Wearables im Alter aus geriatrischer Sicht
Ontology highlight
ABSTRACT:
SUBMITTER: Fotteler M
PROVIDER: S-EPMC8122216 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description: Not available
| S-EPMC8122194 | biostudies-literature
Project description:A genetic resource for studying genetic architecture of agronomic traits and environmental adaptation is essential for crop improvements. Here, we report the development of a rice nested association mapping population (aus-NAM) using 7 aus varieties as diversity donors and T65 as the common parent. Aus-NAM showed broad phenotypic variations. To test whether aus-NAM was useful for quantitative trait loci (QTL) mapping, known flowering genes (Ehd1, Hd1, and Ghd7) in rice were characterized using single-family QTL mapping, joint QTL mapping, and the methods based on genome-wide association study (GWAS). Ehd1 was detected in all the seven families and all the methods. On the other hand, Hd1 and Ghd7 were detected in some families, and joint QTL mapping and GWAS-based methods resulted in weaker and uncertain peaks. Overall, the high allelic variations in aus-NAM provide a valuable genetic resource for the rice community.
| S-EPMC8234321 | biostudies-literature
Project description:Die dramatische Verbreitung des SARS-CoV-2/Covid-19-Virus (Corona-Virus) ist vor allem für Entscheidende eine große Herausforderung. Denn ihnen obliegt die Verantwortung für effiziente und zielgerichtete Prozesse ebenso wie für ein Personalmanagement, welches den Kriterien der Ausführbarkeit, Erträglichkeit, Zumutbarkeit und der Zufriedenheit entspricht. Vor dem Hintergrund eines komplexen Versorgungsauftrages, bei welchem die Notfall- sowie die Intensivmedizin eine Schlüsselrolle einnehmen, kann das beschriebene Delegationsmodell eine zielgerichtete Aufgabenverteilung unterstützen und eine Grundlage für Entscheidungen sein. Zusatzmaterial online: Zu diesem Beitrag sind unter 10.1007/s41906-020-0721-z für autorisierte Leser zusätzliche Dateien abrufbar.
| S-EPMC7206219 | biostudies-literature
Project description:BACKGROUND:Traditional varieties and landraces belonging to the aus-type group of rice (Oryza sativa L.) are known to be highly tolerant to environmental stresses, such as drought and heat, and are therefore recognized as a valuable genetic resource for crop improvement. Using two aus-type (Dular, N22) and two drought intolerant irrigated varieties (IR64, IR74) an untargeted metabolomics analysis was conducted to identify drought-responsive metabolites associated with tolerance. RESULTS:The superior drought tolerance of Dular and N22 compared with the irrigated varieties was confirmed by phenotyping plants grown to maturity after imposing severe drought stress in a dry-down treatment. Dular and N22 did not show a significant reduction in grain yield compared to well-watered control plants, whereas the intolerant varieties showed a significant reduction in both, total spikelet number and grain yield. The metabolomics analysis was conducted with shoot and root samples of plants at the tillering stage at the end of the dry-down treatment. The data revealed an overall higher accumulation of N-rich metabolites (amino acids and nucleotide-related metabolites allantoin and uridine) in shoots of the tolerant varieties. In roots, the aus-type varieties were characterised by a higher reduction of metabolites representative of glycolysis and the TCA cycle, such as malate, glyceric acid and glyceric acid-3-phosphate. On the other hand, the oligosaccharide raffinose showed a higher fold increase in both, shoots and roots of the sensitive genotypes. The data further showed that, for certain drought-responsive metabolites, differences between the contrasting rice varieties were already evident under well-watered control conditions. CONCLUSIONS:The drought tolerance-related metabolites identified in the aus-type varieties provide a valuable set of protective compounds and an entry point for assessing genetic diversity in the underlying pathways for developing drought tolerant rice and other crops.
| S-EPMC5785456 | biostudies-literature
Project description:BackgroundWearables are intriguing way to promote physical activity and reduce sedentary behavior in populations with and without chronic diseases. However, the contemporary evidence demonstrating the effectiveness of wearables on physical health during the COVID-19 pandemic has yet to be explored.AimThe present review aims to provide the readers with a broader knowledge of the impact of wearables on physical health during the pandemic.MethodsFive electronic databases (Web of Science, Scopus, Ovid Medline, Cumulative Index to Nursing and Allied Health Literature and Embase) were searched. The eligibility criteria of the studies to be included were based on PICOT criteria: population (adults, children and elderly), intervention (wearable, smartphones), comparison (any behavioral intervention), outcome (physical activity or sedentary behavior levels) and time frame (between December 1st, 2019 and November 19th, 2021). The present scoping review was framed as per the guidelines of the Arksey and O'Malley framework.ResultsOf 469 citations initially screened, 17 articles were deemed eligible for inclusion and potential scoping was done. Smartphone-based applications with inbuilt accelerometers were commonly used, while a few studies employed smart bands, smartwatches for physical health monitoring. Most of the studies observed the increased use of wearables in healthy adults followed by elderly, children and pregnant women. Considerable reduction (almost-50%) in physical activity during the pandemic: daily step count (- 2812 steps/min), standing (- 32.7%) and walking (- 52.2%) time was found.ConclusionWearables appears to be impending means of improving physical activity and reducing sedentary behavior remotely during the COVID-19 pandemic.Supplementary informationThe online version contains supplementary material available at 10.1007/s11332-021-00885-x.
| S-EPMC8739535 | biostudies-literature
Project description:Die Linke ist mit mehreren Herausforderungen konfrontiert: In Ostdeutschland als ihrer klassischen Hochburg verliert die Partei sowohl Mitglieder als auch Wählerstimmen, während im Westen Gewinne zu verbuchen sind, sodass sich (langfristig) die innerparteilichen Kräfteverhältnisse verschieben. Außerdem diskutiert Die Linke über den Umgang mit der AfD als neuer Konkurrenz um Protestwähler und die Ausrichtung ihrer Migrationspolitik. Diese und andere Aspekte der Entwicklung seit der Bundestagswahl 2017 greift das Kapitel auf.
| S-EPMC7336691 | biostudies-literature
Project description:IntroductionSevere cutaneous adverse reactions (SCAR) are a group of T cell-mediated hypersensitivities associated with significant morbidity, mortality and hospital costs. Clinical phenotypes include Stevens-Johnson syndrome (SJS), toxic epidermal necrolysis (TEN), drug reaction with eosinophilia and systemic symptoms (DRESS) and acute generalised exanthematous pustulosis (AGEP). In this Australasian, multicentre, prospective registry, we plan to examine the clinical presentation, drug causality, genomic predictors, potential diagnostic approaches, treatments and long-term outcomes of SCAR in Australia and New Zealand.Methods and analysisAdult and adolescent patients with SCAR including SJS, TEN, DRESS, AGEP and another T cell-mediated hypersensitivity, generalised bullous fixed drug eruption, will be prospectively recruited. A waiver of consent has been granted for some sites to retrospectively include cases which result in early mortality. DNA will be collected for all prospective cases. Blood, blister fluid and skin biopsy sampling is optional and subject to patient consent and site capacity. To develop culprit drug identification and prevention, genomic testing will be performed to confirm human leukocyte antigen (HLA) type and ex vivo testing will be performed via interferon-γ release enzyme linked immunospot assay using collected peripheral blood mononuclear cells. The long-term outcomes of SCAR will be investigated with a 12-month quality of life survey and examination of prescribing and mortality data.Ethics and disseminationThis study was reviewed and approved by the Austin Health Human Research Ethics Committee (HREC/50791/Austin-19). Results will be published in peer-reviewed journals and presented at relevant conferences.Trial registration numberAustralian New Zealand Clinical Trials Registry (ACTRN12619000241134).
| S-EPMC9389100 | biostudies-literature
Project description:PURPOSE OF REVIEW:This review discusses how wearable devices-sensors externally applied to the body to measure a physiological signal-can be used in heart failure (HF) care. RECENT FINDINGS:Most wearables are marketed to consumers and can measure movement, heart rate, and blood pressure; detect and monitor arrhythmia; and support exercise training and rehabilitation. Wearable devices targeted at healthcare professionals include ECG patch recorders and vests, patches, and textiles with in-built sensors for improved prognostication and the early detection of acute decompensation. Integrating data from wearables into clinical decision-making has been slow due to clinical inertia and concerns regarding data security and validity, lack of evidence of meaningful impact, interoperability, regulatory and reimbursement issues, and legal liability. Although few studies have assessed how best to integrate wearable technologies into clinical practice, their use is rapidly expanding and may support improved decision-making by patients and healthcare professionals along the whole patient pathway.
| S-EPMC7343723 | biostudies-literature
Project description:Monitoring older adults with wearable sensors and IoT devices requires collecting data from various sources and proliferates the number of data that should be collected in the monitoring center. Due to the large storage space and scalability, Clouds became an attractive place where the data can be stored, processed, and analyzed in order to perform the monitoring on large scale and possibly detect dangerous situations. The use of fuzzy sets in the monitoring and detection processes allows incorporating expert knowledge and medical standards while describing the meaning of various sensor readings. Calculations related to fuzzy processing and data analysis can be performed on the Edge devices which frees the Cloud platform from performing costly operations, especially for many connected IoT devices and monitored people. In this paper, we show a solution that relies on fuzzy rules while classifying health states of monitored adults and we investigate the computational cost of rules evaluation in the Cloud and on the Edge devices.
| S-EPMC7302848 | biostudies-literature
Project description:Depression is a multifaceted illness with large interindividual variability in clinical response to treatment. In the era of digital medicine and precision therapeutics, new personalized treatment approaches are warranted for depression. Here, we use a combination of longitudinal ecological momentary assessments of depression, neurocognitive sampling synchronized with electroencephalography, and lifestyle data from wearables to generate individualized predictions of depressed mood over a 1-month time period. This study, thus, develops a systematic pipeline for N-of-1 personalized modeling of depression using multiple modalities of data. In the models, we integrate seven types of supervised machine learning (ML) approaches for each individual, including ensemble learning and regression-based methods. All models were verified using fourfold nested cross-validation. The best-fit as benchmarked by the lowest mean absolute percentage error, was obtained by a different type of ML model for each individual, demonstrating that there is no one-size-fits-all strategy. The voting regressor, which is a composite strategy across ML models, was best performing on-average across subjects. However, the individually selected best-fit models still showed significantly less error than the voting regressor performance across subjects. For each individual's best-fit personalized model, we further extracted top-feature predictors using Shapley statistics. Shapley values revealed distinct feature determinants of depression over time for each person ranging from co-morbid anxiety, to physical exercise, diet, momentary stress and breathing performance, sleep times, and neurocognition. In future, these personalized features can serve as targets for a personalized ML-guided, multimodal treatment strategy for depression.
| S-EPMC8187630 | biostudies-literature