Background and objective: Serious pathologies of the neck can potentially result in cranial nerve palsy. Knowledge about cranial nerve examination (CNE) seems sparse, and its use is still unknown. We aim to investigate the knowledge, skills, and utilization of CNE of Italian physiotherapists. Materials and Methods: An online cross-sectional survey. Results: 396 completed the survey, reaching the required sample size. Although Italian physiotherapists consider CNE relevant (mean ± SD = 7.6/10 ± 2.0), over half of all responders (n = 229 (57.8%)) were not trained in the fundamentals and around a third did not use it in their daily practice (n = 138 (34.8%)). Additionally, participants were unconfident and insecure in conducting (n = 152 (38.4%) and n = 147 (37.1%)), interpreting (n = 140 (35.4%) and n = 164 (41.4%)), and managing the CNE (n = 141 (35.6%) and n = 154 (38.9%)). Possessing a musculoskeletal specialization was associated with an increased value attributed to clinical practice guidelines and reduced the lack of confidence in conducting, interpreting, and managing the CNE (respectively, n = 35 (25.5%), p = 0.0001; n = 32 (23.4%) p = 0.0002; n = 32 (23.4%) p = 0.0002). Working in a direct access setting significantly increased the considered relevance of guidelines and the concerns about arterial (p = 0.004) and other serious pathologies (p = 0.021). Pain and visual disturbances were considered the main indicators to CNE, demonstrating limited knowledge of signs and symptoms’ indicating CNE. Participants considered specific training in CNE as relevant (mean ± SD = 7.6/10 = 2.1). Conclusions: a substantial proportion of Italian physiotherapists are not schooled in the fundamentals of cranial nerve examination. Given the number of physiotherapists who work in first contact roles, this is a professional concern.
Background and objective: Serious pathologies of the neck can potentially result in cranial nerve palsy. Knowledge about cranial nerve examination (CNE) seems sparse, and its use is still unknown. We aim to investigate the knowledge, skills, and utilization of CNE of Italian physiotherapists. Materials and Methods: An online cross-sectional survey. Results: 396 completed the survey, reaching the required sample size. Although Italian physiotherapists consider CNE relevant (mean ± SD = 7.6/10 ± 2.0), over half of all responders (n = 229 (57.8%)) were not trained in the fundamentals and around a third did not use it in their daily practice (n = 138 (34.8%)). Additionally, participants were unconfident and insecure in conducting (n = 152 (38.4%) and n = 147 (37.1%)), interpreting (n = 140 (35.4%) and n = 164 (41.4%)), and managing the CNE (n = 141 (35.6%) and n = 154 (38.9%)). Possessing a musculoskeletal specialization was associated with an increased value attributed to clinical practice guidelines and reduced the lack of confidence in conducting, interpreting, and managing the CNE (respectively, n = 35 (25.5%), p = 0.0001; n = 32 (23.4%) p = 0.0002; n = 32 (23.4%) p = 0.0002). Working in a direct access setting significantly increased the considered relevance of guidelines and the concerns about arterial (p = 0.004) and other serious pathologies (p = 0.021). Pain and visual disturbances were considered the main indicators to CNE, demonstrating limited knowledge of signs and symptoms’ indicating CNE. Participants considered specific training in CNE as relevant (mean ± SD = 7.6/10 = 2.1). Conclusions: a substantial proportion of Italian physiotherapists are not schooled in the fundamentals of cranial nerve examination. Given the number of physiotherapists who work in first contact roles, this is a professional concern.
Although governments are investing heavily in big data analytics, reports show mixed results in terms of performance. Whilst big data analytics capability provided a valuable lens in business and seems useful for the public sector, there is little knowledge of its relationship with governmental performance. This study aims to explain how big data analytics capability led to governmental performance. Using a survey research methodology, an integrated conceptual model is proposed highlighting a comprehensive set of big data analytics resources influencing governmental performance. The conceptual model was developed based on prior literature. Using a PLS-SEM approach, the results strongly support the posited hypotheses. Big data analytics capability has a strong impact on governmental efficiency, effectiveness, and fairness. The findings of this paper confirmed the imperative role of big data analytics capability in governmental performance in the public sector, which earlier studies found in the private sector. This study also validated measures of governmental performance.
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Low back pain is the leading cause of disability worldwide and a significant contributor to work incapacity. Although effective therapeutic options are scarce, exercises supervised by a physiotherapist have shown to be effective. However, the effects found in research studies tend to be small, likely due to the heterogeneous nature of patients' complaints and movement limitations. Personalized treatment is necessary as a 'one-size-fits-all' approach is not sufficient. High-tech solutions consisting of motions sensors supported by artificial intelligence will facilitate physiotherapists to achieve this goal. To date, physiotherapists use questionnaires and physical examinations, which provide subjective results and therefore limited support for treatment decisions. Objective measurement data obtained by motion sensors can help to determine abnormal movement patterns. This information may be crucial in evaluating the prognosis and designing the physiotherapy treatment plan. The proposed study is a small cohort study (n=30) that involves low back pain patients visiting a physiotherapist and performing simple movement tasks such as walking and repeated forward bending. The movements will be recorded using sensors that estimate orientation from accelerations, angular velocities and magnetometer data. Participants complete questionnaires about their pain and functioning before and after treatment. Artificial analysis techniques will be used to link the sensor and questionnaire data to identify clinically relevant subgroups based on movement patterns, and to determine if there are differences in prognosis between these subgroups that serve as a starting point of personalized treatments. This pilot study aims to investigate the potential benefits of using motion sensors to personalize the treatment of low back pain. It serves as a foundation for future research into the use of motion sensors in the treatment of low back pain and other musculoskeletal or neurological movement disorders.
