Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 4 de 4
Filtrar
Más filtros











Base de datos
Intervalo de año de publicación
1.
Parkinsonism Relat Disord ; 48: 74-81, 2018 03.
Artículo en Inglés | MEDLINE | ID: mdl-29307560

RESUMEN

BACKGROUND: Lower levels of physical activity are associated with lower Health-Related Quality of Life (HRQoL) in Parkinson's disease (PD). We evaluated the influence of quantitative physical activity parameters among other (disease-related) features representing other domains of the WHO International model for classification of Function, Disability, and Health (ICF) on HRQoL in PD. METHODS: Home-based movement data (DynaPort MiniMod®) was collected in 47 PD patients. Nine stepwise regression models were calculated, with consecutive outcome variables: Parkinson's Disease Questionnaire (PDQ) Summary Index (SI), PDQ-Mobility, PDQ-Activities of Daily Living (ADL). Demographic variables, disease-specific features, and quantitative physical activity parameters, were included as predicting variables in all analyses. The following three physical activity parameters were alternately included for both sedentary and active episodes: 'percentage' of 24 h spent within these episodes, 'number of bouts', and 'mean bout lengths' (MBL). RESULTS: Depression and 'Total Energy Expenditure' were the main predictors of overall HRQoL (PDQ-SI), independent of the permutation of activity parameters. The same parameters predicted the PDQ-Mobility score. However, this result was altered when 'MBL' parameters were included into the model, 'MBL' of sedentary episodes additionally predicted HRQoL-Mobility. The PDQ-ADL score was associated with demographic, motor, and non-motor variables including cognitive status. After exclusion of demented PD patients, older age and cognitive impairment no longer constrained HRQoL-ADL. DISCUSSION: For the first time, we showed the influence of objective, home-based measured physical activity among depression and cognition on HRQoL in PD. This suggests that a multifactorial treatment approach would be most successful to increase HRQoL in PD.


Asunto(s)
Trastornos del Conocimiento/etiología , Depresión/etiología , Ejercicio Físico , Enfermedad de Parkinson/complicaciones , Enfermedad de Parkinson/psicología , Calidad de Vida/psicología , Acelerometría , Adulto , Anciano , Anciano de 80 o más Años , Estudios Transversales , Femenino , Humanos , Masculino , Persona de Mediana Edad , Pruebas Neuropsicológicas , Escalas de Valoración Psiquiátrica , Índice de Severidad de la Enfermedad , Encuestas y Cuestionarios
2.
Neurodegener Dis ; 17(4-5): 135-144, 2017.
Artículo en Inglés | MEDLINE | ID: mdl-28441649

RESUMEN

BACKGROUND: For the early diagnosis of Parkinson disease dementia (PDD), objective home-based tools are needed to quantify even mild stages of dysfunction of the activities of daily living (ADL). OBJECTIVES: In this pilot study, home-based physical behavior was assessed to examine whether it is possible to distinguish mild cognitive impairment (PD-MCI) from PDD. METHODS: Fifty-five patients with mild to severe Parkinson disease (PD) participated in this cross-sectional study. Based on comprehensive neuropsychological testing, PD patients were classified as cognitively nonimpaired (PD-NC), PD-MCI or PDD. For physical behavior assessments, patients wore the accelerometer DynaPort® (McRoberts) for 3 days. Ordinal logistic regression models with continuous Y were applied to correct results for motor impairment and depressive symptoms. RESULTS: After excluding 7 patients due to insufficient wearing time, 48 patients with a mean of 2 recorded days were analyzed (17 PD-NC, 22 PD-MCI, 9 PDD). ADL-impaired PDD patients showed fewer sedentary bouts than non-ADL-impaired PD-MCI (p = 0.01, odds ratio [OR] = 8.9, 95% confidence interval [CI] = 1.8-45.2) and PD-NC (p = 0.01, OR = 10.3, CI = 1.6-67.3) patients, as well as a longer sedentary bout length (PD-NC: p = 0.02, OR = 0.1, CI = 0.02-0.65; PD-MCI: p = 0.02, OR = 0.14, CI = 0.03-0.69). These differences were mainly caused by fewer (PD-NC: p = 0.02, OR = 9.6, CI = 1.5-62.4; PD-MCI: p = 0.01, OR = 8.5, CI = 1.5-37.3) but longer sitting bouts (PD-NC: p = 0.03, OR = 0.12, CI = 0.02-0.80; PD-MCI: p = 0.04, OR = 0.19, CI = 0.04-0.93). Tests assessing executive function, visuoconstruction and attention correlated significantly with specific activity parameters (ρ ≥ 0.3; p < 0.05). CONCLUSION: Objective assessment of physical behavior, in particular the detection of sedentary bouts, is a promising contributor to the discrimination between PD-MCI and PDD.


Asunto(s)
Actividades Cotidianas/psicología , Disfunción Cognitiva/clasificación , Disfunción Cognitiva/diagnóstico , Ejercicio Físico/fisiología , Enfermedad de Parkinson/diagnóstico , Acelerometría , Adulto , Anciano , Anciano de 80 o más Años , Algoritmos , Estudios Transversales , Metabolismo Energético/fisiología , Femenino , Humanos , Locomoción/fisiología , Masculino , Persona de Mediana Edad , Pruebas Neuropsicológicas , Proyectos Piloto , Estadística como Asunto , Estadísticas no Paramétricas
3.
Respir Med ; 109(2): 286-8, 2015 Feb.
Artículo en Inglés | MEDLINE | ID: mdl-25596920

RESUMEN

INTRODUCTION: Objective of this study was to validate an activity monitor (DynaPort MoveMonitor [MM], McRoberts, The Hague, The Netherlands) against night-vision video analysis during sleep. METHODS: Twenty patients (65 ± 11 years old, mean body-mass-index: 27 ± 6 kg/m(2)) with different chronic lung diseases were recruited to participate in this validation study. Patients performed a polysomnography measurement during one single night while wearing the MM. The activity monitor data of the MM were then validated against the analysis of the night-vision video by an independent investigator. In total, four different lying positions (supine, left, right and prone), sitting upright, out of bed as well as large, medium, small and sitting transitions were classified. RESULTS: A mean duration of 7.6 ± 0.9 h per night of video and MM classification was available for analysis. In total, 702 different postures were registered on the video from which 678 postures (96.6%) were detected correctly by the MM compared to the video classification. These results yielded a total degree of sensitivity of 93.9% and specificity 94.9% in detecting postures during the night. In total, 682 transitions (394 small, 189 medium, 15 large and 84 sitting transitions) were detected of which 482 were also detected by the MM. The MM detected 70% of the transitions correctly (51.0% small, 97.4% medium, 100% large and 97.6% sitting transitions). CONCLUSION: The MM is an activity monitor showing a high degree of sensitivity and specificity to detect different nocturnal postures as well as medium and large sized transitions in patients with chronic respiratory disorders.


Asunto(s)
Enfermedades Pulmonares/fisiopatología , Polisomnografía/métodos , Sueño/fisiología , Anciano , Humanos , Persona de Mediana Edad , Reproducibilidad de los Resultados , Sensibilidad y Especificidad , Grabación en Video
4.
J Neurol Neurosurg Psychiatry ; 86(1): 32-7, 2015 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-24777169

RESUMEN

INTRODUCTION: There is a need for prodromal markers to diagnose Parkinson's disease (PD) as early as possible. Knowing that most patients with overt PD have abnormal nocturnal movement patterns, we hypothesised that such changes might occur already in non-PD individuals with a potentially high risk for future development of the disease. METHODS: Eleven patients with early PD (Hoehn & Yahr stage ≤2.5), 13 healthy controls and 33 subjects with a high risk of developing PD (HR-PD) were investigated. HR-PD was defined by the occurrence of hyperechogenicity of the substantia nigra in combination with prodromal markers (eg, slight motor signs, olfactory dysfunction). A triaxial accelerometer was used to quantify nocturnal movements during two nights per study participant. Outcome measurements included mean acceleration, and qualitative axial movement parameters, such as duration and speed. RESULTS: Mean acceleration of nocturnal movements was lower in patients with PD compared to controls. Frequency and speed of axial movements did not differ between patients with PD and controls, but mean size and duration were lower in PD. The HR-PD group did not significantly differ from the control group in any of the parameters analysed. CONCLUSIONS: Compared with controls, patients with PD had an overall decreased mean acceleration, as well as smaller and shorter nocturnal axial movements. These changes did not occur in our potential HR-PD individuals, suggesting that relevant axial movement alterations during sleep have either not developed or cannot be detected by the means applied in this at-risk cohort.


Asunto(s)
Movimiento/fisiología , Enfermedad de Parkinson/diagnóstico , Enfermedad de Parkinson/fisiopatología , Sueño/fisiología , Acelerometría , Anciano , Anciano de 80 o más Años , Biomarcadores , Estudios de Casos y Controles , Diagnóstico Precoz , Femenino , Humanos , Masculino , Persona de Mediana Edad , Trastornos del Olfato/complicaciones , Trastornos del Olfato/fisiopatología , Enfermedad de Parkinson/complicaciones , Síntomas Prodrómicos , Sustancia Negra/fisiopatología
SELECCIÓN DE REFERENCIAS
DETALLE DE LA BÚSQUEDA