Occupational Health Applied Infodemiological Studies of Nutritional Diseases and Disorders: Scoping Review with Meta-Analysis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Design
2.2. Data Source
2.3. Information Processing
2.4. Final Selection of Articles
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- Inclusion: clinical trials published in peer-reviewed journals and written in English, Spanish, and Portuguese;
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- Exclusion: articles for which the full text could not be found, those that included a non-adult population (under 18 years of age), and those that did not present a relationship between the intervention and the outcome under study (causality criterion). Interventions had to focus on the field of infodemiology and include a Web 2.0 tool. This intervention should have been carried out in the workplace to improve/detect/plan problems related to nutritional diseases or nutritional disorders. The outcome had to be measurable and preferably facilitate differences in body mass index or its effect on body weight.
2.5. Documentary Quality, Level of Evidence and Recommendation, and Study of Bias
2.6. Data Extraction
2.7. Goal of the Meta-Analysis
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- Determine the effectiveness of the methodologies applied in the different studies;
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- Study the significant results for the outcomes of Body Weight and Body Mass Index;
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- Observe the general trends, if any, of the results presented.
2.8. Data Analysis
2.9. Ethical Aspects
Population | Intervention | Results | |||||||
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Size | Age (Years) | Gender (% M/F/O) | Country | Disorder/Disease | Platform | Compilation Dates | Outcomes | p-Value | |
Hene et al., 2022 [31] | N = 300 ni = 100 | >18 | Not reported | South Africa | Cardiovascular Disease | 12 months | ΔBW = −1.5 | <0.01 | |
Kariuki et al., 2022 [32] | N = 82 ni = 41 | >18 | Not reported | USA | Overweight | YouTube | 6 months | ΔBW = −5.6 ΔBMI = −1.8 | <0.05 |
Napolitano et al., 2022 [33] | N = 456 ni = 304 | 18–35 | Not reported | USA | Overweight | 55 months | ΔBMI = −3.6 | <0.05 | |
Moholdt et al., 2021 [34] | N = 24 ni = 16 | 30–40 | 100/0/0 | Australia | Overweight | AUC Web | 6 months | ΔBW = −6.7 ΔBMI = −0.75 | <0.05 |
Biederman et al., 2021 [35] | N = 27 ni = 20 | >18 | 0/100/0 | USA | Overweight | 1.25 months | ΔBW = −1.4 ΔBMI = −0.9 | <0.05 | |
Xu et al., 2021 [36] | N = 47 ni = 39 | 35–65 | 4/94/2 | USA | Overweight | 3 months | ΔBW = −1.25 | <0.05 | |
Osborn et al., 2020 [37] | N = 99 ni = 49 | >18 | 27/73/0 | USA | Diabetes Mellitus | One Drop App | 6 months | ΔBMI = −1.0 | <0.001 |
Hawkins et al., 2020 [38] | N = 369 ni = 299 | >18 | 13/87/0 | Finland | Nutritional Disorder | 10 months | ΔBMI = −0.6 | <0.01 | |
Dagan et al. 2015 [39] | N = 63 ni = 30 | >18 | 30/70/0 | USA | Dietary Habits | 3 months | ΔBMI = −1.2 | 0.25 | |
Huei Phing et al., 2015 [40] | N = 120 ni = 60 | >18 | 18/82/0 | Malaysia | Metabolic Syndrome | 4 months | ΔBW = −7.0 ΔBMI = −3.4 | <0.001 | |
Merchant et al., 2014 [41] | N = 404 ni = 202 | 18–35 | Not reported | USA | Overweight | 12 months | ΔBMI = −0.3 | <0.001 |
Intervention | Results | Conclusion | |
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Hene et al., 2022 [31] | Financial sector employees were randomly assigned to three intervention groups (Facebook plus Health Professionals, Facebook only groups, and control) to reduce 10-year cardiovascular disease risk (Framingham risk score FRS). | Overweight and diabetes risk reduced significantly in Facebooks groups versus control. | Social network lifestyles could be included in workplace health promotional programs to improve certain non-communicable disease risk factors. |
Kariuki et al., 2022 [32] | 12-week follow-up randomised controlled trial with control and intervention (YouTube and FitBit remote coach) groups to increase physical activity (PA). | Increase of PA time of the intervention group (89.5%). The intervention group shows improvement in weight, BMI, body fat, waist circumference, and systolic BP. | This trial demonstrated that the intervention is feasible and acceptable and provided preliminary efficacy in promoting PA among adults with overweight/obesity. |
Napolitano et al., 2022 [33] | A randomised trial of university students to translate and deliver programs via social media (Facebook) of social media treatments, Social Support, and Daily text messages. Eighteen months of individual follow-up to improve the quality of life of the overweight population | 6- and 18-month BMI significantly decrease in the intervention group versus the control group. | The results of this study have the potential to significantly impact the delivery of obesity treatment services on Collages Campuses. |
Moholdt et al., 2021 [34] | Australian male university workers with overweight problems are enrolled in a three-arm trial. The goal is to monitor the effects of morning vs. evening training vs. no training and monitor with social media University website. | Both training groups show BMI and weight loss against the no-training group. There is no significance in these parameters between the morning and evening groups. | Improvements in cardiorespiratory fitness were similar regardless of the time of day of exercise training. |
Biederman et al., 2021 [35] | A randomised trial of 5-week physical activity (PA) program for African American women of working age using intervention Facebook groups. Intervention groups were monitored with an Omron Alvita pedometer to monitor the daily steps and number of days they were physically active. | The intervention group had significantly increased their weekly steps by 190% as compared to the control group. The intervention group showed a decrease in both BMI and Body Weight. | Technologies such as social media and pedometers can assist in educating individuals and improving physical activity. These findings are relevant to public health nurses when implementing programs to increase physical activity for African American women. |
Xu et al., 2021 [36] | A clinical trial of a 12-week intervention period with a dietary and physical activity intervention group of participants via Facebook. Performed mediation analyses to explore how the effects of social network measures on weight loss could be mediated by the theoretical mediators. | Increases in the number of posts, comments, and reactions significantly predicted weight loss. Receiving comments positively predicted changes in self-efficacy. Active participants show significant Body Weight loss over baseline. | The potential of using social network analysis to understand the social processes and mechanisms through which web-based behavioural interventions affect participants’ psychological and behavioural outcomes. |
Osborn et al., 2020 [37] | Social media were used to recruit T1 Diabetes adults. Two groups were generated, one with the One-Drop activity tracker and the other without any activity tracker. Three-month follow-up for the activity modification on the T1D behaviour. | Participants in the One Drop activity tracker condition had a significantly lower 3-month haemoglobin A1c level. It also shows a significant decrease in BMI levels for the intervention group. | Participants exposed to the One Drop activity tracker had a significantly lower 3-month haemoglobin A1c level compared to that of participants not exposed to One Drop during the same timeframe. |
Hawkins et al., 2020 [38] | This study examined whether four perceived norms (perceived descriptive, injunctive, liking, and frequency norms) about Facebook users’ eating habits and preferences predicted participants’ own food consumption and BMI. The four users’ consumptions were fruit, vegetables, energy-dense snacks, and sugar-sweetened beverages. The study aims to change consumption behaviour with interventions on the perceived norms of two advertising groups. | When the Facebook users were shown the different perceived norms, their eating behaviour changed significantly. In that regard, there was a significant decrease in the BMI of the participants of the intervention group. | These findings suggest that perceived norms concerning actual consumption (descriptive and frequency) and norms related to approval (injunctive) may guide the consumption of low and high-energy-dense foods and beverages differently. |
Dagan et al., 2015 [39] | As an intervention, it was developed Food Hero, an online platform within Facebook for nutritional education in which players feed a virtual character according to their own nutritional needs and complete a set of virtual sports challenges. The platform was developed in 2 versions: a “private version”, in which a user can see only his or her own score, and a “social version”, in which a user can see other players’ scores, including preexisting Facebook friends. | Users that have the “social version” tend to behave more responsibly that those with the “private version”. In spite of that, the BMI decrease of the “public group” was not significantly lower than the “private group”. | This work focused on isolating the social networks’ social effects to help guide future online interventions. Our results indicate that the social exposure provided by SNSs is associated with increased engagement and learning in an online nutritional educational platform. |
Huei Phing et al., 2015 [40] | The purpose is to ascertain the effect of a physical activity intervention using a combination of Facebook and standing banners on improvements in metabolic syndrome. Government employees with metabolic syndrome were randomly placed in a two-arm trial. A Lifecorder e-STEP accelerometer was utilised to quantify physical activity. | There were significantly higher step counts in the intervention group as compared to the control group over time. Both the Body Weight and the BMI of the intervention group improved over the control group. | The findings show that delivering information on physical activity through an easily implemented and low-cost physical activity intervention via a combination of Facebook and standing banners was successful in improving step counts and metabolic parameters among individuals with metabolic syndrome. |
Merchant et al., 2014 [41] | The basis of the intervention campaign model was five self-regulatory techniques: intention formation, action planning, feedback, goal review, and self-monitoring. Participants were encouraged to engage their existing social network to meet their weight loss goals. A health coach moderated the page and modified content based on usage patterns and user feedback. | There was significant variability among quantifiable (i.e., visible) engagement. Approximately 40% of the participants interviewed reported passively engaging with the Facebook posts by reading but not visibly interacting with them. The more engaged users showed a significantly lower BMI than those less engaged in the activity. | Facebook can be used to remotely deliver weight loss intervention content with the help of a health coach who can iteratively tailor content and interact with participants. |
3. Results
3.1. Bias Study
3.2. Interventions Performed in the Reviewed Trials
3.3. Results Obtained from the Interventions Implemented
4. Discussion
4.1. Bias Study
4.2. Interventions in the Reviewed Trials
4.3. Results of the Carried-Out Interventions
4.4. Limitations
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | Total | % | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Hene et al., 2022 [31] | 0.5 | 1 | 0.5 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0.5 | 1 | 1 | 1 | 1 | 1 | NA | 1 | 1 | 1 | 0 | 0 | 1 | 17.5 | 72.9 |
Kariuki et al., 2022 [32] | 0.5 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 0.5 | 1 | 1 | 1 | 0 | NA | 1 | 1 | 1 | 1 | 1 | 1 | 21.0 | 87.5 |
Napolitano et al., 2022 [33] | 0.5 | 1 | 0.5 | 1 | 1 | 1 | 0.5 | 0.5 | 0 | 1 | 0 | 1 | 1 | 0.5 | 1 | 1 | 1 | 0 | NA | 1 | 1 | 1 | 0 | 0 | 1 | 16.5 | 68.8 |
Moholdt et al., 2021 [34] | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | NA | 1 | 1 | 1 | 1 | 1 | 1 | 24.0 | 100.0 |
Biederman et al., 2021 [35] | 0.5 | 1 | 0.5 | 0.5 | 1 | 0.5 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | 0 | 1 | 1 | 1 | 1 | NA | 0 | 1 | 1 | 0 | 0 | 0 | 11.5 | 47.9 |
Xu et al., 2021 [36] | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | 0 | 1 | 1 | 1 | 1 | NA | 1 | 1 | 1 | 1 | 0 | 1 | 16.5 | 68.8 |
Osborn et al., 2020 [37] | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0.5 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | NA | 1 | 1 | 1 | 1 | 1 | 1 | 23.5 | 97.9 |
Hawkins et al., 2020 [38] | 0.5 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0.5 | 1 | 1 | 1 | 1 | 0 | NA | 1 | 1 | 1 | 0 | 0 | 1 | 19.0 | 79.2 |
Dagan et al. 2015 [39] | 0.5 | 1 | 0.5 | 0.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | NA | 1 | 1 | 1 | 1 | 0 | 1 | 13.5 | 56.3 |
Huei Phing et al., 2015 [40] | 0.5 | 1 | 1 | 1 | 1 | 1 | 0.5 | 1 | 1 | 0 | 0 | 0.5 | 1 | 1 | 1 | 1 | 1 | 1 | NA | 1 | 1 | 1 | 0 | 0 | 0 | 17.5 | 72.9 |
Merchant et al., 2014 [41] | 0.5 | 1 | 1 | 1 | 1 | 0.5 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | NA | 1 | 1 | 1 | 1 | 0 | 1 | 17.0 | 70.8 |
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Palomo-Llinares, R.; Sánchez-Tormo, J.; Wanden-Berghe, C.; Sanz-Valero, J. Occupational Health Applied Infodemiological Studies of Nutritional Diseases and Disorders: Scoping Review with Meta-Analysis. Nutrients 2023, 15, 3575. https://doi.org/10.3390/nu15163575
Palomo-Llinares R, Sánchez-Tormo J, Wanden-Berghe C, Sanz-Valero J. Occupational Health Applied Infodemiological Studies of Nutritional Diseases and Disorders: Scoping Review with Meta-Analysis. Nutrients. 2023; 15(16):3575. https://doi.org/10.3390/nu15163575
Chicago/Turabian StylePalomo-Llinares, Ruben, Julia Sánchez-Tormo, Carmina Wanden-Berghe, and Javier Sanz-Valero. 2023. "Occupational Health Applied Infodemiological Studies of Nutritional Diseases and Disorders: Scoping Review with Meta-Analysis" Nutrients 15, no. 16: 3575. https://doi.org/10.3390/nu15163575
APA StylePalomo-Llinares, R., Sánchez-Tormo, J., Wanden-Berghe, C., & Sanz-Valero, J. (2023). Occupational Health Applied Infodemiological Studies of Nutritional Diseases and Disorders: Scoping Review with Meta-Analysis. Nutrients, 15(16), 3575. https://doi.org/10.3390/nu15163575