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162 results for “Mobile Technologies”
Accurately Inferring Personality Traits from the Use of Mobile Technology
<p>This dataset contains the features extracted from Spatio-Temporal Mobility and Context of Use and the Big5 scores from the 50-item IPIP survey of 55 volunteers from 6 countries located in 2 continents.</p> <p>The authors predict the Big5 traits by fitting 5 regularized linear regression models, one per trait, and select the regularization parameter and evaluate the prediction performance through nested leave-one-out cross validation.</p> <p><em><strong>Feature extraction pipeline</strong></em></p> <p>For each volunteer, we start the pipeline with 5 time series encoding, in time, her WGS84 coordinates (latitude and longitude), measurements related to her smartphone's battery (charging status and level), surrounding WiFi APs and BT devices, and whether her phone was connected to a WiFi access point.</p> <p>First, we refine the 5 raw time series to accurately describe the spatio-temporal mobility and the context of our volunteers. For example, we create a binary time series that peaks when the user is at home, or when the user is at work, and so on.</p> <p>Next, we process both the refined and the raw time series to extract the features, as follows:</p> <ol> <li><strong>Statistical Features</strong>: We divide the raw time series in intervals of one day. We aggregate the different values within each day into a single numerical measurement (e.g., by computing the average, the count of unique values, the information entropy, or the repetitiveness). Finally, we aggregate the measurements obtained across all days into a single value --- the value of that feature for the selected user --- by measuring the mean (<em>avg</em>), the standard deviation (<em>std</em>), and the coefficient of variation (<em>cov</em>). Features prefixed with <em>avg</em>, <em>std</em>, or <em>cov, </em>have been extracted as described here.</li> <li><strong>Spectral Analysis Features</strong>: We first apply the DFT to the raw time series. Then, we measure: <ol> <li>The frequency of highest energy (we prefix its name with <em>top_frequency</em>);</li> <li>The <em>periodicity</em> of the series in the frequency domain;</li> <li>The energy at the daily and weekly frequencies (<em>daily_energy </em>and <em>weekly_energy</em>);</li> <li>The frequency, the periodicity, and the daily and weekly energy obtained after processing the time series with Welch's method and a two weeks window (<em>w_top_frequency</em>, <em>w_periodicity</em>, <em>w_daily_energy, w_weekly_energy);</em></li> <li>The euclidean distance between the DFT and a pure sine wave with period equivalent to the top frequency of the series (<em>distance_from_sine</em>).</li> </ol> </li> </ol> <p>The string <em>b_day </em>in each name specifies that the features only consider business days (i.e. they exclude holidays and weekends).</p> <p>The 5 columns named O, C, E, A, and N, score the users on the Big5 and represent the prediction targets.</p> <p><em><strong>Source code</strong></em></p> <p>The Python source code developed to engineer and evaluate the embeddings is available <a href="https://www.dropbox.com/s/0nmivoftdfzq4ss/OCEAN_sources.zip?dl=0">here</a>.</p>
Mobile Technology and Data Analytics to Identify Real-time Predictors of Caregiver Well-Being
ClinicalTrials.gov study NCT04556591. IPD Sharing: YES. Countries: 1. Publications: 4.
Using Mobile Technology to Improve Self-Regulation
ClinicalTrials.gov study NCT03774433. IPD Sharing: YES. Countries: 1. Publications: 1.
CHALO! 2.0: A Mobile Technology Based Intervention to Accelerate HIV Testing and Linkage to Preventive Treatment.
ClinicalTrials.gov study NCT04814654. IPD Sharing: YES. Countries: 1. Publications: 1.
Using Mobile Technology to Better Understand and Measure Self-Regulation
ClinicalTrials.gov study NCT03352713. IPD Sharing: YES. Countries: 1. Publications: 2.
Patent Landscape of Mobile Device Technologies in India
<p>This dataset contains a landscape of 23,569 patents and patent applications registered in India and relevant to the domain of mobile technology. These patents and patent applications are held by 50 Indian and non-Indian companies operating in the country. The patent landscape has been released under the Creative Commons-Attribution-Share Alike 4.0 (CC-BY-SA 4.0) License as a part of the Pervasive Technologies research project at the Centre for Internet and Society, India. </p> <p>This study was conducted by Prof Jorge Contreras at the University of Utah, USA, and Rohini Lakshané at the Centre for Internet and Society, India. </p> <p>For the detailed methodology used for drawing up this landscape, refer to: <a href="http://cis-india.org/a2k/blogs/patent-landscaping-in-the-indian-mobile-device-market">http://cis-india.org/a2k/blogs/patent-landscaping-in-the-indian-mobile-device-market</a></p> <p>A paper titled "<a href="https://scholarship.law.vanderbilt.edu/vjtl/vol50/iss1/1/">Patents and Mobile Devices in India: An Empirical Survey</a>" published in the Vanderbilt Journal of Transnational Law (2017) presents an analysis of this patent landscape.</p> <p>For queries regarding the dataset or its reuse, write to rohini dot lakshane at gmail dot com. </p>
Black American women's attitudes toward seeking mental health services and use of mobile technology to support the management of anxiety
<p><strong>Objective</strong>: This study aimed to understand Black American women's attitudes toward seeking mental health services and using mobile technology to receive support for managing anxiety.</p> <p><strong>Methods</strong>: A self-administered web-based questionnaire was launched in October 2019 and closed in January 2020. Women who identify as Black/African American were eligible to participate. The survey consisted of approximately 70 questions and covered topics such as attitudes toward seeking professional psychological help, acceptability of using a mobile phone to receive mental health care, and screening for anxiety.</p> <p><strong>Results</strong>: The findings of the study (N=395) showed that younger Black women were more likely to have greater severity of anxiety than their older counterparts. Respondents were most comfortable with the use of a voice call or video call to communicate with a professional to receive support to manage anxiety in comparison to text messaging or mobile app. Younger age, higher income, and greater scores for psychological openness and help-seeking propensity increased the odds of indicating agreement with using mobile technology to communicate with a professional. Black women in the South region of the U.S. had twice the odds of agreeing to the use of mobile apps than women in the Midwest and Northeast regions.</p> <p><strong>Discussion</strong>: Black American women, in general, have favorable views toward the use of mobile technology to receive support to manage anxiety.</p> <p><strong>Conclusion</strong>: Preferences and cultural appropriateness of resources should be assessed on an individual basis to increase the likelihood of adoption of and engagement with digital mental health interventions for management of anxiety. </p>
Mobile Technology for Blood Pressure Management
ClinicalTrials.gov study NCT04841317. IPD Sharing: NO. Countries: 1. Publications: 7.
Use of Mobile Technology to Prevent Progression of Pre-hypertension in Latin American Urban Settings
ClinicalTrials.gov study NCT01295216. IPD Sharing: Not stated. Countries: 3. Publications: 3.
Mindfulness-based Smoking Cessation Enhanced With Mobile Technology
ClinicalTrials.gov study NCT04965181. IPD Sharing: Not stated. Countries: 1. Publications: 2.
VillageWhere: Innovative Mobile Technology for Youth With Conduct Disorder and Their Parents
ClinicalTrials.gov study NCT03065517. IPD Sharing: YES. Countries: 1. Publications: 1.
Using Cognitive-Behavioral Change and Mobile Technology to Improve RN Sleep and Fatigue
ClinicalTrials.gov study NCT06105307. IPD Sharing: YES. Countries: 1. Publications: 1.
Smoking, Stress, HIV and Mobile Technology
ClinicalTrials.gov study NCT05709002. IPD Sharing: NO. Countries: 1. Publications: 1.
mDiet - Ecological Momentary Intervention of Diet Via Mobile Technology
ClinicalTrials.gov study NCT00415870. IPD Sharing: Not stated. Countries: 1. Publications: 2.
SMS Mobile Technology for Vaccine Coverage and Acceptance
ClinicalTrials.gov study NCT01663636. IPD Sharing: Not stated. Countries: 1. Publications: 1.
TeleRehab for Stroke Patients Using Mobile Technology
ClinicalTrials.gov study NCT02615132. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Using Mobile Technology to Address the Trauma Mental Health Treatment Gap
ClinicalTrials.gov study NCT05374408. IPD Sharing: NO. Countries: 1. Publications: 3.
Mobile Technology to Extend Clinic-based Counseling for HIV+s in Uganda
ClinicalTrials.gov study NCT03928418. IPD Sharing: YES. Countries: 1. Publications: 7.
Reminiscence Technology Balance and Mobility
ClinicalTrials.gov study NCT04043416. IPD Sharing: NO. Countries: 1. Publications: 10.
Applying Mobile Persuasive Technologies to Increase Physical Activity in Women
ClinicalTrials.gov study NCT01280812. IPD Sharing: NO. Countries: 1. Publications: 9.
ScienceDex guides
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