scholarly journals Mobile applications for data collection in longitudinal epidemiological studies: Possibilities, problems and perspectives identified by a scoping review (Preprint)

2020 ◽  
Author(s):  
Florian Fischer ◽  
Sina Kleen
Keyword(s):  
Data Collection ◽  
Real Time ◽  
Scoping Review ◽  
Mobile Applications ◽  
Patient Data ◽  
Smartphone App ◽  
Smartphone Apps ◽  
Epidemiological Research ◽  
Related Data ◽  
Health Apps

BACKGROUND The broad availability of smartphones and the number of health applications (health apps) in app stores have risen in recent years. Health apps have benefits for individuals to monitor their health as well as for the researcher to collect data in population-based, clinical, and observational studies. Although the number of health apps on the global app market is huge and its potential seems to be high, smartphone app-based questionnaires for collecting patient-related data have not played an important role so far. OBJECTIVE This study aims to provide an overview of studies that have collected patient data using an app-based approach, with a particular focus on longitudinal studies. This literature review describes the current state of affairs in terms of the extent to which smartphones have been used for collecting (patient) data for research purposes, and the potentials and challenges associated with this approach. METHODS A scoping review of studies using data collection via apps was conducted. PubMed was used to identify studies describing the utilization of smartphone app questionnaires for collecting data over time. Overall, 17 articles were included in the summary. RESULTS There are only a few studies integrating smartphone apps into data-collection approaches. Studies dealing with the collection of health-related data via smartphone apps have mainly been developed in the field of psychosomatic, neurodegenerative, respiratory, and cardiovascular diseases, as well as malign neoplasm. The study duration for data collection varied from four weeks to twelve months, and the participants’ mean ages ranged from 7 to 69 years. Potential can be seen for real-time information transfer, fast data synchronization from entry to provision (which saves time and increases effectivity), and the possibility of tracking responses longitudinally. Furthermore, smartphone-based data-collection techniques might prevent biases such as reminder bias or mistakes occurring during manual data transfers. In chronic diseases, real-time communication with the physician and early detection of symptoms enable rapid modifications in the management of the disease. CONCLUSIONS The results indicate that using mobile technologies can help to overcome challenges linked with data collection in epidemiological research. However, further feasibility studies need to be conducted in the near future to test the applicability and acceptance of these mobile applications for epidemiological research in various subpopulations. CLINICALTRIAL

scholarly journals Guidelines and Benchmarks for Deployment of Deep Learning Models on Smartphones as Real-Time Apps

2019 ◽  
Vol 1 (1) ◽  
pp. 450-465 ◽  
Author(s):  
Abhishek Sehgal ◽  
Nasser Kehtarnavaz
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Open Source Software ◽  
Mobile Applications ◽  
Network Models ◽  
Software Tools ◽  
Learning Tools ◽  
Smartphone Apps ◽  
Learning Models ◽  
Neural Network Models

Deep learning solutions are being increasingly used in mobile applications. Although there are many open-source software tools for the development of deep learning solutions, there are no guidelines in one place in a unified manner for using these tools toward real-time deployment of these solutions on smartphones. From the variety of available deep learning tools, the most suited ones are used in this paper to enable real-time deployment of deep learning inference networks on smartphones. A uniform flow of implementation is devised for both Android and iOS smartphones. The advantage of using multi-threading to achieve or improve real-time throughputs is also showcased. A benchmarking framework consisting of accuracy, CPU/GPU consumption, and real-time throughput is considered for validation purposes. The developed deployment approach allows deep learning models to be turned into real-time smartphone apps with ease based on publicly available deep learning and smartphone software tools. This approach is applied to six popular or representative convolutional neural network models, and the validation results based on the benchmarking metrics are reported.

Co-Designing Mobile Applications for Data Collection in Citizen Science Projects – Challenges and Lessons Learned within the Nachtlicht-BüHNE Project

2020 ◽  
Author(s):  
Friederike Klan ◽  
Christopher C.M. Kyba ◽  
Nona Schulte-Römer ◽  
Helga U. Kuechly ◽  
Jürgen Oberst ◽  
...  
Keyword(s):  
Data Collection ◽  
Citizen Science ◽  
Mobile Applications ◽  
Mobile Application ◽  
Lessons Learned ◽  
Smartphone Apps ◽  
Science Project ◽  
Science Projects ◽  
Citizen Science Project

<p>Data contributed by citizen scientists raise increasing interest in many areas of scientific research. Increasingly, projects rely on information technology such as mobile applications (apps) to facilitate data collection activities by lay people. When developing such smartphone apps, it is essential to account for both the requirements of the scientists interested in acquiring data and the needs of the citizen scientists contributing data. Citizens and participating scientists should therefore ideally work together during the conception, design and testing of mobile applications used in a citizen science project. This will benefit both sides, as both scientists and citizens can bring in their expectations, desires, knowledge, and commitment early on, thereby making better use of the potential of citizen science. Such processes of app co-design are highly transdisciplinary, and thus pose challenges in terms of the diversity of interests, skills, and background knowledge involved.</p><p>Our “Nachtlicht-BüHNE” citizen science project addresses these issues. Its major goal is the development of a co-design process enabling scientists and citizens to jointly develop citizen science projects based on smartphone apps. This includes (1) the conception and development of a mobile application for a specific scientific purpose, (2) the design, planning and organization of field campaigns using the mobile application, and (3) the evaluation of the approach. In Nachtlicht-BüHNE, the co-design approach is developed within the scope of two parallel pilot studies in the environmental and space sciences. Case study 1 deals with the problem of light pollution. Currently, little is known about how much different light source types contribute to emissions from Earth. Within the project, citizens and researchers will develop and use an app to capture information about all types of light sources visible from public streets. Case study 2 focuses on meteors. They are of great scientific interest because their pathways and traces of light can be used to derive dynamic and physical properties of comets and asteroids. Since the surveillance of the sky with cameras is usually incomplete, reports of fireball sightings are important. Within the project, citizens and scientists will create and use the first German-language app that allows reporting meteor sightings.</p><p>We will share our experiences on how researchers and communities of citizen scientists with backgrounds in the geosciences, space research, the social sciences, computer science and other disciplines work together in the Nachtlicht-BüHNE project to co-design mobile applications. We highlight challenges that arose and present different strategies for co-design that evolved within the project accounting for the specific needs and interests of the communities involved.</p>

scholarly journals Personalization in Real-Time Physical Activity Coaching Using Mobile Applications: A Scoping Review

2020 ◽  
Vol 24 (6) ◽  
pp. 1738-1751 ◽  
Author(s):  
Francisco Monteiro-Guerra ◽  
Octavio Rivera-Romero ◽  
Luis Fernandez-Luque ◽  
Brian Caulfield
Keyword(s):  
Physical Activity ◽  
Real Time ◽  
Scoping Review ◽  
Mobile Applications

scholarly journals An Assessment of Physical Activity Data Collected via a Smartphone App and a Smart Band in Breast Cancer Survivors: Observational Study (Preprint)

2019 ◽  
Author(s):  
Il Yong Chung ◽  
Miyeon Jung ◽  
Sae Byul Lee ◽  
Jong Won Lee ◽  
Yu Rang Park ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Data Collection ◽  
Cancer Survivors ◽  
Mobile Devices ◽  
Breast Cancer Survivors ◽  
Compliance Rate ◽  
Smartphone App ◽  
Smartphone Apps ◽  
Factors Associated ◽  
Distress Level

BACKGROUND Although distress screening is crucial for cancer survivors, it is not easy for clinicians to recognize distress. Physical activity (PA) data collected by mobile devices such as smart bands and smartphone apps have the potential to be used to screen distress in breast cancer survivors. OBJECTIVE The aim of this study was to assess data collection rates of smartphone apps and smart bands in terms of PA data, investigate the correlation between PA data from mobile devices and distress-related questionnaires from smartphone apps, and demonstrate factors associated with data collection with smart bands and smartphone apps in breast cancer survivors. METHODS In this prospective observational study, patients who underwent surgery for breast cancer at Asan Medical Center, Seoul, Republic of Korea, between June 2017 and March 2018 were enrolled and asked to use both a smartphone app and smart band for 6 months. The overall compliance rates of the daily PA data collection via the smartphone walking apps and wearable smart bands were analyzed in a within-subject manner. The longitudinal daily collection rates were calculated to examine the dropout pattern. We also performed multivariate linear regression analysis to examine factors associated with compliance with daily collection. Finally, we tested the correlation between the count of daily average steps and distress level using Pearson correlation analysis. RESULTS A total of 160 female patients who underwent breast cancer surgeries were enrolled. The overall compliance rates for using a smartphone app and smart bands were 88.0% (24,224/27,513) and 52.5% (14,431/27,513), respectively. The longitudinal compliance rate for smartphone apps was 77.8% at day 180, while the longitudinal compliance rate for smart bands rapidly decreased over time, reaching 17.5% at day 180. Subjects who were young, with other comorbidities, or receiving antihormonal therapy or targeted therapy showed significantly higher compliance rates to the smartphone app. However, no factor was associated with the compliance rate to the smart band. In terms of the correlation between the count of daily steps and distress level, step counts collected via smart band showed a significant correlation with distress level. CONCLUSIONS Smartphone apps or smart bands are feasible tools to collect data on daily PA data in breast cancer survivors. PA data from mobile devices are correlated with participants’ distress data, which suggests the potential role of mobile devices in the management of distress in breast cancer survivors. CLINICALTRIAL ClinicalTrials.gov NCT03072966; https://clinicaltrials.gov/ct2/show/NCT03072966

scholarly journals Remote evaluations of violence against women and girls interventions: a rapid scoping review of tools, ethics and safety

2021 ◽  
Vol 6 (9) ◽  
pp. e006780
Author(s):  
Ilana Seff ◽  
Luissa Vahedi ◽  
Samantha McNelly ◽  
Elfriede Kormawa ◽  
Lindsay Stark
Keyword(s):  
Data Collection ◽  
Best Practices ◽  
Violence Against Women ◽  
Scoping Review ◽  
Mobile Applications ◽  
Best Practice ◽  
Online Surveys ◽  
Humanitarian Settings ◽  
Telephone Interviews ◽  
Women And Girls

Although programmes and policies targeting violence against women and girls (VAWG) have increased in the past decade, there is a paucity of evidence on the effectiveness of these interventions. To expand this evidence base, researchers increasingly employ remote data collection (RDC)—including online surveys, mobile applications and telephone interviews—in their evaluations. Although RDC allows for evaluations without in-person interactions—which are restricted during crises such as the COVID-19 pandemic— information about these methods is necessary to understand their potential usefulness and limitations. This scoping review examines remote evaluations of VAWG interventions to describe the landscape of RDC methods, reflect on safety and ethical considerations, and offer best practices for RDC in VAWG research. Fourteen studies met eligibility criteria, with seven, five, and two studies employing telephone interviews, online surveys, and mobile applications, respectively. Studies commonly stated that participants were asked to use a safe email or device, but the method for verifying such safety was rarely specified. Best practices around safety included creating a ‘quick escape’ button for online data collection to use when another individual was present, explaining to participants how to erase browsing history and application purchases, and asking participants to specify a safe time for researchers to call. Only eight studies established referral pathways for respondents as per best practice. None of the eligible studies took place in low/middle-income countries (LMICs) or humanitarian settings, likely reflecting the additional challenges to using RDC methods in lower resource settings. Findings were used to create a best practice checklist for programme evaluators and Institutional Review Boards using RDC for VAWG interventions. The authors found that opportunities exist for researchers to safely and effectively use RDC methodologies to gather VAWG data, but that further study is needed to gauge the feasibility of these methods in LMICs and humanitarian settings.

scholarly journals An Assessment of Physical Activity Data Collected via a Smartphone App and a Smart Band in Breast Cancer Survivors: Observational Study

10.2196/13463 ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. e13463 ◽  
Author(s):  
Il Yong Chung ◽  
Miyeon Jung ◽  
Sae Byul Lee ◽  
Jong Won Lee ◽  
Yu Rang Park ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Physical Activity ◽  
Data Collection ◽  
Cancer Survivors ◽  
Mobile Devices ◽  
Breast Cancer Survivors ◽  
Compliance Rate ◽  
Smartphone App ◽  
Smartphone Apps ◽  
Distress Level

Background Although distress screening is crucial for cancer survivors, it is not easy for clinicians to recognize distress. Physical activity (PA) data collected by mobile devices such as smart bands and smartphone apps have the potential to be used to screen distress in breast cancer survivors. Objective The aim of this study was to assess data collection rates of smartphone apps and smart bands in terms of PA data, investigate the correlation between PA data from mobile devices and distress-related questionnaires from smartphone apps, and demonstrate factors associated with data collection with smart bands and smartphone apps in breast cancer survivors. Methods In this prospective observational study, patients who underwent surgery for breast cancer at Asan Medical Center, Seoul, Republic of Korea, between June 2017 and March 2018 were enrolled and asked to use both a smartphone app and smart band for 6 months. The overall compliance rates of the daily PA data collection via the smartphone walking apps and wearable smart bands were analyzed in a within-subject manner. The longitudinal daily collection rates were calculated to examine the dropout pattern. We also performed multivariate linear regression analysis to examine factors associated with compliance with daily collection. Finally, we tested the correlation between the count of daily average steps and distress level using Pearson correlation analysis. Results A total of 160 female patients who underwent breast cancer surgeries were enrolled. The overall compliance rates for using a smartphone app and smart bands were 88.0% (24,224/27,513) and 52.5% (14,431/27,513), respectively. The longitudinal compliance rate for smartphone apps was 77.8% at day 180, while the longitudinal compliance rate for smart bands rapidly decreased over time, reaching 17.5% at day 180. Subjects who were young, with other comorbidities, or receiving antihormonal therapy or targeted therapy showed significantly higher compliance rates to the smartphone app. However, no factor was associated with the compliance rate to the smart band. In terms of the correlation between the count of daily steps and distress level, step counts collected via smart band showed a significant correlation with distress level. Conclusions Smartphone apps or smart bands are feasible tools to collect data on the physical activity of breast cancer survivors. PA data from mobile devices are correlated with participants’ distress data, which suggests the potential role of mobile devices in the management of distress in breast cancer survivors. Trial Registration ClinicalTrials.gov NCT03072966; https://clinicaltrials.gov/ct2/show/NCT03072966

Enhancing Clinical Supervision Using High-Definition Real-Time Digital Recording, Annotation, and Bluetooth Technology

2019 ◽  
Vol 4 (2) ◽  
pp. 356-362
Author(s):  
Jennifer W. Means ◽  
Casey McCaffrey
Keyword(s):  
Graduate Students ◽  
Data Collection ◽  
Real Time ◽  
Clinical Supervision ◽  
Video Recording ◽  
Advanced Technology ◽  
High Definition ◽  
Recording Technology ◽  
Self Confidence ◽  
Additional Data Collection

Purpose The use of real-time recording technology for clinical instruction allows student clinicians to more easily collect data, self-reflect, and move toward independence as supervisors continue to provide continuation of supportive methods. This article discusses how the use of high-definition real-time recording, Bluetooth technology, and embedded annotation may enhance the supervisory process. It also reports results of graduate students' perception of the benefits and satisfaction with the types of technology used. Method Survey data were collected from graduate students about their use and perceived benefits of advanced technology to support supervision during their 1st clinical experience. Results Survey results indicate that students found the use of their video recordings useful for self-evaluation, data collection, and therapy preparation. The students also perceived an increase in self-confidence through the use of the Bluetooth headsets as their supervisors could provide guidance and encouragement without interrupting the flow of their therapy sessions by entering the room to redirect them. Conclusions The use of video recording technology can provide opportunities for students to review: videos of prospective clients they will be treating, their treatment videos for self-assessment purposes, and for additional data collection. Bluetooth technology provides immediate communication between the clinical educator and the student. Students reported that the result of that communication can improve their self-confidence, perceived performance, and subsequent shift toward independence.

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