Data mining of a remote behavioral tracking system for Type 2 Diabetes patients: A prospective, cohort study
| dc.contributor.author | Wayne, N. | |
| dc.contributor.author | Cercone, N. | |
| dc.contributor.author | Li, J. | |
| dc.contributor.author | Zohar, Ariel | |
| dc.contributor.author | Katz, Joel | |
| dc.contributor.author | Brown, Patrick | |
| dc.contributor.author | Ritvo, Paul | |
| dc.date.accessioned | 2016-11-16T16:16:47Z | |
| dc.date.available | 2016-11-16T16:16:47Z | |
| dc.date.issued | 2016-01 | |
| dc.description.abstract | Background: Complications from type 2 diabetes mellitus can be prevented when patients perform health behaviors such as vigorous exercise and glucose-regulated diet. The use of smartphones for tracking such behaviors has demonstrated success in type 2 diabetes management while generating repositories of analyzable digital data, which, when better understood, may help improve care. Data mining methods were used in this study to better understand self-monitoring patterns using smartphone tracking software. Objective: Associations were evaluated between the smartphone monitoring of health behaviors and HbA1c reductions in a patient subsample with type 2 diabetes who demonstrated clinically significant benefits after participation in a randomized controlled trial. Methods: A priori association-rule algorithms, implemented in the C language, were applied to app-discretized use data involving three primary health behavior trackers (exercise, diet, and glucose monitoring) from 29 participants who achieved clinically significant HbA1c reductions. Use was evaluated in relation to improved HbA1c outcomes. Results: Analyses indicated that nearly a third (9/29, 31%) of participants used a single tracker, half (14/29, 48%) used two primary trackers, and the remainder (6/29, 21%) of the participants used three primary trackers. Decreases in HbA1c were observed across all groups (0.97-1.95%), but clinically significant reductions were more likely with use of one or two trackers rather than use of three trackers (OR 0.18, P=.04). Conclusions: Data mining techniques can reveal relevant coherent behavior patterns useful in guiding future intervention structure. It appears that focusing on using one or two trackers, in a symbolic function, was more effective (in this sample) than regular use of all three trackers. | en_US |
| dc.description.sponsorship | The authors would like to thank NexJ Systems Inc. for their partnership in this trial and for the use of the Connected Wellness Platform as a clinical research tool. Funding was provided by the Public Health Agency of Canada and the Federal Development Agency of Southern Ontario. We offer special thanks to the staff of the Black Creek Community Health Centre and trial participants from the Jane-Finch community of Toronto, Ontario. Joel Katz is supported by a Canadian Institutes of Health Research Canada Research Chair in Health Psychology. The authors would like to acknowledge with sadness the untimely passing of study co-author, colleague, and friend, Dr. Nicholas Cercone. Dr. Cercone’s expertise and mentorship on data mining theory and technique was invaluable. His inspiring and supportive presence will be deeply missed. | |
| dc.identifier.citation | Wayne, N., Cercone, N., Li, J., Zohar, A., Katz, J., Brown, P., Ritvo, P. (2016). Data mining of a remote behavioral tracking system for Type 2 Diabetes patients: A prospective, cohort study. JMIR Diabetes, 1(1), e1-e14. doi:10.2196/diabetes.4506 | |
| dc.identifier.issn | 2371-4379 | |
| dc.identifier.uri | http://hdl.handle.net/10315/32589 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | JMIR Publications | en_US |
| dc.rights | Attribution-NonCommercial-NoDerivs 2.5 Canada | * |
| dc.rights.article | https://diabetes.jmir.org/2016/1/e1/ | |
| dc.rights.journal | https://diabetes.jmir.org/ | en_US |
| dc.rights.publisher | https://jmirpublications.com/ | en_US |
| dc.subject | diabetes mellitus, type 2; health coaching; mhealth; telehealth; data mining | en_US |
| dc.title | Data mining of a remote behavioral tracking system for Type 2 Diabetes patients: A prospective, cohort study | |
| dc.type | Article | en_US |