Yes / National policies and evidence reviews recommend psychosocial interventions (PIs) as an essential support, particularly in the period following dementia diagnosis. However, the availability and uptake of these interventions is comparatively low. One of the reasons for this is that clinicians lack information about what might be provided and the potential benefits of different interventions. This paper identifies and describes psychosocial interventions for community dwelling people following diagnosis of mild to moderate dementia and presents the available evidence to inform practice decisions. A systematic scoping review was employed to map the evidence relating to PIs for this group. This identified 63 relevant studies, testing 69 interventions, which could be grouped into six categories; 20 cognition-oriented interventions; 11 behaviour-oriented; 11 stimulation-oriented; 13 emotion-oriented, 5 social-oriented and 9 multi-modal. There were three targets for outcome measurement of these PIs; the person with dementia, the family carer and the person-carer dyad. Over 154 outcome measures were identified in the studies with outcomes measured across 11 main domains. The lack of a classification framework for PIs means it is difficult to create a meaningful synthesis of the breadth of relevant evidence to guide clinical practice. Possible dimensions of a classification framework are proposed to begin to address this gap.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24034号 / 情博第790号 / 新制||情||134(附属図書館) / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 黒田 知宏, 教授 吉川 正俊, 教授 神田 崇行 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Yes / Key points: The focus on living well with dementia encourages a more positive and empowering approach. The right support can improve the experience of living with dementia. An holistic approach to assessing the needs of people with dementia and identifying the factors that impact on their well-being is essential. Enabling people to live better requires a broad approach that encompasses both health and social systems and the wider community. / The IDEAL study was funded jointly by the Economic and Social Research Council (ESRC) and the National Institute for Health Research (NIHR) through grant ES/L001853/2. The IDEAL2 study’ is funded by Alzheimer’s Society, grant number 348, AS-PR2-16-001. The support of ESRC, NIHR and Alzheimer’s Society is gratefully acknowledged. LC acknowledges support from the NIHR Applied Research Collaboration South-West Peninsula. / Research Development Fund Publication Prize Award winner, Sep 2021

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Submitted by Maria Aparecida (cidazen@gmail.com) on 2017-04-17T11:57:59Z No. of bitstreams: 1 Alfredo Costa Oliveira Júnior.pdf: 789446 bytes, checksum: c5c9858983f5e6384177bda8d1ae2a0a (MD5) / Made available in DSpace on 2017-04-17T11:57:59Z (GMT). No. of bitstreams: 1 Alfredo Costa Oliveira Júnior.pdf: 789446 bytes, checksum: c5c9858983f5e6384177bda8d1ae2a0a (MD5) Previous issue date: 2017-02-16 / Atrial fibrilation(AF) is one of the most common cardiac arrhythmias worldwide. Thus, there are ample efforts to implement AF diagnosis systems. The main noninvasive way to assess cardiac health is through electrocardiogram (ECG) signal analysis, which represents the electrical activity of the cardiac muscle, and has characteristic temporal markings: P, Q, R, S and T waves. Some authors use filtering techniques, statistical analysis and even neural networks for detecting AF based on the RR interval, that is given by the temporal difference between the peaks of the R wave. However, analises of the RR interval allows for evaluating changes occurring only in the R wave of the ECG signal, not allowing to assess, for example, variations in the P wave provoked by the AF. In face of that, we propose characterize the ECG signal amplitude aiming at classifying both healthy and AF patients. The ECG signal was analyzed in the proposed methodology through the following statistics: variance, asymmetry, and kurtosis. Herein, we use the MIT-BIH Atrial Fibrillation and MIT-BIH Normal Sinus Rhythm database signals to evaluate AF and normal heartbeat intervals. Our study shown that kurtosis outperfomed variance and asymmetry with respect to sensibility (Se = 100%), specificity (Sp = 88.33%) and accuracy (Ac = 91.33%). The results were expected since kurtosis is a non-Gaussian measure and the ECG signal has sparse distribution. The proposed methodology also requires a lower number of pre-processing stages, and its simplicity allows for implementations in imbedded systems supporting the clinical diagnosis. / A Fibrilação atrial (FA) é uma das arritmias cardíacas mais comuns em todo o mundo. Por isso, amplos são os esforços para implementar sistemas que apoiem o diagnóstico de FA. A principal forma não invasiva de avaliar a saúde cardíaca, é através da análise do sinal de eletrocardiograma (ECG), o qual representa a atividade elétrica do músculo cardíaco, e possui marcações temporais características: as ondas P, Q, R, S e T. Alguns autores utilizaram técnicas de filtragem, análise estatística e até redes neurais para detectar FA com base no intervalo RR, que é dado pela diferença temporal entre os picos da onda R. Entretanto, a análise do intervalo RR permite avaliar apenas as variações que ocorrem na onda R do sinal de ECG, não permitindo avaliar, por exemplo, as alterações na onda P, provocadas pela FA. Diante disso, propõe-se caracterizar a amplitude do sinal de ECG, a fim de classificar pacientes com FA e saudáveis. Na metodologia proposta, o sinal de ECG, foi analisado por meio das seguintes estatísticas: variância, assimetria e curtose. Para avaliar o classificador proposto, usou-se sinais obtidos das bases de dados MIT-BIH Atrial Fibrillation e MIT-BIH Normal Sinus Rhythm referentes aos pacientes com FA e com ritmo cardíaco normal, respectivamente. Dentre as estatísticas analidadas, a curtose foi a que apresentou resultados superiores em termos de sensibilidade (Se = 100%), especificidade (Sp = 88, 33%) e acurácia (Ac = 91, 33%). Esses resultados são de se esperar pelo fato de que a curtose é uma medida de não-gaussianidade e que o sinal de ECG possui distribuição esparsa. A metodologia proposta também requer um número menor de etapas de pré-processamento, e sua simplicidade permite implementações em sistemas embarcados que apoiarão o diagnóstico clínico.