Examining the influence of technology affordances of fitness trackers and health psychographic factors on physical activity
2019
- 17Usage
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage17
- Downloads15
- Abstract Views2
Thesis / Dissertation Description
Fitness trackers have immense potential to improve individuals’ health behaviors and health outcomes. Studies in disciplines such as public health, communication technology and information systems, have examined factors that can predict use of fitness trackers and their influence on health-related behaviors. However, not much is known about (1) which element(s) of fitness trackers affect individuals’ health behaviors, and (2) the psychological mechanism that guides the relationships between individuals’ health-related beliefs and use of fitness trackers on physical activity behaviors. Guided by the Motivational Technology Model (MTM) and Self-Determination Theory (SDT), this dissertation examined the influence of technology affordances (i.e., customization, interactivity, navigability), psychological feelings (i.e., relatedness, autonomy and competence), engagement with fitness tracker, and health psychographic factors on physical activity behavior in a sample of 970 American adults. Path modeling results showed that technology affordances significantly predicted individuals’ psychological feelings, which in turn led to users’ engagement with and actual use of fitness trackers. Of the three technology affordances, customization had a direct positive effect on users’ engagement with fitness trackers. In addition, compared to fitness tracker use, health psychographic factors more significantly predicted physical activity behaviors. This dissertation has tested the first integrated model of the influence of health and technology attributes on fitness tracker use and consequent physical activity. This has theoretical implications for the MTM and SDT, technical implications for the design of fitness tracker technology, and applied implications for the strategic use of fitness trackers in physical activity interventions.
Bibliographic Details
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know