General practitioners (GPs) play a pivotal role in dementia recognition, yet research suggests that dementia often remains undetected in primary care. Lack of knowledge might be a major contributing factor to low recognition rates. Our objective was to address a gap in the scientific literature by exploring GPs' knowledge on dementia and mild cognitive impairment (MCI) for the first time in Hungary by conducting a cross-sectional, questionnaire study among practicing GPs. Recruitment of the participants (n = 402) took place at mandatory postgraduate training courses and at national GP-conferences; the applied questionnaire was self-administered and contained both openended and fixed-response questions. Results showed that GPs highlighted vascular and metabolic factors (38.3% of the answer items) and unhealthy lifestyle (29.1% of the answer items) as dementia risk factors. They perceived vascular dementia as the most common dementia form, followed by Alzheimer's disease. Almost half of the respondents (44.9%) were not familiar with MCI. Most GPs identified memory problems (98.4%) and personality change (83.2%) as the leading symptoms of dementia. In summary, GPs demonstrated adequate knowledge on areas more relevant to their practices and scope of duties (risk and preventive factors, main types and symptoms of dementia); however, uncertainties were uncovered regarding epidemiology, MCI, and pharmacological therapy. As only one-fifth (19.4%) of the GPs could participate recently in dementia-focused trainings, continued education might be beneficial to improve dementia detection rates in primary care. Background Dementia (or major neurocognitive disorder) is a usually progressive clinical syndrome that encompasses deterioration of memory, thinking, learning, language, orientation, and behavior (American Psychiatric Association, 2013; World Health Organization, 2012). The deficits may interfere with patients' independence and affect their overall quality of life, challenging not only the families involved, but also imposing a huge economic burden on the health-care system (Wimo, Jönsson, Bond, Prince, & Winblad, 2013). Dementia currently affects about 6% of the population over the age of 60 in Europe, and the number is increasing rapidly with 4.6 million new cases every year worldwide (Ferri et al., 2005; Prince, Wimo, & Guerchet, 2015). In Hungary, the number of residents over the age of 65 has increased CONTACT Nóra Imre
Mild Cognitive Impairment (MCI) is a heterogeneous clinical syndrome, often considered as the prodromal stage of dementia. It is characterized by the subtle deterioration of cognitive functions, including memory, executive functions and language. Mainly due to the tenuous nature of these impairments, a high percentage of MCI cases remain undetected. There is evidence that language changes in MCI are present even before the manifestation of other distinctive cognitive symptoms, which offers a chance for early recognition. A cheap non-invasive way of early screening could be the use of automatic speech analysis. Earlier, our research team developed a set of speech temporal parameters, and demonstrated its applicability for MCI detection. For the automatic extraction of these attributes, a Hungarianlanguage ASR system was employed to match the native language of the MCI and healthy control (HC) subjects. In practical applications, however, it would be convenient to use exactly the same tool, regardless of the language spoken by the subjects. In this study we show that our temporal parameter set, consisting of articulation rate, speech tempo and various other attributes describing the hesitation of the subject, can indeed be reliably extracted regardless of the language of the ASR system used. For this purpose, we performed experiments both on English-speaking and on Hungarian-speaking MCI patients and healthy control subjects, using English and Hungarian ASR systems in both cases. Our experimental results indicate that the language on which the ASR system was trained only slightly affects the MCI classification performance, because we got quite similar scores (67-92%) as we did in the monolingual cases (67-92% as well). As our last investigation, we compared the proposed attribute values for the same utterances, utilizing both the English and the Hungarian ASR models. We found that the articulation rate and speech tempo values calculated based on the two ASR models were highly correlated, and so were the attributes corresponding to silent pauses; however, noticeable differences were found regarding the filled pauses (still, these attributes remained indicative for both languages). Our further analysis revealed that this is probably due to a difference regarding the annotation of the English and the Hungarian ASR training utterances.
Hungarian GPs were aware of the benefits of early dementia recognition. Most GPs do not use cognitive tests for case-finding. Besides providing longer consultation times, the primary way to improve the efficacy of recognition would be to construct a cost-and time-effective dementia identification strategy applicable in GPs' practices.
Background: The development of automatic speech recognition (ASR) technology allows the analysis of temporal (time-based) speech parameters characteristic of mild cognitive impairment (MCI). However, no information has been available on whether the analysis of spontaneous speech can be used with the same efficiency in different language environments. Objective: The main goal of this international pilot study is to address the question whether the Speech-Gap Test® (S-GAP Test®), previously tested in the Hungarian language, is appropriate for and applicable to the recognition of MCI in other languages such as English. Method: After an initial screening of 88 individuals, English-speaking (n = 33) and Hungarian-speaking (n = 33) participants were classified as having MCI or as healthy controls (HC) based on Petersen’s criteria. Speech of each participant was recorded via a spontaneous speech task. 15 temporal parameters were determined and calculated by means of ASR. Results: Seven temporal parameters in the English-speaking sample and 5 in the Hungarian-speaking sample showed significant differences between the MCI and the HC group. Receiver operating characteristics (ROC) analysis clearly distinguished the English-speaking MCI cases from the HC group based on speech tempo and articulation tempo with 100% sensitivity, and on three more temporal parameters with high sensitivity (85.7%). In the Hungarian-speaking sample, the ROC analysis showed similar sensitivity rates (92.3%). Conclusion: The results of this study in different native-speaking populations suggest that changes in acoustic parameters detected by the S-GAP Test® might be present across different languages.
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