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2,781 results for “Older Adults”
Perceptions of heat and air pollution among older adults experiencing homelessness in Phoenix, Arizona (USA) (June 2024)
This dataset consists of survey responses from 40 older adults experiencing homelessness in Phoenix, Arizona (USA), assessing the perceptions of environmental hazards—specifically heat and air pollution—and attitudes toward coping resources and behaviors. The survey includes 51 questions co-created with community members across five categories: demographics and behavior, movement/transportation, climate perceptions, resource availability, and local knowledge mapping. Surveys were conducted indoors at a local service provider over two days in June 2024, when outdoor temperatures reached 42 degrees C and 45 degrees C. The dataset offers insights into potential public service reforms to mitigate heat and air pollution risks among Arizona’s unhoused population. The survey was approved by the Institutional Review Board of Arizona State University (IRB approval number: STUDY00018399).
EEG, ECG and pupil data from young and older adults: rest and auditory cued reaction time tasks
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Supporting Material for "Enhancing Mental Health and Cognitive Function in Older Adults: A Swiss Perspective on Public Health Interventions and Stigma Mitigation Strategies Informed by a Desk Review"
<p>This dataset contains all supporting material for the paper "Enhancing Mental Health and Cognitive Function in Older Adults: A Swiss Perspective on Public Health Interventions and Stigma Mitigation Strategies Informed by a Desk Review", published in the journal Swiss Psychology Open:</p> <p><em>Mack, M., Scarampi, C., Joly-Burra, E., Zuber, S., de Freitas, C., Teixeira, R. and Kliegel, M. (2025) ‘Enhancing Mental Health and Cognitive Function in Older Adults: A Swiss Perspective on Public Health Interventions and Stigma Mitigation Strategies Informed by a Desk Review’, Swiss Psychology Open, 5(1), p. 2. Available at: <a href="https://doi.org/10.5334/spo.81.">https://doi.org/10.5334/spo.81</a>.</em></p> <p>It includes the following documents and files:</p> <p><strong>S1. Protocol:</strong> ADVANCE Protocol for desk reviews</p> <p><strong>S2. Search strategy</strong></p> <p><strong>S3. Guidelines for title and abstract screening:</strong> Guidelines for the selection of articles included in the desk review</p> <p><strong>S4. Guidelines full-text screening:</strong> Guidelines for the selection of articles included in the desk review</p> <p><strong>S5. Guidelines data extraction:</strong> ADVANCE Guidelines/codebook data extraction</p> <p><strong>data extraction_desk review_switzerland.xlsx</strong></p> <p>This desk review was conducted as part of the ADVANCE project, which aims to enhance our understanding of mental health promotion and prevention. This desk review evaluates the current state of interventions for mental health and cognitive functioning among older adults in Switzerland focusing on the features of these interventions as well as on Swiss-specific contextual factors that contribute to vulnerability and stigma. This results of the desk review has been submitted for publication to 'LIVES Working Papers' and 'Swiss Psychology Open' . The two versions of the desk review differ slightly. The version for LIVES Working Papers, included the results of the Delphi survey and the resulting intervention scenarios. The version for Swiss Psychology Open, did not include the Delphi survey results and the resulting intervention scenarios, but included a more detailed discussion of the review results.</p>
Examining effects of arousal on responses to salient and non-salient stimuli in younger and older adults
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Perception and evaluation of (modified) wood by older adults from Slovenia and Norway (Datasets, R analysis code, and supplementary tables)
<p>This entry contains datasets, R analysis code, and supplementary tables for the article <em>Perception and evaluation of (modified) wood by older adults from Slovenia and Norway.</em></p> <p>The article investigates human perception and evaluation of handrails made of different materials. Our goal was to identify how older adults perceive handrails made of unmodified wood, modified wood, and steel. We examined if certain materials are more preferred than others, which material properties might be associated with differences in human preference, and what are the roles of tactile and tactile-visual domains in material perception. Our analysis is based on the results from an 11-item rating scale and a ranking task.</p>
Data and materials for Wallace et al (2018) Self-report versus electronic medical record recorded healthcare utilisation in older community-dwelling adults: comparison of two prospective cohort studies v1.2
<p>This comprises the data and materials for the study: Wallace E, Moriarty F, McGarrigle C, Smith SM, Kenny RA, Fahey T. (2018) Self-report versus electronic medical record recorded healthcare utilisation in older community-dwelling adults: Comparison of two prospective cohort studies. PLOS ONE 13(10): e0206201. <a href="https://doi.org/10.1371/journal.pone.0206201">https://doi.org/10.1371/journal.pone.0206201</a></p> <p>The anonymised TILDA dataset is publicly available to researchers who meet the criteria for access, at no monetary cost, from the Irish Social Science Data Archive (ISSDA) at University College Dublin (<a href="https://emea01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.ucd.ie%2Fissda%2Fdata%2Ftilda%2F&data=02%7C01%7C%7Ccc2345c4f5c543bbcddc08d5fd28200c%7C607041e7a8124670bd3030f9db210f06%7C0%7C0%7C636693271128219875&sdata=%2Fcochi1RuRtYSUa5sF9uA%2BjOOoNYIg7DPpk0mZl5D2s%3D&reserved=0">http://www.ucd.ie/issda/data/tilda/</a>) and the Interuniversity Consortium for Political and Social Research (ICPSR) at the University of Michigan (<a href="https://emea01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.icpsr.umich.edu%2Ficpsrweb%2FICPSR%2Fstudies%2F34315&data=02%7C01%7C%7Ccc2345c4f5c543bbcddc08d5fd28200c%7C607041e7a8124670bd3030f9db210f06%7C0%7C0%7C636693271128219875&sdata=7LHSSqU8xotACMsalpAjVrV5m95DlapgQViyr4P%2FsXY%3D&reserved=0">http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/34315</a>). For the CPCR cohort, no provision for data sharing was included in the original ethical approval and participant consent form. As a minimal data set necessary to replicate the present study could not be deidentified due to the large number of demographic variables considered, a synthetic version of the study dataset was produced using the synthpop package in R: <a href="https://emea01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcran.r-project.org%2Fweb%2Fpackages%2Fsynthpop%2Findex.html&data=02%7C01%7C%7Ccc2345c4f5c543bbcddc08d5fd28200c%7C607041e7a8124670bd3030f9db210f06%7C0%7C0%7C636693271128229884&sdata=j3If%2FNe%2F1eGsGAt9hyg3ICMqmLec4aOrjRVKppaRSFU%3D&reserved=0">https://cran.r-project.org/web/packages/synthpop/index.html</a>. This dataset and the analytical code for the present study are presented here. Code developed on the synthetic data can be sent to frankmoriarty@rcsi.ie or <a href="mailto:enquiries.cpcr@rcsi.ie">enquiries.cpcr@rcsi.ie</a> to be run on the original data.</p> <p>v1.2 includes a more detailed description of how the dataset was synthesised.</p>
Prevalence of Multimorbidity among Urban–Rural Older Adults in Mongolia: A Cross-Sectional Study
<p>A face-to-face, questionnaire-based cross-sectional study was conducted with 800 valid participants aged ≥60 years in Mongolia from June to September 2023.</p>
Literature search for publication: A meta-analysis on the role older adults with cancer favour in treatment decision making
<p>Dataset belonging to 10.5281/zenodo.7308287 including data of literature search regardign outcome preference scale in geriatric oncology</p>
Data for Project 'Feasibility, Usability and Acceptance of a Newly Developed Exergame-Based Training Concept for Older Adults with Mild Neurocognitive Disorder - A Pilot Randomized Controlled Trial'
<p>Data for Project 'Feasibility, Usability and Acceptance of a Newly Developed Exergame-Based Training Concept for Older Adults with Mild Neurocognitive Disorder - A Pilot Randomized Controlled Trial' (trial registered at clinicaltrials.gov (<a href="https://clinicaltrials.gov/ct2/show/NCT04996654">NCT04996654</a>; date of registration: 11 July 2021), consisting of:</p> <p>(1) the original and complete data set for all primary outcomes ('Data_Primary-Outcomes_Brain-IT-Pilot-Feasibility-RCT_for-publication.xlsx');</p> <p>(2) the original and complete data set for all secondary outcomes ('Data_Secondary-Outcomes_Brain-IT-Pilot-Feasibility-RCT_for-publication.xlsx');</p> <p>(3) the original and complete data set for all other outcomes (i.e. baseline factors (demographic data, type of usual care interventions) and training heart rate; 'Data_Other-Outcomes_Brain-IT-Pilot-Feasibility-RCT_for-publication.xlsx');</p> <p>(4) folder including the raw and processed heart rate variability (HRV) and electroencephalography (EEG) data for all participants and measurements (HRV-and-EEG_raw-and-processed-data.zip);</p> <p>(5) a corresponding README file including (a) general information, (b) data and file overview, (c) sharing and access information, (d) methodological information, and (e) data-specific information.</p>
Transcriptions of interviews with older adults on the use of WhatsApp
<p>The file contains German transcriptions of semi-structured qualitative interviews conducted with older adults 65+ in Switzerland in 2019. The topic of the interviews was the older adults' use of the instant-messaging service WhatsApp and its perceived effects on their social relationships.</p>
BLE RSSI Database for Analyzing Routines of Community-Dwelling Older Adults
<p>The database includes the RSSI information emitted by BLE beacons installed in an elderly care home and collected by smartwatches worn by volunteers. Raw data and processed data are provided. Also, files in the Python programming language are included to facilitate data manipulation.</p> <p> </p> <p>S. Lluva Plaza, R. Montoliu Colás, A. Jiménez Martín, J.M. Villadangos Carrizo, E. Sansano Sansano and J.J. García Domínguez, "BLE RSSI Database for Analyzing Routines of Community-Dwelling Older Adults," <em>2022 14th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)</em>, Valencia, Spain, 2022, pp. 50-55, doi: 10.1109/ICUMT57764.2022.9943410.</p>
Evaluation Tools for Human-AI Interactions Involving Older Adults with Mild Cognitive Impairments
<h1>Abstract</h1> <p>As artificial intelligence (AI) systems have already proven useful in human lives generally, there is an opportunity for specialized human-AI interaction (HAI) systems to support and provide care for older adults with mild cognitive impairment (MCI). However, the integration of this technology in this population must be thoughtfully designed to accommodate specific needs and limitations. This includes careful measurement of both humans and systems. We developed an evolving dataset categorizing relevant measurement tools into five groups: cognitive ability, demographics & personality, activity level, state of mind, and perceptions of the AI system. Each instance of the tool being used in the literature cataloged in the dataset is qualified in terms of how likely we would recommend using it in the domain of HAI for older adults with MCI based on contextual factors and internal reliability measures. This dataset will serve as a valuable resource for future research, aiding in the identification of promising areas and trends in AI systems for older adults with MCI as well as providing essential tools for future studies.</p> <h1>Methodology</h1> <p>This dataset was not derived through a typical literature review or survey process, but rather followed a more flexible research method. To collect resources for the dataset, we searched numerous databases to identify studies and review types of publications in journals and conferences between the dates of 2000 to 2022. For the papers that contained extensive reviews of literature or cited original tools, we would further look into the citations of those papers, taking us beyond our limited date range. The tools used were categorized into five groups to broadly distinguish their usage in a study, measuring:</p> <ol> <li>Cognitive ability</li> <li>Demographics, personality, and experiences</li> <li>Activity level</li> <li>State of mind</li> <li>Perceptions of the AI system</li> </ol> <p>Subsequently, we conducted an examination of their Cronbach’s 𝛼 scores to assess internal reliability. We created tiers based on how likely we would be to recommend using each tool in the domain of human-AI (HAI) with older adults with MCI, as follows:</p> <ul> <li>Tier 1 included tools with Cronbach’s 𝛼 ≥ 0.7 when used with older adults with MCI in experimental settings interacting with AI</li> <li>Tier 2 included tools with Cronbach’s 𝛼 ≥ 0.7 when used with older adults, with or without MCI, in experimental settings with or without AI interaction</li> <li>Tier * included tools that satisfy the criteria for Tier 1, but, to the best of our knowledge, lack reported Cronbach’s 𝛼 scores</li> <li>Tier 3 included all remaining tools that do not meet the criteria for Tier 1, 2, or *</li> </ul> <p>It should be emphasized that a tool may be found in one or more tiers because multiple studies used the same tool yet resulted in varying reliability scores, contexts, etc.</p> <h1>Contribute</h1> <p>Readers are encouraged to reach out to Adam Norton (adam[underscore]norton[at]uml.edu) to recommend additional tools and entries to the dataset.</p> <h1>Publication</h1> <p>This dataset is published as a short contribution to the Human-Robot Interaction (HRI) 2024 conference. The corresponding paper citation is below:</p> <p>Daisy M. Kiyemba, Jasmin Marwad, Elizabeth J. Carter, and Adam Norton. <strong>Evaluation Tools for Human-AI Interactions Involving Older Adults with Mild Cognitive Impairments</strong>. In Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’24), March 11–14, 2024, Boulder, CO, USA. ACM, New York, NY, USA, 4 pages. <a href="https://doi.org/10.1145/3610977.3637474" target="_blank" rel="noopener">https://doi.org/10.1145/3610977.3637474</a></p> <h1>Acknowledgements</h1> <p>This work was supported by the National Science Foundation (IIS-2112633) as part of the AI-CARING Institute: <a href="https://ai-caring.org/" target="_blank" rel="noopener">https://ai-caring.org/</a></p>
The prevalence and correlates of depression and anxiety symptoms among older adults in Shenzhen, China
<p><strong>Objectives: </strong>To investigate the prevalence of depression and anxiety symptoms among older adults in an urban district in China, as well as their associated factors.</p> <p><strong>Participants:</strong> A total of 5,372 community-dwelling older adults aged 65 years or older were initially recruited. Ultimately, 5,331 participants met the inclusion criteria and were included in this study.</p> <p><strong>Methods:</strong> Participants completed a sociodemographic questionnaire, along with assessments including the Patient Health Questionnaire-9, Generalized Anxiety Scale-7, UCLA Loneliness Simplification Scale, Insomnia Severity Index Scale, Community Dementia Brief Screening Scale, and the 8-item Dementia Screening Questionnaire. Statistical analyses included the Shapiro‒Wilk test, independent t-test, Wilcoxon rank test, c<sup>2</sup> test, and univariate and multivariate linear regression analysis.</p> <p><strong>Results:</strong> The prevalence of depression and anxiety symptoms among older adults in Shenzhen communities was 10.4% and 11.3%, respectively. In multivariate analysis, age (B=-0.01, <em>P</em><0.05), relatively poor health status in the past year (B=1.00, <em>P</em><0.01), poor health status in the past year (B=2.40, <em>P</em><0.01), ISI score (B=0.21, <em>P</em><0.01), AD8 score (B=0.22, <em>P</em><0.01), ULS score (B=0.24, <em>P</em><0.01) were significantly associated with the severity of depression symptom, Compared to their respective reference categories, relatively poor health status in the past year (B=0.50, <em>P</em><0.01), poor health status in the past year (B=1.32, <em>P</em><0.01), ISI score (B=0.23, <em>P</em><0.01), sleep duration (B=0.05, <em>P</em><0.01), AD8 score (B=0.21, <em>P</em><0.01), CSID score (B=0.13, <em>P</em><0.01), ULS score (B=0.22, <em>P</em><0.01) were significantly associated with the severity of anxiety symptom.</p> <p><strong>Conclusions: </strong>We observed a high prevalence of depression and anxiety symptoms among older adults in this study. The existing welfare system and infrastructure should remain and targeted mental health programs addressing the identified risk factors should be proposed.</p>
Sex-specific tuning of modular muscle activation patterns for locomotion in young and older adults
<p>There is increasing evidence that including sex as a biological variable is of crucial importance to promote rigorous, repeatable and reproducible science. In spite of this, the body of literature that accounts for the sex of participants in human locomotion studies is small and often produces controversial results. Here, we investigated the modular organization of muscle activation patterns for human locomotion using the concept of muscle synergies with a double purpose: i) uncover possible sex-specific characteristics of motor control and ii) assess whether these are maintained in older age. We recorded electromyographic activities from 13 ipsilateral muscles of the lower limb in young and older adults of both sexes walking (young and old) and running (young) on a treadmill. The data set obtained from the 215 participants was elaborated through non-negative matrix factorization to extract the time-independent (i.e., motor modules) and time-dependent (i.e., motor primitives) coefficients of muscle synergies. We found sparse sex-specific modulations of motor control. Motor modules showed a different contribution of hip extensors, knee extensors and foot dorsiflexors in various synergies. Motor primitives were wider (i.e., lasted longer) in males in the propulsion synergy for walking (but only in young and not in older adults) and in the weight acceptance synergy for running. Moreover, the complexity of motor primitives was similar in younger adults of both sexes, but lower in older females as compared to older males. In essence, our results revealed the existence of small but defined sex-specific differences in the way humans control locomotion and that these strategies are not entirely maintained in older age.</p> <p>In this supplementary data set we made available: a) the metadata with anonymized participant information; b) the raw EMG, already concatenated for the overground trials; c) the touchdown and lift-off timings of the recorded limb, d) the code to process the data. In total, 520 trials from 215 participants are included in the supplementary data set.</p> <p>The file “metadata.dat” is available in ASCII format and contains:</p> <ul> <li>Code: the participant’s code</li> <li>Group: the participant's group (G1=young adults, walking; G2=old adults, walking; G3=young adults, running)</li> <li>Sex: the participant’s sex (M or F)</li> <li>Locomotion: the type of locomotion (walking or running)</li> <li>Speed: the speed at which the recordings were conducted in [m/s]</li> <li>Speed_type: the distinction between fixed (decided by the researchers) or preferred (selected by the participant) speed</li> <li>Age: the participant’s age in years</li> <li>Height: the participant’s height in [cm]</li> <li>Mass: the participant’s body mass in [kg].</li> </ul> <p>The "RAW_DATA.RData" R list consists of elements of S3 class "EMG", each of which is a human locomotion trial containing cycle segmentation timings and raw electromyographic (EMG) data from 13 muscles of the right-side leg. Cycle times are structured as data frames containing two columns that correspond to touchdown (first column) and lift-off (second column). Raw EMG data sets are also structured as data frames with one row for each recorded data point and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with the following muscle abbreviations: ME = gluteus medius, MA = gluteus maximus, FL = tensor fasciæ latæ, RF = rectus femoris, VM = vastus medialis, VL = vastus lateralis, ST = semitendinosus, BF = biceps femoris, TA = tibialis anterior, PL = peroneus longus, GM = gastrocnemius medialis, GL = gastrocnemius lateralis, SO = soleus. Trials are named like “ID0020_M_YOUNG_TW_01,” where the characters “ID0020” indicate the participant number (in this example the 20th), the character “M” indicates the sex, the characters “YOUNG” indicate the age group, the characters “TW” indicate the locomotion type and environment (T=treadmill, W=walking, R=running), and the numbers “01” indicate the trial number.</p> <p><strong>Old versions not compatible with the R package <a href="https://CRAN.R-project.org/package=musclesyneRgies">musclesyneRgies</a></strong></p> <p>The files containing the gait cycle breakdown are available in RData format, in the file named “CYCLE_TIMES.RData”. The files are structured as data frames with one row for each gait cycle and two columns. The first column contains the touchdown incremental times in seconds. The second column contains the duration of each stance phase in seconds. Each trial is saved as an element of a single R list. Trials are named like “CYCLE_TIMES_ID0020_M_YOUNG_TW_01,” where the characters “CYCLE_TIMES” indicate that the trial contains the gait cycle breakdown times, the characters “ID0020” indicate the participant number (in this example the 20th), the character “M” indicates the sex, the characters “YOUNG” indicate the age group, the characters “TW” indicate the locomotion type and environment (T=treadmill, W=walking, R=running), and the numbers “01” indicate the trial number.</p> <p>The files containing the raw, filtered, and the normalized EMG data are available in RData format, in the files named “RAW_EMG.RData” and “FILT_EMG.RData”. The raw EMG files are structured as data frames with one row for each recorded data point and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with the following muscle abbreviations: ME = gluteus medius, MA = gluteus maximus, FL = tensor fasciæ latæ, RF = rectus femoris, VM = vastus medialis, VL = vastus lateralis, ST = semitendinosus, BF = biceps femoris, TA = tibialis anterior, PL = peroneus longus, GM = gastrocnemius medialis, GL = gastrocnemius lateralis, SO = soleus. Each trial is saved as an element of a single R list. Trials are named like “RAW_EMG_ID0003_F_OLD_TW_01”, where the characters “RAW_EMG” indicate that the trial contains raw emg data, the characters “ID0003” indicate the participant number (in this example the 3rd), the character “F” indicates the sex, the characters “OLD” indicate the age group, the characters “TW” indicate the locomotion type and environment (see above), and the numbers “01” indicate the trial number.</p> <p>All the code used for the pre-processing of EMG data and the extraction of muscle synergies is available in R format. Explanatory comments are profusely present throughout the script “muscle_synergies.R”. The latest version of this code can be found at https://github.com/alesantuz/musclesyneRgies.</p>
Use of health care services in community-dwelling older adults in two regions of Spain
<p>This dataset contains data on use of health care resources of community-dwelling older adults aged 70 or over, who were functionally independent. Data of health resources use included contacts along two consecutive years with: the general practitioner, primary care nurse, the specialists, visits to emergency rooms, and hospital admissions and length of stay. The data included also information about sex, region, polipharmacy, age-adjusted Charlson Comorbidity Index and funcionality, measured by Timed Up and Go test. The data collection was performed in two Spanish regions. Baseline assessment was done between 2015 and 2016, and patients were followed for 2 years. There were in total 1488 registries considering both years.</p>
# Replication code and data for: Global projections of heat exposure of older adults
<p># Replication code and data for: Global projections of heat exposure of older adults<br>By Giacomo Falchetta, Enrica De Cian, Ian Sue Wing and Deborah Carr<br>Nature Communications. DOI: 10.1038/s41467-024-47197-5</p> <p>An output data file, containing grid-cell level counts of people by age group, of climate hazard indicators, and of heat exposure metrics for both the historical climate and current demography and for future scenarios and projections is contained in the repository ("aging_climate_output_data.csv"). </p> <p>Software requirements:<br>- R v4.3+: https://cran.r-project.org/bin/windows/base/<br>- RStudio: v2023.06.0+: https://posit.co/download/rstudio-desktop/<br>- Package dependencies: raster, sf, tidyverse, rasterVis, rgdal, maptools, pbapply, terra, knitr, kableExtra, modelsummary, openxlsx, xtable, ggforce, maptools, weights, spatstat, rworldmap, scales, patchwork, stars, viridis, devtools, stargazer, readxl, nominatimlite, urbnmapr</p> <p>To replicate the analysis:<br>- Clone the replication code repository from https://github.com/giacfalk/aging_climate<br>- Download input data from this Zenodo data repository<br>- Download all the 1km age and gender-stratified global population counts rasters from the following WorldPop page https://hub.worldpop.org/geodata/summary?id=24798 and put them in a subdirectory of the working directory called "AGEPOP"<br>- Run the "project_pop.R" script to generate gridded age-stratified population data for each SSP scenario<br>- Run the "compare_pop_projections.R" file to compare the generated gridded age-stratified population data with an array of pre-existing sources from different countries and produce a summary comparison table (NOTE: before running the script, decompress the "new_comparison_data.zip" folder into the working directory)<br>- Run "projections_exposure_m.R" to quantify heat exposure and generate the figures and tables reported in the paper</p> <p>To process the data and run succesfully, the script requires a computer with at least 32GB RAM. The running time varies based on CPU characteristics, but a runtime of at least 2 hours should be expected to generate all the output data, figures, and tables. All output files are saved in the working directory.</p> <p>___</p> <p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.</p> <div> <div> <div> </div> <div> <div> <div> </div> <div> <p> </p> <p> </p> </div> </div> </div> </div> </div>
Data set for "Understanding Older Adults' Needs: Psychosocial Wellbeing in Context of Perceived and Objective Built Environment"
<p>This entry contains datasets for the article "Understanding Older Adults' Needs: Psychosocial Wellbeing in Context of Perceived and Objective Built Environment".</p>
Raw data: Validity of fitness trackers when worn by older adults
<p>Co-authors of the dataset:</p> <p>Marina Dobnik <sup>2</sup> , Stefan Loefler <sup>3,4,6</sup> , Christian Hofer <sup>4</sup> and Nejc Šarabon <sup>2,5</sup></p> <p><sup>1</sup> University of Primorska, Andrej Marušič Institute, Muzejski trg 2, 6000 Koper, Slovenia; kaja.kastelic@iam.upr.si</p> <p><sup>2</sup> University of Primorska, Faculty of Health Sciences, Polje 42, 6310 Izola, Slovenia; <a href="mailto:nejc.sarabon@fvz.upr.si">nejc.sarabon@fvz.upr.si</a></p> <p><sup>3</sup> Physiko- & Rheumatherapie, Institute for Physical Medicine and Rehabilitation, 3100 St. Pölten, Austria; stefan.loefler@kern-reha.at</p> <p><sup>4</sup> Ludwig Boltzmann Institute for Rehabilitation Research, Neugebäudeplatz 1, 3100 St. Pölten, Austria; christian.hofer@rehabilitationresearch.eu</p> <p><sup>5</sup> InnoRenew CoE, Livade 6, 6310 Izola, Slovenia</p>
Technology use characteristics among older adults during the COVID-19 pandemic: A cross-cultural survey - DATASET
<p>Data to support the findings included in the Journal paper</p> <p>Elimelech, O. C., Ferrante, S., Josman, N., Meyer, S., Lunardini, F., Gómez-Raja, J., ... & Rosenblum, S. (2022). Technology use characteristics among older adults during the COVID-19 pandemic: A cross-cultural survey. <em>Technology in Society</em>, <em>71</em>, 102080.</p> <p>These data were collected in the context of the ESSENCE project, and are aimed at understanding technology use characteristics among older adults during the COVID-19 pandemic. Indeed, the ESSENCE project aims at transforming the lessons learnt from the COVID-19<strong> </strong>in a huge opportunity that exploits technology toward a deep evolution of the services targeting vulnerable populations: non- or pre-frail seniors (targets of this study), and children of the first years of primary school. To contribute to the public health response in the context of the ongoing epidemic, and preparedness for future emergencies, ESSENCE aims at boosting the creation of a new model of home-based care that relies on stimulation, remote monitoring, tele-assistance, and connection between users, families, and professionals. Indeed, it was important to understand technology usage in the target population.</p> <p>Data were collected from Israel, Spain and France, in a multi-country design.</p> <p> </p> <p>The .PDF file contains the questionnaire. For more details, refer to the journal paper linked to this dataset.</p> <p>The .SAV file is an SPSS file that contains the answers to the questionnaire and their encoding.</p>
Contact data of older adults (70+) in the Netherlands in 2021
<p>SCONE (Studying CONtacts of Elderly): Contact data of frail and non-frail older adults (70+) collected in the Netherlands during two survey periods in 2021</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.