@article{HebestreitZeidlerSchippersetal.2022,
  author    = {Hebestreit, Helge and Zeidler, Cornelia and Schippers, Christopher and de Zwaan, Martina and Deckert, J{\"u}rgen and Heuschmann, Peter and Krauth, Christian and Bullinger, Monika and Berger, Alexandra and Berneburg, Mark and Brandstetter, Lilly and Deibele, Anna and Dieris-Hirche, Jan and Graessner, Holm and G{\"u}ndel, Harald and Herpertz, Stephan and Heuft, Gereon and Lapstich, Anne-Marie and L{\"u}cke, Thomas and Maisch, Tim and Mundlos, Christine and Petermann-Meyer, Andrea and M{\"u}ller, Susanne and Ott, Stephan and Pfister, Lisa and Quitmann, Julia and Romanos, Marcel and Rutsch, Frank and Schaubert, Kristina and Schubert, Katharina and Schulz, J{\"o}rg B. and Schweiger, Susann and T{\"u}scher, Oliver and Ungeth{\"u}m, Kathrin and Wagner, Thomas O. F. and Haas, Kirsten},
  title     = {Dual guidance structure for evaluation of patients with unclear diagnosis in centers for rare diseases (ZSE-DUO): study protocol for a controlled multi-center cohort study},
  series = {Orphanet Journal of Rare Diseases},
  volume    = {17},
  journal   = {Orphanet Journal of Rare Diseases},
  number    = {1},
  doi       = {10.1186/s13023-022-02176-1},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-300440},
  year      = {2022},
  abstract  = {Background In individuals suffering from a rare disease the diagnostic process and the confirmation of a final diagnosis often extends over many years. Factors contributing to delayed diagnosis include health care professionals' limited knowledge of rare diseases and frequent (co-)occurrence of mental disorders that may complicate and delay the diagnostic process. The ZSE-DUO study aims to assess the benefits of a combination of a physician focusing on somatic aspects with a mental health expert working side by side as a tandem in the diagnostic process. Study design This multi-center, prospective controlled study has a two-phase cohort design. Methods Two cohorts of 682 patients each are sequentially recruited from 11 university-based German Centers for Rare Diseases (CRD): the standard care cohort (control, somatic expertise only) and the innovative care cohort (experimental, combined somatic and mental health expertise). Individuals aged 12 years and older presenting with symptoms and signs which are not explained by current diagnoses will be included. Data will be collected prior to the first visit to the CRD's outpatient clinic (T0), at the first visit (T1) and 12 months thereafter (T2). Outcomes Primary outcome is the percentage of patients with one or more confirmed diagnoses covering the symptomatic spectrum presented. Sample size is calculated to detect a 10 percent increase from 30\% in standard care to 40\% in the innovative dual expert cohort. Secondary outcomes are (a) time to diagnosis/diagnoses explaining the symptomatology; (b) proportion of patients successfully referred from CRD to standard care; (c) costs of diagnosis including incremental cost effectiveness ratios; (d) predictive value of screening instruments administered at T0 to identify patients with mental disorders; (e) patients' quality of life and evaluation of care; and f) physicians' satisfaction with the innovative care approach. Conclusions This is the first multi-center study to investigate the effects of a mental health specialist working in tandem with a somatic expert physician in CRDs. If this innovative approach proves successful, it will be made available on a larger scale nationally and promoted internationally. In the best case, ZSE-DUO can significantly shorten the time to diagnosis for a suspected rare disease.},
  language  = {en}
}
@article{ZieglerMeyerOtteetal.2022,
  author    = {Ziegler, Alice and Meyer, Hanna and Otte, Insa and Peters, Marcell K. and Appelhans, Tim and Behler, Christina and B{\"o}hning-Gaese, Katrin and Classen, Alice and Detsch, Florian and Deckert, J{\"u}rgen and Eardley, Connal D. and Ferger, Stefan W. and Fischer, Markus and Gebert, Friederike and Haas, Michael and Helbig-Bonitz, Maria and Hemp, Andreas and Hemp, Claudia and Kakengi, Victor and Mayr, Antonia V. and Ngereza, Christine and Reudenbach, Christoph and R{\"o}der, Juliane and Rutten, Gemma and Schellenberger Costa, David and Schleuning, Matthias and Ssymank, Axel and Steffan-Dewenter, Ingolf and Tardanico, Joseph and Tschapka, Marco and Vollst{\"a}dt, Maximilian G. R. and W{\"o}llauer, Stephan and Zhang, Jie and Brandl, Roland and Nauss, Thomas},
  title     = {Potential of airborne LiDAR derived vegetation structure for the prediction of animal species richness at Mount Kilimanjaro},
  series = {Remote Sensing},
  volume    = {14},
  journal   = {Remote Sensing},
  number    = {3},
  issn      = {2072-4292},
  doi       = {10.3390/rs14030786},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-262251},
  year      = {2022},
  abstract  = {The monitoring of species and functional diversity is of increasing relevance for the development of strategies for the conservation and management of biodiversity. Therefore, reliable estimates of the performance of monitoring techniques across taxa become important. Using a unique dataset, this study investigates the potential of airborne LiDAR-derived variables characterizing vegetation structure as predictors for animal species richness at the southern slopes of Mount Kilimanjaro. To disentangle the structural LiDAR information from co-factors related to elevational vegetation zones, LiDAR-based models were compared to the predictive power of elevation models. 17 taxa and 4 feeding guilds were modeled and the standardized study design allowed for a comparison across the assemblages. Results show that most taxa (14) and feeding guilds (3) can be predicted best by elevation with normalized RMSE values but only for three of those taxa and two of those feeding guilds the difference to other models is significant. Generally, modeling performances between different models vary only slightly for each assemblage. For the remaining, structural information at most showed little additional contribution to the performance. In summary, LiDAR observations can be used for animal species prediction. However, the effort and cost of aerial surveys are not always in proportion with the prediction quality, especially when the species distribution follows zonal patterns, and elevation information yields similar results.},
  language  = {en}
}
@article{GruendahlWeissMaieretal.2022,
  author    = {Gr{\"u}ndahl, Marthe and Weiß, Martin and Maier, Lisa and Hewig, Johannes and Deckert, J{\"u}rgen and Hein, Grit},
  title     = {Construction and validation of a scale to measure loneliness and isolation during social distancing and its effect on mental health},
  series = {Frontiers in Psychiatry},
  volume    = {13},
  journal   = {Frontiers in Psychiatry},
  issn      = {1664-0640},
  doi       = {10.3389/fpsyt.2022.798596},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-269446},
  year      = {2022},
  abstract  = {A variety of factors contribute to the degree to which a person feels lonely and socially isolated. These factors may be particularly relevant in contexts requiring social distancing, e.g., during the COVID-19 pandemic or in states of immunodeficiency. We present the Loneliness and Isolation during Social Distancing (LISD) Scale. Extending existing measures, the LISD scale measures both state and trait aspects of loneliness and isolation, including indicators of social connectedness and support. In addition, it reliably predicts individual differences in anxiety and depression. Data were collected online from two independent samples in a social distancing context (the COVID-19 pandemic). Factorial validation was based on exploratory factor analysis (EFA; Sample 1, N = 244) and confirmatory factor analysis (CFA; Sample 2, N = 304). Multiple regression analyses were used to assess how the LISD scale predicts state anxiety and depression. The LISD scale showed satisfactory fit in both samples. Its two state factors indicate being lonely and isolated as well as connected and supported, while its three trait factors reflect general loneliness and isolation, sociability and sense of belonging, and social closeness and support. Our results imply strong predictive power of the LISD scale for state anxiety and depression, explaining 33 and 51\% of variance, respectively. Anxiety and depression scores were particularly predicted by low dispositional sociability and sense of belonging and by currently being more lonely and isolated. In turn, being lonely and isolated was related to being less connected and supported (state) as well as having lower social closeness and support in general (trait). We provide a novel scale which distinguishes between acute and general dimensions of loneliness and social isolation while also predicting mental health. The LISD scale could be a valuable and economic addition to the assessment of mental health factors impacted by social distancing.},
  language  = {en}
}
@article{HaberstumpfForsterLeinweberetal.2022,
  author    = {Haberstumpf, Sophia and Forster, Andr{\´e} and Leinweber, Jonas and Rauskolb, Stefanie and Hewig, Johannes and Sendtner, Michael and Lauer, Martin and Polak, Thomas and Deckert, J{\"u}rgen and Herrmann, Martin J.},
  title     = {Measurement invariance testing of longitudinal neuropsychiatric test scores distinguishes pathological from normative cognitive decline and highlights its potential in early detection research},
  series = {Journal of Neuropsychology},
  volume    = {16},
  journal   = {Journal of Neuropsychology},
  number    = {2},
  doi       = {10.1111/jnp.12269},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-318932},
  pages     = {324 -- 352},
  year      = {2022},
  abstract  = {Objective Alzheimer's disease (AD) is a growing challenge worldwide, which is why the search for early-onset predictors must be focused as soon as possible. Longitudinal studies that investigate courses of neuropsychological and other variables screen for such predictors correlated to mild cognitive impairment (MCI). However, one often neglected issue in analyses of such studies is measurement invariance (MI), which is often assumed but not tested for. This study uses the absence of MI (non-MI) and latent factor scores instead of composite variables to assess properties of cognitive domains, compensation mechanisms, and their predictability to establish a method for a more comprehensive understanding of pathological cognitive decline. Methods An exploratory factor analysis (EFA) and a set of increasingly restricted confirmatory factor analyses (CFAs) were conducted to find latent factors, compared them with the composite approach, and to test for longitudinal (partial-)MI in a neuropsychiatric test battery, consisting of 14 test variables. A total of 330 elderly (mean age: 73.78 ± 1.52 years at baseline) were analyzed two times (3 years apart). Results EFA revealed a four-factor model representing declarative memory, attention, working memory, and visual-spatial processing. Based on CFA, an accurate model was estimated across both measurement timepoints. Partial non-MI was found for parameters such as loadings, test- and latent factor intercepts as well as latent factor variances. The latent factor approach was preferable to the composite approach. Conclusion The overall assessment of non-MI latent factors may pose a possible target for this field of research. Hence, the non-MI of variances indicated variables that are especially suited for the prediction of pathological cognitive decline, while non-MI of intercepts indicated general aging-related decline. As a result, the sole assessment of MI may help distinguish pathological from normative aging processes and additionally may reveal compensatory neuropsychological mechanisms.},
  language  = {en}
}
@article{HaberstumpfLeinweberLaueretal.2022,
  author    = {Haberstumpf, Sophia and Leinweber, Jonas and Lauer, Martin and Polak, Thomas and Deckert, J{\"u}rgen and Herrmann, Martin J.},
  title     = {Factors associated with dropout in the longitudinal Vogel study of cognitive decline},
  series = {The European Journal of Neuroscience},
  volume    = {56},
  journal   = {The European Journal of Neuroscience},
  number    = {9},
  doi       = {10.1111/ejn.15446},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-318945},
  pages     = {5587 -- 5600},
  year      = {2022},
  abstract  = {Dementia, including Alzheimer's disease, is a growing problem worldwide. Prevention or early detection of the disease or a prodromal cognitive decline is necessary. By means of our long-term follow-up 'Vogel study', we aim to predict the pathological cognitive decline of a German cohort (mean age was 73.9 ± 1.55 years at first visit) with three measurement time points within 6 years per participant. Especially in samples of the elderly and subjects with chronic or co-morbid diseases, dropouts are one of the biggest problems of long-term studies. In contrast to the large number of research articles conducted on the course of dementia, little research has been done on the completion of treatment. To ensure unbiased and reliable predictors of cognitive decline from study completers, our objective was to determine predictors of dropout. We conducted multivariate analyses of covariance and multinomial logistic regression analyses to compare and predict the subject's dropout behaviour at the second visit 3 years after baseline (full participation, partial participation and no participation/dropout) with neuropsychiatric, cognitive, blood and lifestyle variables. Lower performance in declarative memory, attention and visual-spatial processing predicted dropout rather than full participation. Lower performance in visual-spatial processing predicted partial participation as opposed to full participation. Furthermore, lower performance in mini-mental status examination predicted whether subjects dropped out or participated partially instead of full participation. Baseline cognitive parameters are associated with dropouts at follow-up with a loss of impaired participants. We expect a bias into a healthier sample over time.},
  language  = {en}
}