Sex differences in osteoporosis self-efficacy among community-residing older adults presenting for DXA

Solimeo, Samantha L.; Nguyen, Vu; Edmonds, Stephanie W.; Lou, Yiyue; Roblin, Douglas W.; Cram, Peter; Wolinsky, Fredric D.

doi:10.1007/s00198-019-04854-6

Cited by 4 publications

(2 citation statements)

References 34 publications

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“…To obtain a complete nutritional evaluation of older adults, body composition should be considered for both the nutritional diagnosis (risk of malnutrition or malnutrition) ( 20 , 21 ) as well as to determine the different body compartments and assess more precisely if the patient presents obesity ( 22 ), sarcopenia (decrease in appendicular skeletal muscle mass) ( 23 ) or osteoporosis (decrease in bone mineral density) ( 24 ).…”

Section: Introductionmentioning

confidence: 99%

Comparison of body fat percentage assessments by bioelectrical impedance analysis, anthropometrical prediction equations, and dual-energy X-ray absorptiometry in older women

et al. 2022

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BackgroundIndividuals with high body fat have a higher risk of mortality. Numerous anthropometric-based predictive equations are available for body composition assessments; furthermore, bioelectrical impedance analysis (BIA) estimates are available. However, in older adults, the validity of body fat estimates requires further investigation.ObjectiveTo assess the agreement between percentage body fat (BF%) estimates by BIA and five predictive equations based on anthropometric characteristics using dual X-ray absorptiometry (DXA) as reference method. A secondary objective was to identify whether excluding short-stature women improves the agreement of BF% estimates in a group of community-dwelling, older Mexican women.MethodsA concordance analysis of BF% was performed. A total of 121 older women participated in the study. Anthropometric information, BIA, and DXA body composition estimates were obtained. Five equations using anthropometric data were evaluated in order to determine body fat percentage (BF%) using DXA as reference method. Paired t-test comparisons and standard error of estimates (SEE) were obtained. The Bland-Altman plot with 95% limits of agreement and the concordance correlation coefficient (CCC) were used to evaluate the BF% prediction equations and BIA estimates.ResultsThe mean age of the study participants was 73.7 (±5.8) years old. BIA and the anthropometric based equations examined showed mean significant differences when tested in the entire sample. For the taller women (height > 145 cm), no significant difference in the paired comparison was found between DXA and BIA of BF% estimates. The mean BF% was 40.3 (±4.8) and 40.7 (±6.2) for DXA and BIA, respectively. The concordance between methods was good (CCC 0.814), (SEE 2.62). Also, in the taller women subset, the Woolcott equation using waist-to-height ratio presented no significant difference in the paired comparison; however, the error of the estimates was high (SEE 3.37) and the concordance was moderate (CCC 0.693).ConclusionThis study found that BIA yielded good results in the estimation of BF% among women with heights over 145 cm. Also, in this group, the Woolcott predictive equation based on waist circumference and height ratio showed no significant differences compared to DXA in the paired comparison; however, the large error of estimates observed may limit its application. In older women, short stature may impact the validity of the body fat percentage estimates of anthropometric-based predictive equations.

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Section: Introductionmentioning

confidence: 99%

Comparison of body fat percentage assessments by bioelectrical impedance analysis, anthropometrical prediction equations, and dual-energy X-ray absorptiometry in older women

et al. 2022

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“…In recent years, computer technology has been widely used in the medical field, such as computed tomography (CT) and X-ray imaging technology [1]. Different tissues can absorb different number of rays.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Self-Efficacy X-Ray Images in the Evaluation of Rheumatoid Arthritis Combined with Osteoporosis Nursing

Geng

Liu

Ding

et al. 2021

Scientific Programming

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To explore the application of self-efficacy in X-ray image analysis based on deep convolutional neural network (DCNN) in the care and treatment of osteoporosis patients with rheumatoid arthritis. In this study, 90 patients with osteoporosis were divided into the control group and the experimental group for DCNN combined with X-ray diagnosis. Patients in the control group were given routine nursing care, and those in the experimental group were given comprehensive nursing care. The bone mineral content, self-efficacy, anxiety, and depression in the femur and lumbar spine after care were compared. The results showed that the accuracy, sensitivity, and false-negative rate of X-ray image recognition of osteoporosis based on DCNN were 91%, 98%, and 2%, respectively. The bone mineral contents of femur and lumbar vertebra in the experimental group were significantly higher than those in the control group ( P < 0.05 ). The anxiety, depression, and self-efficacy scores of patients in the experimental group were significantly higher than those in the control group ( P < 0.05 ). In conclusion, the accuracy rate of DCNN combined with X-ray plain film imaging in the detection of osteoporosis is high. Comprehensive nursing intervention can improve the curative effect and self-efficacy of patients. The improvement of self-efficacy is a related factor for the improvement of patients’ negative emotions and quality of life.

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Factors associated with the contemplative stage of readiness to initiate osteoporosis treatment

et al. 2020

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Sex differences in osteoporosis self-efficacy among community-residing older adults presenting for DXA

Cited by 4 publications

References 34 publications

Comparison of body fat percentage assessments by bioelectrical impedance analysis, anthropometrical prediction equations, and dual-energy X-ray absorptiometry in older women

Comparison of body fat percentage assessments by bioelectrical impedance analysis, anthropometrical prediction equations, and dual-energy X-ray absorptiometry in older women

Deep Learning-Based Self-Efficacy X-Ray Images in the Evaluation of Rheumatoid Arthritis Combined with Osteoporosis Nursing

Factors associated with the contemplative stage of readiness to initiate osteoporosis treatment

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