Online platform for healthy weight loss in adults with overweight and obesity - the “POEmaS” project: a randomized controlled trial

Du, Huiyun; Andrade, André Q; Diniz, Maria de Fátima Haueisen Sander; Álvares, R. S. R.; Ribeiro, Antônio Luiz Pinho

doi:10.1186/s12889-018-5882-y

Cited by 13 publications

(12 citation statements)

References 14 publications

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“…The Online Platform for Healthy Weight Loss (POEmaS, from the abbreviation in Portuguese) study has been registered on ClinicalTrials.gov (NCT03435445), and the protocol with details of the intervention has been published elsewhere [ 13 ]. In brief, we conducted a 3-arm (1:1:1), parallel, randomized controlled trial, which recruited university students and staff in the Universidade Federal de Minas Gerais in Brazil.…”

Section: Methodsmentioning

confidence: 99%

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

Du¹,

Andrade²,

Diniz³

et al. 2020

J Med Internet Res

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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/m2, 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

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Section: Methodsmentioning

confidence: 99%

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

Du¹,

Andrade²,

Diniz³

et al. 2020

J Med Internet Res

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“…In a surgical context, recent evidence has linked better patient physical preparedness before surgery with improved outcomes and benefits after surgery 11–13 . More specifically, improvements in a patient’s dietary intake 14 , physical activity levels 15 , and smoking cessation 16 have been linked to improved recovery, better tolerance of postoperative treatment, and prevention of related disease in the long term 8 , 17–19 . At present, variable amounts of support and education, however, are made available for patients undergoing elective surgery in order to motivate health behaviour changes.…”

Section: Introductionmentioning

confidence: 99%

Digital technology to support lifestyle and health behaviour changes in surgical patients: systematic review

et al. 2020

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Background Digital technologies (such as smartphone applications, activity trackers, and e-learning platforms) have supported patients with long-term conditions to change their lifestyle health behaviours. The aim of this study was to examine the effectiveness of digital technologies in supporting patients undergoing elective surgery to change their health behaviours. Methods A systematic review was conducted of articles reporting a digital intervention supporting behaviour change in adult patients who underwent elective bariatric, oncological or orthopaedic surgery. MEDLINE, Embase, CINAHL, PsycINFO, Web of Science, and Scopus were searched from inception to March 2019 for quantitative intervention studies with a specific focus on physical activity, dietary intake, and weight loss in patients before and after surgery (PROSPERO: CRD42019127972). The Joanna Briggs Institute critical appraisal checklist was used to assess study quality. Results Of 3021 citations screened, 17 studies were included comprising 4923 surgical patients; these included experimental (pre–post design, feasibility studies, and RCTs) and observational studies. Three factors were identified as effective for supporting health behaviour change in elective surgical populations: digital technology delivery, implementation, and theoretical underpinning. Six of eight studies that referred to behaviour change theories observed significant improvements in health behaviour relating to reduced weight regain, and improved lifestyle choices for physical activity and diet. Meta-analysis was not possible because of heterogeneous outcome measures. Conclusion Digital technologies may effectively support behavioural change in patients undergoing elective surgery.

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“…However, this research had several limitations, as well. The respondent's weight and height were self-reported in this study, despite previous research works which have also utilized this method, and although it is valid [84][85][86], it can be a possible limitation of the study. Furthermore, physical activity that we measured was defined as regular exercise (e.g., fast walking, jogging, cycling, swimming), which were mentioned in the questionnaire.…”

Section: Discussionmentioning

confidence: 99%

Postpartum depression symptoms in survey-based research: a structural equation analysis

2021

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Background Since the last decade, postpartum depression (PPD) has been recognized as a significant public health problem, and several factors have been linked to PPD. Mothers at risk are rarely undetected and underdiagnosed. Our study aims to determine the factors leading to symptoms of depression using Structural Equation Modeling (SEM) analysis. In this research, we introduced a new framework for postpartum depression modeling for women. Methods We structured the model of this research to take into consideration the Malaysian culture in particular. A total of 387 postpartum women have completed the questionnaire. The symptoms of postpartum depression were examined using the Edinburgh Postnatal Depression Scale (EPDS), and they act as a dependent variable in this research model. Results Four hundred fifty mothers were invited to participate in this research. 86% of the total distributed questionnaire received feedback. The majority of 79.6% of respondents were having depression symptoms. The highest coefficients of factor loading analysis obtained in every latent variable indicator were income (β = 0.77), screen time (β = 0.83), chips (β = 0.85), and anxiety (β = 0.88). Lifestyle, unhealthy food, and BMI variables were directly affected by the dependent variable. Based on the output, respondents with a high level of depression symptoms tended to consume more unhealthy food and had a high level of body mass indexes (BMI). The highest significant impact on depression level among postpartum women was unhealthy food consumption. Based on our model, the findings indicated that 76% of the variances stemmed from a variety of factors: socio-demographics, lifestyle, healthy food, unhealthy food, and BMI. The strength of the exogenous and endogenous variables in this research framework is strong. Conclusion The prevalence of postpartum women with depression symptoms in this study is considerably high. It is, therefore, imperative that postpartum women seek medical help to prevent postpartum depressive symptoms from worsening.

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Online platform for healthy weight loss in adults with overweight and obesity - the “POEmaS” project: a randomized controlled trial

Cited by 13 publications

References 14 publications

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

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

Digital technology to support lifestyle and health behaviour changes in surgical patients: systematic review

Postpartum depression symptoms in survey-based research: a structural equation analysis

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