Validering av Alberta Context Tool (ACT) för bruk inom svensk kommunal äldrevård : Ett verktyg för implementering av evidensbaserad vård

The aim of this study was to conduct an instrument test of the Canadian questionnaire Alberta Context Tool (ACT) version Long-Term care for Swedish conditions. ACT is designed in order to measure the context in the care environment and different behaviours related to the changes in clinical practice. In total, 159 Licensed Practical Nurses (LPNs) and Registered Nurses (RNs) within municipality care of the elderly were included in the survey. The test included the instrument's reliability and face validity.The reliability test was implemented through calculation of Cronbach´s Alpha, and showed internal consistency for five of the scales of the ACT-instrument with Cronbach´s Alpha values ranging between 0,728 and 0,873. However, three dimensions got lower values (0,558 - 0,683).The analysis was carried out with content analysis and carried out for LPNs and RNs in separate groups. The majority of LPNs expressed that it was easy to respond to the questions (56%), while nine percent considered it as difficult. Eleven comments were given about questions that were perceived to be unclear, complicated or contained difficult words. In the RN group only 30 percent considered that the questions were easy to respond to. One third of the RNs considered that part of the questions were unclear, and six RNs expressed also which questions they experienced as unclear. In general, the questions in the ACT were perceived as relevant. The instrument's relevance as a tool to measure contextual factors that influence the implementation of evidence based nursing can also be considered to be determined. By modifying the content in the questionnaire in accordance with what appeared in this survey and to implement yet another test, the instrument should be considered to be relevant for use within Swedish municipality care of the elderly. ACT can be used both as a tool in the work on improvement of clinical practice and as a tool for further research about implementation of evidence based nursing.

First-principle data-driven models for assessment of motor disorders in Parkinson’s disease

Parkinson’s disease (PD) is an increasing neurological disorder in an aging society. The motor and non-motor symptoms of PD advance with the disease progression and occur in varying frequency and duration. In order to affirm the full extent of a patient’s condition, repeated assessments are necessary to adjust medical prescription. In clinical studies, symptoms are assessed using the unified Parkinson’s disease rating scale (UPDRS). On one hand, the subjective rating using UPDRS relies on clinical expertise. On the other hand, it requires the physical presence of patients in clinics which implies high logistical costs. Another limitation of clinical assessment is that the observation in hospital may not accurately represent a patient’s situation at home. For such reasons, the practical frequency of tracking PD symptoms may under-represent the true time scale of PD fluctuations and may result in an overall inaccurate assessment. Current technologies for at-home PD treatment are based on data-driven approaches for which the interpretation and reproduction of results are problematic.  The overall objective of this thesis is to develop and evaluate unobtrusive computer methods for enabling remote monitoring of patients with PD. It investigates first-principle data-driven model based novel signal and image processing techniques for extraction of clinically useful information from audio recordings of speech (in texts read aloud) and video recordings of gait and finger-tapping motor examinations. The aim is to map between PD symptoms severities estimated using novel computer methods and the clinical ratings based on UPDRS part-III (motor examination). A web-based test battery system consisting of self-assessment of symptoms and motor function tests was previously constructed for a touch screen mobile device. A comprehensive speech framework has been developed for this device to analyze text-dependent running speech by: (1) extracting novel signal features that are able to represent PD deficits in each individual component of the speech system, (2) mapping between clinical ratings and feature estimates of speech symptom severity, and (3) classifying between UPDRS part-III severity levels using speech features and statistical machine learning tools. A novel speech processing method called cepstral separation difference showed stronger ability to classify between speech symptom severities as compared to existing features of PD speech. In the case of finger tapping, the recorded videos of rapid finger tapping examination were processed using a novel computer-vision (CV) algorithm that extracts symptom information from video-based tapping signals using motion analysis of the index-finger which incorporates a face detection module for signal calibration. This algorithm was able to discriminate between UPDRS part III severity levels of finger tapping with high classification rates. Further analysis was performed on novel CV based gait features constructed using a standard human model to discriminate between a healthy gait and a Parkinsonian gait. The findings of this study suggest that the symptom severity levels in PD can be discriminated with high accuracies by involving a combination of first-principle (features) and data-driven (classification) approaches. The processing of audio and video recordings on one hand allows remote monitoring of speech, gait and finger-tapping examinations by the clinical staff. On the other hand, the first-principles approach eases the understanding of symptom estimates for clinicians. We have demonstrated that the selected features of speech, gait and finger tapping were able to discriminate between symptom severity levels, as well as, between healthy controls and PD patients with high classification rates. The findings support suitability of these methods to be used as decision support tools in the context of PD assessment.