ROLE OF KINESIOPHOBIA ON PAIN, DISABILITY AND QUALITY OF LIFE IN PATIENTS WITH CHRONIC LOW BACK PAIN: A SYSTEMATIC REVIEW

Background and Aim: The majority of people suffered with low back pain (LBP) at least once during their lifetime. As such, LBP is a highly prevalent and costly condition. People respond inappropriately as a result of current or possible risks and establish defensive habits (for example, hyper-vigilance) that aim at avoiding new injuries. A continued reconciling of studies which provide various answers for the same issue will be necessary for treatment decisions. This study is performed to conclude the function of Kinesiophobia and check it on   Pain, Disability and Quality of Life in Patients that are suffering from Chronic Low Back Pain: A Systematic Review. Methodology: A Systematic Review has been conducted. Secondary data collected from Electronic database including PubMed, Medline and Cochrain Library from inception to 2010. Total 554 Article found out of which 10 articles included in the study after excluding the duplicate article, Quality screening through Pedro Scale, and article don’t fulfilling the inclusion criteria of the study. Review completed within 9 months after approval of synopsis. Results: According to this Review total Sample size was 554 with mean Sample size 130±90, mean Age 46±5 years, Mean of Pain Intensity (VAS 0-10) 6.12±1.5, mean Pain Duration 30±14, mean Kinesiophobia Measures (Tampa Scale of Kinesiophobia 0-68) 37±6.5, mean Disability (Oswestry Disability Index 0-100%) 56±27, mean Quality of Life (SF 36 0-100) 39.17±15.197.  Conclusion: TSK scores showed a statistically significant correlation with Pain, Disability, education level, and SF-36 QOL. As the education level decreases, kinesiophobia scores increase and as kinesiophobia scores increase, Level of disability increases and the quality of life decreases. Patients with kinesiophobia presented greater pain intensity, a greater fear of movement and of performing physical activities and it was also associated with worse quality of life.

Road Damage Detection and Health Report Generation​

The aim is to automate the process of manual health inspection and report generation of roads in Karachi. A device attached to a moving vehicle that captures geo tagged images of the road and sends them to the cloud. The data is passed through a Convolutional Neural Network based detector that detects and classifies the road damage into five categories (i.e. line cracks, potholes, alligator cracks, raveling and no damage). After the damages are detected, they are displayed on a map. A GUI is created that allows report generation of a selected road. It also gives the road priority for repair based on the area selected. ​