scholarly journals Factors Affecting the Quality of Person-Generated Wearable Device Data and Associated Challenges: Rapid Review (Preprint)

2020 ◽  
Author(s):  
Sylvia Cho ◽  
Ipek Ensari ◽  
Chunhua Weng ◽  
Michael Kahn ◽  
Karthik Natarajan
Keyword(s):  
Data Quality ◽  
Heterogeneous Data ◽  
Wearable Device ◽  
Rapid Review ◽  
Data Governance ◽  
Related Factors ◽  
Data Accessibility ◽  
Factors Affecting ◽  
Address Data

BACKGROUND There is increasing interest to reuse person-generated wearable device data for research purposes, which raises concerns about data quality. However, the literature on data quality challenges, specifically for person-generated wearable device data, is sparse. OBJECTIVE The objective of this study is to systematically review the literature on factors affecting quality of person-generated wearable device data and identify challenges associated with their secondary uses. METHODS We searched PubMed, ACM, IEEE, and Google Scholar with search terms related to wearable device and data quality. Using PRISMA guidelines, we reviewed the papers to find factors affecting the quality of wearable device data. We annotated those papers and categorized semantically similar factors. If any data quality challenges were mentioned in the study, we extracted those contents as well. RESULTS Twenty-six papers were included. We identified 3 high-level factors: device and technical, user-related, and data governance factors. Device and technical factors include problems with hardware, software, connectivity; user-related factors include device non-wear and user error; and data governance factors include lack of standardization and data accessibility issues. The identified factors potentially can lead to data quality problems such as incomplete, inaccurate, and heterogeneous data. CONCLUSIONS Our study identifies potential data quality challenges that could occur when analyzing wearable device data for research and 3 major contributing factors for these challenges. As poor data quality can compromise the reliability and accuracy of research results, further investigation is warranted on how to address data quality challenges facing wearable devices.

scholarly journals Factors Affecting the Quality of Person-Generated Wearable Device Data and Associated Challenges: Rapid Systematic Review

10.2196/20738 ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. e20738
Author(s):  
Sylvia Cho ◽  
Ipek Ensari ◽  
Chunhua Weng ◽  
Michael G Kahn ◽  
Karthik Natarajan
Keyword(s):  
Data Quality ◽  
Wearable Devices ◽  
Heterogeneous Data ◽  
Wearable Device ◽  
Bring Your Own Device ◽  
Contributing Factors ◽  
Data Governance ◽  
Related Factors ◽  
Factors Affecting

Background There is increasing interest in reusing person-generated wearable device data for research purposes, which raises concerns about data quality. However, the amount of literature on data quality challenges, specifically those for person-generated wearable device data, is sparse. Objective This study aims to systematically review the literature on factors affecting the quality of person-generated wearable device data and their associated intrinsic data quality challenges for research. Methods The literature was searched in the PubMed, Association for Computing Machinery, Institute of Electrical and Electronics Engineers, and Google Scholar databases by using search terms related to wearable devices and data quality. By using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, studies were reviewed to identify factors affecting the quality of wearable device data. Studies were eligible if they included content on the data quality of wearable devices, such as fitness trackers and sleep monitors. Both research-grade and consumer-grade wearable devices were included in the review. Relevant content was annotated and iteratively categorized into semantically similar factors until a consensus was reached. If any data quality challenges were mentioned in the study, those contents were extracted and categorized as well. Results A total of 19 papers were included in this review. We identified three high-level factors that affect data quality—device- and technical-related factors, user-related factors, and data governance-related factors. Device- and technical-related factors include problems with hardware, software, and the connectivity of the device; user-related factors include device nonwear and user error; and data governance-related factors include a lack of standardization. The identified factors can potentially lead to intrinsic data quality challenges, such as incomplete, incorrect, and heterogeneous data. Although missing and incorrect data are widely known data quality challenges for wearable devices, the heterogeneity of data is another aspect of data quality that should be considered for wearable devices. Heterogeneity in wearable device data exists at three levels: heterogeneity in data generated by a single person using a single device (within-person heterogeneity); heterogeneity in data generated by multiple people who use the same brand, model, and version of a device (between-person heterogeneity); and heterogeneity in data generated from multiple people using different devices (between-person heterogeneity), which would apply especially to data collected under a bring-your-own-device policy. Conclusions Our study identifies potential intrinsic data quality challenges that could occur when analyzing wearable device data for research and three major contributing factors for these challenges. As poor data quality can compromise the reliability and accuracy of research results, further investigation is needed on how to address the data quality challenges of wearable devices.

Quality of life in breast cancer sufferers

2016 ◽  
Vol 29 (7) ◽  
pp. 721-732 ◽  
Author(s):  
Ahmed Essmat Shouman ◽  
Nahla Fawzy Abou El Ezz ◽  
Nivine Gado ◽  
Amal Mahmoud Ibrahim Goda
Keyword(s):  
Breast Cancer ◽  
Quality Of Life ◽  
Family Income ◽  
Early Stage ◽  
Functional Domain ◽  
Related Factors ◽  
Specific Patient ◽  
Content Type ◽  
Factors Affecting

Purpose – The purpose of this paper is to measure health-related quality of life (QOL) among patients with early stage cancer breast under curative treatment at department of oncology and nuclear medicine at Ain Shams University Hospitals. Identify factors affecting QOL among these patients. Design/methodology/approach – A cross-sectional study measured QOL among early stage female breast cancer (BC) patients and determined the main factors affecting their QOL. Three interviewer administered questionnaires were used. Findings – The physical domain mostly affected in BC patients and the functional domain least. Socio-demographic factors that significantly affected BC patients QOL scores were patient age, education, having children and family income. Specific patient characteristics include caregiver presence – a factor that affected different QOL scores. Age at diagnosis, affection in the side of the predominant hand, post-operative chemotherapy and difficulty in obtaining the medication were the disease-related factors that affected QOL scores. Originality/value – The final model predicting QOL for early stage female BC patients included age, education and difficulty in obtaining the medication as determinants for total QOL score. Carer presence was the specific patient characteristic that affected different QOL scores.

scholarly journals Quality of Open Research Data: Values, Convergences and Governance

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 175 ◽  
Author(s):  
Tibor Koltay
Keyword(s):  
Big Data ◽  
Data Quality ◽  
Academic Research ◽  
Research Data ◽  
Data Governance ◽  
Quality Of Data ◽  
Open Research ◽  
Research Environments

This paper focuses on the characteristics of research data quality, and aims to cover the most important issues related to it, giving particular attention to its attributes and to data governance. The corporate word’s considerable interest in the quality of data is obvious in several thoughts and issues reported in business-related publications, even if there are apparent differences between values and approaches to data in corporate and in academic (research) environments. The paper also takes into consideration that addressing data quality would be unimaginable without considering big data.

scholarly journals Patients’ Concerns at Their Admission in Hospital and Its Related Factors in Kermanshah, Iran

2021 ◽  
Vol 10 (1) ◽  
pp. 23-28
Author(s):  
Soraya Siabani ◽  
Leila Solouki ◽  
Afshin Almasi ◽  
Sina Siabani ◽  
Motahareh Khaledi ◽  
...  
Keyword(s):  
Hospital Costs ◽  
Length Of Hospital Stay ◽  
Final Diagnosis ◽  
Medical Sciences ◽  
Related Factors ◽  
Factors Affecting ◽  
Hospital Administrators ◽  
Multi Stage ◽  
Significant Difference

Background: One of the critical factors affecting patients’ outcomes is their concerns about different issues during their admission to the hospital. Clarifying these concerns and providing appropriate approaches could improve the quality of care, result in better outcomes, and reduce treatment costs. The present study aimed to investigate patients’ concerns during hospitalization, and the likely related factors of the educational hospitals in Kermanshah, western Iran. Materials and Methods: This analytical-descriptive study included 600 adult patients selected via a multi-stage sampling method and admitted to all four educational hospitals affiliated to Kermanshah University of Medical Sciences )KUMS) in 2016. Required data were collected using a survey with 15 questions on demographic information, current disease, medical records, and a researcher-developed questionnaire on factors causing concern in the Likert scale. Results: Of 600 patients who participated in the survey, 336 (56%) were female and 486 )81%) were married. The most frequent concerns were the length of admission, failure in treatment or recovery, and hospital costs, respectively. The length of hospital stay, income, and level of education were significantly associated with the concern scores. Also, there was a significant difference between concern score distributions in groups with a definite diagnosis of illnesses (P<0.05). Conclusion: The results of this study suggested a correlation between variables such as education, income, the final diagnosis of a sickness, and the concern level of admitted patients. Our findings could help managers and hospital administrators better understand the concerns of admitted patients and find solutions to remove them.

scholarly journals COVID-19 related factors affecting the experiences of neurodivergent persons in the workplace: A rapid review

Work ◽  
2021 ◽  
pp. 1-10
Author(s):  
Damian Mellifont
Keyword(s):  
Social Justice ◽  
Thematic Analysis ◽  
Rapid Review ◽  
Journal Articles ◽  
Related Factors ◽  
Factors Affecting ◽  
Employee Recruitment ◽  
Policy Responses ◽  
Diversity And Inclusion ◽  
Challenges And Opportunities

BACKGROUND: Policy responses to the COVID-19 pandemic offer possibilities to advance social justice. One such prospect is to make workplaces more inclusive of neurodivergence. OBJECTIVE: This research addresses the question of, in what ways might COVID-19 affect the experiences of neurodivergent persons in the workplace? METHODS: Conducting a rapid review, the author has applied thematic analysis to a total of 50 documents comprised of journal articles, news articles, and guides as retrieved from purposive searches of ProQuest Central, ProQuest Newsstream International, Google Scholar, and Google databases. RESULTS: Research results have revealed themes of challenges and opportunities, and sub-themes of accommodating (i.e., remote working, employee recruitment, retainment or advancement and/or access); and diversity and inclusion (i.e., acceptance, empathy and/or ERGs). CONCLUSION: This study has informed a baseline COVID-19-related guide to accommodating and including neurodivergence in the workplace. The review concludes by offering possibilities as to what a COVID-19 inspired ‘new normal’ might mean for supporting neurodivergent staff (and prospective staff).

scholarly journals Aspects of Data Quality in Psychology: Missing Data and Aberrant Responses

2021 ◽  
Author(s):  
Maxwell Hong ◽  
Matt Carter ◽  
Cheyeon Kim ◽  
Ying Cheng
Keyword(s):  
Missing Data ◽  
Data Quality ◽  
Developmental Psychology ◽  
Data Preprocessing ◽  
Self Report ◽  
Listwise Deletion ◽  
The Social ◽  
Address Data ◽  
Missing Data Treatment

Data preprocessing is an integral step prior to analyzing data in the social sciences. The purpose of this article is to report the current practices psychological researchers use to address data preprocessing or quality concerns with a focus on issues pertaining to aberrant responses and missing data in self report measures. 240 articles were sampled from four journals: Psychological Science, Journal of Personality and Social Psychology, Developmental Psychology, and Abnormal Psychology from 2012 to 2018. We found that nearly half of the studies did not report any missing data treatment (111/240; 46.25%) and if they did, the most common approach to handle missing data was listwise deletion (71/240; 29.6%). Studies that remove data due to missingness removed, on average, 12% of the sample. We also found that most studies do not report any methodology to address aberrant responses (194/240; 80.83%). For studies that reported issues with aberrant responses, a study would classify 4% of the sample, on average, as suspect responses. These results suggest that most studies are either not transparent enough about their data preprocessing steps or maybe leveraging suboptimal procedures. We outline recommendations for researchers to improve the transparency and/or the data quality of their study.

scholarly journals METODE PERANCANGAN STRUKTUR FUNGSI DAN PERAN TATA KELOLA DATA BERBASIS COBIT

2012 ◽  
Vol 6 (2) ◽  
pp. 146
Author(s):  
Budi Yuwono ◽  
Aditya Arinanda
Keyword(s):  
Data Quality ◽  
Organizational Structure ◽  
Data Governance ◽  
Function Structure ◽  
Theoretical Frameworks ◽  
Organizational Work ◽  
Work Units ◽  
Unit Unit

Tulisan ini mengusulkan suatu metode untuk merancang struktur fungsi dan peran tata kelola data suatu organisasi. Metode ini berorientasi pada permasalahan, yaitu menyusun rancangan struktur berangkat dari permasalahan yang dihadapi organisasi dan memperhatikan unit-unit kerja organisasi yang ada saat ini. Tulisan ini menguraikan penerapan metode ini dalam merancang kerangka kerja tata kelola data untuk meningkatkan dan menjaga kualitas data suatu organisasi. Dari permasalahan kualitas data yang dihadapi, diidentifikasi artifak tata kelola – seperti ketentuan, standar, arsitektur – yang dibutuhkan untuk mengendalikan permasalahan-permasalahan tersebut; diidentifikasi aktivitas-aktivitas yang dibutuhkan untuk menghasilkan dan mengelola artifak tersebut; diidentifikasi peran dan fungsi yang terlibat dalam aktivitas-aktivitas tersebut; dan akhirnya menata peran-peran tersebut dalam suatu struktur organisasi. Tulisan ini menunjukkan bahwa struktur yang dihasilkan setara dengan struktur yang disusun berdasarkan kerangka-kerangka kerja teoritis, dengan kelebihan adanya spesifikasi tentang siapa yang layak memegang peran dalam struktur tersebut dan apa tanggung jawabnya. This paper proposes a method for designing the function structure and of the role of an organization's data governance. The method is oriented to the problem, by arranging the structure of a draft set of issues faced by the organization and showing to organizational work units that exist today. This paper describes the application of this method in designing a data governance framework to improve and maintain the quality of organization's data. From data quality problems, we identified governance artifacts - such as regulations, standards, architecture – that is needed to control these problems; identified the activities required to generate and manage these artifacts; identified the roles and functions involved in activities these, and finally organize these roles in an organizational structure. This paper shows that the resulting structure is equivalent to a structure based on theoretical frameworks, with an excess of the specification of who should play a role in the structure and what are the responsibilities.

scholarly journals A Multisource Retrospective Audit Method for Data Quality Optimization and Evaluation

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Li Jiang ◽  
Hao Chen ◽  
Yueqi Ouyang ◽  
Canbing Li
Keyword(s):  
Data Integration ◽  
Data Quality ◽  
Rapid Development ◽  
Original Data ◽  
Heterogeneous Data ◽  
Integration Model ◽  
Heterogeneous Data Integration ◽  
Model Based ◽  
Retrospective Audit

With the rapid development of information technology and the coming of the era of big data, various data are constantly emerging and present the characteristics of autonomy and heterogeneity. How to optimize data quality and evaluate the effect has become a challenging problem. Firstly, a heterogeneous data integration model based on retrospective audit is proposed to locate the original data source and match the data. Secondly, in order to improve the integrated data quality, a retrospective audit model and associative audit rules are proposed to fix incomplete and incorrect data from multiple heterogeneous data sources. The heterogeneous data integration model based on retrospective audit is divided into four modules including original heterogeneous data, data structure, data processing, and data retrospective audit. At last, some assessment criteria such as redundancy, sparsity, and accuracy are defined to evaluate the effect of the optimized data quality. Experimental results show that the quality of the integrated data is significantly higher than the quality of the original data.

scholarly journals Factors Influencing Fog Computing Adoption based on Quality of Results (QoR) for Heterogeneous Data Analysis: A Proposed Framework

2019 ◽  
Vol 8 (2) ◽  
pp. 2760-2766
Keyword(s):  
Data Analysis ◽  
Data Quality ◽  
Fog Computing ◽  
Heterogeneous Data ◽  
Quality Level ◽  
Quality Of Results ◽  
Iot Devices ◽  
Dynamic Execution ◽  
Systems Framework

The rapid increase of data generated has brought challenges on data quality level. Fog computing in general has been supporting the requirements of end user devices that could not be met by cloud computing solution and it is acknowledged to have a major impact on how an organisation decides to adopt for preprocessing a huge amount of data being generated by the devices. Since IoT devices generating very heterogeneous and dynamic data, there are challenges for the level of data quality. The limitation has hindered the development of fog systems framework that capable operating the dynamic execution of edge devices that handling generation and collection large amounts of data on-premise and off-premise. Thus,sufficient operations of identifying Quality of Result enable user to detect any problems when conducting the decision making. The aim of this paper is to address the factors that perceived likely to influence the adoption of fog computing in evaluating the data analysis on data transmitted from the ever increases devices.A conceptual framework has been constructed considering attributes such as heterogeneous data analysis (on-premise and off-premise) and Quality of Results (quality indicators, quality control, validity outcome and reliability outcome).Potential benefits from the implementation of this framework to organisation is it enable to provide greater value and benefits to the business process. The framework of this study could also be influencing and inhibiting the adoption of fog computing.Quality of result has higher chances to satisfy the defined industrial’s requirement. In addition, fog-computing adoption is important for serving an environment for industry to execute, monitor, and analyze a large form of data in a fog landscape.

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