Data Quality Indicators Research Articles

A Collection of Data Quality Indicators for Health Research: Rationale for an Update.

Structured data are the capital of empirical health research. The value of these data relates to their quality and to their fit for use. A German guideline for the management of data quality in registries and cohort studies lists 51 quality indicators organized into the categories organization, integrity, and trueness. An update of the guideline will take into account the current view on dimensions of data, the appropriate structure for the definition of an indicator, and the collection of quality indicators itself. In the next version, the collection will explicitly address measures of metadata quality. The first step of a literature review revealed a high number of potential sources of evidence. These will be categorized into the topics dimensions, structure, and indicators respectively. Special attention will be paid to new challenges of data quality control arising from big data and artificial intelligence.

Electronic Health Record Data Quality and Performance Assessments: Scoping Review.

Electronic health records (EHRs) have an enormous potential to advance medical research and practice through easily accessible and interpretable EHR-derived databases. Attainability of this potential is limited by issues with data quality (DQ) and performance assessment. This review aims to streamline the current best practices on EHR DQ and performance assessments as a replicable standard for researchers in the field. PubMed was systematically searched for original research articles assessing EHR DQ and performance from inception until May 7, 2023. Our search yielded 26 original research articles. Most articles had 1 or more significant limitations, including incomplete or inconsistent reporting (n=6, 30%), poor replicability (n=5, 25%), and limited generalizability of results (n=5, 25%). Completeness (n=21, 81%), conformance (n=18, 69%), and plausibility (n=16, 62%) were the most cited indicators of DQ, while correctness or accuracy (n=14, 54%) was most cited for data performance, with context-specific supplementation by recency (n=7, 27%), fairness (n=6, 23%), stability (n=4, 15%), and shareability (n=2, 8%) assessments. Artificial intelligence-based techniques, including natural language data extraction, data imputation, and fairness algorithms, were demonstrated to play a rising role in improving both dataset quality and performance. This review highlights the need for incentivizing DQ and performance assessments and their standardization. The results suggest the usefulness of artificial intelligence-based techniques for enhancing DQ and performance to unlock the full potential of EHRs to improve medical research and practice.

Multidimensional Indicator for Data Quality Assessment in Wireless Sensor Networks: Challenges and Opportunities

Wireless Sensor Networks (WSN) are equipped with numerous sensors that generate vast quantities of data, essential for operational efficiency and informed decision-making. However, the value of this data is contingent upon its suitability for the specific applications it serves. A significant challenge in WSNs is the selection of appropriate data quality dimensions and metrics necessary to construct robust Data Quality Indicators (DQI) and comprehensively assess data quality in various contexts. This systematic literature review seeks to identify the key data quality dimensions and the corresponding measurement metrics within WSNs, while exploring the use of multi-dimensional data quality criteria in developing DQI. A thorough search of SCOPUS and Web of Science databases yielded 475 potential research articles, from which 64 primary studies were selected for in-depth analysis. The findings highlight four key data quality dimensions in WSN: accuracy, timeliness, completeness, and consistency. However, choosing measurement metrics for each dimension requires an in-depth understanding of the data's context. Various approaches for obtaining DQI in WSN research were identified, including weighted linear average models and application-specific contextual information. Effective DQI incorporates weights to each dimension, reflecting the priorities of specific data users, and leverages contextual information pertinent to the sensors’ data. It is crucial to evaluate whether the data collected by WSNs meets established quality standards, a key aspect of WSN operation. These insights will aid in developing more robust and reliable WSNs, ensuring the provision of high-quality data essential for effective operation and decision-making.

Data quality assessment of aggregated LCI datasets: A case study on fossil‐based and bio‐based plastic food packaging

AbstractEnvironmental impacts resulting from plastic food packaging, made from both fossil‐based and bio‐based polymers, are increasingly analyzed in life cycle assessment (LCA) studies. However, the literature reveals significant variations in results for the same polymer within the same scope. To enhance the reliability of these assessments, data quality assessment (DQA) plays a relevant role. However, despite most of the LCA studies employing aggregated life cycle inventory (LCI) datasets, in the literature, DQA methods for aggregated processes are not available. To fill this gap, in this paper, a DQA for aggregated LCI datasets is proposed and demonstrated through its application to 101 aggregated LCI datasets, extracted from Ecoinvent and GaBi databases. The DQA method has been developed by adapting and integrating the pedigree matrix and the data quality ranking proposed by the recently published EC Plastic LCA method. The three data quality indicators (DQIs) used are technological, geographical, and time‐related representativeness. The application of this method exhibits an overall positive evaluation of the selected datasets with differences among the three DQIs. Moreover, it highlights the role of metadata structure in adequately supporting a robust DQA. Indeed, in the absence of a common framework that defines, assesses, and provides access to data quality information, transparency must be assured by the operator in the metadata interpretation and related assumptions along the DQA process. Finally, although the proposed DQA method was developed for the plastic sector, its application can be extended to LCI aggregated datasets relevant to other sectors, materials, and products.

The impact of company size on dual VET quality in Spanish companies

PurposeThe purpose of this paper is to provide a comprehensive description and analysis of quality levels with regard to dual Vocational Education and Training (VET) at companies participating in this programme in Spain, with a specific focus on company size.Design/methodology/approachThe study is based on a quantitative cross-sectional analysis, collecting quality indicator data from 829 participating companies through a structured questionnaire. Subsequently, both descriptive and inferential analyses were conducted to address the specific objectives.FindingsThe study reveals that, on the whole, companies implement dual VET with a high level of quality, achieving compliance rates of above 70% in dimensions related to process, resources, staff and training. However, in this study the dimensions of evaluation and synergies showed lower compliance rates, with figures of 47.4 and 25.7%, respectively. Notably, medium-sized and large companies outperformed micro-SMEs and SMEs in training, evaluation and synergies, while SMEs surpassed large companies in resource management. The study provides recommendations for enhancing dual VET in companies.Research limitations/implicationsThe main limitations of this study pertain to an absence of data concerning the distribution of the population by company sizes and a lack of information regarding the specific sectors to which these companies belong.Originality/valueThis study offers significant value, as it provides data on the quality of dual VET in companies considering size as a key factor. These findings are particularly noteworthy given that research on the quality of dual VET in companies remains limited within the scientific literature.

7816 Implementation And Evaluation Of Clinical Care Structures For People With Differences Of Sex Development (DSD) In Germany

Abstract Disclosure: S.O. Hiort: None. M. Schnoor: None. U. Doehnert: None. M. Juergensen: None. J. Scherf: None. A. Heidenreich: None. M. Rapp: None. A. Katalinic: None. People with Differences of Sex Development (DSD) pose a challenge for healthcare. Lifelong holistic care requires a broad range of diagnostic approaches, qualified psychosocial, peer, fertility and sexual counselling, and structured therapeutic strategies including gonadal monitoring, hormone or surgical therapy. A quest has been taken to define centers of expertise for DSD and to substantiate and evaluate quality of care. Based on the criteria defined by the European Reference Network (ERN) for Rare Endocrine Conditions, 10 health care providers in Germany qualify for specialized DSD care. Quality indicators (QI) were developed through a Donabedian model to define structure, process and outcome quality mainly based on the German national guideline ”Differences of Sex Development”. Additionally, a consensus was reached though a structured process involving participating centers as well as patient organizations. Structural QIs are collected by an annual survey from the participating centers. Patient-related process and outcome data are recorded in a central registry. Patients and legal guardians receive questionnaires to collect patient-related outcome and experience measures after visits. These are measured by CHC-SUN/ YHC-SUN for treatment satisfaction and KIDDY KINDL, KIDSSCREEN 52, or WHOQOL-BREF for quality of life. An annual evaluation of all data results in a benchmarking of the participating centers. 24 indicators for structural quality were consented, 7 for process and 8 for outcome quality. Indicators of process quality comprise e.g. diagnostic procedures, case conferences, peer counseling or patient education. For structural quality there was an increase of centers fulfilling the QIs in 14 items including e.g. team composition and qualification during the study period from 2020 until 2022. Still very few centers had fixed standard operating procedures e.g. for gonadal monitoring or long-term follow up after surgery. 601 people with DSD were included into the registry from May 2021 until June 2023. In process quality there were considerable differences between centers both in QI compliance and data quality. The proportion of individuals with a genetic diagnosis varied by center from 44% to 96%. Information from questionnaires was available from 445 participants. Satisfaction with care was high. Parents reported in 86% to be very or highly satisfied with the visit, children and adolescents in 76%, and adults in 77%. Our approach allows for detailed evaluation of structural, process, and outcome quality. Health care insurers have secured appropriate funding for continuous evaluation of DSD care and the maintenance of the registry. This will allow for sustainability of the benchmarking and improvement of health care provision for people with DSD. Presentation: 6/1/2024

Analyzing spatio-temporal dynamics of dissolved oxygen for the River Thames using superstatistical methods and machine learning

By employing superstatistical methods and machine learning, we analyze time series data of water quality indicators for the River Thames (UK). The indicators analyzed include dissolved oxygen, temperature, electrical conductivity, pH, ammonium, turbidity, and rainfall, with a specific focus on the dynamics of dissolved oxygen. After detrending, the probability density functions of dissolved oxygen fluctuations exhibit heavy tails that are effectively modeled using q-Gaussian distributions. Our findings indicate that the multiplicative Empirical Mode Decomposition method stands out as the most effective detrending technique, yielding the highest log-likelihood in nearly all fittings. We also observe that the optimally fitted width parameter of the q-Gaussian shows a negative correlation with the distance to the sea, highlighting the influence of geographical factors on water quality dynamics. In the context of same-time prediction of dissolved oxygen, regression analysis incorporating various water quality indicators and temporal features identify the Light Gradient Boosting Machine as the best model. SHapley Additive exPlanations reveal that temperature, pH, and time of year play crucial roles in the predictions. Furthermore, we use the Transformer, a state-of-the-art machine learning model, to forecast dissolved oxygen concentrations. For long-term forecasting, the Informer model consistently delivers superior performance, achieving the lowest Mean Absolute Error (0.15) and Symmetric Mean Absolute Percentage Error (21.96%) with the 192 historical time steps that we used. This performance is attributed to the Informer’s ProbSparse self-attention mechanism, which allows it to capture long-range dependencies in time-series data more effectively than other machine learning models. It effectively recognizes the half-life cycle of dissolved oxygen, with particular attention to critical periods such as morning to early afternoon, late evening to early morning, and key intervals between the 16th and 26th quarter-hours of the previous half-day. Our findings provide valuable insights for policymakers involved in ecological health assessments, aiding in accurate predictions of river water quality and the maintenance of healthy aquatic ecosystems.

Assessing Hospital Data Quality: Application of a Data Quality Tool in 15 Countries

IntroductionGlobally, differences in coding practices and guidelines lead to variations in hospital-administrative data. Studies on patient safety found that better hospital-data quality is associated with higher rates of patient safety events. This study outlines our process of creating a standardized tool for countries to assess their hospital-administrative data quality. ApproachWe developed 24 indicators across five different dimensions through a Delphi-consensus method. To test applicability of these indicators, we approached representatives from 50 countries with an online survey comprised of qualitative and quantitative questions. Characteristics of each country and indicator scores were noted. A data quality overall score, out of 20, was calculated for each country based on survey responses. The score was classified into 3 data quality categories: high (≥18), moderate (13-17.9), and low (<13). ResultsOf the 50 countries invited, 17 responded. Surveys from two countries were excluded due to insufficient data. Country responses (n=15) were evaluated and scored by dimension. The data quality indicators showed positive face validity and were applicable for most countries providing comparative information for development of the tool with good discrimination. Canada, USA, New Zealand, UK, and Spain were among the countries with an overall high data quality score (≥18). Most countries scored high in 3 out of 5 dimensions of data quality. A few countries scored 0 out of 4 in ‘Relevance’ and ‘Timeliness’ dimensions resulting in a lower overall score. ConclusionWe propose our developed tool be used for comparing hospital-administrative data quality for applying the same standard across countries.

Intensive Care Quality Indicators Dashboard Using Observational Medical Outcomes Partnership Common Data Model.

This paper presents the development of a visualization dashboard for quality indicators in intensive care units (ICUs), using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The dashboard enables the user to visualize quality indicator data using histograms, pie charts and tables. Our project uses the OMOP CDM, ensuring a seamless implementation of our dashboard across various hospitals. Future directions for our research include expanding the dashboard to incorporate additional quality indicators and evaluating clinicians' feedback on its effectiveness.

Study on Life-Cycle Carbon Footprints and an Uncertainty Analysis of Mega Sporting Events: An Analysis in China

This study proposes a model for the quantitative evaluation of the life-cycle carbon footprints of large sporting events and the uncertainties related to them. The model was used to analyze the case of a mega sporting event in Beijing, China. First, the quantitative model for the evaluation of the carbon footprints of mega sporting events includes a preparation stage, a holding stage, and an end stage. These stages consider the energy and resources used for construction, operation, transportation, catering, and accommodation. Second, this study proposes a prediction model using model-based and simulation-based methods to address the difficulty of obtaining traffic activity. Third, a semi-quantitative method that combines a data quality indicator and stochastic simulation is adopted for the uncertainty analysis of mega sporting events. Finally, a case study is used to indicate that the preparation stage of a mega sporting event accounts for the highest CO2 emissions at 92.1%, followed by 7.5% in the holding stage and 0.4% in the end stage. The total life-cycle CO2 emissions of a sustainable scenario of a mega sporting event in Beijing amount to 205,080.3 t CO2e, and the per capita CO2 emissions during the event’s holding stage amount to 0.26 t CO2e/person. The uncertainty in the input parameters is 0.0617, indicating that the uncertainty of the model is low, and the reliability of the results is high.

Hospital quality classification based on quality indicator data during the COVID-19 pandemic

This research aim is to propose a machine learning approach to automatically evaluate or categories hospital quality status using quality indicator data. This research was divided into six stages: data collection, pre-processing, feature engineering, data training, data testing, and evaluation. In 2020, we collected 5,542 data values for quality indicators from 658 Indonesian hospitals. However, we analyzed data from only 275 hospitals due to inadequate submission. We employed methods of machine learning such as decision tree (DT), gaussian naïve Bayes (GNB), logistic regression (LR), k-nearest neighbors (KNN), support vector machine (SVM), linear discriminant analysis (LDA) and neural network (NN) for research archive purposes. Logistic regression achieved a 70% accuracy rate, SVM a 68% accuracy rate, and neural network a 59.34% of accuracy. Moreover, K-nearest neighbors achieved a 54% of accuracy and decision tree a 41% accuracy. Gaussian-NB achieved a 32% accuracy rate. The linear discriminant analysis achieved the highest accuracy with 71%. It can be concluded that linear discriminant analysis is the algorithm suitable for hospital quality data in this research.

Uncertainty in the embodied energy assessment of buildings: a case of EWS type low-cost house in India

Studies on embodied energy of buildings often present results as a single-point estimate without considering the variability in input parameters and the uncertainty it poses on results. Uncertainty analysis is useful to improve the reliability of results. This study examines uncertainty in the embodied energy of a single-storey EWS type residential building due to variability in the embodied energy intensity of materials and material wastage factors. Data quality indicator (DQI) based semi-qualitative method is used for modelling the variability in input parameters. Monte Carlo Simulation is used for uncertainty propagation. The building embodied energy is found to vary from 4 to 9 GJ/m2 based on variation in input data. The mean building embodied energy is found to be 6.3 GJ/m2. The embodied energy intensity of materials is found to cause more variation in building embodied energy in comparison with mass of materials. 'Brick’ is identified as the significant key parameter based on the relative contribution to uncertainty and embodied energy at the building level. Cement and rebar are identified as key parameters. The DQI based methodology presented in this study is useful for Life Cycle Assessment practitioners to evaluate uncertainty in the embodied energy assessment of residential buildings.

Quantifying data quality after removing respondents who fail data quality checks

ABSTRACT Check Questions (CQs) are used to scrutinise whether respondents are providing quality data and these are becoming more important to ensure data quality, especially with the use of online panels. Often the proportion of people passing these questions are used as a summary of data quality, but this is flawed and of limited value when assessing the quality of a study. This paper introduces a new statistic, the data quality index Q ^ , to measure the quality of data after respondents who fail CQs are removed. This index is interpretable as the estimated proportion of respondents (after excluding respondents who fail the CQs) who answer the survey conscientiously. The index is derived using simple probability, its properties explored, and directions provided on how it should be used and interpreted. The use of Q ^ in published research will increase transparency and confidence in the quality of research findings.

The surveys on quality indicators for the total testing process in clinical laboratories of Fujian Province in China from 2018 to 2023.

This study investigates the application of 15 Quality Indicators (QIs) in clinical laboratories in Fujian Province, China, from 2018 to 2023. It identifies the main causes of laboratory errors and explores issues in the application of QIs, providing a reference for establishing provincial state-of-the-art and operational quality specifications (QSs). All clinical laboratories in Fujian Province were organized to submit general information and original QIs data through the online External Quality Assessment (EQA) system of the National Clinical Laboratory Center (NCCL) for a survey of 15 QIs. Data from 2018 to 2023 were downloaded for statistical analysis, and the current QSs for the 15 QIs in Fujian Province were compared and analyzed with those published by the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Working Group on Laboratory Errors and Patient Safety (WG-LEPS). QIs data from 542 clinical laboratories were collected. The survey on data sources showed that the number of laboratories recording QIs data using Laboratory Information Systems (LIS) increased annually, but the growth was modest and the proportion was less than 50 %. Among the laboratories using LIS to record QIs data, 133 continuously participated in this survey for six years, reporting different QIs. Over the six years, all reported QIs showed significant improvement or at least remained stable. The best median Sigma (σ) metrics were for the percentage of critical values notification and timely critical values notification, reaching 6σ, followed by the percentage of incorrect laboratory reports, with σ metrics ranging from 4.9σ to 5.1σ. In contrast, the percentage of tests covered by internal quality control (IQC) (1.5σ-1.7σ) and inter-laboratory comparison (0.1σ) remained consistently low. Compared to the QSs published by IFCC WG-LEPS, the QSs for the 15 QIs in Fujian Province in 2023 were stricter or roughly equivalent, except for the percentage of incorrect laboratory reports (Fujian Province: 0-0.221, IFCC WG-LEPS: 0-0.03). 1. The application of QIs has significantly improved the quality of testing in clinical laboratories in Fujian Province, but the percentage of tests covered by IQC and inter-laboratory comparison remain low; 2. Effective application of QIs requires the establishment of comprehensive LIS, unified calculation standards, and other supporting measures.

Development of Data Quality Indicators for Improving Hospital International Classification of Diseases-Coded Health Data Quality Globally.

Hospital inpatient data, coded using the International Classification of Diseases (ICD), is widely used to monitor diseases, allocate resources and funding, and evaluate patient outcomes. As such, hospital data quality should be measured before use; however, currently, there is no standard and international approach to assess ICD-coded data quality. To develop a standardized method for assessing hospital ICD-coded data quality that could be applied across countries: Data quality indicators (DQIs). To identify a set of candidate DQIs, we performed an environmental scan, reviewing gray and academic literature on data quality frameworks and existing methods to assess data quality. Indicators from the literature were then appraised and selected through a 3-round Delphi process. The first round involved face-to-face group and individual meetings for idea generation, while the second and third rounds were conducted remotely to collect online ratings. Final DQIs were selected based on the panelists' quantitative and qualitative feedback. Participants included international experts with expertise in administrative health data, data quality, and ICD coding. The resulting 24 DQIs encompass 5 dimensions of data quality: relevance, accuracy and reliability; comparability and coherence; timeliness; and Accessibility and clarity. These will help stakeholders (eg, World Health Organization) to assess hospital data quality using the same standard across countries and highlight areas in need of improvement. This novel area of research will facilitate international comparisons of ICD-coded data quality and be valuable to future studies and initiatives aimed at improving hospital administrative data quality.

Research on multiscale OpenStreetMap in China: data quality assessment with EWM-TOPSIS and GDP modeling

ABSTRACT OpenStreetMap (OSM) is a voluntary platform designed to provide free and up-to-date geographic data. Since OSM is based on multisource geographic data provided by the public, the quality of the data has become a concern of researchers. In this study, a unified measure for evaluating OSM data quality was constructed, and the entropy weight method (EWM) and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) model were used to evaluate the quality of multiscale OSM data in China from 2014 to 2020. In addition to evaluating the data quality, the use of OSM data quality index factors in economic modeling at different spatial scales in China was explored by using a geographic information system (GIS) analysis method and a geographically and temporally weighted regression (GTWR) model. Four machine learning models, SVM, RF, XGBoost and CatBoost, were used to simulate the grid-scale GDP, and the effectiveness of these simulations was discussed. The results showed that (1) the weights of OSM data quality indicator factors vary across different spatial scales. (2) From 2014 to 2020, the quality of national-scale OSM data first increased, then decreased and then gradually stabilized. In addition, the quality of OSM data at the provincial and municipal scales is significantly different, and the distribution is affected by the population and geographical environment. (3) Over time, the spatial clustering characteristics of OSM data quality at different spatial scales in China has continuously strengthened. In addition, the quality of Chinese OSM data displays obvious local spatial autocorrelation characteristics, which are dominated by H-H clustering and L-L clustering. (4) The GTWR model performs well in predicting and revealing the spatiotemporal correlation characteristics between GDP and OSM data quality indicators at the provincial and municipal scales in China. The correlations increase with decreasing spatial scale (provincial to municipal). Moreover, the GDP modeling ability is better in economically underdeveloped Northwest China and economically developed East China. (5) Four machine learning models coupled with road network length completeness, relative linear density, road name attribute completeness, POI name attribute completeness, road network accuracy, road network update frequency, POI update frequency, topological consistency and directional similarity yielded the best grid-scale GDP simulation values. Notably, the CatBoost model provided the best accuracy, and the results further verified that it is feasible to use the proposed OSM data quality index system to predict the regional economic development level in China.

Long-term impact of wildfire on soil physical, chemical and biological properties within a pine forest

AbstractAnthropogenic fires pose a serious threat to many terrestrial ecosystems because they can cause loss of biodiversity and carbon stocks in the biosphere. Specifically, wildfires impacting natural conservation areas such as European Natura 2000 sites (N2K) are of particular concern. The main study objective was to evaluate the long-term effects of wildfires on the organic layer and some physical, chemical and biological properties of the underlying soil mineral layer, linked to soil quality. Here, we studied two coastal Mediterranean Aleppo pine stands within an N2K site differing for the fires’ years of occurrence, the time between fires (TBF) and the time since last fire (TSLF) throughout 24 years. Furthermore, in each stand, differences in fire frequency (FF) were considered by selecting three sites—double-fire, single-fire and control (unburnt). Our results show the absence of the O-layer in double-fire sites, indicating a loss of this organic carbon (if compared to control) pool of 204 g m−2 in R2F and 139 g m−2 in M2F. Despite this loss being offset by the Corg increase in soil mineral layer, the disappearance of O-layer may compromise the ecosystem services provided by soil. In each stand, long-term fire effects were evident at both single-fire and double-fire sites for some chemical as well as biological soil properties and depended on TSLF. Increased rates of nitrogen mineralization and nitrification were found at all burned sites, persisting up to 24 years post-fire. Soil quality indicators data highlighted the recovery handicap of the microbial community within the considered period. Since our outcomes showed wildfires enduring consequences, mainly relating to TSLF and FF, on different organic and mineral soil properties, we advocate employing prompt strategies to mitigate recurring fires.

Carbon footprint accounting of prefabricated buildings: A circular economy perspective

Prefabricated buildings have gained significant attention as a solution for reducing carbon footprint in the construction industry. However, there is a lack of a comprehensive accounting approach tailored to the construction features of prefabricated buildings. To address this issue, this study develops a framework to account the life-cycle carbon footprint of prefabricated buildings within the circular economy principles. The developed framework is applied to a prefabricated building in Chengdu, China, revealing that a significant carbon footprint is attributed to the material production and transportation stages. Moreover, an uncertainty analysis is conducted based on Data Quality Indicator evaluation method and Monte Carlo Simulation to validate the accounting framework, and a minor deviation of 0.085 % between the uncertain and calculated values validates the proposed accounting framework. Furthermore, key processes to mitigate the carbon footprint are identified. The results show that cast-in-place concrete is a key process at the construction materials level. Building upon these findings, this study offers recommendations on accurately and efficiently accounting the carbon footprint of prefabricated buildings. The research findings contribute to fostering the accounting of the life-cycle carbon footprint for prefabricated buildings, establishing a theoretical and empirical basis for circular economy implementation in prefabricated buildings, and offering insights for making informed carbon emission reduction decisions in prefabricated buildings.

Instruments for Measuring Youth Digital Media Use

In the context of adolescents’ increasing engagement with digital media, accurately assessing their media usage becomes challenging. This complexity arises from factors such as heightened multitasking, shorter attention spans, and peer influence. The Mobile Experience Sampling (MES) method has emerged as an alternative in digital media research with the strong advantage of measuring media use in situ several times a day over an extensive period. However, there is a lack of research investigating how MES studies among teenagers should be set up and which specific MES tools should be used to ensure high response rates. To answer this question, we conducted a multi-method study (N = 34) comparing data quality indicators (i.e., response rate and response delay) and perceived usability of a web-based and an app-based MES tool among Austrian middle and high school students, consisting of a one-week experimental MES study followed by qualitative interviews. In the MES part, participants were notified by three beeps on three days (NMES = 306) to complete a short survey measuring their time use in the last 60 minutes. The qualitative interviews revealed that adolescents perceived both MES tools as positive regarding usability (except for installation). The MES data, however, show great differences in data quality, highlighting that the web mode generated higher response rates and fewer errors (e.g., for response delays due to technical problems) than the app mode. This study provides a deeper understanding of technological considerations that should be made when planning an MES study and offers methodological guidance for future research.

Association between sleep traits and sarcopenia-related traits: A two-sample bidirectional Mendelian randomization study.

Despite limited evidence regarding the impact of sleep quality on sarcopenia, it is widely recognized as being associated with various diseases. This study aimed to explore the causal relationship between sleep traits and sarcopenia-related traits. This study utilized a two-sample bidirectional Mendelian randomization analysis. Genetic genome-wide summary data of sleep quality indicators, including chronotype, morning wake-up time, sleep duration, daytime napping, insomnia and daytime dozing, were used. Data on sarcopenia-related traits, such as appendicular lean mass, grip strength of both hands, walking pace and waist circumference, were collected from a large cohort study. The primary method used was the inverse-variance weighted analysis. A causal association was found between chronotype and appendicular lean mass (odds ratio [OR] 1.019, 95% confidence interval [CI] 1.016-1.211, P = 0.021). Napping during the day was connected with walking pace (OR 0.879, 95% CI 0.834-0.928, P = 2.289 × 10-6) and waist circumference (OR 1.234, 95% CI 1.081-1.408, P = 0.002). Insomnia was related to lower grip strength of the right hand (OR 0.844, 95% CI 0.747-0.954, P = 0.007), left hand (OR 0.836, 95% CI 0.742-0.943, P = 0.003), as well as walking pace (OR 0.871, 95% CI 0.798-0.951, P = 0.002). Furthermore, the reverse Mendelian randomization analysis showed associations between certain sarcopenia-related traits and poor sleep quality. Some sleep traits were associated with the occurrence of sarcopenia. These findings emphasized the significance of prioritizing sleep quality as a preventive measure against sarcopenia. Geriatr Gerontol Int 2024; 24: 537-545.