Personalized Web-Based Weight Loss Behavior Change Program With and Without Dietitian Online Coaching for Adults With Overweight and Obesity: Randomized Controlled Trial

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Abstract

Background

The effect of computer- or human-delivered personalized feedback on the effectivess of web-based behavior change platforms for weight loss is unclear.

Objective

We aimed to compare the effectiveness of a web-based behavior change intervention personalized through either computerized or human-delivered feedback with a nonpersonalized intervention in promoting weight loss in community-based adults with overweight or obesity.

Methods

This pragmatic, 3-group, parallel-arm, randomized trial recruited students and staff in a Brazilian public university who were aged 18 to 60 years, had a BMI of ≥25 kg/m ², and were not pregnant. Participants were allocated to one of 3 groups: platform only (24-week behavior change program delivered using a web platform with personalized computer-delivered feedback), platform plus coaching (same 24-week web-based behavior change program plus 12 weeks of personalized feedback delivered online by a dietitian), or waiting list (nonpersonalized dietary and physical activity recommendations delivered through an e-booklet and videos). Self-reported weight at 24 weeks was the primary outcome. Changes in dietary and physical activity habits within 24 weeks were secondary outcomes.

Results

Among the 1298 participants, 375 (28.89%) were lost to follow-up. In the intention-to-treat analysis, the platform-only and platform plus coaching groups had greater mean weight loss than the waiting-list group at 24 weeks (–1.08 kg, 95% CI –1.41 to –0.75 vs –1.57 kg, 95% CI –1.92 to –1.22 vs –0.66 kg, 95% CI –0.98 to –0.34, respectively). The platform-only and platform plus coaching groups, compared with the waiting list group, had a greater increase in the consumption of vegetables (3%, 95% CI 1% to 6% vs 5%, 95% CI 2% to 8% vs –3%, 95% CI –5% to 0%) and fruits (9%, 95% CI 6% to 12% vs 6%, 95% CI 2% to 9% vs 2%, 95% CI 0% to 6%) and a larger reduction in ultraprocessed food intake (–18%, 95% CI –23% to –13% vs –25%, 95% CI –30% to –20% vs –12%, 95% CI –16% to –8%). Changes in physical activity did not differ across the groups. Engagement was higher in the platform plus coaching group than in the platform-only group (7.6 vs 5.2 completed sessions; P=.007). Longer usage of the platform was associated with clinically meaningful (≥5%) weight loss (odds ratio 1.02, 95% CI 1.01 to 1.04).

Conclusions

The web-based behavior change programs with computer- and human-delivered personalized feedback led to greater, albeit small-magnitude, weight loss within 24 weeks. Improvement in multiple dietary habits, but not physical activity, were also greater in the personalized programs compared with the nonpersonalized one. The human-delivered personalized feedback by the online dietitian coach increased user engagement with the program and was associated with a significantly higher chance of clinically meaningful weight loss.

Trial Registration

ClinicalTrials.gov NCT03435445; https://clinicaltrials.gov/ct2/show/NCT03435445

International Registered Report Identifier (IRRID)

RR2-10.2196/10.1186/s12889-018-5882-y

Related collections

Most cited references 34

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The behaviour change wheel: A new method for characterising and designing behaviour change interventions

Susan Michie, Maartje M. van Stralen, Robert H. West (2012)

Background Improving the design and implementation of evidence-based practice depends on successful behaviour change interventions. This requires an appropriate method for characterising interventions and linking them to an analysis of the targeted behaviour. There exists a plethora of frameworks of behaviour change interventions, but it is not clear how well they serve this purpose. This paper evaluates these frameworks, and develops and evaluates a new framework aimed at overcoming their limitations. Methods A systematic search of electronic databases and consultation with behaviour change experts were used to identify frameworks of behaviour change interventions. These were evaluated according to three criteria: comprehensiveness, coherence, and a clear link to an overarching model of behaviour. A new framework was developed to meet these criteria. The reliability with which it could be applied was examined in two domains of behaviour change: tobacco control and obesity. Results Nineteen frameworks were identified covering nine intervention functions and seven policy categories that could enable those interventions. None of the frameworks reviewed covered the full range of intervention functions or policies, and only a minority met the criteria of coherence or linkage to a model of behaviour. At the centre of a proposed new framework is a 'behaviour system' involving three essential conditions: capability, opportunity, and motivation (what we term the 'COM-B system'). This forms the hub of a 'behaviour change wheel' (BCW) around which are positioned the nine intervention functions aimed at addressing deficits in one or more of these conditions; around this are placed seven categories of policy that could enable those interventions to occur. The BCW was used reliably to characterise interventions within the English Department of Health's 2010 tobacco control strategy and the National Institute of Health and Clinical Excellence's guidance on reducing obesity. Conclusions Interventions and policies to change behaviour can be usefully characterised by means of a BCW comprising: a 'behaviour system' at the hub, encircled by intervention functions and then by policy categories. Research is needed to establish how far the BCW can lead to more efficient design of effective interventions.

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Health Effects of Overweight and Obesity in 195 Countries over 25 Years.

Ashkan Afshin, Mohammad Forouzanfar, Marissa Reitsma … (2018)

Background While the rising pandemic of obesity has received significant attention in many countries, the effect of this attention on trends and the disease burden of obesity remains uncertain. Methods We analyzed data from 67.8 million individuals to assess the trends in obesity and overweight prevalence among children and adults between 1980 and 2015. Using the Global Burden of Disease study data and methods, we also quantified the burden of disease related to high body mass index (BMI), by age, sex, cause, and BMI level in 195 countries between 1990 and 2015. Results In 2015, obesity affected 107.7 million (98.7-118.4) children and 603.7 million (588.2- 619.8) adults worldwide. Obesity prevalence has doubled since 1980 in more than 70 countries and continuously increased in most other countries. Although the prevalence of obesity among children has been lower than adults, the rate of increase in childhood obesity in many countries was greater than the rate of increase in adult obesity. High BMI accounted for 4.0 million (2.7- 5.3) deaths globally, nearly 40% of which occurred among non-obese. More than two-thirds of deaths related to high BMI were due to cardiovascular disease. The disease burden of high BMI has increased since 1990; however, the rate of this increase has been attenuated due to decreases in underlying cardiovascular disease death rates. Conclusions The rapid increase in prevalence and disease burden of elevated BMI highlights the need for continued focus on surveillance of BMI and identification, implementation, and evaluation of evidence-based interventions to address this problem.

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The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions.

Susan Michie, Michelle Richardson, Marie Johnston … (2014)

CONSORT guidelines call for precise reporting of behavior change interventions: we need rigorous methods of characterizing active content of interventions with precision and specificity. The objective of this study is to develop an extensive, consensually agreed hierarchically structured taxonomy of techniques [behavior change techniques (BCTs)] used in behavior change interventions. In a Delphi-type exercise, 14 experts rated labels and definitions of 124 BCTs from six published classification systems. Another 18 experts grouped BCTs according to similarity of active ingredients in an open-sort task. Inter-rater agreement amongst six researchers coding 85 intervention descriptions by BCTs was assessed. This resulted in 93 BCTs clustered into 16 groups. Of the 26 BCTs occurring at least five times, 23 had adjusted kappas of 0.60 or above. "BCT taxonomy v1," an extensive taxonomy of 93 consensually agreed, distinct BCTs, offers a step change as a method for specifying interventions, but we anticipate further development and evaluation based on international, interdisciplinary consensus.

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Author and article information

Contributors

Alline Beleigoli:

ORCID: https://orcid.org/0000-0002-7848-3183

Flinders Digital Health Research CentreFlinders UniversitySturt North (N213)GPO Box 2100Adelaide, 5001Australia61 8 82013303alline.beleigoli@flinders.edu.au

Journal

Journal ID (nlm-ta): J Med Internet Res

Journal ID (iso-abbrev): J Med Internet Res

Journal ID (publisher-id): JMIR

Title: Journal of Medical Internet Research

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Print): 1439-4456

ISSN (Electronic): 1438-8871

Publication date Collection: November 2020

Publication date (Electronic): 5 November 2020

Volume: 22

Issue: 11

Electronic Location Identifier: e17494

Affiliations

[1 ] Flinders Digital Health Research Centre Flinders University Adelaide Australia

[2 ] Caring Futures Institute Flinders University Adelaide Australia

[3 ] Post Graduation Course of Adult Health Sciences Universidade Federal de Minas Gerais Belo Horizonte Brazil

[4 ] Quality Use of Medicines and Pharmacy Research Centre University of South Australia Adelaide Australia

[5 ] Department of Internal Medicine Universidade Federal de Minas Gerais Belo Horizonte Brazil

[6 ] Centre of Telehealth of the Hospital das Clinicas Universidade Federal de Minas Gerais Belo Horizonte Brazil

Author notes

Corresponding Author: Alline Beleigoli alline.beleigoli@ 123456flinders.edu.au

Author information

Alline Beleigoli https://orcid.org/0000-0002-7848-3183

Andre Q Andrade https://orcid.org/0000-0001-6587-3169

Maria De Fatima Diniz https://orcid.org/0000-0001-9146-5003

Antonio Luiz Ribeiro https://orcid.org/0000-0002-0364-3584

Article

Publisher ID: v22i11e17494

DOI: 10.2196/17494

PMC ID: 7677024

PubMed ID: 33151151

SO-VID: 2ffde377-e119-425f-86a4-2a81ba313fed

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

History

Date received : 16 December 2019

Date revision requested : 10 March 2020

Date revision received : 6 August 2020

Date accepted : 3 October 2020

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