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A pilot study on a specific measure for sleep disorders in Parkinson’s disease: SCOPA-Sleep P. Martínez-Martín a, E. Cubo-Delgado a,b, M. Aguilar-Barberà c, A. Bergareche d, S. Escalante c,
A. Rojo c, J. Campdelacreu c, B. Frades-Payo a, S. Arroyo a, on behalf of the ELEP Group e
A PILOT STUDY ON A SPECIFIC MEASURE FOR SLEEP DISORDERS IN PARKINSON'S DISEASE: SCOPA-SLEEP Summary. Introduction. There is a high prevalence of sleep disorders in Parkinson’s disease (PD). Aims. To assess some basic
metric attributes of the SCOPA-Sleep scale, a measure for PD patients; secondary objective: to check the impact caused by the
sleep disorder on the health-related quality of life (HRQoL) of patients and their caregivers. Subjects and methods. 68 PD
patients and their main caregivers; measures: Hoehn and Yahr staging, SCOPA-Motor, Clinical Impression of Severity Index
(CISI-PD), PDSS, Hospital Anxiety and Depression Scale, SCOPA-Psychosocial, and EuroQoL. Carers filled in a PDSS
questionnaire about patient sleep and HRQoL measures (SF-36, EuroQoL). SCOPA-Sleep acceptability, scaling assumptions,
internal consistency, construct validity and precision were determined. Results. SCOPA-Sleep acceptability and scaling
assumptions resulted satisfactory, although the nocturnal sleep subescale (SC-Ns) showed a mild ceiling effect (22.1%) and a
defective convergent validity was found for daytime sleepiness (SC-Ds) item 6. Internal consistency also was satisfactory for
both scales (alpha = 0.84 and 0.75, respectively). The correlation between SC-Ns and PDSS was high (r = –0.70), as it was
between SC-Ns and PDSS questionnaire by caregiver (r = –0.53). The corresponding coefficients with the SC-Ds gained lower values (r = –0.41 y –0.50). Standard error of measurement was 1.45 for the SC-Ns and 1.76 for the SC-Ds. Both, patient and caregiver HRQoL showed a loose association with the sleep measures. Conclusion. SCOPA-Sleep is a feasible,
consistent, and useful scale for assessment of sleep disorder in PD patients. A weak association between sleep disorder and
HRQoL was found. [REV NEUROL 2006; 43: 577-83]
Key words. Assessment. CISI-PD. Health-related quality of life. Parkinson’s disease. Parkinson’s Disease Rating Scale. SCOPA-
Sleep. Sleep disorder.
INTRODUCTION
such instruments would enable the magnitude of the alterations While clinical manifestations of Parkinson’s disease (PD) typi- and the effect of therapies to be quantified. The problem posed cally include motor disorders, such as tremor, rigidity, hypo- by this deficit will soon be resolved, however: specific scales kinesia, and gait disturbances, there is also a wide variety of for some of these dysfunctions are already available [4-7] and ‘non-motor’ symptoms, to which increasing attention is being there are several initiatives under way aimed at designing a uni- paid. Some noteworthy non-motor symptoms are neuropsychi- fied scale for non-motor symptoms [1,8,9].
atric disturbances, sleep disorders, gastrointestinal and auto- A very frequent problem in PD is upset sleep, which includes nomic manifestations, sensory symptoms, and a miscellany insomnia (difficulty falling or staying asleep at night), parasom- that includes fatigue, visual troubles, seborrhea, and weight nias –such as REM (rapid-eye movement) sleep behavior disor- der–, daytime hypersomnia, and sleep attacks [10-12].
Yet, despite the huge impact these symptoms have on Non-specific scales for assessment of nocturnal sleep, such patients’ overall health and quality of life, they are frequently as the Pittsburgh scale [13], or daytime sleepiness, such as the overlooked. Indeed, this is so even in the specialized setting, Epworth scale [14], have been used for evaluation of sleep dis- where health professionals tend to be more attentive to the motor turbances in PD. In 2002, Chaudhuri et al [4] published the first ever specific scale for evaluation of nocturnal sleep quality in One of the reasons for this situation has been the absence of PD. Recently, this Parkinson’s Disease Sleep Scale (PDSS) has simple, valid measurement instruments for systematic applica- undergone independent validation and cross-cultural adaptation tion in daily practice and clinical research. The availability of to Spain [15]. In 2003, Marinus et al [5] published another spe- guez, F. Rodríguez-Sanz (Segovia), L.J. López del Val (Zaragoza), J. Chacón- Peña, M. Carballo (Sevilla), J.M. Fernández-García (Bilbao), V. Campos- Neuroepidemiology Unit. National Center for Epidemiology. Carlos III Arillo (Málaga), A. Rojo-Sebastián (Terrassa, Barcelona), M. Álvarez-Saúco, Institute of Public Health. Madrid. b Department of Neurology. Nuestra C. Leiva (Alicante), A. Castro, A. Sesar (Santiago de Compostela, A Coru- Señora del Rosario Clinic. Madrid. c Department of Neurology, Mútua de ña), A. Ortega-Moreno (Granada), R. Luquin (Pamplona). Terrassa Hospital. Terrassa, Barcelona, d Department of Neurology. BidasoaHospital. Hondarribia, Guipúzcoa. e Head Researcher: P. Martínez-Martín Corresponding author: Dr. P. Martínez Martín. National Center for Epidemi- (Madrid). Steering Committee: P. Martínez-Martín (Madrid), G. Linazasoro ology. Carlos III Institute of Public Health. Sinesio Delgado, 6. E-28029 (Guipúzcoa), J. Kulisevsky (Barcelona), M. Aguilar-Barberà (Terrassa, Bar- Madrid. Fax: +34 913 877 815. E-mail: pmartinez@isciii.es celona). Technical Committee: J. de Pedro, E. Cubo, M.J. Forjaz, J.M. Fer-nández-Castrillo (Madrid), A. Bergareche (Hondarribia, Guipúzcoa), M. This study was partially funded by the Carlos III Institute of Public Health, Blázquez-Estrada (Oviedo). Members: L. Menéndez-Guisasola, C. Salva- namely: E. Cubo by the CIEN Network of Excellence (C03-06); B. Frades dor-Aguiar, S. González-González (Oviedo), A. Bayes-Rusiñol, F. Vallde- by the IRYSS Network of Excellence (G03-202); and S. Arroyo by the Intra- oriola (Barcelona), B. Frades-Payo, L. Vela-Desojo, J. Benito-León, F. Vi- mural Research Program (ELEP Project: EPY1271/05). vancos-Matellano, M.J. Catalán-Alonso (Madrid), S. García-Muñozguren (Al-bacete), C. Durán-Herrera (Badajoz), J. Duarte-García, A. Mendoza-Rodrí- cific scale for PD (SCOPA-Sleep), designed to evaluate noctur- themselves or by their caregivers [4].The maximum total PDSS score is 150: the lower the score, the worse the quality of sleep.
The main aim of this study was to assess some basic metric attributes of the Spanish-version SCOPA-Sleep scale applied to This scale has two sections, Nocturnal Sleep (SC-NS) and Daytime Sleepi- a series of PD patients. As a secondary objective, it sought to ness (SC-DS), which evaluate problems in these respective domains during analyze the association between sleep disorders and patients’ the ‘last month’. The SC-NS consists of five items addressing trouble and their caregivers’ health-related quality of life (HRQL).
falling asleep, fragmentation and duration of sleep, early waking, and feel-ing of having had too little sleep. Score options for items range from 0 (noproblem) to 3 (a lot of problems), with the limits of the total score being 0 SUBJECTS AND METHODS
and 15. Following this section is a global evaluation of nighttime sleep with This was the first independent study on the metric properties of the SCOPA- seven response options (1, ‘very well’ to 7, ‘very bad’). The SC-DS scale Sleep and a pilot study for the Spanish version. A multicenter, open, cross- evaluates daytime hypersomnia in the preceding month. It includes 6 items sectional, one point-in-time evaluation study.
dealing with the frequency of falling asleep in certain situations (e.g., unex- Consecutive patients older than 40 years, both genders, with diagnosis of pectedly, sitting down peacefully, watching television or reading, or speak- PD as per modified United Kingdom PD Society Brain Bank Criteria [16].
ing to somebody). Each item can score from 0 (never) to 3 (frequently), thus The modifications consisted of considering ‘clear beneficial response to dopaminergic treatment’ (not only to levodopa) and ‘maintained responseto dopaminergic treatment’ (instead of response to levodopa treatment for Hospital Anxiety and Depression Scale (HADS) [24] more than 5 years) as support criteria (Section 3). This is composed of 14 items, seven identifying anxiety and seven for depres- As an additional inclusion criterion, patients were required to have a sta- sion. Each item scores from 0 (no problem) to 3 (extreme problem). Scores ble caregiver, and both patients and carers were required to be ‘able to read, higher than 10 on each subscale are indicative of anxiety or depression, to understand and to answer questionnaires’ in the participant neurologist’s respectively. Marinus et al. [25] report that the HADS’ metric properties mean that it can be applied to PD patients.
Exclusion criteria were defined as the absence of one or more inclusion criteria and the presence of any comorbidity that could interfere with or sig- nificantly modify evaluation of the effects caused by PD (e.g., blindness, This scale was designed to evaluate the psychosocial impact of PD. It con- serious systemic illness, residual hemiplegia, etc.). sists of 11 items, each of which assesses the severity of a particular problem Informed consent was obtained from all participant patients and care- during the preceding month, using a score ranging from 0 (not at all) to 3 givers. This study forms part of the Longitudinal PD Patient Study –Estudio (very much). It includes information on psychosocial functioning and diffi- Longitudinal de pacientes con Enfermedad de Parkinson (ELEP)–, approved culties vis-à-vis daily living and recreational activities, relationships with by the Clinical Research Ethics Committees of the Princesa Hospital family and friends, dependence, isolation and concern about the future.
(Madrid) and the Carlos III Institute of Public Health [17].
Neurologist-based assessments
Intended for use in econometrics, this is an instrument designed to measure HRQL on the basis of preferences. It contains a descriptive part, comprising In the present study, we applied the version included in the Unified Parkin- five items with three answer levels (1 = there are no problems or symptoms, to 3 = problems or severe symptoms). The descriptive system can thus gen-erate 243 different health profiles. To each of these profiles, a preference Mini-Mental State Examination (MMSE) [20] index or social tariff can be assigned, ranging from 1.0 (perfect health state) This test was applied to ascertain the cognitive state of patients included in to 0.0 (death). Such an index is obtained by means of techniques such as time trade-off (the indices used in the present study) or the analogue visual scale. SCOPA-Motor (SC-M) [21]. The SCOPA-Motor scale was designed The EuroQoL also includes a question on the course of respondents’ gen- within a program to develop specific PD measures –Scales for Outcomes eral state of health in the previous 12 months and a visual analogue scale for in Parkinson’s disease (SCOPA)–. It is made up of the following 3 sec- evaluation of their current (‘today’) health state (from 0 = worst imaginable tions: 1) Motor evaluation (‘clinical examination’ subscale, 8 items, and health state, to 100 = best imaginable health state). ‘historical information’ subscale, 2 items); 2) Activities of daily living(ADL) (7 items); and 3) Motor complications (4 items). Each item is scored Caregiver-based assessments
from 0 (normal) to 3 (severe). The average time spent on administering this scale is 8.1 ± 1.9 minutes [21]. A cross-culturally validated Spanish version A questionnaire containing the same items as the PDSS was purpose-designed to obtain an evaluation by caregivers (evaluation by proxy) of sleep distur-bances that might go unnoticed by patients. Clinical Impression of Severity Index (CISI-PD) [23] This is a clinimetric index comprising four items (motor signs, disability, Hospital Anxiety & Depression Scale (HADS) motor complications, and cognitive state) that are scored by the neurologist Administered to assess caregivers’ mood.
after the interview and examination. Each item is scored from 0 (normal)to 6 (severe). An index is obtained from the sum of these scores (range, 0 to 24), which reflects the neurologist’s impression as regards the severity of Administered to assess caregivers’ own perceived health state.
Patient-based assessments
This is a generic measure of health-related quality of life, which includes Parkinson’s Disease Sleep Scale (PDSS) [4] eight dimensions of health state focusing on: This scale is composed of fifteen items, fourteen of which explore seven – Functional aspects, such as physical functioning (10 items), social func- aspects relating to nocturnal sleep, such as global quality of nighttime sleep, tioning (2 items), and role limitations due to physical (4 items) and emo- difficulty falling sleep, presence of hallucinations, nocturia, etc. One item (item 15) evaluates the presence of unexpectedly falling asleep during the – Well-being, which integrates the domains of mental health (5 items), day. The time span explored is the preceding week. On a visual analogue vitality (4 items) and bodily pain (2 items).
scale that runs from ‘always’ (0) to ‘never’ (10), patients indicate their level of disability for each aspect assessed. The scale can be completed by patients – Change in health status over time (1 item).
Table I. Store distribution of the applied measures.
data and their location have been considered. The maximum acceptable lim-it for missing and non-analyzable data is 5% [32]. The acceptability of the measure indicates to what extent the distribution of the scores represents the true distribution of health state in the assessed sample. To determine this property, parameters such as the distance betweenthe mean and the median, floor and ceiling effects (ideally less than 15%) [33] and skewness (acceptable limits: –1 to +1) [34] are taken into account.
Scaling assumptions refer to the correct grouping of items in the corre- sponding scales or dimensions, and to what extent it is appropriate for the respective scores to be directly added to produce a total score representativeof the construct to be measured. To this end, item-total correlation, duly corrected for overlap, was analyzed. A value of 0.40 [35] was taken as theminimum standard limit. Items should demonstrate higher correlations (+ 2 × standard error of the correlation coefficient) with their own scale than with the other in the multitrait analysis [32]. Internal consistency is one of the attributes of a measure’s reliability.
This property is based on the homogeneity (intercorrelation) of the itemsthat comprise the scale. The most appropriate statistic for exploring this property is Cronbach’s α coefficient. A value of 0.70 was taken as the low-er limit for α [35]. Other techniques for ascertaining this attribute are item homogeneity coefficient (the mean of the inter-item correlation coefficients; acceptable lower limit = 0.30) [36] and factor analysis.
Validity assessment tests whether an instrument really measures what it purports to measure. Construct validity refers to the evidence that enablesscores to be interpreted according to the theoretical implications associated with the construct that is being measured; convergent validity refers to the correlation with other accepted measures for the same or related constructs(in which case the coefficients should be high); and divergent or discriminant validity refers to the relationships with variables that measure other unrelatedconstructs (in this case correlation coefficients should be low). We hypothe- sized that there would be: a high correlation between the SC-NS and PDSS(r ≥ 0.60) and a moderate correlation between the SC-DS and PDSS (r = 0.30-0.59); a weak association between SC-Sleep subscales and patients’ age, duration of PD, HY, and MMSE (r = 0.10-0.29); a moderate relationshipbetween SC-Sleep and SC-M, CISI-PD, HADS, SC-PS and EuroQoL (r = 0.30-0.59) [15, 37]; and a high correlation between the SC-NS and the PDSS-based questionnaire completed by caregivers. Since the data did not fit a nor- mal distribution, the Spearman rank correlation coefficient was used. The ability of a measure to detect differences at a point in time among patients who are ranked according to different levels of severity, is known as discriminative validity. This was assessed using the Mann-Whitney andKruskal-Wallis tests, with differences being deemed statistically significant The precision (sensitivity) of a measure is its ability to detect small dif- ferences. The statistic recommended for this purpose is standard error of measurement (SEM = SD × √1 – r , where SD is the standard deviation and r the coefficient of reliability) [38,39]. The association between sleep dysfunction and deterioration in patients’ HRQL was determined by the correlation between PDSS and SC-Sleep scores and EuroQoL and SC-PS parameters. To analyze the impact of patients’ sleep dysfunction on caregiver’s HRQL, sleep scales scores were correlatedwith caregivers’ EuroQoL and SF-36 indices.
MMSE: Mini-Mental State Examination; CISI-PD: Clinical Impression of Seve- rity; Index for Parkinson’s disease; HADS: Hospital Anxiety and Depression A total of sixty-eight PD patients, 61.8% males, were included (Table I).
Scale; PDSS: Parkinson’s Disease Sleep Scale; SC-NS: SCOPA-Nocturnal sleep;SC-DS: SCOPA-Daytime sleepiness; SD: standard deviation According to HY, the patients distribution was as follows: stage 1, 10.6%;stage 1.5, 6.1%; stage 2, 59.1%; stage 2.5, 9.1%; stage 3, 7.6%; stage 4, 4.5%;and stage 5, 3.0%. Patients were receiving treatment with: levodopa, 82.35%;dopamine agonists, 63.24%; selegiline, 13.24%; amantadine, 2.94%; and apo- For each dimension, scores are standardized, ranging from 0 (worst health morphine, 1.47%. Their level of education was: university or equivalent, 13.4%; state) to 100 (best health state). Finally, the individual dimension scores are high school, 20.9%; primary, 53.7%; and no formal education, 11.9%. combined to provide a physical and mental component index [31].
The mean age of caregivers, 77.3%, women, was 62.9 ± 12.3 years. Their level of education was: university, 21.5%; high school, 21.5%; primary, The following metric attributes of the SC-Sleep were analyzed: acceptability; The descriptive statistics of the scales applied to or used by the patients scaling assumptions; internal consistency; construct validity; and precision. are shown in table I. A total of 39 caregivers were requested to complete the Data quality refers to the instrument’s fitness for use in a clinical context PDSS-based questionnaire on patients’ sleep (mean score: 96.1 ± 31.5; range: and is determined by the proportion of fully computable data, after missing One patient failed to answer SC-DS items 5 and 6 (missing data, 1.5%; Table II. SCOPA-Sleep scaling assumptions (n = 67).
computable, 98.5%). All SC-NS data were available (100%). Accordingly,data quality was satisfactory. The scores registered for all SC-Sleep items covered the complete theo- retical range. In contrast, the total score of both subscales failed to reach the higher theoretical score limit (Table I). The distance of the mean to the median was 0.63/15 (4.2%) for the SC-NS and 0.55/18 (3.05%) for the SC-DS. Although the SC-NS displayed no floor effect (5.90%), it nevertheless showed a mild ceiling effect (22.1%), with the corresponding values for theSC-DS being 3.0% and 10.45%, respectively. Skewness proved to be 0.47 for the SC-NS and 1.20 for the SC-DS. To sum up, a slight ceiling effect forthe SC-NS and skewness for the SC-DS were observed.
Item-total correlations were higher than the standard, 0.40 [35], except for item 6 of the SC-DS (r = 0.21), which registered substandard convergentvalidity (Table II). Hence, with single exception of SC-DS item 6, all items on both subscales were deemed to fit the scaling assumptions (Table II).
Cronbach α coefficient values were 0.84 for the SC-NS and 0.75 for the SC-DS, with item homogeneity coefficient values of 0.52 and 0.36, respec-tively. All these coefficients proved higher than the established minimum limit. The exploratory factor analysis (principal components, orthogonal rota- tion) showed one factor explaining 62% of the variance in the SC-NS, andtwo factors explaining 68% of the variance in the SC-DS. The first of these latter two factors comprised the first three items of the SC-DS (falling asleepunexpectedly, falling asleep while sitting peacefully, falling asleep while watching television or reading), and the second comprised the last three items(falling asleep while talking to someone, problems staying awake during day, and experiencing falling asleep during the day as a problem).
Correlation coefficients between the SCOPA-Sleep subscales and the other measures applied in the study are shown in the table III. In line with a Spearman rank correlation coefficients (rs standard error = 0.12). SC-NS: SCOPA- our working hypothesis, the correlation between the SC-NS and PDSS Nocturnal sleep; SC-DS: SCOPA-Daytime sleepiness.
(which also measures quality of the nocturnal sleep) was high (r = –0.70), and the relationship between the SC-DS and PDSS was moderate (r = –0.41). The SC-NS registered moderate associations (r = 0.30-0.59) with Table III. Correlation a between SCOPA-Sleep and the other measures
the HADS (anxiety and depression sections) and Motor complications of the SC-M. The SC-DS displayed moderate coefficient values with HY and theCISI-PD (Table III). The remaining correlations were weak. No significant association was observed between sleep scales scores (including the PDSS)and patients’ age or disease duration.
The SC-NS showed a significant correlation with the question on global evaluation of nocturnal sleep (r = 0.81) and with item 1 (global quality of night sleep) of the PDSS (r = –0.65, p <0.0001). The correlation between SC-DS and PDSS item 15 (unexpectedly falling asleep during the day) wasmoderate (r = –0.52, p <0.0001), as was the correlation between SCOPA- Sleep and the PDSS-based questionnaire completed by caregivers (r = –0.50 with the SC-NS; r = –0.53 with the SC-DS) (Table III).
There were no significant gender-related differences in the SCOPA-Sleep scores. The SC-NS score displayed a non-statistically significant rising trend as HY stage increased. The SC-DS registered a non-linear trend, with highest values in stage 3 (7.75 points) and inferior values in the lower and higherstages (e.g., 2.4 in stage 1 and 5.5 in stage 5) (Kruskal-Wallis, p = 0.03). Mean SC-NS scores increased significantly with global evaluation of night sleep (Table IV) (Kruskal-Wallis, p <0.0001). The SEM was 1.45 for The correlation coefficients between patients’ HRQL measures and sleep rating scales (both SC-Sleep and PDSS) were weak overall (r = –0.06 at –0.27). The SC-NS and PDSS showed a moderate association with the SC-PS (r = 0.37 and –0.36; p = 0.002 and 0.004, respectively). With respect to the impact of patients’ sleep dysfunction on caregivers’ HRQL, the correlation between patients’ sleep rating scales and caregivers’ HRQL measures ranged from –0.01 (SC-DS and the physical component of the SF-36) to –0.23 (SC-DS and the EuroQoL tariff). The PDSS-based ques- Spearman rank correlation coefficient. CISI-PD: Clinical Impression of Severity Index for Parkinson's Disease; HADS: Hospital Anxiety and Depression Scale; tionnaire completed by caregivers correlated moderately with the EuroQoL PDSS: Parkinson's Disease Sleep Scale; SC-NS: SCOPA-Nocturnal sleep; SC-DS: tariff (r = 0.34, p <0.05) and weakly with the other caregiver HRQL parame- ters (r = 0.03-0.29; p = n.s.). DISCUSSION
has led to the design of numerous evaluation methods over the Valid, specific measures are required to assess the diversity of last five decades [40]. Recent years have witnessed increasing manifestations that may be present in PD patients. This need recognition of the importance of a complete evaluation that Table IV. SCOPA-Nocturnal sleep score distribution by the anchor question.
As hypothesized, a close association was found between each SC-Sleep subscale and the respective PDSS parameters for nocturnal sleep and daytime hypersomnia. The correlation between SC-NS and the question on global evaluation of night sleep proved similar to that of the original study (0.81vs. 0.85) [5]. The convergent validity of the SC-Sleep scale is therefore viewed as satisfactory. As for the other measures, the SC-NS showed moderate correlations with mood distur-bances and motor complications. In addition, a moderate asso- ciation was found between the SC-DS and PD severity meas- ures, suggesting that nocturnal and daytime sleep dysfunctionshave different relationships with the range of aspects evaluated As in the original study [5], the SC-Sleep failed to identify significant differences among patients with different levels of Test de Kruskal-Wallis, p < 0.0001.
severity or disease duration. Similarly, these differences werenot observed when the PDSS was used, either in this or in otherprevious studies [15]. This suggests that: 1) relationships between encompasses the great variety of non-motor manifestations that sleep dysfunction and disease severity, motor or cognitive status can affect patients’ quality of life [1-3,41]. tend to be loose; 2) the type of sleep disturbance could change Practically all PD patients suffer night sleep disturbances over time without significantly modifying total scale scores; and/or day hypersomnia [4,41]. Useful instruments, capable or 3) sleep disturbances are present from the beginning of the of reflecting the type and severity of these dysfunctions and disease and do not increase despite the progression of the dis- their response to therapeutic strategies, are therefore regarded The SC-NS displayed excellent discriminative validity vis- The first specific scale for assessing sleep disorders in PD à-vis global evaluation of night sleep. The lack of a similar anchor (PDSS) was published by Chaudhuri et al in 2002 [4]. Subse- question in the SC-DS means that this particular attribute can- quently, the validation of the PDSS was completed in an inde- not be explored in the same way for this subscale.
pendent study conducted in Spain, after the necessary cross- The influence of sleep disturbances on PD patients’ HRQL cultural adaptation [15]. Marinus et al published another spe- has been highlighted [43-45], but this relationship has yielded cific scale for evaluation of sleep disturbances in PD, known as low-to-moderate correlation coefficients between specific the SC-Sleep [5]. To our knowledge, this scale has, as yet, nei- measures that evaluated both aspects (PDQ-39 and PDSS) in ther been subjected to independent validation nor been adapted previous studies (|r | = 0.26-0.39) [15,46]. In the present study, for use in a Spanish setting. The main objective of this study, while a moderate correlation was observed between the sleep albeit preliminary, was to assess some basic metric attributes of scales (SC-NS and PDSS) and the SC-PS, the correlation between both scales and the EuroQoL was low or nonexistent. Further Analysis of data quality and acceptability shows that the studies are called for, in order to apply the data furnished by the SC-Sleep is a viable scale, with a mild ceiling effect in the SC- new specific measures and thereby enhance our knowledge of NS domain (22.1%), in line with the data reported in the origi- Although patients’ sleep disorders influence caregivers’ sleep In our study, item 6 of the SC-DS was shown by the scaling and quality of life [47], the present study failed to find a signif- assumptions analysis to be substandard. In contrast, the study icant association between patients’ sleep disorders and care- by Marinus et al [5] showed that all the item-total correlation givers’ HRQL. However, a PDSS questionnaire adapted for coefficients exceeded the standard criterion of 0.40. Neverthe- proxy assessment showed that there was a moderate relation- less, in view of the differences in size and characteristic of the ship between the EuroQoL index and caregiver evaluation of two samples, no conclusion can be drawn on this point.
Both the SC-NS and SC-DS obtained α and item-homo- The limitations of this study are linked to the characteristics geneity coefficients higher than the established limit, demon- of the sample, with scant representation of patients in the most strating that their internal consistency is satisfactory. However, advanced stages of the disease and those with the most severe there was a qualitative difference with respect to the findings by sleep disturbances. These facts limit the generalizability of the Marinus et al [5], according to which α was almost equivalent for results. Yet the quality of the relevant SC-Sleep metric attrib- the two subscales (difference = 0.03), with it being slightly high- utes, assessment of which constituted the main objective of this er for the SC-DS. Yet, in our study, not only was the difference pilot study, was nevertheless confirmed. Stability of the meas- between the subscales greater (0.09), but it was also in favour of the SC-NS. At all events, both studies coincide in substantiating The SC-Sleep is a viable scale, with appropriate scaling the reliability of the two subscales. While the exploratory factor assumptions, internal consistency, and construct validity. On the analysis confirmed the unidimensionality of the SC-NS, the fol- whole, the impact of sleep dysfunctions on patients’ and care- lowing two factors were identified in the SC-DS: the first givers’ HRQL proved to be low, yet these relationships should included items 1 to 3 and could be defined as ‘drowsiness in be explored by means of specific studies, which have a design inactivity’; and the second contained items 4 to 6 and was relat- different to ours and implement newly-developed specific meas- ed to ‘inappropriate daytime sleepiness’.
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ESPECÍFICA PARA LOS TRASTORNOS DEL SUEÑO ESPECÍFICA PARA AS PERTURBAÇÕES DO SONO DE LA ENFERMEDAD DE PARKINSON: SCOPA-SUEÑO ASSOCIADAS À DOENÇA DE PARKINSON: SCOPA-SONO Resumen. Introducción. En la enfermedad de Parkinson (EP) exis-
Resumo. Introdução. A doença de Parkinson (DP) associa-se a uma
te una alta prevalencia de trastornos del sueño. Objetivos. Compro- elevada prevalência de perturbações do sono. Objectivos. Comprovar bar los atributos métricos básicos de la escala SCOPA-sueño para os atributos métricos básicos da escala SCOPA-sono para doentes pacientes con EP; objetivo secundario: analizar el impacto del tras- com DP; objectivo secundário: analisar o impacto das perturbaçõ- torno del sueño en la calidad de vida relacionada con la salud es do sono na qualidade de vida relacionada com a saúde (QVRS) (CVRS) del paciente y de su cuidador principal. Sujetos y métodos.
do doente e do seu principal cuidador. Sujeitos e métodos. Foram 68 pacientes con EP y sus cuidadores principales. Se aplicaron: estudados 68 doentes com DP e respectivos cuidadores. Aplicaram- Hoehn y Yahr, SCOPA-motor, impresión clínica de gravedad (CISI- se as escalas: Hoehn e Yahr, SCOPA-motor, Clinical Impression of PD), escala PDSS, Hospital Anxiety and Depression Scale, SCO- Severity Index for Parkinson’s Disease (CISI-PD), escala PDSS, PA-psicosocial y EuroQoL. El cuidador cumplimentó un cuestiona- Hospital Anxiety and Depression Scale, SCOPA-psicosocial e Euro- rio PDSS sobre el sueño del paciente y las medidas de la CVRS QoL. O cuidador preencheu um questionário PDSS sobre o sono do (SF-36, EuroQoL). Se analizaron la aceptabilidad, las asunciones doente e as medidas da QVRS (SF-36, EuroQoL). Foram analisadas escalares, la consistencia interna, la validez de constructo y la pre- a aceitabilidade, as assunções escalares a consistência interna, a cisión de la SCOPA-sueño. Resultados. La SCOPA-sueño mostró validade de construção e a precisão da SCOPA-sono. Resultados. A aceptabilidad satisfactoria y asunciones escalares. La subescala SCOPA-sono revelou aceitabilidade satisfatória e assunções das es- sueño nocturno (SC-Sn) presentó leve efecto techo (22,1%), y la calas. A subescala sono nocturno (SC-Sn) apresentou um discreto subescala somnolencia diurna (SC-Sd), defectuosa validez conver- efeito tecto (22,1%) e a subescala sonolência diurna (SC-Sd) uma gente del ítem 6; la consistencia interna de ambas resultó satisfac- validade convergente imperfeita do item 6; a consistência interna de toria (alfa = 0,84 y 0,75, respectivamente). SC-Sn correlacionó sig- ambas resultou satisfatória (alfa = 0,84 e 0,75, respectivamente). nificativamente con la PDSS (r = –0,70) y con el cuestionario PDSS SC-Sn correlacionou-se significativamente com a PDSS (r = –0,70) cumplimentado por el cuidador (r = –0,53), y fueron menores los e com o questionário PDSS preenchido pelo cuidador (r = –0,53), e valores respectivos para la SC-Sd (r = –0,41 y –0,50). Error están- foram menores os valores respectivos para a SC-Sd (r = –0,41 dar de la medida: SC-Sn, 1,45; SC-Sd, 1,76. La CVRS del paciente e –0,50). O erro standard das medidas foi: SC-Sn, 1,45; SC-Sd, y la del cuidador mostraron una escasa correlación con las me- 1,76. A QVRS do doente e do cuidador revelou uma ténue correla- didas de sueño. Conclusiones. La escala SCOPA-sueño es viable, ção com as medidas do sono. Conclusões. A escala SCOPA-sono é consistente y útil para evaluar el trastorno del sueño en pacientes viável, consistente e útil para avaliar a perturbação do sono em con EP. La relación entre la CVRS y la alteración del sueño fue doentes com DP. Detectou-se uma ténue relação entre a QVRS e a débil. [REV NEUROL 2006; 43: 577-83] alteração do sono. [REV NEUROL 2006; 43: 577-83] Palabras clave. Calidad de vida relacionada con la salud. CISI-PD.
Palavras chave. Avaliação. CISI-PD. Doença de Parkinson. Esca-
Enfermedad de Parkinson. Evaluación. Parkinson’s Disease Rating la para avaliação da doença de Parkinson. Perturbação do sono. Scale. SCOPA-sueño. Trastorno del sueño. Qualidade de vida relacionada com a saúde. SCOPA-sono.

Source: http://www.scopa-propark.eu/pdfdocs/RevNeuro.pdf