Asymptomatic reference values for the Disability of Arm, Shoulder and Hand and Patient-Rated Wrist/Hand Evaluation – electronic data collection and its clinical implications

2018 ◽  
Vol 43 (9) ◽  
pp. 988-993
Author(s):  
James M. McLean ◽  
Afsana P. Hasan ◽  
Jake Willet ◽  
Matthew Jennings ◽  
Kimberly Brown ◽  
...  

The purpose of this study was to establish normal asymptomatic population values for the Disability of Arm, Shoulder and Hand and Patient-Rated Wrist/Hand Evaluation in healthy, asymptomatic individuals of different age, gender, ethnicity, handedness and nationality, using electronic data collection. Two-hundred and ninety-two Australian and 293 Canadian citizens with no active wrist pain, injury or pathology in their dominant hand, were evaluated. Participants completed an electronically administered questionnaire and were assessed clinically. There was no statistically significant association between both wrist scores and nationality. There was a statistically significant association between both wrist scores and age, demonstrating that as age increased, normal wrist function declined. This study has established an electronic, asymptomatic control group for future studies using these scores. When using the Disability of Arm, Shoulder and Hand and Patient-Rated Wrist/Hand Evaluation, the control group can be sourced from a pre-established control group within a database, without necessarily being sourced from the same country of origin. Level of evidence: II

2016 ◽  
Vol 27 (4) ◽  
pp. 389-396 ◽  
Author(s):  
James M. McLean ◽  
Jacob Cappelletto ◽  
Jock Clarnette ◽  
Catherine L. Hill ◽  
Tiffany Gill ◽  
...  

Background The aim of this study was to assess whether the Harris Hip Score (HHS) and the Oxford Hip Score (OHS) were comparable in normal, healthy, pathology-free individuals of different age, gender, ethnicity, handedness and nationality. The purpose of this study was to establish normal population values for the HHS and OHS using an electronic data collection system. Methods 317 Australian and 310 Canadian citizens with no active hip pain, injury or pathology in the ipsilateral hip corresponding to their dominant arm, were evaluated. Participants completed an electronically-administered questionnaire and were assessed clinically. Chi-square tests, Fisher's exact test and Poisson regression models were used where appropriate, to investigate the association between hip scores, ethnicity, nationality, gender, handedness and age. Results There was a statistically significant association between the OHS and age (p<0.0001) and the HHS and age (p = 0.0006); demonstrating that as age increased, normal hip scores decreased. There was no statistically significant association between the HHS and gender (p = 0.1389); or HSS and nationality, adjusting for age (p = 0.5698) and adjusting for gender (p = 0.6997). There was no statistically significant association between the OHS and gender (p = 0.1350). Australians reported a statistically significant 4.2% higher overall OHS value compared to Canadians (p = 0.0490). There was no statistically significant association between the OHS and nationality in age groups 18-79 years. Participants >80 years reported a statistically significant association between the OHS and nationality (p<0.0001). Conclusions Studies using an electronic control group should consider differences in gender, age, ethnicity and nationality when using the HHS and OHS to assess patient outcomes. This study has established an electronic, normal control group for studies using the HHS and OHS. When using the OHS, the control group should be sourced from the same country of origin. When using the HHS, the control group should be sourced from a pre-established control group within a database, without necessarily being sourced from the same country of origin.


2019 ◽  
Author(s):  
Benedikt Ley ◽  
Komal Raj Rijal ◽  
Jutta Marfurt ◽  
Nabaraj Adhikari ◽  
Megha Banjara ◽  
...  

Abstract Objective: Electronic data collection (EDC) has become a suitable alternative to paper based data collection (PBDC) in biomedical research even in resource poor settings. During a survey in Nepal, data were collected using both systems and data entry errors compared between both methods. Collected data were checked for completeness, values outside of realistic ranges, internal logic and date variables for reasonable time frames. Variables were grouped into 5 categories and the number of discordant entries were compared between both systems, overall and per variable category. Results: Data from 52 variables collected from 358 participants were available. Discrepancies between both data sets were found in 12.6% of all entries (2352/18,616). Differences between data points were identified in 18.0% (643/3,580) of continuous variables, 15.8% of time variables (113/716), 13.0% of date variables (140/1,074), 12.0% of text variables (86/716), and 10.9% of categorical variables (1,370/12,530). Overall 64% (1,499/2,352) of all discrepancies were due to data omissions, 76.6% (1,148/1,499) of missing entries were among categorical data. Omissions in PBDC (n=1002) were twice as frequent as in EDC (n=497, p<0.001). Data omissions, specifically among categorical variables were identified as the greatest source of error. If designed accordingly, EDC can address this short fall effectively.


2021 ◽  
Author(s):  
Michael Reichold ◽  
Miriam Hess ◽  
Peter L. Kolominsky-Rabas ◽  
Elmar Gräßel ◽  
Hans-Ulrich Prokosch

BACKGROUND Digital registries have shown to provide an efficient way better to understand the clinical complexity and long-term progression of diseases. The paperless way of electronic data collection during a patient interview saves both: time and resources. In the prospective multicenter 'Digital Dementia Registry Bavaria - digiDEM Bayern', interviews are also conducted on-site in rural areas with unreliable internet connectivity. It must be ensured that electronic data collection can still be performed there, and it is no need to fall back on paper-based questionnaires. Therefore, the EDC system REDCap offers, in addition to a web-based data collection solution, the option to collect data offline via an app and synchronize it afterward. OBJECTIVE This study evaluates the usability of the REDCap app as an offline electronic data collection option for a lay user group and examines the necessary technology acceptance using mobile devices for data collection. Thereby, the feasibility of the app-based offline data collection in the dementia registry project was evaluated before going live. METHODS The study was conducted with an exploratory mixed-method in the form of an on-site usability test with the 'Thinking Aloud' method combined with a tailored semi-standardized online questionnaire including System Usability Score (SUS). The acceptance of mobile devices for the data collection was surveyed based on the technology acceptance model (TAM) with five categories. RESULTS Using the Thinking Aloud method, usability problems were identified and solutions were derived therefore. The evaluation of the REDCap app resulted in a SUS score of 74, which represents 'good' usability. After evaluating the technology acceptance questionnaire, it can be stated that the lay user group is open to mobile devices as interview tools. CONCLUSIONS The usability evaluation results show that a lay user group like the data collecting partners in the digiDEM project can handle the REDCap app well overall. The usability test provided statements about positive aspects and was able to identify usability problems of the REDCap app. In addition, the current technology acceptance in the sample showed that heterogeneous groups of different ages with different experiences in handling mobile devices are also ready for the use of app-based EDC systems. Based on the results, it can be assumed that the offline use of an app-based EDC system on mobile devices is a viable solution to collect data in a registry-based research project.


Author(s):  
Michael Farrugia ◽  
Neil Hurley ◽  
Diane Payne ◽  
Aaron Quigley

In this chapter, the authors will discuss the differences between manual data collection and electronic data collection to understand the advantages and the challenges brought by electronic social network data. They will discuss in detail the processes that are used to transform electronic data to social network data and the procedures that can be used to validate the resultant social network.


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