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Developing a system that can automatically detect health changes using transfer times of older adults

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Organization
Abstract
Background: As gait speed and transfer times are considered to be an important measure of functional ability in older adults, several systems are currently being researched to measure this parameter in the home environment of older adults. The data resulting from these systems, however, still needs to be reviewed by healthcare workers which is a time-consuming process. Methods: This paper presents a system that employs statistical process control techniques (SPC) to automatically detect both positive and negative trends in transfer times. Several SPC techniques, Tabular cumulative sum (CUSUM) chart, Standardized CUSUM and Exponentially Weighted Moving Average (EWMA) chart were evaluated. The best performing method was further optimized for the desired application. After this, it was validated on both simulated data and real-life data. Results: The best performing method was the Exponentially Weighted Moving Average control chart with the use of rational subgroups and a reinitialization after three alarm days. The results from the simulated data showed that positive and negative trends are detected within 14 days after the start of the trend when a trend is 28 days long. When the transition period is shorter, the number of days before an alert is triggered also diminishes. If for instance an abrupt change is present in the transfer time an alert is triggered within two days after this change. On average, only one false alarm is triggered every five weeks. The results from the real-life dataset confirm those of the simulated dataset. Conclusions: The system presented in this paper is able to detect both positive and negative trends in the transfer times of older adults, therefore automatically triggering an alarm when changes in transfer times occur. These changes can be gradual as well as abrupt.
Keywords
Assisted living, Gerontechnology, Change detection algorithms, Statistical process control, Log logistic distributions, Gait speed, GAIT SPEED, HOME, PREDICTOR, FALLS, RISK

Citation

Please use this url to cite or link to this publication:

Chicago
Baldewijns, Greet, Stijn Luca, Bart Vanrumste, and Tom Croonenborghs. 2016. “Developing a System That Can Automatically Detect Health Changes Using Transfer Times of Older Adults.” Bmc Medical Research Methodology 16.
APA
Baldewijns, G., Luca, S., Vanrumste, B., & Croonenborghs, T. (2016). Developing a system that can automatically detect health changes using transfer times of older adults. BMC MEDICAL RESEARCH METHODOLOGY, 16.
Vancouver
1.
Baldewijns G, Luca S, Vanrumste B, Croonenborghs T. Developing a system that can automatically detect health changes using transfer times of older adults. BMC MEDICAL RESEARCH METHODOLOGY. 2016;16.
MLA
Baldewijns, Greet et al. “Developing a System That Can Automatically Detect Health Changes Using Transfer Times of Older Adults.” BMC MEDICAL RESEARCH METHODOLOGY 16 (2016): n. pag. Print.
@article{8581143,
  abstract     = {Background: As gait speed and transfer times are considered to be an important measure of functional ability in older adults, several systems are currently being researched to measure this parameter in the home environment of older adults. The data resulting from these systems, however, still needs to be reviewed by healthcare workers which is a time-consuming process. 
Methods: This paper presents a system that employs statistical process control techniques (SPC) to automatically detect both positive and negative trends in transfer times. Several SPC techniques, Tabular cumulative sum (CUSUM) chart, Standardized CUSUM and Exponentially Weighted Moving Average (EWMA) chart were evaluated. The best performing method was further optimized for the desired application. After this, it was validated on both simulated data and real-life data. 
Results: The best performing method was the Exponentially Weighted Moving Average control chart with the use of rational subgroups and a reinitialization after three alarm days. The results from the simulated data showed that positive and negative trends are detected within 14 days after the start of the trend when a trend is 28 days long. When the transition period is shorter, the number of days before an alert is triggered also diminishes. If for instance an abrupt change is present in the transfer time an alert is triggered within two days after this change. On average, only one false alarm is triggered every five weeks. The results from the real-life dataset confirm those of the simulated dataset. 
Conclusions: The system presented in this paper is able to detect both positive and negative trends in the transfer times of older adults, therefore automatically triggering an alarm when changes in transfer times occur. These changes can be gradual as well as abrupt.},
  articleno    = {23},
  author       = {Baldewijns, Greet and Luca, Stijn and Vanrumste, Bart and Croonenborghs, Tom},
  issn         = {1471-2288},
  journal      = {BMC MEDICAL RESEARCH METHODOLOGY},
  language     = {eng},
  pages        = {17},
  title        = {Developing a system that can automatically detect health changes using transfer times of older adults},
  url          = {http://dx.doi.org/10.1186/s12874-016-0124-4},
  volume       = {16},
  year         = {2016},
}

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