ABSTRACT
Background: Social media presents an opportunity to analyze popular opinion about patient experiences. Idiopathic scoliosis is a spinal pathology commonly identified in younger patients who are the largest users of social media. Objective: To analyze posts on the social media platform, TikTok, to better understand the scoliotic patient condition. Methods: TikTok posts were searched manually by screening for "#Scoliosis." Variables assessed included number of likes, conveyed tone, gender, activities of daily living, incisional scar, imaging, involved spine level, spinal curvature, pain, formal physical therapy, multiple operations/reoperation, brace use, self-image, mobility, and educational/awareness posts. Number of responses per category were evaluated for the total they represented and the percentage of available posts containing those elements. Odds ratios with 95% confidence intervals were calculated for each collected variable. Results: More posts were positive than negative (P < 0.001) and from female users than male users (P < 0.001). Self-image was the most prevalent subject, with many posts not mentioning activities of daily living, incisional scars, imaging, pain, physical therapy, timing, awareness/education, or involved spine levels. Conclusions: More females post about scoliosis than males, with most posts containing positive self-image-related themes. This may represent a positive public attitude about scoliosis; however, further research is needed.
ABSTRACT
Fractures are costly to treat and can significantly increase morbidity. Although dual-energy x-ray absorptiometry (DEXA) is used to screen at risk people with low bone mineral density (BMD), not all areas have access to one. We sought to create a readily accessible, inexpensive, high-throughput prediction tool for BMD that may identify people at risk of fracture for further evaluation. Anthropometric and demographic data were collected from 492 volunteers (â275, â217; [44 â± â20] years; Body Mass Index (BMI) = [27.6 â± â6.0] kg/m2) in addition to total body bone mineral content (BMC, kg) and BMD measurements of the spine, pelvis, arms, legs and total body. Multiple-linear-regression with step-wise removal was used to develop a two-step prediction model for BMC followed by BMC. Model selection was determined by the highest adjusted R2, lowest error of estimate, and lowest level of variance inflation (α â= â0.05). Height (HTcm), age (years), sexm=1, f=0, %body fat (%fat), fat free mass (FFMkg), fat mass (FMkg), leg length (LLcm), shoulder width (SHWDTHcm), trunk length (TRNKLcm), and pelvis width (PWDTHcm) were observed to be significant predictors in the following two-step model (p â< â0.05). Step1: BMC (kg) = (0.006 3 × HT) â+ â(-0.002 4 × AGE) â+ â(0.171 2 × SEXm=1, f=0) â+ â(0.031 4 × FFM) â+ â(0.001 × FM) â+ â(0.008 9 × SHWDTH) â+ â(-0.014 5 × TRNKL) â+ â(-0.027 8 × PWDTH) - 0.507 3; R2 â= â0.819, SE â± â0.301. Step2: Total body BMD (g/cm2) = (-0.002 8 × HT) â+ â(-0.043 7 × SEXm=1, f=0) â+ â(0.000 8 × %FAT) â+ â(0.297 0 × BMC) â+ â(-0.002 3 × LL) â+ â(0.002 3 × SHWDTH) â+ â(-0.002 5 × TRNKL) â+ â(-0.011 3 × PWDTH) â+ â1.379; R2 â= â0.89, SE â± â0.054. Similar models were also developed to predict leg, arm, spine, and pelvis BMD (R2 â= â0.796-0.864, p â< â0.05). The equations developed here represent promising tools for identifying individuals with low BMD at risk of fracture who would benefit from further evaluation, especially in the resource or time restricted setting.
