» Articles » PMID: 32098894

Embedding Mobile Health Technology into the Nurses' Health Study 3 to Study Behavioral Risk Factors for Cancer

Overview
Date 2020 Feb 27
PMID 32098894
Citations 10
Authors
Affiliations
Soon will be listed here.
Abstract

Background: Physical activity and sleep are behavioral risk factors for cancer that may be influenced by environmental exposures, including built and natural environments. However, many studies in this area are limited by residence-based exposure assessment and/or self-reported, time-aggregated measures of behavior.

Methods: The Nurses' Health Study 3 (NHS3) Mobile Health Substudy is a pilot study of 500 participants in the prospective NHS3 cohort who use a smartphone application and a Fitbit for seven-day periods, four times over a year, to measure minute-level location, physical activity, heart rate, and sleep.

Results: We have collected data on 435 participants, comprising over 6 million participant-minutes of heart rate, step, sleep, and location. Over 90% of participants had five days of ≥600 minutes of Fitbit wear-time in their first sampling week, and this percentage dropped to 70% for weeks 2 to 4. Over 819 sampling weeks, we observed an average of 7,581 minutes of heart rate and step data [interquartile range (IQR): 6,651-9,645] per participant-week, and >2 million minutes of sleep in over 5,700 sleep bouts. We have recorded location data for 5,237 unique participant-days, averaging 104 location observations per participant-day (IQR: 103-107).

Conclusions: This study describes a protocol to incorporate mobile health technology into a nationwide prospective cohort to measure high-resolution objective data on environment and behavior.

Impact: This project could provide translational insights into interventions for urban planning to optimize opportunities for physical activity and healthy sleep patterns to reduce cancer risk.

Citing Articles

Using a Consumer Wearable Activity Monitoring Device to Study Physical Activity and Sleep Among Adolescents in Project Viva: Cohort Study.

Zhang Y, Bornkamp N, Hivert M, Oken E, James P JMIR Pediatr Parent. 2025; 8:e59159.

PMID: 39903900 PMC: 11813160. DOI: 10.2196/59159.


Associations of seasonally available global positioning systems-derived walkability and objectively measured sleep in the Nurses' Health Study 3 Mobile Health Substudy.

Hu C, Wilt G, Roscoe C, Iyer H, Kessler W, Laden F Environ Epidemiol. 2024; 8(6):e348.

PMID: 39399736 PMC: 11469837. DOI: 10.1097/EE9.0000000000000348.


Measuring Environmental and Behavioral Drivers of Chronic Diseases Using Smartphone-Based Digital Phenotyping: Intensive Longitudinal Observational mHealth Substudy Embedded in 2 Prospective Cohorts of Adults.

Yi L, Hart J, Straczkiewicz M, Karas M, Wilt G, Hu C JMIR Public Health Surveill. 2024; 10:e55170.

PMID: 39392682 PMC: 11512133. DOI: 10.2196/55170.


Vaping habits and respiratory symptoms using a smartphone app platform.

Lee M, Eum K, Allen J, Onnela J, Christiani D BMC Public Health. 2024; 24(1):2047.

PMID: 39080563 PMC: 11289986. DOI: 10.1186/s12889-024-19439-0.


The integration of geographic methods and ecological momentary assessment in public health research: A systematic review of methods and applications.

Zhang Y, Li D, Li X, Zhou X, Newman G Soc Sci Med. 2024; 354:117075.

PMID: 38959816 PMC: 11629396. DOI: 10.1016/j.socscimed.2024.117075.


References
1.
Krieger N . Embodiment: a conceptual glossary for epidemiology. J Epidemiol Community Health. 2005; 59(5):350-5. PMC: 1733093. DOI: 10.1136/jech.2004.024562. View

2.
Perchoux C, Chaix B, Cummins S, Kestens Y . Conceptualization and measurement of environmental exposure in epidemiology: accounting for activity space related to daily mobility. Health Place. 2013; 21:86-93. DOI: 10.1016/j.healthplace.2013.01.005. View

3.
Vooijs M, Alpay L, Snoeck-Stroband J, Beerthuizen T, Siemonsma P, Abbink J . Validity and usability of low-cost accelerometers for internet-based self-monitoring of physical activity in patients with chronic obstructive pulmonary disease. Interact J Med Res. 2014; 3(4):e14. PMC: 4259960. DOI: 10.2196/ijmr.3056. View

4.
Stahl S, An H, Dinkel D, Noble J, Lee J . How accurate are the wrist-based heart rate monitors during walking and running activities? Are they accurate enough?. BMJ Open Sport Exerc Med. 2016; 2(1):e000106. PMC: 5117066. DOI: 10.1136/bmjsem-2015-000106. View

5.
Adam Noah J, Spierer D, Gu J, Bronner S . Comparison of steps and energy expenditure assessment in adults of Fitbit Tracker and Ultra to the Actical and indirect calorimetry. J Med Eng Technol. 2013; 37(7):456-62. DOI: 10.3109/03091902.2013.831135. View