Dataset Information

Structured relearning of activities of daily living in dementia: the randomized controlled REDALI-DEM trial on errorless learning.

ABSTRACT:

Background

Errorless learning (EL) is a method for optimizing learning, which uses feed-forward instructions in order to prevent people from making mistakes during the learning process. The majority of previous studies on EL taught patients with dementia artificial tasks of little or no relevance for their daily lives. Furthermore, only a few controlled studies on EL have so far been performed and just a handful of studies have examined the long-term effects of EL. Tasks were not always trained in the patients' natural or home environment, limiting the external validity of these studies. This multicenter parallel randomized controlled trial examines the effects of EL compared with trial and error learning (TEL) on the performance of activities of daily living in persons with Alzheimer's or mixed-type dementia living at home.

Methods

Patients received nine 1-hour task training sessions over eight weeks using EL or TEL. Task performance was measured using video observations at week 16. Secondary outcome measures were task performance measured at week 26, satisfaction with treatment, need for assistance, challenging behavior, adverse events, resource utilization and treatment costs.

Results

A total of 161 participants were randomized, of whom 71 completed the EL and 74 the TEL arm at week 11. Sixty-nine EL patients and 71 TEL patients were assessed at the 16-week follow-up (the primary measurement endpoint). Intention-to-treat analysis showed a significantly improved task performance in both groups. No significant differences between the treatment groups were found for primary or secondary outcomes.

Conclusions

Structured relearning improved the performance of activities of daily living. Improvements were maintained for 6 months. EL had no additional effect over TEL.

Trial registration

German Register of Clinical Trials DRKS00003117 . Registered 31 May 2011.

SUBMITTER: Voigt-Radloff S

PROVIDER: S-EPMC5364615 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Structured relearning of activities of daily living in dementia: the randomized controlled REDALI-DEM trial on errorless learning.

Voigt-Radloff Sebastian S de Werd Maartje M E MM Leonhart Rainer R Boelen Danielle H E DH Olde Rikkert Marcel G M MG Fliessbach Klaus K Klöppel Stefan S Heimbach Bernhard B Fellgiebel Andreas A Dodel Richard R Eschweiler Gerhard W GW Hausner Lucrezia L Kessels Roy P C RP Hüll Michael M

Alzheimer's research & therapy 20170323 1

<h4>Background</h4>Errorless learning (EL) is a method for optimizing learning, which uses feed-forward instructions in order to prevent people from making mistakes during the learning process. The majority of previous studies on EL taught patients with dementia artificial tasks of little or no relevance for their daily lives. Furthermore, only a few controlled studies on EL have so far been performed and just a handful of studies have examined the long-term effects of EL. Tasks were not always ...[more]

PMID: 28335810

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Project description:Dementia is a neurological and cognitive condition that affects millions of people around the world. At any given time in the United Kingdom, 1 in 4 hospital beds are occupied by a person with dementia, while about 22% of these hospital admissions are due to preventable causes. In this paper we discuss using Internet of Things (IoT) technologies and in-home sensory devices in combination with machine learning techniques to monitor health and well-being of people with dementia. This will allow us to provide more effective and preventative care and reduce preventable hospital admissions. One of the unique aspects of this work is combining environmental data with physiological data collected via low cost in-home sensory devices to extract actionable information regarding the health and well-being of people with dementia in their own home environment. We have worked with clinicians to design our machine learning algorithms where we focused on developing solutions for real-world settings. In our solutions, we avoid generating too many alerts/alarms to prevent increasing the monitoring and support workload. We have designed an algorithm to detect Urinary Tract Infections (UTI) which is one of the top five reasons of hospital admissions for people with dementia (around 9% of hospital admissions for people with dementia in the UK). To develop the UTI detection algorithm, we have used a Non-negative Matrix Factorisation (NMF) technique to extract latent factors from raw observation and use them for clustering and identifying the possible UTI cases. In addition, we have designed an algorithm for detecting changes in activity patterns to identify early symptoms of cognitive decline or health decline in order to provide personalised and preventative care services. For this purpose, we have used an Isolation Forest (iForest) technique to create a holistic view of the daily activity patterns. This paper describes the algorithms and discusses the evaluation of the work using a large set of real-world data collected from a trial with people with dementia and their caregivers.

Project description:Objectives:To evaluate application of the Barthel Index (BI) in assessing basic activities of daily living (ADL) of patients with dementia using Rasch analysis. Design:A multi-country cross-sectional study. Setting and Participants:Nineteen long-term care facilities located in China, Japan, South Korea, and Thailand. A total of 644 patients with dementia were included. Methods:Unidimensionality, global and item fit, local dependence, person-item targeting, threshold disordering, and differential item functioning (DIF) were examined. Negative correlations between scores for DIF items and Neuropsychiatric Inventory Nursing Home version (NPI-NH) were evaluated. Results:Item reliability (1.0) and person reliability (.88) were acceptable. The Rasch dimension explained 72.9% of the variance (Eigenvalue = 27), while the first contrast explained 6.6% (Eigenvalue = 2.4). The "mobility" was misfitting to the Rasch model (infit mean square = 1.86). The overall difficulty of the BI exceeded patients' ability (person location = -2.27 logits). The "stairs climbing" and "mobility" showed narrow category thresholds (< 1.4 logits). The location of "controlling bladder" and "toilet use" overlapped. Removing "stairs climbing", collapsing categories with narrow threshold widths in "mobility", and combining "controlling bowel" and "controlling bladder" into one item, improved unidimensionality, and item fit of the scale. Only three items ("grooming", "dressing", and "toilet use") were free from DIF across countries. The scores for "feeding" were negatively related to scores for "disinhibition" (r = -0.46, P < 0.01), and scores for "controlling bowel" were negatively related to scores for "disinhibition" (r = -0.44, P < 0.01), "agitation" (r = -0.32, P < 0.05), and "aggression" (r = -0.27, P < 0.01) in Japanese samples. Conclusions and Implications:The performance of the BI for assessing patients with dementia might be compromised by misfit items, person-item mistargeting, measurement gaps, redundant items, narrow threshold width, and item bias. Mobility ability might not be helpful for determining capability of basic ADL in the patients. Comparisons of BI scores between countries should be undertaken with caution due to item bias. Neuropsychiatric symptoms might interact with basic ADL abilities of the patients. We will not suggest using the instrument in patients with dementia, without future refining to improve its performance.

Dataset Information

Structured relearning of activities of daily living in dementia: the randomized controlled REDALI-DEM trial on errorless learning.

Background

Methods

Results

Conclusions

Trial registration

Publications

Structured relearning of activities of daily living in dementia: the randomized controlled REDALI-DEM trial on errorless learning.

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