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1,047 results for “disability”
Characterization of a novel mutation in O-GlcNAc transferase that segregates with disease in a family with X-linked intellectual disability
GEO Series GSE74263. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
RNA sequencing and pathway analysis identify important pathways involved in hypertrichosis and intellectual disability in patients with Wiedemann-Steiner syndrome
GEO Series GSE115969. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing.
Data for: Proteomics reveal biomarkers for diagnosis, disease activity and long-term disability outcomes in multiple sclerosis
<p>Protein levels were measured in cerebrospinal fluid samples (CSF; n = 186) and plasma samples (n = 165) from persons with multiple sclerosis and healthy controls. CSF samples and plasma samples were taken from 92 persons with CIS or RRMS at Linköping University Hospital, Sweden and 51 persons with CIS or RRMS at the Karolinska University Hospital, Sweden. Everyone fulfilled the revised McDonald criteria from 2010 and 2017 for CIS or Multiple sclerosis (MS). Age-matched healthy controls (HC) were recruited from healthy blood donors (23 at the Linköping University hospital and 20 at the Karolinska University Hospital). The concentration of 1463 proteins were measured using the Olink Explore platform which uses Proximity Extension Assay (PEA) technology. The proteins were preselected from four Olink panels: Explore 384 Cardiometabolic, Explore 384 Inflammation, Explore 384 Neurology, and Explore 384 Oncology. The protein concentrations are given as Olink’s relative protein quantification unit on log2 scale: Normalized Protein Expression (NPX). The NPX values were intensity normalized by Olink.</p>
Human Activity Recognition Dataset for Pedestrians with Mobility Disabilities
<p>Human Activity Recognition Dataset for Pedestrians with Mobility Disabilities</p> <p><strong>Note</strong>: The dataset is licensed and shared under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/. If you want to refer to this dataset in a publication, please use the reference below: Author names: removed for anonymous review. 2024. Human Activity Recognition Dataset for Pedestrians with Mobility Disabilities Sci. Data 00, 000.</p> <p>1 HAR-PMD Dataset</p> <p>HAR-PMD(Human Activity Recognition for Pedestrians with Mobility Disabilities) dataset consists of smartphone and smartwatch sensor data from six pedestrian activities for people with mobility difficulties: still, walking, crutches, walker, manual wheelchairs, and electric wheelchairs. Sixty participants collected smartphone data, and sixty additional participants collected both smartphone and smartwatch data. Each activity was conducted in both indoor and outdoor environments. Thirteen smartphone sensors and five smartwatch sensors were collected. As a result, the dataset consists of 14,400 minutes of data from 120 participants.</p> <p>For detailed descriptions of activities, environments, sensors, positions, data collection application, and data collection procedure, see METHODS in the paper.</p> <p> </p> <p>2 DATA FORMAT</p> <p>2.1 HAR-PMD Dataset</p> <p>The dataset consists of 120 folders for each individual. Each folder contains metadata, smartphone, and smartwatch data if collected. Smartphone and smartwatch data are stored in comma-separated values (CSV) format files.</p> <p>2.1.1 Metadata</p> <ul> <li><user_id>/device.txt – smartphone model of the participant</li> <li><user_id>/os_version.txt – Android OS version of the smartphone</li> </ul> <p>2.1.2 Smartphone</p> <ul> <li><user_id>/<device>/<user_id>_<activity>_<device>_<environment>.csv – collected smartphone sensor data <ul> <li><user_id>: 1 – 120</li> <li><device>: phone</li> <li><activity>: still, walking, crutches, walker, manual (manual wheelchair), or electric (electric wheelchair)</li> <li><environment>: indoor or outdoor</li> </ul> </li> </ul> <p>2.1.3 Smartwatch</p> <ul> <li><user_id>/<device>/<user_id>_<activity>_<device>_<environment>.csv – collected smartwatch sensor data <ul> <li><user_id>: 61 – 120</li> <li><device>: watch</li> <li><activity>: still, walking, crutches, walker, manual (manual wheelchair), or electric (electric wheelchair)</li> <li><environment>: indoor or outdoor</li> </ul> </li> </ul> <p>2.2 Data Files</p> <p>2.2.1 Smartphone sensor data</p> <p>Table 1 describes each column in a smartphone sensor data CSV file. For more detailed descriptions, see <a href="https://developer.android.com/guide/topics/sensors/sensors_overview">Android Developers Sensors Documents</a>.</p> <p><Table 1: Smartphone sensor data description for each column></p> <table> <tbody> <tr> <th>Columns</th> <th>Description (unit)</th> </tr> <tr> <td>Time</td> <td>Timestamp (s)</td> </tr> <tr> <td>LAccX</td> <td>Acceleration force excluding gravity along the x-axis (m/s²)</td> </tr> <tr> <td>LAccY</td> <td>Acceleration force excluding gravity along the y-axis (m/s²)</td> </tr> <tr> <td>LAccZ</td> <td>Acceleration force excluding gravity along the z-axis (m/s²)</td> </tr> <tr> <td>GyrX</td> <td>Rate of rotation around the x axis (rad/s)</td> </tr> <tr> <td>GyrY</td> <td>Rate of rotation around the y axis (rad/s)</td> </tr> <tr> <td>GyrZ</td> <td>Rate of rotation around the z axis (rad/s)</td> </tr> <tr> <td>MagX</td> <td>Geomagnetic field strength along the x-axis (μT)</td> </tr> <tr> <td>MagY</td> <td>Geomagnetic field strength along the y-axis (μT)</td> </tr> <tr> <td>MagZ</td> <td>Geomagnetic field strength along the z-axis (μT)</td> </tr> <tr> <td>GraX</td> <td>Force of gravity in the x-axis (m/s²)</td> </tr> <tr> <td>GraY</td> <td>Force of gravity in the y-axis (m/s²)</td> </tr> <tr> <td>GraZ</td> <td>Force of gravity in the z-axis (m/s²)</td> </tr> <tr> <td>AccX</td> <td>Acceleration force including gravity along the x-axis (m/s²)</td> </tr> <tr> <td>AccY</td> <td>Acceleration force including gravity along the y-axis (m/s²)</td> </tr> <tr> <td>AccZ</td> <td>Acceleration force including gravity along the z-axis (m/s²)</td> </tr> <tr> <td>Ori_Azimuth</td> <td>Angle around the x-axis (rad)</td> </tr> <tr> <td>Ori_Pitch</td> <td>Angle around the y-axis (rad)</td> </tr> <tr> <td>Ori_Roll</td> <td>Angle around the z-axis (rad)</td> </tr> <tr> <td>RotVec_0</td> <td>Rotation vector component along the x-axis (unitless)</td> </tr> <tr> <td>RotVec_1</td> <td>Rotation vector component along the y-axis (unitless)</td> </tr> <tr> <td>RotVec_2</td> <td>Rotation vector component along the z-axis (unitless)</td> </tr> <tr> <td>RotVec_3</td> <td>Scalar component of the rotation vector (unitless)</td> </tr> <tr> <td>Game_RotVec_0</td> <td>Rotation vector without using geomagnetic filed component along the x-axis (unitless)</td> </tr> <tr> <td>Game_RotVec_1</td> <td>Rotation vector without using geomagnetic filed component along the y-axis (unitless)</td> </tr> <tr> <td>Game_RotVec_2</td> <td>Rotation vector without using geomagnetic filed component along the z-axis (unitless)</td> </tr> <tr> <td>Game_RotVec_3</td> <td>Scalar component without using geomagnetic filed of the rotation vector (unitless)</td> </tr> <tr> <td>Pressure</td> <td>Ambient air pressure (hPa)</td> </tr> <tr> <td>Height</td> <td>Altitude (m)</td> </tr> <tr> <td>Light</td> <td>Illuminance (lx)</td> </tr> <tr> <td>Step</td> <td>Number of steps (steps)</td> </tr> <tr> <td>Proxi</td> <td>Proximity (cm)</td> </tr> </tbody> </table> <p> </p> <p>2.2.2 Smartwatch sensor data</p> <p>Table 2 describes each column in a smartwatch sensor data CSV file. For more detailed descriptions, see <a href="https://developer.android.com/guide/topics/sensors/sensors_overview">Android Developers Sensors Documents</a>.</p> <p><Table 2: Smartwatch sensor data description for each column></p> <table style="width: 42.344%; height: 382.282px;"> <tbody> <tr style="height: 19.5938px;"> <th style="width: 16.3311%; height: 19.5938px;">Columns</th> <th style="width: 83.6689%; height: 19.5938px;">Description (unit)</th> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">Time</td> <td style="width: 83.6689%; height: 19.5938px;">Timestamp (s)</td> </tr> <tr style="height: 39.1875px;"> <td style="width: 16.3311%; height: 39.1875px;">LAccX</td> <td style="width: 83.6689%; height: 39.1875px;">Acceleration force excluding gravity along the x-axis (m/s²)</td> </tr> <tr style="height: 39.1875px;"> <td style="width: 16.3311%; height: 39.1875px;">LAccY</td> <td style="width: 83.6689%; height: 39.1875px;">Acceleration force excluding gravity along the y-axis (m/s²)</td> </tr> <tr style="height: 39.1875px;"> <td style="width: 16.3311%; height: 39.1875px;">LAccZ</td> <td style="width: 83.6689%; height: 39.1875px;">Acceleration force excluding gravity along the z-axis (m/s²)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">GyrX</td> <td style="width: 83.6689%; height: 19.5938px;">Rate of rotation around the x axis (rad/s)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">GyrY</td> <td style="width: 83.6689%; height: 19.5938px;">Rate of rotation around the y axis (rad/s)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">GyrZ</td> <td style="width: 83.6689%; height: 19.5938px;">Rate of rotation around the z axis (rad/s)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">MagX</td> <td style="width: 83.6689%; height: 19.5938px;">Geomagnetic field strength along the x-axis (μT)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">MagY</td> <td style="width: 83.6689%; height: 19.5938px;">Geomagnetic field strength along the y-axis (μT)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">MagZ</td> <td style="width: 83.6689%; height: 19.5938px;">Geomagnetic field strength along the z-axis (μT)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">GraX</td> <td style="width: 83.6689%; height: 19.5938px;">Force of gravity in the x-axis (m/s²)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">GraY</td> <td style="width: 83.6689%; height: 19.5938px;">Force of gravity in the y-axis (m/s²)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">GraZ</td> <td style="width: 83.6689%; height: 19.5938px;">Force of gravity in the z-axis (m/s²)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">AccX</td> <td style="width: 83.6689%; height: 19.5938px;">Acceleration force including gravity along the x-axis (m/s²)</td> </tr> <tr style="height: 10px;"> <td style="width: 16.3311%; height: 10px;">AccY</td> <td style="width: 83.6689%; height: 10px;">Acceleration force including gravity along the y-axis (m/s²)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">AccZ</td> <td style="width: 83.6689%; height: 19.5938px;">Acceleration force including gravity along the z-axis (m/s²)</td> </tr> </tbody> </table> <p> </p> <p><span lang="EN-US">3. Benchmark Experiment</span></p> <p><span lang="EN-US">We applied four machine learning models (Decision Tree, Random Forest, XGBoost, and Support Vector Machine) and four deep learning models (Multilayer Perceptron, Convolutional Neural Networks, Long Short-Term Memory (LSTM), and Transformer) to conduct the benchmark experiments. </span></p> <p><span lang="EN-US">We had three validation scenarios: a) Mobility disability; b) Walking aids & wheelchairs; and c) Mobility in detail. Data from all participants and activities were used for each validation scenarios. Each scenario was evaluated using two validation methods: user-depedent (random) evaluation and user-independent evaluation. In the user-dependent (random) evaluation, we randomly shuffled the data and divided the 14,400 min of data into five subsets of 2,880 min each for smartphone sensor data. After shuffling and splitting the data, one subset was allocated for testing while the other four subsets were used for training the model, and this process was repeated for all five subsets. For the combination of smartphone and smartwatch sensors, 7,200 min of data was randomly shuffled and divided the five subsets of 1,440 min data. Each subset was again allocated for testing while the other four subsets were used for training. In the user-independent (UI) evaluation, we used leave-one-group-out 5-fold cross-validation. For smartphone sensor data, 120 participants who comprised the data were divided into five equally distributed subsets of 24 participants each. The models were trained using the data of 96 participants and tested one of the data for the remaining 24 participants. For the combination of smartphone and smartwatch sensors, as the smartwatch data consisted of 60 participants, the data from 48 participants were used for training and the remaining 12 participants were used for testing. This list of participants included in each fold is shown in the below table. In the combination of sensor, we used three sensor combinations: a) linear accelerometer; b) linear accelerometer and gyroscope, and c) linear accelerometer, gyroscope, and magnetometer. Data from all participants and activities were used for each sensor scenarios.</span></p> <p> </p> <table style="width: 67.8639%; height: 136.172px;"> <tbody> <tr style="height: 33.6094px;"> <th style="width: 16.4575%; height: 33.6094px;"> </th> <th style="width: 17.0153%; height: 33.6094px;">Fold 1</th> <th style="width: 16.8759%; height: 33.6094px;">Fold 2</th> <th style="width: 16.5962%; height: 33.6094px;">Fold 3</th> <th style="width: 16.3188%; height: 33.6094px;">Fold 4</th> <th style="width: 16.7364%; height: 33.6094px;">Fold 5</th> </tr> <tr style="height: 47.1875px;"> <td style="width: 16.4575%; height: 47.1875px;">Smartphone only</td> <td style="width: 17.0153%; height: 47.1875px;">1-24</td> <td style="width: 16.8759%; height: 47.1875px;">25-48</td> <td style="width: 16.5962%; height: 47.1875px;">49-72</td> <td style="width: 16.3188%; height: 47.1875px;">73-96</td> <td style="width: 16.7364%; height: 47.1875px;">96-120</td> </tr> <tr style="height: 55.375px;"> <td style="width: 16.4575%; height: 55.375px;">Smartphone & smartwatch</td> <td style="width: 17.0153%; height: 55.375px;">61-72</td> <td style="width: 16.8759%; height: 55.375px;">73-84</td> <td style="width: 16.5962%; height: 55.375px;">85-96</td> <td style="width: 16.3188%; height: 55.375px;">97-108</td> <td style="width: 16.7364%; height: 55.375px;">109-120</td> </tr> </tbody> </table> <p> </p>
Dataset for "Understanding the Predictors of Perceived Disability in Older Adults: A Bayesian Structural Equation Model Approach" study
<p>SF-12, CES-D, GES, Activity level & GARS responses sourced from 473 participants via online questinnaire. </p>
Dietary Inflammatory Index and Elderly Disability in Guangzhou
<p>This is a dataset of 262 disabled elderly individuals aged 65 and above from Guangzhou, including demographic information, disability assessments, and dietary data.</p>
Mechanisms of Disability in Peripheral Arterial Disease
ClinicalTrials.gov study NCT00046592. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Circadian regulation of Intellectual Disability genes in the hippocampus, a brain oscillator
GEO Series GSE66875. Mus musculus. 12 samples. Type: Expression profiling by array.
The X-Linked Intellectual Disability gene, ZDHHC9, is important for oligodendrocyte maturation and myelin formation
GEO Series GSE236665. Mus musculus. 20 samples. Type: Expression profiling by high throughput sequencing.
Wnt/β-catenin pathway and cell adhesion deregulation in CSDE1-related intellectual disability and autism spectrum disorders
GEO Series GSE162954. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
DNA methylation fingerprint of a KDM5C loss-of-function mutation in patients with intellectual disability
GEO Series GSE108423. Homo sapiens. 21 samples. Type: Methylation profiling by genome tiling array.
Integrated Genomic Analysis of a Stem Cell Model of Overgrowth Syndrome with Intellectual Disability (WGBS)
GEO Series GSE116420. Mus musculus. 13 samples. Type: Methylation profiling by high throughput sequencing.
Affymetrix CytoScan 750K and HD data for developmental delay and/or physical disability phenotypes samples
GEO Series GSE120624. Homo sapiens. 83 samples. Type: Genome variation profiling by SNP array; Genome variation profiling by genome tiling array.
Investigating learning disabilities: fetal alcohol exposed rats. Perrone-Bizzozero-5R01NS030255-12-2
GEO Series GSE1996. Rattus norvegicus. 18 samples. Type: Expression profiling by array.
Transcriptome profiling reveals novel Disabled-2-associated downstream effectors in mouse embryonic stem cells (ESC)
GEO Series GSE17592. Mus musculus. 2 samples. Type: Expression profiling by array.
Rehabilitation Outcomes of Patients With Severe Disability Poststroke
<p><strong>Objective:</strong> To characterize rehabilitation outcomes of patients with severe poststroke motor impairment (MI) and develop a predictive model for treatment failure.</p> <p><strong>Design</strong>: Retrospective cohort study. Correlates of treatment failure, defined as the persistence of severe MI after rehabilitation, were identified using logistic regression analysis. Then, an integer-based scoring rule was developed from the logistic model.</p> <p><strong>Setting</strong>: Three specialized inpatient rehabilitation facilities.</p> <p><strong>Participants</strong>: Patients (N=1265) classified as case-mix groups (CMGs) 0108, 0109, and 0110 of the Medicare classification system.</p> <p><strong>Interventions</strong>: Not applicable.</p> <p><strong>Main outcome measure</strong>: Change in the severity of MI, as assessed by the FIM, from admission to discharge.</p> <p><strong>Results</strong>: Median FIM-motor (FIM-M) score increased from 17 (interquartile range [IQR] 14-23) to 38 (IQR, 25-55) points. Median proportional recovery, as expressed by FIM-M effectiveness, was 26% (IQR, 12-47). Median FIM-M change was 18 (IQR, 9-34) points. About 38.5% patients achieved the minimal clinically important difference. Eighteen point six percent and 32.0% of the patients recovered to a stage of either mild (FIM-M ≥62) or moderate (FIM-M 38-61) MI, respectively. All between-CMG differences were statistically significant. Outcomes have also been analyzed according to classification systems used in Australia and Canada. The scoring rule had an area under the curve of 0.833 (95% confidence interval, 0.808-0.858). Decision curve analysis displayed large net benefit of using the risk score compared with the treat all strategy.</p> <p><strong>Conclusions</strong>: This study provides a snapshot of rehabilitation outcomes in a large cohort of patients with severe poststroke MI, thus filling a gap in knowledge. The scoring rule accurately identified the patients at risk for treatment failure.</p> <p> </p>
Effects of cervical traction, infrared therapy, and selected biomarkers on neck disability indexes among people with cervical spondylosis: A sequential clinical trial
<p><strong>Background</strong></p> <p><strong>Background </strong></p> <p>Cervical traction (CT) is among the adjunct physiotherapy technique for conservative management of cervical spondylosis CS. The mechanism of action of traction involves vertebral joint distraction which loses adhesions within the dural sleeves, reduces axial forces on intervertebral discs and intervertebral foramen, reduces nerve root irritation, inflammatory pain responses, and muscle spasm, and improve circulation within the epidural space. Furthermore, bloodborne biomarker of inflammation (interleukin-6 [IL-6], and osteoprotegerin [OPG]), hormones (estradiol [E2], and testosterone [TES]), and biochemical mediators of bone metabolism such as calcium (Ca2), and magnesium (Mg) may play a significant role in CS.</p> <p><strong>Objectives</strong></p> <p>To investigate (a) the effects of six-week alternate CT and infrared therapy (IRT) on neck pain and neck disability index (NDI), (b) and the influence of serum levels of IL-6, OPG, E2, TES, Ca2, Mg, and Ca2/Mg ratio on the reported levels of pain intensity (PI) and NDI among patients with cervical spondylosis, referred for physiotherapy in Enugu, Nigeria.</p> <p><strong>Dataset/Variables Description </strong></p> <p>Sex (Male =1, Female =2), Age, Weight, Height, and Pain duration were measure only at baseline.</p> <p>Systole, Diastole, and Pulse Rate were measured at baseline (labelled as PreSystole, PreDiastole, and PrePR) and after the final intervention (13 weeks) labelled as PostSystole, PostDiastole, and PostPR.</p> <p>Numeric Pain Rating (NRS), Neck Disability Index (NDI), OPG, IL6, EST(E2), TES, Ca2, and Mg were measured four times: </p> <p>At baseline labelled with "a" such as NRS_a, NDI_a, OPG_a etc</p> <p>After first intervention (6 weeks) and labelled with "b" example IL6_b, TES_b</p> <p>After wash out (1 week rest) labelled as "c"</p> <p>After the final intervention (6 weeks) labelled as "d"</p>
Disability and mortality in a cohort of MS patients: how the real-world scenario is changed
<p>Demographical (sex and age) and clinical findings (functional system involved at MS onset, MS type, MS disability evaluated via EDSS scale, time to MS progression and use of disease modifying treatment) collected in a cohort of 86 patients with MS and with first hospitalization at IRCCS Mondino, Pavia (italy) in 2005-2006 and followed until 2018. </p>
dataset related to article "Adverse Events in Neurosurgery: The Novel Therapy-Disability-Neurology Grade"
<p>Dataset related to publication at title</p>
Dataset related to article: "Pain, disability and adherence to home exercises in patients with chronic neck pain: long term effects of phone surveillance. A randomized controlled study"
<p>We provide the raw data used for the following article:</p> <ul> <li>Gialanella B, Comini L, Olivares A, Gelmini E, Ubertini E, Grioni G.<br> <em>Pain, disability and adherence to home exercises in patients with chronic neck pain: long term effects of phone surveillance. A randomized controlled study.</em><br> "Eur J Phys Rehabil Med." 2020 Feb;56(1):104-111. doi: 10.23736/S1973-9087.19.05686-7. Epub 2019 Jun 4. PMID: 31165606.</li> </ul> <p> </p> <p><strong>ABSTRACT<br> BACKGROUND: </strong>Effectiveness of phone surveillance and other communication technologies in the management of neck pain patients have been evaluated previously only at the suspension of the service.<br> <strong>AIM: </strong>To verify whether a phone surveillance program can improve pain, disability, and adherence to home exercises in neck pain patients, and whether the improvement achieved continues to be maintained also after suspension of the support.<br> <strong>DESIGN:</strong> This is a randomized controlled study.<br> <strong>SETTING:</strong> Outpatients of Rehabilitation Unit.<br> <strong>POPULATION:</strong> 84 out of 100 outpatients consecutively randomized (by blocks of four) to Phone group (N.=42, performing a 6-month home-based phone surveillance program) or to Control group (N.=42, with the only recommendation to continue exercising at home without phone surveillance) were considered. The home-based phone surveillance program consisted of 12 scheduled phone calls, each performed every fortnight by a nurse-tutor with expertise in rehabilitation for the first six months of the study. At the end of phone surveillance period, Phone patients were encouraged to continue home exercises for a further 6 months period.<br> <strong>METHODS:</strong> Pain severity (assessed with Pain VAS), disability (Neck Disability Index), and adherence to exercises performed at home (classified as: ≥5 sessions/week, 2-4 sessions/week, occasional or no sessions) were outcome measures. Pain severity and disability were assessed at entry, at 6 and 12 months, while adherence to exercises was self-reported and recorded at 6 and 12 months. Differences between groups were analyzed with χ2 test, Student’s t-test or ANOVA.<br> <strong>RESULTS:</strong> At 6 months, Pain VAS (P=0.013) and Neck Disability Index scores (P=0.012) were lower in Phone patients than Controls. At 12 months, Neck Disability Index scores (P=0.026) continued to be lower in Phone patients than Controls. At 6 months, 97.6% of Phone patients and 80.9% of Controls performed rehabilitation at home with a range of 2 to 7 sessions/week; while at 12 months, the respective percentages of Phone and Control patients were 92.9% and 73.8%. Adherence to home exercises was higher in Phone patients than in Controls at 6 (P=0.013) and 12 months (P=0.019).<br> <strong>CONCLUSIONS: </strong>In patients with chronic neck pain, the positive effects of phone surveillance on the neck disability and adherence to home exercises still persist 6 months after the suspension of the support.<br> <strong>CLINICAL REHABILITATION IMPACT:</strong> This knowledge can be useful for physicians to plan home rehabilitation of neck pain patients.</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.