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Zoszak K, Batterham M, Simpson-Yap S, Probst Y. Web scraping of user-simulated online nutrition information for people with multiple sclerosis. Mult Scler Relat Disord 2024; 88:105746. [PMID: 38959592 DOI: 10.1016/j.msard.2024.105746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 05/23/2024] [Accepted: 06/21/2024] [Indexed: 07/05/2024]
Abstract
BACKGROUND People diagnosed with multiple sclerosis (MS) often seek to modify their diet guided by online advice, however this advice may not align with national dietary guidelines. The aim of this study was to simulate an online search for dietary advice conducted by a person with MS and evaluate the content. It was hypothesised that a variety of eating patterns are promoted for MS online and these dietary approaches can be contradictory. METHODS An online search was simulated using Google Trends-informed search terms and Google and Bing search engines. URLs were extracted using R. Nutrition data were extracted including recommendations for diets, foods, supplements, and health professional consultation. Statistical analyses were conducted using R. RESULTS 73 URLs from 49 websites were extracted, with only 14 results common to both search engines. Dietary recommendations included overall eating patterns (58 webpages, 79%), individual foods (55 webpages, 75%), and supplements (33 webpages, 45%). The most promoted eating pattern for MS was a balanced diet (33 recommendations, 48%), more likely by nonprofit organisations and health information websites (14 and 17 recommendations, 100% and 89%); lifestyle program websites were more likely to recommend restrictive diets (19 recommendations, 100%) (p<0.001). 52% pages advised consulting a health professional, most often a doctor or dietitian. CONCLUSION A balanced diet is the most recommended eating pattern for MS online, though advice promoting restrictive diets persists.
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Affiliation(s)
- Karen Zoszak
- School of Medical, Indigenous and Health Sciences, University of Wollongong, Wollongong, Australia.
| | - Marijka Batterham
- Statistical Consulting Centre, School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, Australia
| | - Steve Simpson-Yap
- Neuroepidemiology Unit, Melbourne School of Population & Global Health, The University of Melbourne, Melbourne, Australia
| | - Yasmine Probst
- School of Medical, Indigenous and Health Sciences, University of Wollongong, Wollongong, Australia
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Impact of nutrition counseling on anthropometry and dietary intake of multiple sclerosis patients at Kasr Alainy Multiple Sclerosis Unit, Cairo, Egypt 2019-2020: randomized controlled clinical trial. Arch Public Health 2023; 81:11. [PMID: 36691061 PMCID: PMC9869589 DOI: 10.1186/s13690-022-01013-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 12/12/2022] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND Faulty dietary habits and overnutrition are prevalent among Egyptian patients with multiple sclerosis (MS) who do not receive nutrition care as part of treatment. Thus, this study was conducted to identify the effect of nutrition counseling on the nutritional status of patients with MS. This endeavor might provide evidence for the value of counseling in such a setting and advance the integration of nutrition counseling into the routine management of patients with MS. METHODS A single-blinded, parallel-randomized controlled clinical trial was conducted at Kasr Alainy MS Unit on 120 eligible patients with MS from September 2019 to February 2020. Patients were randomly allocated to either the nutrition counseling intervention group (IG) or the control group (CG). Allocation concealment was performed by using sequentially numbered opaque sealed envelopes. All patients were assessed initially and complied with the Kasr Alainy MS Unit standard management protocol for the study period. Only patients in the IG underwent initial nutrition counseling sessions followed by a monthly evaluation. All patients were assessed at the end of the 3-month follow-up period. Sociodemographic data were gathered through a structured interview. Nutritional status was assessed anthropometrically and via 24-h recall. The 2 groups were compared initially and at the end of the follow-up. Both intention-to-treat and per-protocol analyses were conducted. RESULTS At baseline assessment, the prevalence of overweight and obesity was 31.7% and 32.5%, respectively, and the mean body mass index was 27.7 ± 5.7 kg/m2. Mean waist circumference was 93.5 ± 11.9 and 99.2 ± 13.1 cm for males and females, respectively. Approximately 27.3% of males and 83.9% of females showed abdominal obesity. After 3 months of counseling, weight, body mass index, waist circumference, nutrient intake and adequacy significantly improved in the IG (p < 0.05). CONCLUSION Nutrition counseling significantly improved anthropometric measurements, dietary habits, nutrient intake and adequacy. TRIAL REGISTRATION The study was registered on ClinicalTrial.gov and was given a code (NCT04217564).
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Geng H, Wang S, Jin Y, Cheng N, Song B, Shu S, Li B, Han Y, Han Y, Gao L, Ding Z, Xu Y, Wang X, Ma Z, Sun Y. Nutritional Status and Body Composition in Wilson Disease: A Cross-Sectional Study From China. Front Nutr 2022; 8:790520. [PMID: 35036410 PMCID: PMC8759200 DOI: 10.3389/fnut.2021.790520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 11/24/2021] [Indexed: 12/01/2022] Open
Abstract
Background: Abnormal nutritional status is frequently seen in patients with chronic diseases. To date, no study has investigated the detailed characteristics of abnormal nutritional status among Wilson's disease (WD) patients in the Chinese cohort. This study aimed to describe the nutritional status of WD patients, with a particular focus on the differences between patients with different phenotypes. Methods: The study subjects comprised 119 healthy controls, 129 inpatients (hepatic subtype, n = 34; neurological subtype, n = 95) who were being treated at the affiliated hospital of the Institute of Neurology, Anhui University of Chinese Medicine. All of the subjects were assessed for body composition by using bioelectrical impedance analysis. All WD patients received anthropometry, nutritional risk screening 2002 (NRS2002), and laboratory test (hemocyte and serum biomarkers) additionally. Results: Compared with healthy controls, the fat mass and rate of total body and trunk were significantly higher in WD patients (P < 0.001), the muscle and skeletal muscle mass of total body and trunk were significantly lower in WD patients (P < 0.001). Compared with hepatic subtype patients, the fat mass and rate of total body, trunk, and limbs were significantly lower in neurological subtype patients (P<0.01); while there were no significant differences in muscle and skeletal muscle between these two subtypes. The overall prevalence of abnormal nutritional status in WD patients was 43.41% (56/129). The prevalence of high-nutritional risk and overweight in WD patients was 17.83% (23 of 129) and 25.58% (33 of 129), respectively. Compare with patients with high nutritional risk, macro platelet ratio, alkaline phosphatase, the basal metabolic rate (p < 0.05), creatinine, trunk fat rate (p < 0.01) and appendicular skeletal muscle mass (p < 0.001) were significantly higher in patients without nutritional risk (p < 0.001). Patients with a high nutritional risk tend to have a lower cholinesterase concentration (x2 = 4.227, p < 0.05). Conclusion: Both patients with H-subtype and N-subtype are prone to have an abnormal nutritional status. Longitudinal studies are required to investigate if nutritional status and body composition could reflect prognosis in WD patients, and which of these body composition indexes contribute to malnutrition and worse prognosis.
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Affiliation(s)
- Hao Geng
- Laboratory of Sports and Nutrition Information Technology, Institute of Intelligent Machine, Hefei Institutes of Physical Science, Chinese Academy of Sciences (CAS), Hefei, China.,Department of Biophysics, University of Science and Technology of China, Hefei, China.,Hospital Affiliated to the Institute of Neurology, Anhui University of Chinese Medicine, Hefei, China
| | - Shijing Wang
- Hospital Affiliated to the Institute of Neurology, Anhui University of Chinese Medicine, Hefei, China
| | - Yan Jin
- Hospital Affiliated to the Institute of Neurology, Anhui University of Chinese Medicine, Hefei, China
| | - Nan Cheng
- Hospital Affiliated to the Institute of Neurology, Anhui University of Chinese Medicine, Hefei, China
| | - Bin Song
- Hospital Affiliated to the Institute of Neurology, Anhui University of Chinese Medicine, Hefei, China
| | - Shan Shu
- Hospital Affiliated to the Institute of Neurology, Anhui University of Chinese Medicine, Hefei, China
| | - Bo Li
- Hospital Affiliated to the Institute of Neurology, Anhui University of Chinese Medicine, Hefei, China
| | - Yongsheng Han
- Hospital Affiliated to the Institute of Neurology, Anhui University of Chinese Medicine, Hefei, China
| | - Yongzhu Han
- Hospital Affiliated to the Institute of Neurology, Anhui University of Chinese Medicine, Hefei, China
| | - Lishen Gao
- Laboratory of Sports and Nutrition Information Technology, Institute of Intelligent Machine, Hefei Institutes of Physical Science, Chinese Academy of Sciences (CAS), Hefei, China.,Department of Biophysics, University of Science and Technology of China, Hefei, China
| | - Zenghui Ding
- Laboratory of Sports and Nutrition Information Technology, Institute of Intelligent Machine, Hefei Institutes of Physical Science, Chinese Academy of Sciences (CAS), Hefei, China.,Department of Biophysics, University of Science and Technology of China, Hefei, China
| | - Yang Xu
- Laboratory of Sports and Nutrition Information Technology, Institute of Intelligent Machine, Hefei Institutes of Physical Science, Chinese Academy of Sciences (CAS), Hefei, China.,Department of Biophysics, University of Science and Technology of China, Hefei, China
| | - Xun Wang
- Hospital Affiliated to the Institute of Neurology, Anhui University of Chinese Medicine, Hefei, China
| | - Zuchang Ma
- Laboratory of Sports and Nutrition Information Technology, Institute of Intelligent Machine, Hefei Institutes of Physical Science, Chinese Academy of Sciences (CAS), Hefei, China.,Department of Biophysics, University of Science and Technology of China, Hefei, China
| | - Yining Sun
- Laboratory of Sports and Nutrition Information Technology, Institute of Intelligent Machine, Hefei Institutes of Physical Science, Chinese Academy of Sciences (CAS), Hefei, China.,Department of Biophysics, University of Science and Technology of China, Hefei, China
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