Cargando…
Sleep Tracking and Exercise in Patients With Type 2 Diabetes Mellitus (Step-D): Pilot Study to Determine Correlations Between Fitbit Data and Patient-Reported Outcomes
BACKGROUND: Few studies assessing the correlation between patient-reported outcomes and patient-generated health data from wearable devices exist. OBJECTIVE: The aim of this study was to determine the direction and magnitude of associations between patient-generated health data (from the Fitbit Char...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6008516/ https://www.ncbi.nlm.nih.gov/pubmed/29871856 http://dx.doi.org/10.2196/mhealth.8122 |
_version_ | 1783333192410857472 |
---|---|
author | Weatherall, James Paprocki, Yurek Meyer, Theresa M Kudel, Ian Witt, Edward A |
author_facet | Weatherall, James Paprocki, Yurek Meyer, Theresa M Kudel, Ian Witt, Edward A |
author_sort | Weatherall, James |
collection | PubMed |
description | BACKGROUND: Few studies assessing the correlation between patient-reported outcomes and patient-generated health data from wearable devices exist. OBJECTIVE: The aim of this study was to determine the direction and magnitude of associations between patient-generated health data (from the Fitbit Charge HR) and patient-reported outcomes for sleep patterns and physical activity in patients with type 2 diabetes mellitus (T2DM). METHODS: This was a pilot study conducted with adults diagnosed with T2DM (n=86). All participants wore a Fitbit Charge HR for 14 consecutive days and completed internet-based surveys at 3 time points: day 1, day 7, and day 14. Patient-generated health data included minutes asleep and number of steps taken. Questionnaires assessed the number of days of exercise and nights of sleep problems per week. Means and SDs were calculated for all data, and Pearson correlations were used to examine associations between patient-reported outcomes and patient-generated health data. All respondents provided informed consent before participating. RESULTS: The participants were predominantly middle-aged (mean 54.3, SD 13.3 years), white (80/86, 93%), and female (50/86, 58%). Use of oral T2DM medication correlated with the number of mean steps taken (r=.35, P=.001), whereas being unaware of the glycated hemoglobin level correlated with the number of minutes asleep (r=−.24, P=.04). On the basis of the Fitbit data, participants walked an average of 4955 steps and slept 6.7 hours per day. They self-reported an average of 2.0 days of exercise and 2.3 nights of sleep problems per week. The association between the number of days exercised and steps walked was strong (r=.60, P<.001), whereas the association between the number of troubled sleep nights and minutes asleep was weaker (r=.28, P=.02). CONCLUSIONS: Fitbit and patient-reported data were positively associated for physical activity as well as sleep, with the former more strongly correlated than the latter. As extensive patient monitoring can guide clinical decisions regarding T2DM therapy, passive, objective data collection through wearables could potentially enhance patient care, resulting in better patient-reported outcomes. |
format | Online Article Text |
id | pubmed-6008516 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-60085162018-06-27 Sleep Tracking and Exercise in Patients With Type 2 Diabetes Mellitus (Step-D): Pilot Study to Determine Correlations Between Fitbit Data and Patient-Reported Outcomes Weatherall, James Paprocki, Yurek Meyer, Theresa M Kudel, Ian Witt, Edward A JMIR Mhealth Uhealth Original Paper BACKGROUND: Few studies assessing the correlation between patient-reported outcomes and patient-generated health data from wearable devices exist. OBJECTIVE: The aim of this study was to determine the direction and magnitude of associations between patient-generated health data (from the Fitbit Charge HR) and patient-reported outcomes for sleep patterns and physical activity in patients with type 2 diabetes mellitus (T2DM). METHODS: This was a pilot study conducted with adults diagnosed with T2DM (n=86). All participants wore a Fitbit Charge HR for 14 consecutive days and completed internet-based surveys at 3 time points: day 1, day 7, and day 14. Patient-generated health data included minutes asleep and number of steps taken. Questionnaires assessed the number of days of exercise and nights of sleep problems per week. Means and SDs were calculated for all data, and Pearson correlations were used to examine associations between patient-reported outcomes and patient-generated health data. All respondents provided informed consent before participating. RESULTS: The participants were predominantly middle-aged (mean 54.3, SD 13.3 years), white (80/86, 93%), and female (50/86, 58%). Use of oral T2DM medication correlated with the number of mean steps taken (r=.35, P=.001), whereas being unaware of the glycated hemoglobin level correlated with the number of minutes asleep (r=−.24, P=.04). On the basis of the Fitbit data, participants walked an average of 4955 steps and slept 6.7 hours per day. They self-reported an average of 2.0 days of exercise and 2.3 nights of sleep problems per week. The association between the number of days exercised and steps walked was strong (r=.60, P<.001), whereas the association between the number of troubled sleep nights and minutes asleep was weaker (r=.28, P=.02). CONCLUSIONS: Fitbit and patient-reported data were positively associated for physical activity as well as sleep, with the former more strongly correlated than the latter. As extensive patient monitoring can guide clinical decisions regarding T2DM therapy, passive, objective data collection through wearables could potentially enhance patient care, resulting in better patient-reported outcomes. JMIR Publications 2018-06-05 /pmc/articles/PMC6008516/ /pubmed/29871856 http://dx.doi.org/10.2196/mhealth.8122 Text en ©James Weatherall, Yurek Paprocki, Theresa M Meyer, Ian Kudel, Edward A Witt. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 05.06.2018. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Weatherall, James Paprocki, Yurek Meyer, Theresa M Kudel, Ian Witt, Edward A Sleep Tracking and Exercise in Patients With Type 2 Diabetes Mellitus (Step-D): Pilot Study to Determine Correlations Between Fitbit Data and Patient-Reported Outcomes |
title | Sleep Tracking and Exercise in Patients With Type 2 Diabetes Mellitus (Step-D): Pilot Study to Determine Correlations Between Fitbit Data and Patient-Reported Outcomes |
title_full | Sleep Tracking and Exercise in Patients With Type 2 Diabetes Mellitus (Step-D): Pilot Study to Determine Correlations Between Fitbit Data and Patient-Reported Outcomes |
title_fullStr | Sleep Tracking and Exercise in Patients With Type 2 Diabetes Mellitus (Step-D): Pilot Study to Determine Correlations Between Fitbit Data and Patient-Reported Outcomes |
title_full_unstemmed | Sleep Tracking and Exercise in Patients With Type 2 Diabetes Mellitus (Step-D): Pilot Study to Determine Correlations Between Fitbit Data and Patient-Reported Outcomes |
title_short | Sleep Tracking and Exercise in Patients With Type 2 Diabetes Mellitus (Step-D): Pilot Study to Determine Correlations Between Fitbit Data and Patient-Reported Outcomes |
title_sort | sleep tracking and exercise in patients with type 2 diabetes mellitus (step-d): pilot study to determine correlations between fitbit data and patient-reported outcomes |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6008516/ https://www.ncbi.nlm.nih.gov/pubmed/29871856 http://dx.doi.org/10.2196/mhealth.8122 |
work_keys_str_mv | AT weatheralljames sleeptrackingandexerciseinpatientswithtype2diabetesmellitusstepdpilotstudytodeterminecorrelationsbetweenfitbitdataandpatientreportedoutcomes AT paprockiyurek sleeptrackingandexerciseinpatientswithtype2diabetesmellitusstepdpilotstudytodeterminecorrelationsbetweenfitbitdataandpatientreportedoutcomes AT meyertheresam sleeptrackingandexerciseinpatientswithtype2diabetesmellitusstepdpilotstudytodeterminecorrelationsbetweenfitbitdataandpatientreportedoutcomes AT kudelian sleeptrackingandexerciseinpatientswithtype2diabetesmellitusstepdpilotstudytodeterminecorrelationsbetweenfitbitdataandpatientreportedoutcomes AT wittedwarda sleeptrackingandexerciseinpatientswithtype2diabetesmellitusstepdpilotstudytodeterminecorrelationsbetweenfitbitdataandpatientreportedoutcomes |