A comparative study to assess thoracic kyphotic index in adults with and without mechanical neck pain.

OBJECTIVE: To assess thoracic kyphotic index in adults, and to find out the correlation between Neck Disability Index and Thoracic Kyphotic Index. METHODS: The analytical cross-sectional study was conducted from October 2020 to January 2021 at the Shifa Tameer-e-Millat University, Islamabad, Pakistan, and comprised adults aged 18-35 years. Those with neck pain formed group A, while those without neck pain formed group B. The Numeric Pain Rating Scale and the Neck Disability Index were used to measure mechanical neck pain, while the Thoracic Kyphotic Index value was measured using a flexicurve ruler. Data was analyzed using SPSS 24. RESULTS: Of the 74 subjects, 37(50%) were in each of the two groups. There were 19 (51.40%) females and 18(48.60%) males in group A, while in group B there were 18(48.60%) females and 19(51.40%) males. The overall mean age of the sample was 23.35±3.31 years. Group A showed higher Thoracic Kyphotic Index value than group B (p=0.0001). The Neck Disability Index showed negative weak (r=-0.18, p=0.28) correlation with Thoracic Kyphotic Index in group B and moderate positive correlation (r=0.33, p=0.04) in group A. CONCLUSIONS: Higher Thoracic Kyphotic Index value was found among adults with mechanical neck pain compared to healthy adults.

Cervical joint positioning error and its association with cervical spine mechanics among undergraduate students.

A cross sectional analytical study was conducted from September 26 to December 28 2018 at Shifa Tameer-e-Millat University Islamabad with 111 undergraduate students aged 17-26 years as participants. The aim of the study was to establish the normative values of cervical joint positioning error (CJPE) and its association with cervical spine mechanics. Neck discomfort was measured using the neck portion of the "student specific Cornell Musculoskeletal Discomfort Questionnaire" (ssCMDQ) and CJPE was measured via cervico-cephalic relocation test using a goniometer. Non-parametric tests of significance were used because the data was not normally distributed in terms of normality testing. Normative values of CJPE were noted to be highest in flexion (9o±9o), rotation towards left (9o±6o) and right (8o±7o), extension (6o±8o), and lastly lateral flexion towards left (5o±7o) and right (5o±5o). Higher CJPE in all movements was observed among females; however, no significant statistical differences were observed (p>0.05). In terms of correlation, important trends included significantly positive correlation of neck discomfort with CJPE in extension, and of CJPE in lateral flexion towards the left with CJPE in lateral flexion towards the right and flexion (p<0.05).

Assessment of functional ability by anthropometric and physiological parameters during six-minute walk test in healthy children.

OBJECTIVE: To assess the functional ability and vitals of young children using six-minute walk test. METHODS: The analytical cross-sectional study was conducted from October 2019 to January 2020 at public and private schools of Rawalpindi and Islamabad after approval from the ethics review committee of Riphah College of Rehabilitation Sciences, Westridge Campus Rawalpindi, Pakistan, and comprised healthy children aged 7-12 years who were subjected to the six-minute walk test according to standardised guidelines. Data was collected using a semi-structured questionnaire. Anthropometric measurements, distance walked in six minutes, heart rate, respiratory rate, oxygen saturation, and rate of perceived exertion were the outcome variables. Data was analysed using SPSS 26. RESULTS: Of the 376 subjects, 225(59.8%) were boys and 151(40.2%) were girls. The mean age of the sample was 9.25±1.64 years. Mean distance covered by the children was 482.63±119.76 metres. Public school students performed better than those studying in private schools (p=0.001). The difference in gender terms was non-significant (p=0.926). Significant difference was observed in mean heart rate and respiratory rate post-walk (p<0.05). There was a weak positive correlation of the test with age and height (p<0.001), but not with weight, gender and body mass index (p>0.05). Conclusion: The level of functional ability of the young students improved with age and was better among those studying at public schools. Besides, anthropometric and physiological parameters influenced the text performance.

Intelligent framework for diagnosis of frozen shoulder using cross sectional survey and case studies.

OBJECTIVES: Frozen shoulder is a disease in which shoulder becomes stiff. Accurate diagnosis of frozen shoulder is helpful in providing economical and effective treatment for patients. This research provides the classification of unstructured data using data mining techniques. Prediction results are validated by K-fold cross-validation method. It also provides accurate diagnosis of frozen shoulder using Naïve Bayesian and Random Forest models. At the end results are presented by performance measure techniques. METHODS: In this research, 145 respondents (patients) with a severe finding of frozen shoulder are included. They are selected on premise of (clinical) assessment confirmed after by MRI. This data is taken from the department of Orthopedics (Pakistan Institute of Medical Sciences Islamabad and Railway Hospital Rawalpindi) between September 2014 to November 2015. Frozen shoulder is categorized on the basis of MRI result. The predictor variables are taken from patient survey and patient reports, which consisted of 35+ variables. The outcome variable is coded into numeric system of "intact" and "no-intact". The outcome variable is assigned into numeric code, 1 for "intact" and 0 for "no-intact". "Intact" group is used as an indication that tissue is damaged badly and "no-intact" is classified as normal. Distribution of result is 110 patients for "Intact" group and 35 patients for "No-Intact" group (false positive rate was 24 %). In this research we have utilized two methods i.e. Naive Bayes and Random Forest. A statistics regression model (Logistic regression) to categorize frozen shoulder finding into "intact" and "no-intact" classes. In the end, we validated our results by Bayesian theorem. This gives a rough estimate about the probability of frozen shoulder. RESULTS: In this research, our anticipated and predictive procedures gave better outcome as compared to statistical techniques. The specificity and sensitivity ratio of predicting a frozen shoulder are better in the Naïve Bayes as compared to Random Forest. In end the likelihood ratio results are used with Bayesian theorem for final evaluation of the results, from this we conclude predictive model is valid model for classification of frozen shoulder. CONCLUSIONS: We have used three predictive models in our study to classify frozen shoulder. Then we validated our predictive results by Bayesian theorem to give a rough estimate about the probability of occurrence of disease or not. This enhances the clinical decision making regarding frozen shoulder.