Physical Activity Trend eXtraction: A Framework for Extracting Moderate-Vigorous Physical Activity Trends From Wearable Fitness Tracker Data.
activity trackers
exercise
fitness trackers
health
health behavior
mHealth
mental health
perception
physical activity
social network
Journal
JMIR mHealth and uHealth
ISSN: 2291-5222
Titre abrégé: JMIR Mhealth Uhealth
Pays: Canada
ID NLM: 101624439
Informations de publication
Date de publication:
12 03 2019
12 03 2019
Historique:
received:
16
06
2018
accepted:
10
12
2018
revised:
26
11
2018
entrez:
13
3
2019
pubmed:
13
3
2019
medline:
13
3
2019
Statut:
epublish
Résumé
Moderate-vigorous physical activity (MVPA) offers extensive health benefits but is neglected by many. As a result, a wide body of research investigating physical activity behavior change has been conducted. As many of these studies transition from paper-based methods of MVPA data collection to fitness trackers, a series of challenges arise in extracting insights from these new data. The objective of this research was to develop a framework for preprocessing and extracting MVPA trends from wearable fitness tracker data to support MVPA behavior change studies. Using heart rate data collected from fitness trackers, we propose Physical Activity Trend eXtraction (PATX), a framework that imputes missing data, recalculates personalized target heart zones, and extracts MVPA trends. We tested our framework on a dataset of 123 college study participants observed across 2 academic years (18 months) using Fitbit Charge HRs. To demonstrate the value of our frameworks' output in supporting MVPA behavior change studies, we applied it to 2 case studies. Among the 123 participants analyzed, PATX labeled 41 participants as experiencing a significant increase in MVPA and 44 participants who experienced a significant decrease in MVPA, with significance defined as P<.05. Our first case study was consistent with previous works investigating the associations between MVPA and mental health. Whereas the second, exploring how individuals perceive their own levels of MVPA relative to their friends, led to a novel observation that individuals were less likely to notice changes in their own MVPA when close ties in their social network mimicked their changes. By providing meaningful and flexible outputs, PATX alleviates data concerns common with fitness trackers to support MVPA behavior change studies as they shift to more objective assessments of MVPA.
Sections du résumé
BACKGROUND
Moderate-vigorous physical activity (MVPA) offers extensive health benefits but is neglected by many. As a result, a wide body of research investigating physical activity behavior change has been conducted. As many of these studies transition from paper-based methods of MVPA data collection to fitness trackers, a series of challenges arise in extracting insights from these new data.
OBJECTIVE
The objective of this research was to develop a framework for preprocessing and extracting MVPA trends from wearable fitness tracker data to support MVPA behavior change studies.
METHODS
Using heart rate data collected from fitness trackers, we propose Physical Activity Trend eXtraction (PATX), a framework that imputes missing data, recalculates personalized target heart zones, and extracts MVPA trends. We tested our framework on a dataset of 123 college study participants observed across 2 academic years (18 months) using Fitbit Charge HRs. To demonstrate the value of our frameworks' output in supporting MVPA behavior change studies, we applied it to 2 case studies.
RESULTS
Among the 123 participants analyzed, PATX labeled 41 participants as experiencing a significant increase in MVPA and 44 participants who experienced a significant decrease in MVPA, with significance defined as P<.05. Our first case study was consistent with previous works investigating the associations between MVPA and mental health. Whereas the second, exploring how individuals perceive their own levels of MVPA relative to their friends, led to a novel observation that individuals were less likely to notice changes in their own MVPA when close ties in their social network mimicked their changes.
CONCLUSIONS
By providing meaningful and flexible outputs, PATX alleviates data concerns common with fitness trackers to support MVPA behavior change studies as they shift to more objective assessments of MVPA.
Identifiants
pubmed: 30860488
pii: v7i3e11075
doi: 10.2196/11075
pmc: PMC6434402
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
e11075Subventions
Organisme : NHLBI NIH HHS
ID : R01 HL117757
Pays : United States
Informations de copyright
©Louis Faust, Cheng Wang, David Hachen, Omar Lizardo, Nitesh V Chawla. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 12.03.2019.
Références
CMAJ. 2006 Mar 14;174(6):801-9
pubmed: 16534088
Int J Behav Nutr Phys Act. 2010 May 11;7:40
pubmed: 20459784
Lancet. 2012 Jul 21;380(9838):219-29
pubmed: 22818936
J Sports Med (Hindawi Publ Corp). 2017;2017:4641203
pubmed: 28459099
Big Data. 2015 Dec;3(4):249-66
pubmed: 27441406
Int J Epidemiol. 2011 Jun;40(3):685-98
pubmed: 21245072
Br J Sports Med. 2015 Jun;49(11):730-6
pubmed: 24273308
JAMA. 1995 Feb 1;273(5):402-7
pubmed: 7823386
JMIR Mhealth Uhealth. 2016 Nov 23;4(4):e129
pubmed: 27881359
Public Health. 2007 Dec;121(12):909-22
pubmed: 17920646
Front Public Health. 2017 Jan 11;4:289
pubmed: 28123997
J Phys Act Health. 2017 Jul;14(7):513-519
pubmed: 28290744
Sports Med. 1996 May;21(5):326-36
pubmed: 8724201
Support Care Cancer. 2015 Jan;23(1):159-67
pubmed: 25022760
J Biomed Inform. 2016 Oct;63:54-65
pubmed: 27471222
Circulation. 2018 Jul 24;138(4):345-355
pubmed: 29712712
JMIR Mhealth Uhealth. 2017 Oct 20;5(10):e157
pubmed: 29055881
J Am Heart Assoc. 2018 Mar 22;7(6):
pubmed: 29567764
JMIR Mhealth Uhealth. 2018 Aug 09;6(8):e10527
pubmed: 30093371
J Sports Sci. 2018 Aug;36(16):1889-1896
pubmed: 29318916
Scand J Med Sci Sports. 2017 Dec;27(12):1785-1792
pubmed: 27714910
PLoS One. 2015 Mar 18;10(3):e0119607
pubmed: 25786030
J Am Coll Cardiol. 2014 Aug 5;64(5):482-4
pubmed: 25082582
Pediatrics. 2016 Oct;138(4):
pubmed: 27669737
Br J Sports Med. 2019 Apr;53(8):496-503
pubmed: 28739834
Dev Med Child Neurol. 2011 Sep;53(9):861-864
pubmed: 21569015
Arch Dis Child. 2007 Nov;92(11):963-9
pubmed: 17855437
Am J Prev Med. 2011 Aug;41(2):207-15
pubmed: 21767729
Arch Pediatr Adolesc Med. 2001 Aug;155(8):940-6
pubmed: 11483123
Int J Obes Relat Metab Disord. 1993 May;17(5):279-86
pubmed: 8389337
J Reliab Intell Environ. 2017 Aug;3(2):83-98
pubmed: 28966906
Mayo Clin Proc. 2010 Dec;85(12):1138-41
pubmed: 21123641
Knowl Inf Syst. 2017 May;51(2):339-367
pubmed: 28603327