The Erlangen Test of Activities of Daily Living: first results on reliability and validity of a short performance test to measure fundamental activities of daily living in dementia patients

Background: In the absence of an easily applicable performance test for making valid measurements of fundamental activities of daily living (ADL) in dementia patients, this study reports the development of an ADL performance test which constitutes both a reliable and a valid measurement of the relevant autonomous areas of everyday activities for dementia patients. Methods: The Erlangen Test of Activities of Daily Living (E-ADL-Test) consists of five items: pouring a drink, cutting a piece of bread, opening a small cupboard, washing hands and tying a bow. Each test item underwent standardized evaluation on a scale of 0 to 6. To determine retest reliability each assessment was repeated at two-weekly intervals. The Global Deterioration Scale, Mini-mental State Examination (MMSE) and Nurses' Observations Scale for Geriatric Patients (NOSGER) were used to assess construct validity. Spearman's rank correlation coefficient was applied. Forty-six patients (42 women and 4 men) with clinically diagnosed dementia, who were resident in nursing homes, took part in the validation study. Their average age was 86. Results: The E-ADL-Test revealed good inter-individual differentiation ability, particularly in cases of moderate to severe dementia. Cronbach's α was 0.77, retest reliability 0.73. The correlation coefficients were −0.47 with GDS, 0.60 with NOSGER and 0.72 with MMSE. Conclusions: The E-ADL-Test is a suitable performance test for measuring activities of daily living as it is easy to use, reliable, valid and well accepte

The Nachtlichter app: a citizen science tool for documenting outdoor light sources in public space

The relationship between satellite based measurements of city radiance at night and the numbers and types of physical lights installed on the ground is not well understood. Here we present the "Nachtlichter app", which was developed to enable citizen scientists to classify and count light sources along street segments over large spatial scales. The project and app were co-designed: citizen scientists played key roles in the app development, testing, and recruitment, as well as in analysis of the data. In addition to describing the app itself and the data format, we provide a general overview of the project, including training materials, data cleaning, and the result of some basic data consistency checks