology [64], smartphone could be a potential internetbased platform for self-monitoring with capabilities of running game design weight management applications [65] and increase the convenience of self-monitoring [17, 66]. To

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sustain adolescents’ engagement, a possibility would be to leverage on technology to tailor the

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internet-based weight management content and behavioural change strategies specific to the individual [67]. A pedometer (monitoring devices) serves as an objective measure and

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motivational tool that may improve self-regulation and self-reinforcement [68] was incorporated in three trials for self-monitoring. Pedometers measure the number of steps taken; these tools are used with a physical activity goal, such as 10,000 steps, to allow adolescents to self-monitor their goals. Hence, future interventions should consider trials with the use of

tailoring weight management content, game design, pedometers and self-monitoring on smartphones to engage adolescents and promote sustainable behaviour changes [68-70]. 4.2 Effectiveness of internet-based self-monitoring intervention for BMI reduction Consistent with previous narrative reviews on internet-based intervention [27-29] for overweight and obese adolescents, this meta-analysis found that internet-based self-monitoring

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intervention exerted a small effect size on the reduction of BMI amongst adolescents who were overweight and obese. Based on the recommendation by Reinehr and colleagues [71], a

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reduction of 0.25 in the BMI z-score (a BMI reduction of approximately 1.0 kg/m2 for a 13year-old) was recommended as it was associated with improvements in cardiometabolic

outcomes, such as HDL-cholesterol, triglycerides and blood pressure. The second meta-

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analysis identified a reduction in BMI of 0.75 kg/m2, which suggests that internet self-

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monitoring may not be clinically significant but has the potential to treat overweight and obese

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adolescents. The small effect size could be possibly attributed to the frequency of fortnightly self-monitoring, unicomponent self-monitoring, the absence of specific goal setting,

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professional support and parental involvement, which may reduce the effect of internet-based self-monitoring interventions. It was unclear if the reduction of BMI and BMI z-scores with internet-based self-monitoring interventions were sustainable in the long term as only three

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trials presented follow-up data and one trial reporting follow-up beyond one year.

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4.3 Goal setting and daily multicomponent self-monitoring The findings of the subgroup analyses support the use of multicomponent self-

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monitoring with the combination of physical activity and weight or physical activity, weight, diet and specific goal setting. This was similar to the findings from systematic reviews on adult weight management, where a combination of physical activity, diet and weight self-monitoring was found to be effective in losing weight [10, 11, 22] and a higher frequency of selfmonitoring was required for successful behaviour change that facilitated weight loss [23].

Unlike adults, the problem-solving and planning skills of adolescents are limited but continually develop throughout adolescence until early adulthood [72]. Hence, setting specific goals for adolescents could overcome their limitations in problem-solving and planning. Goal setting enhances self-efficacy [73] and promotes adherence to goal-directed activities [74]. Therefore, daily multicomponent self-monitoring with specific goal setting is suggested for

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future interventions for adolescent weight management.

4.4 Incorporation of professional counselling and parental involvement

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Our subgroup analyses observed a greater effect size on the reduction of BMI when internet-based self-monitoring intervention was complemented with face-to-face professional

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counselling and parental involvement. This result may be attributed to the interpersonal

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interaction and professional advice that face-to-face counselling provides that motivate

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behavioural change [75]. The finding that face-to-face counselling increases the effectiveness

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of weight loss is supported by reviews among adults [24, 26]. Professional support also establishes rapport with adolescents to satisfy individual needs [76].

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The use of parental involvement is consistent with previous systematic reviews on internet-based weight management in adolescents [27-29]. Systematic reviews [77, 78] suggested that the intensity of parental involvement is important in childhood weight control

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intervention. Adolescents are significantly influenced by parental beliefs, perception and

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behaviour; thus, parents serve as role models to adolescents in behavioural strategies [78]. Internet-based intervention is an alternative medium to engage parents with additional

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commitments [79]. Our review suggests the possibility of adding professional counselling and parental involvement in internet-based self-monitoring intervention for adolescents who are overweight and obese. 4.5 Strengths and limitations

A meta-analytic approach was used to obtain evidence for the effect of internet-based selfmonitoring intervention on BMI amongst adolescents from seven databases using a rigorous and comprehensive search strategy. This review was registered, and it adhered to the use of the PRISMA statement [31]. Overall and individual evidence assessments were performed using the GRADE system and Cochrane risk of biases. However, our review has several limitations.

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First, the variability of the interventional components and internet-based features differed between trials and the results of the subgroup analyses, where trials were grouped depending

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on the presence of a specific interventional component, should consider the multiplicity of the subgroup and interpretation of the observational results. Second, the small sample sizes in the

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selected trials and the inclusion of trials published in English only may pose small study effects

N

and a potential publication bias. Third, the majority of trials were conducted in Western

A

countries, which may limit generalisation amongst adolescents from Eastern cultures because

M

of cultural and racial differences. Fourth, a majority of the trials presented no follow-up data, and sustainability is unclear. Lastly, the results of this meta-analysis, such as any meta-analysis,

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may be limited by ecological fallacy and/or Simpson’s Paradox [80]. Therefore, the results should be interpreted with caution.

4.6 Recommendations for practice

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Internet-based self-monitoring intervention is a possible approach for overweight and

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obese adolescents to reduce their BMI or BMI z-scores. The results from the subgroup analyses highlighted the use of daily multicomponent self-monitoring with specific goal setting and

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provision of face-to-face professional counselling and parental involvement, could increase the effectiveness of internet-delivered weight management programmes. Use of a monitoring device for physical activities [68], image-assisted dietary assessment [81] and anthropometric measures for nutritional status [82] should be considered for self-monitoring. Furthermore, smartphone applications [83], tailoring weight management content, electronic cameras,

Bluetooth, the cloud and wearable technologies [84] are potential self-monitoring technologies to sustain the engagement of overweight and obese adolescents and self-regulate their health behaviour. 4.7 Recommendations for future research This review highlighted the need for many primary trials with large sample sizes to

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explore the role of parents in promoting self-monitoring of weight management for overweight and obesity in adolescents. In our review, the intervention duration is inconclusive with result

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of the subgroup analysis suggesting at least six months of intervention. Further trials are

recommended to determine the optimal duration. The overall evidence grade was low, thereby

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suggesting that future studies with larger samples and high-quality RCTs should give special

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attention to reducing selection, performance and attrition bias according to the recommendation In addition to

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of the Consolidated Standards of Reporting Trials (CONSORT) [85].

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CONSORT, the review recommends future studies to use TIDieR checklist [86] to provide details on the assessment of intervention fidelity and to identify essential internet-based

5. Conclusion

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features for the engagement of adolescent and the contribution to interventional outcomes.

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The internet provides interactive ways to gain knowledge, exchange information and selfmonitor through a diverse range of advanced functionalities. Internet-based self-monitoring is

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a possible approach for overweight and obese adolescents to reduce their BMI and BMI z-

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scores. Daily multicomponent self-monitoring and goal setting should be incorporated into the intervention with considerations for face-to-face professional counselling and parental involvement. The overall low-quality evidence indicated that further well-designed RCTs with follow-up data amongst a large sample size are required to confirm the effectiveness of internet-based self-monitoring intervention.

Authors’ contributions The authors’ primary responsibilities were as follows: Y.L., T.H, C.L. and S.N.W developed the review question and design the review. S.N.W. supported searching strategies and Y.L., T.H. and C.L. screened, selected and reviewed the data. Y.L. and T.H. synthesized the data and interpreted the results. Y.L. wrote the manuscript and all authors contributed

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Potential conflict of interest: No conflict of interest was declared.

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comments for finalize manuscript. All authors approved the final submitted version.

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Acknowledgements: This study is support by the University Humanities & Social Sciences

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Seed Fund Grant of YL (HSS Seed Funding-1/2016, WBS no: R-545-000-076-646). We would

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like to acknowledge authors for sharing their additional study data, which was a great help for

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us to conduct the meta-analysis.

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[77]. van der Kruk, J.J., et al., Obesity: a systematic review on parental involvement in longterm European childhood weight control interventions with a nutritional focus. Obes Rev, 2013. 14(9): p. 745-60. [78]. Golley, R.K., et al., Interventions that involve parents to improve children's weightrelated nutrition intake and activity patterns - what nutrition and activity targets and behaviour change techniques are associated with intervention effectiveness? Obesity reviews, 2011. 12(2): p. 114-30. [79]. Choi, H., et al., Development and preliminary evaluation of culturally specific web-based intervention for parents of adolescents. Journal of Psychiatric and Mental Health Nursing, 2016. 23(8): p. 489-501. [80]. Cooper, H. and E.A. Patall, The relative benefits of meta-analysis conducted with individual participant data versus aggregated data. Psychological Methods, 2009. 14(2): p. 165-76. [81]. Gemming, L., J. Utter, and C. Ni Mhurchu, Image-assisted dietary assessment: a systematic review of the evidence. Journal of the Academy of Nutrition and Dietetics, 2015. 115(1): p. 64-77. [82]. Gunderson, E.P., Childbearing and obesity in women: weight before, during, and after pregnancy. Obstetrics and Gynecology Clinics of North America, 2009. 36(2): p. 31732, ix. [83]. Rosenbloom, R., et al., Technology delivered self-monitoring application to promote successful inclusion of an elementary student with autism. Assist Technol, 2016. 28(1): p. 9-16. [84]. Whelan, M.E., et al., Brain Activation in Response to Personalized Behavioral and Physiological Feedback From Self-Monitoring Technology: Pilot Study. J Med Internet Res, 2017. 19(11): p. e384. [85]. Schulz, K.F., D.G. Altman, and D. Moher, CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials. BMJ, 2010. 340. [86]. Hoffmann, T.C., et al., Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide. BMJ, 2014. 348.

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No additional records identified through other sources

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6841 records identified PubMed (n = 1894); CINAHL (n = 588); Cochrane Library (n = 2119); EMBASE (n = 1073); ProQuest (n = 258); PsycINFO (n = 256); SCOPUS (n = 653)

Screening

N

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Identification

Figures

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2091 records were duplicates and removed using ENDNOTE program

Included

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Eligibility

M

A

4750 records were founded and screened

Reasons for exclusion: 



Irrelevant Title (n = 2521) Irrelevant Abstract (n = 2161)

)

Reasons for exclusion:  Wrong Population (n = 19)  No Internet (n = 19)  No self-monitoring (n = 5)  Lacks BMI Outcome (n = 5)  Non-RCTs (n = 7)  Non-English language (n = 1)  Protocol (n = 2)

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N

M

A

6 Trials (10 articles) included for systematic review and in meta-analysis)

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68 full-text articles assessed for eligibility

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Figure 1 Selection of trials for inclusion of the systematic and meta-analysis.

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Figure 2 Risk of bias summary.

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Figure 3 Forest plot of standardized mean difference (95%) in change of body mass index for

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M

A

N

Internet-based self-monitoring intervention and control groups.

I Table 1 Characteristics of the selected trials Inclusion Criteria

Celio, 2005 [48]; Doyle et al. 2008 [49]

2-arm RCT

Age: 12-18 yrs BMI%tile: ≥ 85th

Fonseca et al. 2016 [50]

2-arm RCT

Age M (SD)

CC E

Paediatric obesity clinic, Portugal

A

Jones et al. 2008 [52]; 2009 [51]

Nawi et al. 2015 [53]

2-arm RCT Schools, USA

2-arm Cluster RCT Schools, Malaysia

Name / Internetbased SelfMonitoring Intervention Student Bodies 2 / Informational website

Comparator

Outcomes / Measures

Attrition Rate (%)

ITT

Protocol/ Register

GS

Printed materials and requested to follow-up with physicians

BMI; BMI Z-score; EDE-Q

4 mths I: NR C: NR 8 mths I: 16.67 C: 17.07

N

N/N

Y

I: 14.9 (1.7) C: 14.1 (1.6)

Age 12-18 yrs BMI%tile: ≥ 85th

I: 40, C: 40,

I: 14.5 (1.9) C: 14.5 (1.8)

Next.Step / e-therapeutic platform using game design. With paediatric obesity clinic

Paediatric obesity clinic paediatrician, dietician and exercise physiologist

BMI; IWQOL; AWCQ; ALP

3 mths I: NR, C: NR

N

Y/Y

Y

High school students BMI%tile: ≥ 85th Binge/overeati ng behaviour

I: 52 C: 53

I: 15.0 (1.0) C: 15.2 (1.1)

Student Bodies 2 / Informational website

Wait-list control

BMI, BMI Z-score; CES; DFSM

4 mths I: 11.5, C: 9.4 9 mths I: 18.2 C: 17.0

Y

N/Y

Y

Secondary school students BMI: > 25 kg/m2

I: 47 C:50

I: NR C: NR

ObeseGO / Informational website

Printed materials

BMI, QoL/ NR (physical functioning, social functioning)

3 mths I: 14.5, C: 0

N

N/Y

N

ED

I: 42 C: 41

PT

Community, USA

Sample Size

A

Design, Setting, Country

M

Author, Year

N U SC R

Tables

ALP=Adolescent Lifestyle Profile; AWCQ=Adherence to Weight Control Questionnaire; BMI=Body mass index; C=Control group; CES-D=Center for Epidemiologic Studies Depression Scale; DFSM=Dietary Fat Screening Measure; EDE-Q=Eating Disorder Examination Questionnaire; GS=Grant Support; I=Intervention group; ITT=Intention-to-treat Analysis; IWQOL= Impact pf Weight on Quality of Life-Lite; M(SD)=Mean(Standard Deviation); mths=months; N=No; NR=Not reported; QoL=Quality of Life; RCT=Randomized Controlled Trial; Register=Trial Registration; USA – United States of America; Y=Yes; yrs=Years

I Patrick et al. 2013 [54]

Design, Setting, Country 4-arm RCT (4 of interest)

Inclusion Criteria

Sample Size

Age M (SD)

Age: 12-16 BMI%tile: >85th

I (a): 26 I (b): 24 I (c): 26 C: 25

I (a): 14.1 (1.4) I (b): 14.3 (1.8) I (c): 14.3 (1.5) C: 14.5 (1.5)

A

Author, Year

N U SC R

Table 1 (Continued)

2-arm RCT Community,

USA

AfricanAmerican girls Age: 11-15 yrs BMI%tile: ≥ 85th

PT

White,2004 [55]; Williamson et al. 2005 [56]; 2006 [57]

ED

M

Pediatric clinics, USA

I: 28 C: 29

I: 13.1 (1.6) C: 13.2 (1.16)

Name / Internet-based Self-Monitoring Intervention PACEi-DP / I (a): Informational Website. I (b): Same as (a) + SMS reminder from counsellor I (c): Same as (a) + group counselling for adolescents and parents HIP-Teen / Informational website, automated feedback on progress. Counselling with parents.

Comparator

Outcomes / Measures

Attrition Rate (%)

ITT

Protocol/ Register

GS

Printed materials

BMI Z-score; PQOL (total, physical functioning); CES; Behavioural strategies for physical activity / NR; fat intake / NR)

12 mths I (a): 26.9 I (b): 13.6 I (c): 30.7 C: 20.8

Y

N/Y

Y

Informational website without self-monitoring. Involve counselling with parents.

BMI; BFFQ; WLBS; CDSS; PASS

6 mths I: 17.9, C: 6.9 24 mths I: 21.7 C: 18.5

Y

N/N

Y

A

CC E

ALP=Adolescent Lifestyle Profile; BFFQ=Block Food Frequency Questionnaire; BMI=Body mass index; C=Control group; CDSS=Child Dietary Self-Efficacy Scale; CES-D=Center for Epidemiologic Studies Depression Scale; GS=Grant Support; I =Intervention group; ITT= Intention-to-treat Analysis; IWQOL= Impact pf Weight on Quality of Life-Lite; M(SD)=Mean(Standard Deviation); mths=months; N=No; PACEiDP=Pace-Internet for Diabetes Prevention Intervention; PASS=Physical Activity Social Support; PQOL=Pediatric Quality of Life Inventory; QoL= Quality of Life; RCT=Randomized Controlled Trial; SMS=Short message service; Register=Trial Registration; USA=United States of America; WLBS=Weight Loss Behavior Scale;Y=Yes; Yr=Year

I N U SC R

Table 2 GRADE summary of evidence for internet self-monitoring interventions on BMI Certainty assessment Risk of bias

Inconsistency

Indirectness

6 RCTs

serious †

not serious

not serious

M

A

No of studies

Imprecision

Publication bias

serious ‡

none

Overall certainty of evidence

No of participants Intervention

Control

⨁⨁◯◯ LOW

276

229

ED

CI: Confidence interval; SMD: Standardised mean difference Explanations †

1 of the studies had high risk of selection bias and 5 of the studies did not provide allocation concealment. The CI around the treatment is wide.

A

CC E

PT

‡

Effect size (95% CI)

SMD - 0.30 (-0.48 to -0.12)

Table 3 Subgroup analyses of internet-based self-monitoring interventions

Per-Protocol Analysis Intention-to-Treat Analysis No Theory Health Promotion Model Cognitive Behavioural Therapy Behavioural Determinants & Transtheoretical Model Family-based Therapy Fortnightly Daily

3 [48-50,53] 3 [51,52,54-57] 1 [53] 1 [50]

Sample size 243 196 97 80

Statistical Analysis Theory-based intervention

2 [48,49,51,52]

171

0.20

(-0.51, 0.10)

1 [54]

100

0.51

(-0.97, -0.04)

Frequency

1 2 [50,53] 4 [48,49,51,52,54-57]

57 177 328

0.66 0.20 0.36

(-1.19, -0.13) (-0.49, 0.10) (-0.59, -0.13)

Provision of Counselling

No Counselling Face-to-face Counselling

5 [48-53,54(a)(b)] 2 [54(c),55-57]

414 91

0.22 0.69

(-0.42, -0.03) (-0.48, -0.25)

97

0.14

(-0.54, 0.26)

80

0.27

(-0.71, 0.17)

[55-57]

d (95% Confidence Interval) 0.20 0.36 0.14 0.27

(-0.45, 0.05) (-0.65, -0.07) (-0.54, 0.26) (-0.71, 0.17)

1 1

[50]

4 [48,49,51,52,54-57]

328

0.36

(-0.59, -0.13)

Number of SelfMonitoring Components

Weight

1 [53]

97

0.14

(-0.54, 0.26)

Professional Support

2 [53,54(a)] [54(b)]

U

No Professional Support

0.51

(-0.97, -0.04)

N

4 [48-52,55-57]

100 308

0.30

(-0.53, -0.08)

131

0.19

(-0.54, 0.17)

32

0.39

(-1.19, 0.42)

A

1

Physical Activity + Weight + Diet

1

Internet Professional Support

2 [48,49,51,52]

171

0.20

(-0.51, 0.10)

Face-to-Face Professional Support

1 [50]

80

0.27

(-0.71, 0.17)

Face-to-Face + Internet Professional Support

2 [54(c),55-57]

91

0.69

(-1.14, -0.25)

No Parental Support

2 [50,53]

177

0.20

(-0.49, 0.10)

ED

M

SMS Professional Support

[48,49,51,52]

Parents Handbook

2

171

0.20

(-0.51, 0.10)

Parents Website

1 [54(a)(b)]

66

0.38

(-0.95, 0.19)

Parents Counselling + Internet Self-Monitoring

2 [54(c),55-57]

91

0.69

(-1.14, -0.25)

Three Months

2 [50, 53]

177

0.20

(-0.49, 0.10)

2 [48,49,51,52]

171

0.20

(-0.51, 0.10)

57

0.66

(-1.19, -0.13)

100

0.51

(-0.97, -0.04)

CC E

Duration of Intervention

Physical Activity + Weight

PT

Parental Support

[54(a)(b)(c)]

SC R

No Goal Setting Goal Setting (Never Specify Emphasis) Goal Setting (Specified Physical Activity /Nutrition)

[53]

Goal Setting

Four Months

[55-57]

Six Months

1

Twelve Months

1 [54(a)(b)(c)]

Subgroup Difference

p-value for Q (I2%) 0.40 (0)

0.47 (0)

IP T

No of Studies (Ref)

Subgroups

0.39 (0)

0.06 (72.1)

0.63 (0)

0.50 (0)

0.43 (0)

0.27 (23.5)

0.34 (11.0)

A

Patrick et al. 2013[54] has three intervention arms (a, b and c)

33

Table 4 Effectiveness of Internet-based self-monitoring intervention on secondary outcomes

Quality of Life  Physical Functioning 

Social Functioning

Psychosocial  Depression

Trials included

d (95% Confidence Interval)

Nawi et al. 2015 [53] Patrick et al. 2013[54] Fonseca et al. 2016[50] Nawi et al. 2015 [53]

0.45

(-0.10, 0.99)

0.06(60)

0.06

(-0.30, 0.42)

0.23(31)

0.18

(-0.47, 0.11)

1.00(0)

A

CC E

PT

ED

M

A

N

U

SC R

Jones et al. 2008;2009[51,52] Patrick et al. 2013[54]

Subgroup Difference

p-value for Q (I2%)

IP T

Secondary outcomes

34