Unique views on obesity-related behaviors and environments: research using still and video images (Preprint)

2018 ◽  
Author(s):  
Jordan Carlson ◽  
J. Aaron Hipp ◽  
Jacqueline Kerr ◽  
Todd Horowitz ◽  
David Berrigan
Keyword(s):  
Physical Activity ◽  
Ethical Issues ◽  
Data Extraction ◽  
Dietary Assessment ◽  
Extraction Methods ◽  
Image Capture ◽  
Still Images ◽  
Key Informants ◽  
Technological Advances ◽  
Computer Scientists

BACKGROUND Image based data collection for obesity research is in its infancy. OBJECTIVE The present study aimed to document challenges to and benefits from such research by capturing examples of research involving the use of images to assess physical activity- or nutrition-related behaviors and/or environments. METHODS Researchers (i.e., key informants) using image capture in their research were identified through knowledge and networks of the authors of this paper and through literature search. Twenty-nine key informants completed a survey covering the type of research, source of images, and challenges and benefits experienced, developed specifically for this study. RESULTS Most respondents used still images in their research, with only 26.7% using video. Image sources were categorized as participant generated (N = 13; e.g., participants using smartphones for dietary assessment), researcher generated (N = 10; e.g., wearable cameras with automatic image capture), or curated from third parties (N = 7; e.g., Google Street View). Two of the major challenges that emerged included the need for automated processing of large datasets (58.8%) and participant recruitment/compliance (41.2%). Benefit-related themes included greater perspectives on obesity with increased data coverage (34.6%) and improved accuracy of behavior and environment assessment (34.6%). CONCLUSIONS Technological advances will support the increased use of images in the assessment of physical activity, nutrition behaviors, and environments. To advance this area of research, more effective collaborations are needed between health and computer scientists. In particular development of automated data extraction methods for diverse aspects of behavior, environment, and food characteristics are needed. Additionally, progress in standards for addressing ethical issues related to image capture for research purposes are critical. CLINICALTRIAL NA

scholarly journals Unique Views on Obesity-Related Behaviors and Environments: Research Using Still and Video Images

2018 ◽  
Vol 1 (3) ◽  
pp. 143-154
Author(s):  
Jordan A. Carlson ◽  
J. Aaron Hipp ◽  
Jacqueline Kerr ◽  
Todd S. Horowitz ◽  
David Berrigan
Keyword(s):  
Physical Activity ◽  
Ethical Issues ◽  
Data Extraction ◽  
Dietary Assessment ◽  
Extraction Methods ◽  
Image Capture ◽  
Still Images ◽  
Key Informants ◽  
Technological Advances ◽  
Computer Scientists

Objectives: To document challenges to and benefits from research involving the use of images by capturing examples of such research to assess physical activity– or nutrition-related behaviors and/or environments. Methods: Researchers (i.e., key informants) using image capture in their research were identified through knowledge and networks of the authors of this paper and through literature search. Twenty-nine key informants completed a survey covering the type of research, source of images, and challenges and benefits experienced, developed specifically for this study. Results: Most respondents used still images in their research, with only 26.7% using video. Image sources were categorized as participant generated (n = 13; e.g., participants using smartphones for dietary assessment), researcher generated (n = 10; e.g., wearable cameras with automatic image capture), or curated from third parties (n = 7; e.g., Google Street View). Two of the major challenges that emerged included the need for automated processing of large datasets (58.8%) and participant recruitment/compliance (41.2%). Benefit-related themes included greater perspectives on obesity with increased data coverage (34.6%) and improved accuracy of behavior and environment assessment (34.6%). Conclusions: Technological advances will support the increased use of images in the assessment of physical activity, nutrition behaviors, and environments. To advance this area of research, more effective collaborations are needed between health and computer scientists. In particular development of automated data extraction methods for diverse aspects of behavior, environment, and food characteristics are needed. Additionally, progress in standards for addressing ethical issues related to image capture for research purposes is critical.

scholarly journals Sleep quality in the chronic stage of concussion is associated with poorer recovery: A systematic review

2021 ◽  
Vol 5 ◽  
pp. 205970022110208
Author(s):  
Rebecca Ludwig ◽  
Eryen Nelson ◽  
Prasanna Vaduvathiriyan ◽  
Michael A Rippee ◽  
Catherine Siengsukon
Keyword(s):  
Physical Activity ◽  
Sleep Quality ◽  
Sleep Disturbances ◽  
Data Extraction ◽  
Grey Literature ◽  
Chronic Phase ◽  
Emotional Symptoms ◽  
Eligibility Criteria ◽  
Chronic Stage ◽  
Prior Review

Background Recovery from a concussion varies based on a multitude of factors. One such factor is sleep disturbances. In our prior review, it was observed that in the acute phase, sleep disturbances are predictive of poor outcomes following a concussion. The literature gap remains on how sleep in the chronic phase of recovery affects outcomes. Objective To examine the association between sleep quality during the chronic stage of concussion and post-concussion outcomes. Literature Survey: Literature searches were performed during 1 July to 1 August 2019 in selected databases along with searching grey literature. Out of the 733 results, 702 references were reviewed after duplicate removal. Methods Three reviewers independently reviewed and consented on abstracts meeting eligibility criteria ( n = 35). The full-text articles were assessed independently by two reviewers. Consensus was achieved, leaving four articles. Relevant data from each study was extracted using a standard data-extraction table. Quality appraisal was conducted to assess potential bias and the quality of articles. Results One study included children (18–60 months) and three studies included adolescents and/or adults (ranging 12–35 years). The association between sleep and cognition (two studies), physical activity (one study), and emotion symptoms (one study) was examined. Sleep quality was associated with decreased cognition and emotional symptoms, but not with meeting physical activity guidelines six months post-concussion injury. Conclusions The heterogeneity in age of participants and outcomes across studies and limited number of included studies made interpretations difficult. Future studies may consider if addressing sleep quality following concussion will improve outcomes.

Artificial Intelligence and Ethics in Dentistry: A Scoping Review

2021 ◽  
pp. 002203452110138
Author(s):  
C.M. Mörch ◽  
S. Atsu ◽  
W. Cai ◽  
X. Li ◽  
S.A. Madathil ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Scoping Review ◽  
Ethical Issues ◽  
Data Extraction ◽  
Cochrane Library ◽  
Data Sets ◽  
Ethical Challenges ◽  
Internal Validation ◽  
Neural Learning ◽  
Learning Techniques

Dentistry increasingly integrates artificial intelligence (AI) to help improve the current state of clinical dental practice. However, this revolutionary technological field raises various complex ethical challenges. The objective of this systematic scoping review is to document the current uses of AI in dentistry and the ethical concerns or challenges they imply. Three health care databases (MEDLINE [PubMed], SciVerse Scopus, and Cochrane Library) and 2 computer science databases (ArXiv, IEEE Xplore) were searched. After identifying 1,553 records, the documents were filtered, and a full-text screening was performed. In total, 178 studies were retained and analyzed by 8 researchers specialized in dentistry, AI, and ethics. The team used Covidence for data extraction and Dedoose for the identification of ethics-related information. PRISMA guidelines were followed. Among the included studies, 130 (73.0%) studies were published after 2016, and 93 (52.2%) were published in journals specialized in computer sciences. The technologies used were neural learning techniques for 75 (42.1%), traditional learning techniques for 76 (42.7%), or a combination of several technologies for 20 (11.2%). Overall, 7 countries contributed to 109 (61.2%) studies. A total of 53 different applications of AI in dentistry were identified, involving most dental specialties. The use of initial data sets for internal validation was reported in 152 (85.4%) studies. Forty-five ethical issues (related to the use AI in dentistry) were reported in 22 (12.4%) studies around 6 principles: prudence (10 times), equity (8), privacy (8), responsibility (6), democratic participation (4), and solidarity (4). The ratio of studies mentioning AI-related ethical issues has remained similar in the past years, showing that there is no increasing interest in the field of dentistry on this topic. This study confirms the growing presence of AI in dentistry and highlights a current lack of information on the ethical challenges surrounding its use. In addition, the scarcity of studies sharing their code could prevent future replications. The authors formulate recommendations to contribute to a more responsible use of AI technologies in dentistry.

Personal and social factors that influence physical activity levels in community-dwelling stroke survivors: A systematic review of qualitative literature

2021 ◽  
pp. 026921552199369
Author(s):  
Karl R Espernberger ◽  
Natalie A Fini ◽  
Casey L Peiris
Keyword(s):  
Physical Activity ◽  
Social Factors ◽  
Self Efficacy ◽  
Data Extraction ◽  
Community Dwelling ◽  
Stroke Survivors ◽  
Activity Levels ◽  
Post Stroke ◽  
Physical Activity Levels ◽  
Mobile Community

Objectives: To determine the personal and social factors perceived to influence physical activity levels in stroke survivors. Data sources: Four electronic databases (MEDLINE, CINAHL, PubMed and Embase) were searched from inception to November 2020, including reference and citation list searches. Study selection: The initial search yielded 1499 papers, with 14 included in the review. Included articles were peer-reviewed, qualitative studies, reporting on the perceived factors influencing physical activity levels of independently mobile community-dwelling adults, greater than 3 months post stroke. Data extraction: Data extracted included location, study aim, design, participant and recruitment information and how data were collected and analysed. Data synthesis: Thematic analysis was undertaken to identify meanings and patterns, generate codes and develop themes. Five main themes were identified: (i) Social networks are important influencers of physical activity; (ii) Participation in meaningful activities rather than ‘exercise’ is important; (iii) Self-efficacy promotes physical activity and physical activity enhances self-efficacy; (iv) Pre-stroke identity related to physical activity influences post-stroke physical activity; and (v) Formal programmes are important for those with low self-efficacy or a sedentary pre-stroke identity. Conclusions: Physical activity levels in stroke survivors are influenced by social activities and support, pre-stroke identity, self-efficacy levels and completion of activities that are meaningful to stroke survivors.

scholarly journals Promoting Diet and Physical Activity in Nurses

2016 ◽  
Vol 31 (1) ◽  
pp. 19-27 ◽  
Author(s):  
Luciana Torquati ◽  
Toby Pavey ◽  
Tracy Kolbe-Alexander ◽  
Michael Leveritt
Keyword(s):  
Physical Activity ◽  
English Language ◽  
Data Extraction ◽  
Student Nurses ◽  
Inclusion Criteria ◽  
Exclusion Criteria ◽  
Data Synthesis ◽  
Diet And Physical Activity ◽  
Data Source

Objective. To systematically review the effectiveness of intervention studies promoting diet and physical activity (PA) in nurses. Data Source. English language manuscripts published between 1970 and 2014 in PubMed, Scopus, CINAHL, and EMBASE, as well as those accessed with the PICO tool, were reviewed. Study Inclusion and Exclusion Criteria. Inclusion criteria comprised (1) nurses/student nurses working in a health care setting and (2) interventions where PA and/or diet behaviors were the primary outcome. Exclusion criteria were (1) non–peer-reviewed articles or conference abstracts and (2) interventions focused on treatment of chronic conditions or lifestyle factors other than PA or diet in nurses. Data Extraction. Seventy-one full texts were retrieved and assessed for inclusion by two reviewers. Data were extracted by one reviewer and checked for accuracy by a second reviewer. Data Synthesis. Extracted data were synthesized in a tabular format and narrative summary. Results. Nine (n = 737 nurses) studies met the inclusion criteria. Quality of the studies was low to moderate. Four studies reported an increase in self-reported PA through structured exercise and goal setting. Dietary outcomes were generally positive, but were only measured in three studies with some limitations in the assessment methods. Two studies reported improved body composition without significant changes in diet or PA. Conclusions. Outcomes of interventions to change nurses’ PA and diet behavior are promising, but inconsistent. Additional and higher quality interventions that include objective and validated outcome measures and appropriate process evaluation are required.

Estimation of Sand Distribution in Coral Reef Environment using Colour Segmentation and Colour Thresholding Methods

2019 ◽  
Vol 1 (3) ◽  
pp. 96-104
Author(s):  
Pauleen Ong ◽  
Muhammad Suzuri Hitam ◽  
Zainuddin Bachok ◽  
Mohd Safuan Che Din
Keyword(s):  
Coral Reef ◽  
Extraction Methods ◽  
Colour Difference ◽  
Reef Environment ◽  
Still Images ◽  
Point Count ◽  
Automatic Computer ◽  
Computer Based ◽  
Coral Reef Environment ◽  
Colour Segmentation

At present, marine scientists employ manual method to estimate the components in coral reef environment, where Coral Point Count with Excel extensions (CPCe) software is used to determine the coral reef components and substrate coverage. This manual process is laborious and time consuming, and needs experts to conduct the survey. In this paper, a prototype for estimating the distribution of sand cover in coral reef environment from still images by using colour extraction methods was introduced. The colour segmentation called delta E was used to calculate the colour difference between two colour samples. Another method used was colour threshold by setting the range of sand colour pixels. The system was developed by using a MATLAB software with image processing toolbox. The developed system was semi-automatic computer-based system that can be used by researchers even with little knowledge and experience to estimate the percentage of sand coverage in coral reef still images.

Implementation of Web Application for Disease Prediction Using AI

2020 ◽  
pp. 5-9
Author(s):  
Manasvi Srivastava ◽  
◽  
Vikas Yadav ◽  
Swati Singh ◽  
◽  
...  
Keyword(s):  
Web Application ◽  
Ad Hoc ◽  
Data Extraction ◽  
Extraction Methods ◽  
Web Page ◽  
Web Based ◽  
Web Extraction ◽  
Web Scraping ◽  
Audio Video ◽  
Manual Extraction

The Internet is the largest source of information created by humanity. It contains a variety of materials available in various formats such as text, audio, video and much more. In all web scraping is one way. It is a set of strategies here in which we get information from the website instead of copying the data manually. Many Web-based data extraction methods are designed to solve specific problems and work on ad-hoc domains. Various tools and technologies have been developed to facilitate Web Scraping. Unfortunately, the appropriateness and ethics of using these Web Scraping tools are often overlooked. There are hundreds of web scraping software available today, most of them designed for Java, Python and Ruby. There is also open source software and commercial software. Web-based software such as YahooPipes, Google Web Scrapers and Firefox extensions for Outwit are the best tools for beginners in web cutting. Web extraction is basically used to cut this manual extraction and editing process and provide an easy and better way to collect data from a web page and convert it into the desired format and save it to a local or archive directory. In this paper, among others the kind of scrub, we focus on those techniques that extract the content of a Web page. In particular, we use scrubbing techniques for a variety of diseases with their own symptoms and precautions.

A qualitative and quantitative comparison between Web scraping and API methods for Twitter credibility analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Irvin Dongo ◽  
Yudith Cardinale ◽  
Ana Aguilera ◽  
Fabiola Martinez ◽  
Yuni Quintero ◽  
...  
Keyword(s):  
San Francisco ◽  
Data Extraction ◽  
Qualitative Evaluation ◽  
Application Programming Interface ◽  
Extraction Methods ◽  
Content Type ◽  
Qualitative And Quantitative ◽  
Advantages And Disadvantages ◽  
Web Scraping ◽  
Shared Information

Purpose This paper aims to perform an exhaustive revision of relevant and recent related studies, which reveals that both extraction methods are currently used to analyze credibility on Twitter. Thus, there is clear evidence of the need of having different options to extract different data for this purpose. Nevertheless, none of these studies perform a comparative evaluation of both extraction techniques. Moreover, the authors extend a previous comparison, which uses a recent developed framework that offers both alternates of data extraction and implements a previously proposed credibility model, by adding a qualitative evaluation and a Twitter-Application Programming Interface (API) performance analysis from different locations. Design/methodology/approach As one of the most popular social platforms, Twitter has been the focus of recent research aimed at analyzing the credibility of the shared information. To do so, several proposals use either Twitter API or Web scraping to extract the data to perform the analysis. Qualitative and quantitative evaluations are performed to discover the advantages and disadvantages of both extraction methods. Findings The study demonstrates the differences in terms of accuracy and efficiency of both extraction methods and gives relevance to much more problems related to this area to pursue true transparency and legitimacy of information on the Web. Originality/value Results report that some Twitter attributes cannot be retrieved by Web scraping. Both methods produce identical credibility values when a robust normalization process is applied to the text (i.e. tweet). Moreover, concerning the time performance, Web scraping is faster than Twitter API and it is more flexible in terms of obtaining data; however, Web scraping is very sensitive to website changes. Additionally, the response time of the Twitter API is proportional to the distance from the central server at San Francisco.

scholarly journals The COVID-19 pandemic shifted the Veterans Affairs System toward being a payer and virtual care provider: is it sustainable?

2021 ◽  
Author(s):  
Liam Rose ◽  
Linda Diem Tran ◽  
Steven M Asch ◽  
Anita Vashi
Keyword(s):  
Financial Incentives ◽  
Care Delivery ◽  
Data Extraction ◽  
Medical Personnel ◽  
Extraction Methods ◽  
Virtual Care ◽  
Large Expansion ◽  
Virtual Visits ◽  
The Difference ◽  
One Year

Objective: To examine how VA shifted care delivery methods one year into the pandemic. Study Setting: All encounters paid or provided by VA between January 1, 2019 and February 27, 2021. Study Design: We aggregated all VA paid or provided encounters and classified them into community (non-VA) acute and non-acute visits, VA acute and non-acute visits, and VA virtual visits. We then compared the number of encounters by week over time to pre-pandemic levels. Data Extraction Methods: Aggregation of administrative VA claims and health records. Principal Findings: VA has experienced a dramatic and persistent shift to providing virtual care and purchasing care from non-VA providers. Before the pandemic, a majority (63%) of VA care was provided in-person at a VA facility. One year into the pandemic, in-person care at VA's constituted just 33% of all visits. Most of the difference made up by large expansions of virtual care; total VA provided visits (in person and virtual) declined (4.9 million to 4.2 million) while total visits of all types declined only 3.5%. Community provided visits exceeded prepandemic levels (2.3 million to 2.9 million, +26%). Conclusion: Unlike private health care, VA has resumed in-person care slowly at its own facilities, and more rapidly in purchased care with different financial incentives a likely driver. The very large expansion of virtual care nearly made up the difference. With a widespread physical presence across the U.S., this has important implications for access to care and future allocation of medical personnel, facilities, and resources.

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