| 1. |
- Marmstål Hammar, Lena, 1979-, et al.
(författare)
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Being Treated With Respect and Dignity? : Perceptions of Home Care Service Among Persons With Dementia
- 2021
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Ingår i: Journal of the American Medical Directors Association. - New York : Elsevier. - 1525-8610 .- 1538-9375. ; 22:3, s. 656-662
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Studies on the quality of home care services (HCS) offered to persons with dementia (PwDs) reveal the prevalence of unmet needs and dissatisfaction related to encounters and a lack of relationships with staff. The objective of this study was to enhance knowledge of the perceptions of PwDs regarding their treatment with dignity and respect in HCS over time.Design: A mixed longitudinal cohort study was designed to study trends in the period between 2016 and 2018 and compare the results between PwDs (cases) and persons without dementia (controls) living at home with HCS.Setting and Participants: Persons aged 65 years and older with HCS in Sweden.Methods: Data from an existing yearly HCS survey by the Swedish National Board of Health and Welfare (NBHW) was used. The focus was on questions concerning dignity and respect. NBHW data sets on diagnoses, medications, HCS hours, and demographic information were also used. We applied GEE logistic and cumulative logit regression models to estimate effects and trends of interest after controlling for the effects of age, gender, self-rated health, and number of HCS hours.Results: Over the study period, 271,915 (PwDs¼8.1%) respondents completed the survey. The results showed that PwDs were significantly less likely (3%-10% lower odds and cumulative odds) than controls to indicate that they were satisfied in response to questions related to dignity and respect. Both groups experienced a decrease in satisfaction from 2016 to 2018. Females, individuals with poor self-rated health, and individuals granted more HCS hours were found to be more dissatisfied.Conclusions and Implications: The HCS organization needs to shift from a task-oriented system to a person-centered approach, where dignity and respect are of the utmost importance. The HCS organizations need to be developed to focus on competence in person-centered care, and leadership to support staff.
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| 2. |
- Thomas, Ilias, et al.
(författare)
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Sensor-based algorithmic dosing suggestions for oral administration of levodopa/carbidopa microtablets for Parkinson's disease : a first experience
- 2019
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Ingår i: Journal of Neurology. - : Springer Science and Business Media LLC. - 0340-5354 .- 1432-1459. ; 266:3, s. 651-658
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE: Dosing schedules for oral levodopa in advanced stages of Parkinson's disease (PD) require careful tailoring to fit the needs of each patient. This study proposes a dosing algorithm for oral administration of levodopa and evaluates its integration into a sensor-based dosing system (SBDS).MATERIALS AND METHODS: In collaboration with two movement disorder experts a knowledge-driven, simulation based algorithm was designed and integrated into a SBDS. The SBDS uses data from wearable sensors to fit individual patient models, which are then used as input to the dosing algorithm. To access the feasibility of using the SBDS in clinical practice its performance was evaluated during a clinical experiment where dosing optimization of oral levodopa was explored. The supervising neurologist made dosing adjustments based on data from the Parkinson's KinetiGraph™ (PKG) that the patients wore for a week in a free living setting. The dosing suggestions of the SBDS were compared with the PKG-guided adjustments.RESULTS: The SBDS maintenance and morning dosing suggestions had a Pearson's correlation of 0.80 and 0.95 (with mean relative errors of 21% and 12.5%), to the PKG-guided dosing adjustments. Paired t test indicated no statistical differences between the algorithmic suggestions and the clinician's adjustments.CONCLUSION: This study shows that it is possible to use algorithmic sensor-based dosing adjustments to optimize treatment with oral medication for PD patients.
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| 3. |
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| 4. |
- Thomas, Ilias, et al.
(författare)
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A treatment–response index from wearable sensors for quantifying Parkinson's disease motor states
- 2018
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Ingår i: IEEE journal of biomedical and health informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 2168-2194 .- 2168-2208. ; 22:5, s. 1341-1349
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Tidskriftsartikel (refereegranskat)abstract
- The goal of this study was to develop an algorithm that automatically quantifies motor states (off,on,dyskinesia) in Parkinson's disease (PD), based on accelerometry during a hand pronation-supination test. Clinician's ratings using the Treatment Response Scale (TRS), ranging from -3 (very Off) to 0 (On) to +3 (very dyskinetic), was used as target. For that purpose, 19 participants with advanced PD and 22 healthy persons were recruited in a single center open label clinical trial in Uppsala, Sweden. The trial consisted of single levodopa dose experiments for the people with PD (PwP), where participants were asked to perform standardized wrist rotation tests, using each hand, before and at pre-specified time points after the dose. The participants used wrist sensors containing a 3D accelerometer and gyroscope. Features to quantify the level, variation and asymmetry of the sensor signals, three-level Discrete Wavelet Transform features and approximate entropy measures were extracted from the sensors data. At the time of the tests, the PwP were video recorded. Three movement disorder specialists rated the participants’ state on the TRS scale. A Treatment Response Index from Sensors (TRIS) was constructed to quantify the motor states based on the wrist rotation tests. Different machine learning algorithms were evaluated to map the features derived from the sensor data to the ratings provided by the three specialists. Results from cross validation, both in 10-fold and a leave-one-individual out setting, showed good predictive power of a support vector machine model and high correlation to the TRS scale. Values at the end tails of the TRS scale were under and over predicted due to the lack of observations at those values but the model managed to accurately capture the dose - effect profiles of the patients. In addition, the TRIS had good test-retest reliability on the baseline levels of the PD participants (Intraclass correlation coefficient of 0.83) and reasonable sensitivity to levodopa treatment (0.33 for the TRIS). For a series of test occasions the proposed algorithms provided dose - effect time profiles for participants with PD, which could be useful during therapy individualization of people suffering from advanced PD
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| 5. |
- Alam, Md. Moudud, et al.
(författare)
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Computationally feasible estimation of the covariance structure in generalized linear mixed models
- 2008
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Ingår i: Journal of Statistical Computation and Simulation. - London : Taylor & Francis. - 0094-9655 .- 1563-5163. ; 78:12, s. 1229-1239
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we discuss how a regression model, with a non-continuous response variable, which allows for dependency between observations, should be estimated when observations are clustered and measurements on the subjects are repeated. The cluster sizes are assumed to be large.We find that the conventional estimation technique suggested by the literature on generalized linear mixed models(GLMM) is slow and sometimes fails due to non-convergence and lack of memory on standard PCs.We suggest to estimate the random effects as fixed effects by generalized linear model and to derive the covariance matrix from these estimates.A simulation study shows that our proposal is feasible in terms of mean-square error and computation time.We recommend that our proposal be implemented in the software of GLMM techniques so that the estimation procedure can switch between the conventional technique and our proposal, depending on the size of the clusters.
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| 6. |
- Alam, Md. Moudud
(författare)
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Feasible computation of generalized linear mixed models with application to credit risk modelling
- 2010
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis deals with developing and testing feasible computational procedures to facilitate the estimation of and carry out the prediction with the generalized linear mixed model (GLMM) with a scope of applying them to large data sets. The work of this thesis is motivated from an issue arising incredit risk modelling. We have access to a huge data set, consisting of about one million observations, on credit history obtained from two major Swedish banks. The principal research interest involved with the data analysis is to model the probability of credit defaults by incorporating the systematic dependencies among the default events. In order to model the dependent credit defaults we adopt the framework of GLMM which is apopular approach to model correlated binary data. However, existing computational procedures for GLMM did not offer us the flexibility to incorporate the desired correlation structure of defaults events.For the feasible estimation of the GLMM we propose two estimation techniques being the fixed effects (FE) approach and the two-step pseudolikelihood approach (2PL). The preciseness of the estimation techniques and their computational advantages are studied by Monte-Carlo simulations and by applying them to the credit risk modelling. Regarding the prediction issue, we show how to apply the likelihood principle to carryout prediction with GLMM. We also provide an R add-in package to facilitate the predictive inference for GLMM.
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| 7. |
- Alam, Md. Moudud
(författare)
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Feasible estimation of generalized linear mixed models (GLMM) with weak dependency between groups
- 2010
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- This paper presents a two-step pseudo likelihood estimation for generalized linear mixed models with the random effects being correlated between groups. The core idea is to deal with the random intractable integrals in the likelihood function by multivariate Taylor's approximation. The accuracy of the estimation technique is assessed in a Monte-Carlo study: An application of it with binary response variable is presented using a real dara set on credit defaults from two Swedish banks. Thanks to the use of two-step estimation technique, the proposed algorithm outperforms conventional likelihood algoritms in terms of computational time.
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| 8. |
- Alam, Md Moudud, 1976-
(författare)
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Likelihood Prediction for Generalized Linear Mixed Models under Covariate Uncertainty
- 2014
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Ingår i: Communications in Statistics - Theory and Methods. - : Informa UK Limited. - 0361-0926 .- 1532-415X. ; 43:2, s. 219-234
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Tidskriftsartikel (refereegranskat)abstract
- This article presents the techniques of likelihood prediction for the generalized linear mixed models. Methods of likelihood prediction are explained through a series of examples; from a classical one to more complicated ones. The examples show, in simple cases, that the likelihood prediction (LP) coincides with already known best frequentist practice such as the best linear unbiased predictor. This article outlines a way to deal with the covariate uncertainty while producing predictive inference. Using a Poisson errors-in-variable generalized linear model, it has been shown in certain cases that LP produces better results than already known methods.
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