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Cozart JS, Bruce AS, Shook RP, Befort C, Siengsukon C, Simon S, Lynch SG, Mahmoud R, Drees B, Posson P, Hibbing PR, Huebner J, Bradish T, Robichaud J, Bruce JM. Body metrics are associated with clinical, free-living, and self-report measures of mobility in a cohort of adults with obesity and multiple sclerosis. Mult Scler Relat Disord 2023; 79:105010. [PMID: 37776827 DOI: 10.1016/j.msard.2023.105010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 08/29/2023] [Accepted: 09/13/2023] [Indexed: 10/02/2023]
Abstract
BACKGROUND Obesity is associated with multiple sclerosis (MS) onset and may contribute to more rapid disability accumulation. Whether obesity impacts mobility in MS is uncertain. Some studies find that obesity in MS is associated with poorer mobility; other studies find no relationship. Discrepant findings may be due to differences in measurement and methodology. In the present study, we employ a comprehensive battery of anthropometric and mobility measures in a sample of people with MS and obesity. METHODS Participants with MS (N = 74) completed a battery of adiposity measurements (weight, height, waist circumference, and full body dual-energy x-ray absorptiometry [DXA] scans). They also completed validated clinical, free-living (accelerometry), and self-report measures of mobility. Spearman's Rho correlations were used to examine the associations between mobility and obesity measures with Benjamini and Hochberg correction for multiple comparisons. Multiple linear regression was used to examine if adiposity predicted mobility outcomes in people with MS when controlling for age and disease duration. RESULTS The majority of participants (n = 70) were diagnosed with relapsing-remitting MS and reported mild MS-related disability on the Patient Determined Disease Steps (M = 0.77, SD = 1.1). Median BMI was 35.8 (SD = 5.4). Higher percentage body fat (measured via DXA) was associated with poorer self-reported physical functioning (rs = -0.52, p <0.001), less moderate-to-vigorous physical activity (rs = -0.24, p = 0.04), and worse performance on the Six Minute Walk Test (6MWT; rs = -0.44, p <0.001), the Timed 25 Foot Walk (T25FW; rs = 0.45, p <0.001), and the Timed Up and Go test (TUG; rs = 0.35, p = .003). Higher BMI and waist-to-height ratio (WtHR) were associated with worse outcomes on the 6MWT (BMI; rs = -0.35, p <0.01, WtHR; rs = -0.43, p <0.001), T25FW (BMI; rs = 0.32, p <0.01, WtHR; rs = 0.38, p <0.001), and the SF-36 (BMI; rs = -0.29, p <0.005, WtHR; rs = -0.31, p <0.05). Percentage body fat accounted for an additional 17 % of the variance in the T25FW and 6MWT performance, after controlling for age and disease duration. CONCLUSION Higher BMI, WtHR, and percentage body fat were associated with lower levels of mobility (T25FW and 6MWT) in people with MS who have class I, class II, and class III obesity. Higher percentage body fat was associated with significantly worse performance on clinical, free-living, and self-report measures of mobility in people with MS even when accounting for participant age and disease duration. These findings suggest that people with MS and obesity may show improved mobility with weight loss.
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Affiliation(s)
- J S Cozart
- Department of Biomedical and Health Informatics, University of Missouri-Kansas City, School of Medicine, Kansas City, Missouri, USA; Department of Psychology, University of Missouri-Kansas City, Kansas City, Missouri, USA.
| | - A S Bruce
- Center for Children's Healthy Lifestyles and Nutrition, Children's Mercy Hospital, Kansas City, Missouri, USA; Department of Pediatrics, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - R P Shook
- Center for Children's Healthy Lifestyles and Nutrition, Children's Mercy Hospital, Kansas City, Missouri, USA; Department of Pediatrics, Children's Mercy Hospital, Kansas City, Missouri, USA; School of Medicine, University of Missouri-Kansas City, Kansas City, Missouri USA
| | - C Befort
- Department of Population Health, University Kansas Medical Center, Kansas City, Kansas, USA
| | - C Siengsukon
- Department of Physical Therapy, Rehabilitation Science, and Athletic Training, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - S Simon
- Department of Biomedical and Health Informatics, University of Missouri-Kansas City, School of Medicine, Kansas City, Missouri, USA
| | - S G Lynch
- Department of Neurology, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - R Mahmoud
- Department of Neurology, Saint Luke's Hospital, Kansas City, Missouri, USA; Department of Neurology, University of Missouri-Kansas City, Kansas City, Missouri, USA
| | - B Drees
- Department of Biomedical and Health Informatics, University of Missouri-Kansas City, School of Medicine, Kansas City, Missouri, USA; Department of Internal Medicine, University of Missouri-Kansas City School of Medicine, Kansas City, Missouri, USA; Graduate School of the Stowers Institute for Medical Research, Kansas City, Missouri, USA
| | - P Posson
- Center for Children's Healthy Lifestyles and Nutrition, Children's Mercy Hospital, Kansas City, Missouri, USA
| | - P R Hibbing
- Department of Kinesiology & Nutrition, University of Illinois Chicago, Chicago, Illinois, USA
| | - J Huebner
- Department of Community and Family Medicine University Health Lakewood Medical Center, Medicine, Kansas City, Missouri, USA
| | - T Bradish
- Department of Biomedical and Health Informatics, University of Missouri-Kansas City, School of Medicine, Kansas City, Missouri, USA
| | - J Robichaud
- Department of Biomedical and Health Informatics, University of Missouri-Kansas City, School of Medicine, Kansas City, Missouri, USA
| | - J M Bruce
- Department of Biomedical and Health Informatics, University of Missouri-Kansas City, School of Medicine, Kansas City, Missouri, USA; Departments of Neurology and Psychiatry, University Health, Kansas City, Missouri, USA
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Alshehri M, Alkathiry A, Alenazi A, Alothman S, Rucker J, Phadnis M, Miles J, Kluding P, Siengsukon C. 1059 Sleep Parameters In People With Type 2 Diabetes With And Without Insomnia Symptoms. Sleep 2020. [DOI: 10.1093/sleep/zsaa056.1055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Introduction
There is an increasing awareness of the high prevalence of insomnia symptoms in people with type 2 diabetes (T2D). Past studies have demonstrated the importance of measuring sleep parameters in both averages and variabilities using subjective and objective methods. Thus, we aimed to compare the averages and variability of sleep parameters in people with T2D with and without insomnia symptoms.
Methods
Actigraph measurements and sleep diaries were used in 59 participants to assess sleep parameters, including sleep efficiency (SE), sleep latency, total sleep time, and wake after sleep onset over seven nights. Validated instruments were used to assess the symptoms of depression, anxiety, and pain. Circular data were used to describe the distribution of bed distribution with SE as a magnitude for both groups. Mann Whitney U test was utilized to compare averages and variability of sleep parameters between the two groups. Multivariable general linear model to control for demographic and clinical variables. For the secondary aim, multiple linear regression tests were utilized to assess the association between averages and variability values for both groups.
Results
SE was found to be lower in average and higher in variability for participants with T2D and insomnia symptoms, than those with T2D only subjectively and objectively. SE variability was also the only sleep parameter higher in people with T2D and insomnia symptoms, with psychological symptoms potentially playing a role in this difference. We observed that people in T2D+Insomnia tend to go to bed earlier compared to the T2D only group based on objective measures, but no difference was observed between groups in subjective measures. The only significant relationship in both objective and subjective measures was between the averages and variability of SE.
Conclusion
Our findings suggest a discrepancy between subjective and objective measures in only average of total sleep time, as well as agreement in measures of variability in sleep parameters. Also, the relationship between averages and variabilities suggested the importance of improving SE to minimize its variability. Further research is warranted to investigate the complex relationship between sleep parameters and psychological factors in people with T2D and insomnia symptoms.
Support
None
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Affiliation(s)
- M Alshehri
- University of Kansas Medical Center, Kansas City, KS
| | - A Alkathiry
- Physical Therapy department, Majmaah University, Almajmaah, Central Region, Saudi Arabia, SAUDI ARABIA
| | - A Alenazi
- Physical Therapy department, Prince Sattam Bin Abdulaziz University, Alkharj City, Central Region, Saudi Arabia, SAUDI ARABIA
| | - S Alothman
- Physical Therapy and Rehabilitation Science Depart, Kansas City, Kansas, KS
| | - J Rucker
- University of Kansas Medical Center, Kansas City, Kansas, KS
| | - M Phadnis
- Univeristy of Kansas Medical Center, Kansas City, Kansas, KS
| | - J Miles
- University of Kansas Medical Center, Kansas City, KS
| | - P Kluding
- University of Kansas Medical Center, Kansas City, KS
| | - C Siengsukon
- University of Kansas Medical Center, Kansas City, KS
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