3 research outputs found
There is a Great Need for Contextualisation in Southern Africa
No full textThe purpose of this article is to show the need for contextualisation in southern Africa. The author discusses the early missionary activities in Africa and the current problems that the African church faces and how these problems are linked with a lack of contextualisation of the Gospel
Predicting lying, sitting, walking and running using Apple Watch and Fitbit data
No full textObjectives This study’s objective was to examine whether commercial wearable devices could accurately predict lying, sitting and varying intensities of walking and running.Methods We recruited a convenience sample of 49 participants (23 men and 26 women) to wear three devices, an Apple Watch Series 2, a Fitbit Charge HR2 and iPhone 6S. Participants completed a 65 min protocol consisting of 40 min of total treadmill time and 25 min of sitting or lying time. The study’s outcome variables were six movement types: lying, sitting, walking self-paced and walking/running at 3 metabolic equivalents of task (METs), 5 METs and 7 METs. All analyses were conducted at the minute level with heart rate, steps, distance and calories from Apple Watch and Fitbit. These included three different machine learning models: support vector machines, Random Forest and Rotation forest.Results Our dataset included 3656 and 2608 min of Apple Watch and Fitbit data, respectively. Rotation Forest models had the highest classification accuracies for Apple Watch at 82.6%, and Random Forest models had the highest accuracy for Fitbit at 90.8%. Classification accuracies for Apple Watch data ranged from 72.6% for sitting to 89.0% for 7 METs. For Fitbit, accuracies varied between 86.2% for sitting to 92.6% for 7 METs.Conclusion This preliminary study demonstrated that data from commercial wearable devices could predict movement types with reasonable accuracy. More research is needed, but these methods are a proof of concept for movement type classification at the population level using commercial wearable device data
The State of Open Data 2022
No full textWe're proud to release our seventh State of Open Data report.
Based on a global survey, the report is now in its seventh year and provides insights into researchers’ attitudes towards and experiences of open data. With more than 5,400 respondents, the 2022 survey is the largest since the COVID-19 pandemic began.
This year’s report also includes guest articles from open data experts at the National Institutes of Health (NIH), the White House Office of Science and Technology Policy (OSTP), the Computer Network Information Center, Chinese Academy of Sciences (CNIC, CAS), publishers and universities.
Version 5 includes link to full survey results and questionnaire with updated links, corrected author affiliation on the contents page and throughout. </p
