Background: Cognitive Muscular TherapyTM (CMT) is an integrated behavioural intervention developed for knee osteoarthritis. CMT teaches patients to reconceptualise the condition, integrates muscle biofeedback and aims to reduce muscle overactivity, both in response to pain and during daily activities. This nested qualitative study explored patient and physiotherapist perspectives and experiences of CMT.Methods: Five physiotherapists were trained to follow a well-defined protocol and then delivered CMT to at least two patients with knee osteoarthritis. Each patient received seven individual clinical sessions and was provided with access to online learning materials incorporating animated videos. Semi-structured interviews took place after delivery/completion of the intervention and data were analysed at the patient and physiotherapist level.Results: Five physiotherapists and five patients were interviewed. All described a process of changing beliefs throughout their engagement with CMT. A framework with three phases was developed to organise the data according to how osteoarthritis was conceptualised and how this changed throughout their interactions with CMT. Firstly, was an identification of pain beliefs to be challenged and recognition of how current beliefs can misalign with daily experiences. Secondly was a process of challenging and changing beliefs, validated through new experiences. Finally, there was an embedding of changed beliefs into self-management to continue with activities.
Conclusion:This study identified a range of psychological changes which occur during exposure to CMT. These changes enabled patients to reconceptualise their condition, develop a new understanding of their body, understand psychological processes, and make sense of their knee pain.
In this paper, we propose a new analysis for randomized 2-SAT and 3-SAT algorithms, and show that we could determine more precise boundaries for transition probability of Markov chain using Karnaugh map. In our analysis we will show the probability that the selected literal has been flipped correctly is so close to 3 2 and 7 4 , respectively for 2-SAT and 3-SAT with large number of variables. Then we will extend our result to k-SAT and show that both transition probability of Markov chain in randomized algorithm for k-SAT approaches to 0.5. Finally we use this result to determine the probability and complexity of finding the satisfying assignment for randomized k-SAT algorithm. It will be shown that the probability of finding satisfying assignment and its complexity respectively are within a polynomial factor of ) 9272 . 0 ( n and ) 0785 .
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