Comparative effects of behaviour change techniques using eHealth and mHealth in promoting dietary behaviour: protocol for a systematic review and component network meta-analysis
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* Takafumi Fukuda
* Nozomi Matsuura
* Hisashi Noma
* Takahiro Mihara

## Abstract

**Introduction** Globally, it is estimated that dietary habits contribute to 22% of adult deaths and 15% of disability-adjusted life years, highlighting the critical role of dietary behaviour in public health. Despite the known benefits of healthy eating, many individuals find it challenging to change their diet for disease prevention. eHealth and mHealth interventions using behaviour change techniques (BCTs) have emerged as promising strategies to address this issue. However, the specific BCTs that are most effective in promoting dietary behaviour are not well established. This systematic review and component network meta-analysis (CNMA) aims to estimate the effect size of each BCT on fostering healthy eating.

**Methods and analysis** We will include randomised controlled trials that assess the effects of eHealth and mHealth interventions on promoting changes in dietary behaviours among healthy adults. Studies with a minimum follow-up period of 3 weeks will be considered. Searches will be conducted in MEDLINE [PubMed], Embase [Dialogue], Cochrane Central Register of Controlled Trials, PsycInfo [Dialogue], ClinicalTrials.gov, the WHO International Clinical Trials Registry Platform and the University Hospital Medical Information Network Clinical Trials Registry on 27 January 2024. Two independent reviewers will conduct title and abstract screening followed by a full-text review. Disagreements will be resolved through discussion or consultation with a third reviewer. The primary outcome is dietary behaviour, as measured by changes in the diet quality score and the intake of a specific food. Our data synthesis will apply a frequentist random-effects model for pairwise meta-analysis, network meta-analysis and an additive CNMA model to compute the effect size of each BCT. This methodological approach will reveal the positive and negative effects of each BCT and provide a ranking of these techniques, considering both direct and indirect evidence.

**Ethics and dissemination** Ethical approval is not required for this systematic review because it uses existing published data. These results will be submitted for publication in a peer-reviewed journal. The current protocol was submitted to PROSPERO on 16 January 2024 (CRD 42024502217).

*   eHealth
*   Behavior
*   Meta-Analysis

### STRENGTHS AND LIMITATIONS OF THIS STUDY

*   A systematic approach is used to search, screen, assess and synthesise the literature, including the prior registration of the protocol in PROSPERO and the evaluation of the risk of bias using Cochrane risk of bias tools.

*   The importance of this study is identifying behaviour change technique to promote dietary behaviours through component network meta-analysis.

*   Potential limitations include missing unpublished data.

*   As dietary measurements are performed using various methods, a potential limitation of this study is that it is forced to synthesise outcomes using standardised mean differences.

## Introduction

### Rationale and objective

Globally, poor dietary habits have been implicated in approximately 22% of adult deaths and contribute to 15% of disability-adjusted life years.1 These habits particularly influence the prevalence of cardiovascular disease, which is the leading cause of obesity-exacerbated mortality.2 Despite the known benefits of healthy eating, individuals often struggle to make substantial and sustained changes to their dietary patterns to reduce disease risk.3 

To address this issue, eHealth and mHealth interventions that leverage technologies such as smartphones and the Internet have been extensively studied in recent decades.4 Although systematic reviews have evidenced these interventions to be generally effective,5–7 heterogeneity in outcomes suggests a complex interplay of factors that influence their effectiveness.

One factor contributing to this heterogeneity is the complexity of eHealth and mHealth behaviour change interventions. Each intervention incorporated varying components, resulting in non-constant effect sizes. In recent years, the determinants of health behaviour have been identified from the perspective of behavioural science and health psychology, and intervention studies aimed at behaviour change have proliferated.8 Michie *et al* systematised and standardised the elements of these behaviour change techniques (BCTs) to be reproducible and not reducible, publishing them in 2013 as the Behaviour Change Technique Taxonomy v1 (BCTTv1), which has gained international consensus.9 BCTs are categorised into seven cluster solutions and 16 groups and further subdivided into 93 specific techniques. Numerous attempts have been made to enhance eating habits through combined interventions employing BCTs.

However, there is no consensus on the effectiveness of BCTs in promoting dietary behaviour. For example, systematic reviews have indicated that providing feedback, a form of BCT, is effective for behavioural modification.10 11 Conversely, McDermott *et al* contended that the same BCT can produce negative effects.12 Samdal *et al* reviewed the possibility that presenting pros and cons can negatively affect behavioural change using pairwise meta-analysis and meta-regression analysis.13 Therefore, clarifying the direction and magnitude of the effects of each BCT is crucial, as this will enable the effective design of eHealth and mHealth interventions intended to promote dietary behaviour.

Studies that have assessed the effectiveness of BCTs typically rely on pairwise meta-analyses and meta-regression analyses to elucidate the efficacy of each BCT. However, these approaches have methodological limitations when estimating the effect sizes of individual BCTs. In this study, we will first verify the effectiveness of eHealth and mHealth interventions in promoting dietary behaviours using pairwise meta-analysis. Subsequently, we will conduct a component network meta-analysis (CNMA) that decomposes interventions into diverse elements to estimate the effect size of each component. This analysis will focus on randomised controlled trials (RCTs) and aim to estimate the effect size of each BCT on dietary behavioural changes with higher reliability using CNMA compared with traditional methods.

## Methods and analysis

This systematic review protocol was reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P)14 and PRISMA extension for network meta-analysis (NMA)15 and adhered to the Cochrane Handbook for Systematic Reviews of Interventions (second edition). The start date of the study was January 2024, and the planned completion date is December 2024.

### Eligibility criteria

Studies will be selected according to the criteria outlined below.

#### Study designs

If applicable, RCTs, including cluster and crossover RCTs, will be included. Quasi-experimental studies will be excluded.

#### Participants

Healthy adults aged ≥18 years, including overweight or those with obesity but excluding those with specific diseases or conditions, will be included. Adult data will be extracted separately for mixed-population studies.

#### Interventions

We will consider eHealth and mHealth interventions encompassing electronic health records, telemedicine, mobile health apps, health information portals and related technologies designed to encourage dietary behaviour. Studies focused on laboratory feeding trials not intended to assess behavioural changes, those promoting prefabricated foods or meal replacement drinks and those testing dietary supplements such as fish oil will be excluded from our analysis. Additionally, studies with follow-up data of a minimum of 3 weeks after randomisation will be included.

#### Comparators

This study will include a range of comparators, such as different eHealth or mHealth interventions, minimal interventions, usual care and no interventions.

#### Outcomes

The primary outcome of this study is the change in dietary behaviours, evaluated through validated scores such as the Healthy Eating Index,16 Mediterranean Diet Score17 and Dietary Approaches to Stop Hypertension (DASH) score,18 or through the consumption of specific foods recommended in the Mediterranean diet, such as fruits, vegetables, whole grains, nuts, legumes, seafood and fish. These changes will be measured using dietary recall, dietary records, dietary history questionnaires and food frequency questionnaires. Secondary outcomes will be evaluated by integrating the same diet scores or types of food. This study will also examine health-related outcomes, focusing on changes in body weight and body mass index (BMI).

#### Setting

This study will not be restricted by the type of setting.

#### Language

Articles reported in any language will be included; however, the search for studies will be conducted in English.

### Information sources

The following databases will be searched: MEDLINE [PubMed], Embase [Dialogue], Cochrane Central Register of Controlled Trials, PsycInfo [Dialogue], ClinicalTrials.gov, WHO International Clinical Trials Registry Platform (ICTRP) and University Hospital Medical Information Network Clinical Trials Registry (UMIN-CTR). We will search for a full database history from the beginning until 27 January 2024.

### Search strategy

The search strategy will employ a combination of controlled terms and free-text words relevant to eHealth/mHealth and dietary behaviour (see table 1 for the PubMed strategy). Online supplemental file 1 contains the full search strategies for the other databases. The search will be performed on 27 January 2024. Before the final analysis, an updated search will ensure the inclusion of the latest studies.

### Supplementary data

[[bmjopen-2024-084774supp001.pdf]](pending:yes) 

View this table:
[Table 1](http://bmjopen.bmj.com/content/14/10/e084774/T1)

Table 1 
Search strategy for PubMed

### Study records

#### Data management

Two independent reviewers (TF and NM) will screen the titles and abstracts of the papers identified using the search strategies mentioned earlier. Duplicate papers will be removed using Rayyan.19 

#### Selection process

If the research title or abstract does not provide sufficient information to determine eligibility, the full paper will be assessed for acceptance or rejection. In case of disagreement between the two reviewers, a senior researcher (TM) will be consulted for the final decision.

#### Data collection process

*   Two independent reviewers (TF and NM) will extract data from eligible studies, excluding BCTs, in duplicate. The first five titles will be screened in cooperation using a data collection form and discussed by the two reviewers.

*   Data related to BCTs will be extracted by TF and checked by NM.

*   Disagreements in data extraction will be resolved through discussion, and the study authors will be contacted to clarify uncertainties.

#### Data items

*   A data collection sheet will be prepared, which includes the following:

1.  Title/author information.

2.  Year of publication.

3.  Study design.

4.  Eligibility criteria.

5.  Exclusion criteria.

6.  Participants’ characteristics (demographics, number of endpoints, mean or median age and sex ratio).

7.  Settings.

8.  Intervention (including comparator) details for each arm (duration, frequency and BCTs based on BCTTv1, provider and delivery).

9.  Outcome variables for each arm at the longest follow-up (variables and results). If outcomes are assessed at multiple time points, the outcome variables at the shortest follow-up period of more than 3 weeks will also be extracted as secondary data.

10. Methods of dietary assessment.

11. Dropout rate.

#### Coding BCTs

The BCT will be coded only when there is clear evidence of inclusion. The 93 BCTs will be coded as present (1) or absent (0). The BCTs of the intervention and control groups will be identified separately. Reviewers will undergo training using the BCTTv1 online training provided by University College London ([https://www.bct-taxonomy.com/?n=1](https://www.bct-taxonomy.com/?n=1)) before the coding. Additionally, if the same studies as those included in this research have been used in previous systematic reviews of BCTs, the coding of BCTs in those reviews will also be referenced.

### Outcomes and prioritisation

#### Primary outcomes

The primary outcome will be dietary and behavioural changes assessed using the validated methods mentioned above. In cases where multiple dietary behaviour outcomes are reported in a single publication, priority will be given to the primary outcome identified in each article. If no prioritisation of outcomes is provided, preference will be given to composite scores (such as the Healthy Eating Index, Mediterranean Diet Score and DASH score) over individual food items. For studies reporting multiple time point measurements, we will use the score from the longest available follow-up period to ensure the assessment of sustained dietary behaviour changes for the primary outcome.

#### Secondary outcomes

The secondary outcomes will assess changes in dietary intake and related health measures, with a particular focus on the following:

1.  Daily intake of specific foods (vegetables, fruit, whole grains, nuts, seafood and fish)

2.  Combined daily fruit and vegetable intake

3.  Each dietary quality score.

4.  Body weight (kg)

5.  BMI (kg/m²), calculated from height and weight measurements.

If multiple measurements are reported for all secondary outcomes, we will prioritise the data from the longest follow-up period to align with the assessment strategy of the primary outcome.

Additionally, to evaluate the short-term effects of the intervention as secondary outcomes, the primary outcome variables at the shortest follow-up period of more than 3 weeks extracted from each study will be assessed.

### Risk of bias in individual studies

The risk of bias will be assessed independently by two reviewers (TF and NM) using the Cochrane Collaboration tool (RoB 2.0).20 Because the study aims to clarify the effect of assignment to an intervention, we will evaluate bias on the effect of assignment to an intervention (the ‘intention-to-treat effect’) with domain 2 of RoB 2.0. Disagreements will be resolved first by discussion and then by consulting a third author for arbitration.

### Data synthesis

#### Pairwise meta-analysis

We will conduct a pairwise meta-analysis to assess the effectiveness of mHealth and eHealth interventions on each outcome when compared with comparator interventions (eg, minimal intervention, no intervention or usual care) when multiple studies containing comparable pairs are identified. Frequentist random-effects models21 will be used to combine the results. Different scores will be synthesised using SMD, and the same scores will be synthesised using MD with 95%CIs. Heterogeneity will be quantified using the Cochrane Q-test and the Higgins I² statistics.

#### Network meta-analysis

We will perform a random-effects NMA assuming between-study variance across the entire network. We will estimate the summary mean difference (MD) or standardized mean difference (SMD) using the 95% CIs. The surface under the cumulative ranking curve (SUCRA) will be used to assess the relative efficacy of interventions. We will assess the overall evidence for head-to-head comparisons of interventions using network plots.15 In addition, we will perform statistical evaluations of incoherence in networks using global and local approaches. For the global approach, we will use a design-by-treatment interaction model to assess incoherence.22 For the local approach, the Bucher local inconsistency test will be used to quantify the incoherence of all triangular loops in the network.23 We will use the ‘NMA’ package with an R statistical environment.24 

#### Component network meta-analysis

We will conduct a CNMA to assess the collective effectiveness of the various BCTs. Additive CNMA will be performed under the assumption that the effects of combined treatments can be represented as the sum of their individual components.25 This model will be particularly useful for isolating the effects of a single BCT when it is part of a combined intervention. The ranking of the BCTs will be estimated using P-scores.26 The R statistical environment using the ‘netmeta’ package27 will support the CNMA. Discrepancies between the model-estimated effects and the observed data will be used to evaluate heterogeneity within the CNMA framework.28 29 

#### Narrative synthesis

If quantitative synthesis is not feasible owing to between-study heterogeneity or an insufficient number of studies, we will conduct a systematic narrative synthesis. This method will use information from the text and tables to summarise and describe the characteristics and findings of the incorporated studies.

### Additional analysis

#### Subgroup analysis

For a more nuanced understanding of the effectiveness of the interventions, we will perform subgroup analyses within the meta-analysis for the primary outcome if a sufficient number of studies are involved. The factors considered for subgroup analyses will be the participant’s health status, with groups divided by mean subject BMI thresholds (>30 or <30), age categories (>60 or <60 years) and intervention duration (>3 or <3 months). Additionally, dietary assessment methods and the specific provider of the intervention will also be criteria for subgroup analysis. This approach aims to discern differential effects across various populations and intervention contexts.

#### Sensitivity analysis

Sensitivity analyses will be performed to ascertain the stability of the findings. Priority will be given to eliminating studies with a high risk of bias. Further sensitivity assessments will review the robustness of the results across different levels of BCT classification (7, 16 or 93 categories), ensuring that the analysis accounts for the complexity and specificity of behavioural interventions.

#### Small study effects

To investigate reporting bias, we will examine studies with RCT protocols that meet the eligibility criteria but have not published their results. ClinicalTrials.gov, ICTRP and UMIN-CTR will be screened for relevant information. Comparison-adjusted funnel plots and the Egger test will be used to assess potential publication and small study biases.

### Confidence in the cumulative estimate

The overall certainty of evidence will be evaluated using the Grading of Recommendations Assessment, Development and Evaluation framework for pairwise meta-analysis. For NMA, the Confidence in Network Meta-Analysis approach will be considered.30–32 

### Ethics and dissemination

Ethics approval is not required because this systematic review will solely use the existing published data. The results will be submitted for publication in a peer-reviewed journal. Any significant protocol changes will be duly documented, providing a description of the change, its rationale and the date of the amendment, all of which will be included in the final report.

### Patient and public involvement

None

## Discussion

In this systematic review, we rigorously estimate the effect size of each BCT on dietary behaviour. Notably, no systematic review has explicitly addressed this objective, despite numerous publications focusing on BCTs for dietary behaviours. A critical aspect of this research lies in addressing the challenges inherent in deconstructing and comparatively analysing individual components within complex interventions. This is achieved by employing frequentist CNMA, a methodology that enables feasible and effective comparative analyses.

Given that rising healthcare costs are a major global problem, it is important to reduce healthcare costs by preventing disease. Deterioration of dietary behaviours has been widely reported as a risk factor for various diseases.33 34 eHealth and mHealth are powerful tools to promote prevention without relying on human resources.35 However, improving these habits remains a formidable challenge in disease prevention. In this study, estimating the individual effects of BCTs is vital for designing effective strategies to foster behavioural changes. In other words, by identifying behavioural change techniques that are effective in changing eating behaviour and those that have the opposite effect, this research can be used in the design and development of eHealth and mHealth in the future. This approach not only contributes to the academic understanding of BCTs but also has significant practical implications for public health interventions.

The heterogeneity among the studies, particularly in the variety of dietary assessment methods, represents a potential limitation of this study. Information from each study will be carefully extracted, and, where appropriate, sensitivity analyses will be conducted to thoroughly assess comparability.

## Ethics statements

### Patient consent for publication

Not applicable.

## Footnotes

*   Contributors TM is the guarantor. TF, NM and TM drafted the manuscript and contributed to the development of the selection criteria, risk of bias assessment strategy, data extraction criteria and search strategy. HN provided statistical expertise. All authors have read, provided feedback and approved the final manuscript.

*   Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

*   Competing interests None declared.

*   Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

*   Provenance and peer review Not commissioned; externally peer reviewed.

*   Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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