The objective of this study was to investigate the accuracy of eleven prediction equations and one prediction table when estimating isoinertial knee extension and leg press one repetition maximum (1-RM) performance in subjects with knee injuries and knee osteoarthritis. Study Design: A descriptive quantitative research study was undertaken utilizing a cross-sectional design. Background: Traumatic injuries and osteoarthritis are common musculoskeletal pathologies that can disrupt normal function of the knee joint. A frequent sequela of these pathologies is quadriceps femoris muscle weakness. Such weakness can contribute to disability and diminished levels of functional and recreational activity. Therefore, safe and accurate methods of measuring maximal strength are required to identify and quantify quadriceps strength deficits. One option proposed in the literature is the use of 1-RM prediction equations which estimate 1-RM performance from the number of repetitions completed with sub-maximal loads. These equations have been investigated previously using healthy populations and subjects with calf muscle injuries. However, to date, no known study has investigated their accuracy in individuals with joint pathologies. Method: Machine-weight seated knee extension and seated leg press exercises were investigated in this study. Twenty subjects with knee injuries and 12 subjects with knee OA completed the testing procedures for the knee extension exercise. Nineteen subjects with knee injuries and 18 subjects with knee OA completed the testing procedures for the leg press exercise. All subjects attended the testing venue on three occasions. At the first visit a familiarization session was carried out. At the second and third visits each subject was randomly assigned to perform either actual or predicted 1-RM testing for both of the exercises. Twelve different prediction methods were used to estimate 1-RM performance from the results. The estimates of 1-RM strength were then compared to actual 1-RM performance to assess the level of conformity between these measures. Statistical procedures including Bland and Altman analyses, intraclass correlation coefficients, typical error and total error of measurement were used in the analyses of the results. In addition, paired t-tests were performed to determine whether actual 1-RM values were significantly different across the control and affected limbs and whether there were any significant differences in predictive accuracy for each equation across the control and affected limbs. Finally, the number of subjects with predicted 1-RM values within 5% or less of their actual 1-RM values was determined for each equation. Results: When the knee injury group performed the knee extension exercise, the Brown, Brzycki, Epley, Lander, Mayhew et al., Poliquin and Wathen prediction methods demonstrated the greatest levels of predictive accuracy. When two atypical subjects were identified and excluded from the analyses, the accuracy of these equations improved further. Following the removal of these two subjects, no significant differences in predictive accuracy were found for any of the equations across the affected and control limbs (p > 0.05). Typical errors and total errors were low for the more accurate prediction methods ranging from 2.4-2.8% and from 2.4-3.5%, respectively. Overall, the Poliquin table appeared to be the most accurate prediction method for this sample (affected limbs: bias 0.3 kg, 95% limits of agreement (LOA) -5.8 to 6.4 kg, typical error as a coefficient of variation (COV) 2.4%, total error of measurement (total error) 2.4%; control limbs: bias -1.3 kg, 95% LOA -9.0 to 6.3 kg, typical error as a COV 2.7%, total error 2.8%). When the knee OA group performed the knee extension exercise, the Brown, Brzycki, Epley, Lander, Mayhew et al., Poliquin and Wathen prediction methods demonstrated the greatest levels of predictive accuracy. No significant differences in predictive accuracy were found for any of the equations across the affected and control limbs (p > 0.05). When an atypical subject was identified and excluded from the analyses, the accuracy of the equations improved further. Typical errors as COVs and total errors for the more accurate prediction methods ranged from 2.5-2.7% and from 2.4-2.9%, respectively. Overall, the Poliquin table appeared to be the most accurate prediction method for this sample (affected limbs: bias 0.9 kg, 95% LOA -4.5 to 6.3 kg, typical error as a COV 2.5%, total error 2.5%; control limbs: bias -0.1 kg, 95% LOA -6.0 to 5.9 kg, typical error as a COV 2.5%, total error 2.4%). When the knee injury group performed the leg press, the Adams, Berger, Lombardi and O’Connor equations demonstrated the greatest levels of predictive accuracy. No significant differences in predictive accuracy were found for any of the equations across the affected and control limbs (p > 0.05). Typical errors as COVs and total errors for the more accurate equations ranged from 2.8-3.2% and from 2.9-3.3%, respectively. Overall, the Berger (affected limbs: bias -0.4 kg, 95% LOA -7.2 to 6.3 kg, typical error as a COV 3.2%, total error 3.2%; control limbs: bias 0.1 kg, 95% LOA -6.6 to 6.7 kg, typical error as a COV 3.1%, total error 3.0%) and O’Connor equations (affected limbs: bias -0.6 kg, 95% LOA-6.8 to 5.7 kg, typical error as a COV 2.9%, total error 3.0%; control limbs: bias -0.2 kg, 95% LOA -6.9 to 6.4 kg, typical error as a COV 2.9%, total error 2.9%) appeared to be the most accurate prediction methods for this sample. When the knee OA group performed the leg press, the Adams, Berger, KLW, Lombardi and O’Connor equations demonstrated the greatest levels of predictive accuracy. No significant differences in predictive accuracy were found for any of the equations across the affected and control limbs (p > 0.05). The typical errors as COVs and the total error values for the more accurate prediction methods were the highest observed in this study, ranging from 5.8-6.0% and from 5.7-6.2%, respectively. Overall, the Adams, Berger, KLW and O’Connor equations appeared to be the most accurate prediction methods for this sample. However, it is possible that the predicted leg press 1-RM values produced by the knee OA group might not have matched actual 1-RM values closely enough to be clinically acceptable for some purposes. Conclusion: The findings of the current study suggested that the Poliquin table produced the most accurate estimates of knee extension 1-RM performance for both the knee injury and knee OA groups. In contrast, the Berger and O’Connor equations produced the most accurate estimates of leg press 1-RM performance for the knee injury group, while the Adams, Berger, KLW and O’Connor equations produced the most accurate results for the knee OA group. However, the higher error values observed for the knee OA group suggested that predicted leg press 1-RM performance might not be accurate enough for some clinical purposes. Finally, it can be concluded that no single prediction equation was able to accurately estimate both knee extension and leg press 1-RM performance in subjects with knee injuries and knee OA.

The objective of this study was to investigate the accuracy of eleven prediction equations and one prediction table when estimating isoinertial knee extension and leg press one repetition maximum (1-RM) performance in subjects with knee injuries and knee osteoarthritis. Study Design: A descriptive quantitative research study was undertaken utilizing a cross-sectional design. Background: Traumatic injuries and osteoarthritis are common musculoskeletal pathologies that can disrupt normal function of the knee joint. A frequent sequela of these pathologies is quadriceps femoris muscle weakness. Such weakness can contribute to disability and diminished levels of functional and recreational activity. Therefore, safe and accurate methods of measuring maximal strength are required to identify and quantify quadriceps strength deficits. One option proposed in the literature is the use of 1-RM prediction equations which estimate 1-RM performance from the number of repetitions completed with sub-maximal loads. These equations have been investigated previously using healthy populations and subjects with calf muscle injuries. However, to date, no known study has investigated their accuracy in individuals with joint pathologies. Method: Machine-weight seated knee extension and seated leg press exercises were investigated in this study. Twenty subjects with knee injuries and 12 subjects with knee OA completed the testing procedures for the knee extension exercise. Nineteen subjects with knee injuries and 18 subjects with knee OA completed the testing procedures for the leg press exercise. All subjects attended the testing venue on three occasions. At the first visit a familiarization session was carried out. At the second and third visits each subject was randomly assigned to perform either actual or predicted 1-RM testing for both of the exercises. Twelve different prediction methods were used to estimate 1-RM performance from the results. The estimates of 1-RM strength were then compared to actual 1-RM performance to assess the level of conformity between these measures. Statistical procedures including Bland and Altman analyses, intraclass correlation coefficients, typical error and total error of measurement were used in the analyses of the results. In addition, paired t-tests were performed to determine whether actual 1-RM values were significantly different across the control and affected limbs and whether there were any significant differences in predictive accuracy for each equation across the control and affected limbs. Finally, the number of subjects with predicted 1-RM values within 5% or less of their actual 1-RM values was determined for each equation. Results: When the knee injury group performed the knee extension exercise, the Brown, Brzycki, Epley, Lander, Mayhew et al., Poliquin and Wathen prediction methods demonstrated the greatest levels of predictive accuracy. When two atypical subjects were identified and excluded from the analyses, the accuracy of these equations improved further. Following the removal of these two subjects, no significant differences in predictive accuracy were found for any of the equations across the affected and control limbs (p > 0.05). Typical errors and total errors were low for the more accurate prediction methods ranging from 2.4-2.8% and from 2.4-3.5%, respectively. Overall, the Poliquin table appeared to be the most accurate prediction method for this sample (affected limbs: bias 0.3 kg, 95% limits of agreement (LOA) -5.8 to 6.4 kg, typical error as a coefficient of variation (COV) 2.4%, total error of measurement (total error) 2.4%; control limbs: bias -1.3 kg, 95% LOA -9.0 to 6.3 kg, typical error as a COV 2.7%, total error 2.8%). When the knee OA group performed the knee extension exercise, the Brown, Brzycki, Epley, Lander, Mayhew et al., Poliquin and Wathen prediction methods demonstrated the greatest levels of predictive accuracy. No significant differences in predictive accuracy were found for any of the equations across the affected and control limbs (p > 0.05). When an atypical subject was identified and excluded from the analyses, the accuracy of the equations improved further. Typical errors as COVs and total errors for the more accurate prediction methods ranged from 2.5-2.7% and from 2.4-2.9%, respectively. Overall, the Poliquin table appeared to be the most accurate prediction method for this sample (affected limbs: bias 0.9 kg, 95% LOA -4.5 to 6.3 kg, typical error as a COV 2.5%, total error 2.5%; control limbs: bias -0.1 kg, 95% LOA -6.0 to 5.9 kg, typical error as a COV 2.5%, total error 2.4%). When the knee injury group performed the leg press, the Adams, Berger, Lombardi and O’Connor equations demonstrated the greatest levels of predictive accuracy. No significant differences in predictive accuracy were found for any of the equations across the affected and control limbs (p > 0.05). Typical errors as COVs and total errors for the more accurate equations ranged from 2.8-3.2% and from 2.9-3.3%, respectively. Overall, the Berger (affected limbs: bias -0.4 kg, 95% LOA -7.2 to 6.3 kg, typical error as a COV 3.2%, total error 3.2%; control limbs: bias 0.1 kg, 95% LOA -6.6 to 6.7 kg, typical error as a COV 3.1%, total error 3.0%) and O’Connor equations (affected limbs: bias -0.6 kg, 95% LOA-6.8 to 5.7 kg, typical error as a COV 2.9%, total error 3.0%; control limbs: bias -0.2 kg, 95% LOA -6.9 to 6.4 kg, typical error as a COV 2.9%, total error 2.9%) appeared to be the most accurate prediction methods for this sample. When the knee OA group performed the leg press, the Adams, Berger, KLW, Lombardi and O’Connor equations demonstrated the greatest levels of predictive accuracy. No significant differences in predictive accuracy were found for any of the equations across the affected and control limbs (p > 0.05). The typical errors as COVs and the total error values for the more accurate prediction methods were the highest observed in this study, ranging from 5.8-6.0% and from 5.7-6.2%, respectively. Overall, the Adams, Berger, KLW and O’Connor equations appeared to be the most accurate prediction methods for this sample. However, it is possible that the predicted leg press 1-RM values produced by the knee OA group might not have matched actual 1-RM values closely enough to be clinically acceptable for some purposes. Conclusion: The findings of the current study suggested that the Poliquin table produced the most accurate estimates of knee extension 1-RM performance for both the knee injury and knee OA groups. In contrast, the Berger and O’Connor equations produced the most accurate estimates of leg press 1-RM performance for the knee injury group, while the Adams, Berger, KLW and O’Connor equations produced the most accurate results for the knee OA group. However, the higher error values observed for the knee OA group suggested that predicted leg press 1-RM performance might not be accurate enough for some clinical purposes. Finally, it can be concluded that no single prediction equation was able to accurately estimate both knee extension and leg press 1-RM performance in subjects with knee injuries and knee OA.

Background: Osteoarthritis (OA) in the knee is one of the most common joint diseases in the world. The symptoms include local joint pain, joint stiffness, crepitation etc. Treatment follows national clinical guidelines which includes patient education, exercise and weight loss. Manual therapy can be used as an additional treatment and has shown a positive effect on pain, range of motion (ROM) and function but the method is not studied enough. Purpose: To investigate the effect of manual mobilization with the OMT Nordic System method as a complement to exercise and patient education for patients with knee OA on pain, Quality of Life (QoL) and ROM. Method: A Single subject experimental design study with 4 participants was conducted. Participants underwent a standardized patient education followed by a 6-week baseline of physical exercise, then a 3-week intervention phase which added manual therapy of the knee joint two times a week for the entire phase. The data was analysed regarding changes in both trend and level. Result: All participants had a significant positive change in level of pain and one participant had a positive change in trend. QoL varied among the participants, two had no change in level, one had a significantly positive change and one had a significantly negative change. Only one participant had a positive change of trend in QoL. ROM increased significantly in level in three out of four participants and the trend changed positively among two of the participants Conclusion: This study indicates that OMT Nordic system as a complement to physical exercise and patient education have a positive effect on pain and ROM in patients with knee OA in the short term. However, since not both level and trend were all over significant the result lacks in significance. Due to the study’s design the results should not be generalized on a group level. To be able to draw general conclusions further studies needs to investigate the effect of the OMT Nordic System with more participants and in different settings.

Gait analysis is the process of collecting and analyzing quantitative information about walking patterns of the people. Gait analysis enables the clinicians to differentiate gait deviations objectively. Diagnostic decision making from gait data only requires high level of medical expertise of neuromusculoskeletal system trained for the purpose. An automated system is expected to decrease this requirement by a &lsquo / transformed knowledge&rsquo / of these experts. This study presents a clinical decision support system for the detecting and scoring of a knee disorder, namely, Osteoarthritis (OA). Data used for training and recognition is mainly obtained through Computerized Gait Analysis software. Sociodemographic and disease characteristics such as age, body mass index and pain level are also included in decision making. Subjects are allocated into four OA-severity categories, formed in accordance with the Kellgren-Lawrence scale: &ldquo / Normal&rdquo / , &ldquo / Mild&rdquo / , &ldquo / Moderate&rdquo / , and &ldquo / Severe&rdquo / . Different types of classifiers are combined to incorporate the different types of data and to make the best advantages of different classifiers for better accuracy. A decision tree is developed with Multilayer Perceptrons (MLP) at the leaves. This gives an opportunity to use neural networks to extract hidden (i.e., implicit) knowledge in gait measurements and use it back into the explicit form of the decision trees for reasoning. Individual feature selection is applied using the Mahalanobis Distance measure and most discriminatory features are used for each expert MLP. Significant knowledge about clinical recognition of the OA is derived by feature selection process. The final system is tested with test set and a success rate of about 80% is achieved on the average.

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