The effect of time spent outdoors during summer on daily blood glucose and steps in women with type 2 diabetes

  • Molly B. Richardson
  • Courtney Chmielewski
  • Connor Y. H. Wu
  • Mary B. Evans
  • Leslie A. McClure
  • Kathryn W. Hosig
  • Julia M. GohlkeEmail author


This study investigated changes in glycemic control following a small increase in time spent outdoors. Women participants with type 2 diabetes (N = 46) wore an iBUTTON temperature monitor and a pedometer for 1 week and recorded their morning fasting blood glucose (FBG) daily. They went about their normal activities for 2 days (baseline) and were asked to add 30 min of time outdoors during Days 3–7 (intervention). Linear mixed effects models were used to test whether morning FBG values were different on days following intervention versus baseline days, and whether steps and/or heat exposure changed. Results were stratified by indicators of good versus poor glycemic control prior to initiation of the study. On average, blood glucose was reduced by 6.1 mg/dL (95% CI − 11.5, − 0.6) on mornings after intervention days after adjusting for age, BMI, and ambient weather conditions. Participants in the poor glycemic control group (n = 16) experienced a 15.8 mg/dL decrease (95% CI − 27.1, − 4.5) in morning FBG on days following the intervention compared to a 1.6 mg/dL decrease (95%CI − 7.7, 4.5) for participants in the good glycemic control group (n = 30). Including daily steps or heat exposure did not attenuate the association between intervention and morning FBG. The present study suggests spending an additional 30 min outdoors may improve glycemic control; however, further examination with a larger sample over a longer duration and determination of mediators of this relationship is warranted.


T2DM Diabetes Fasting glucose Time spent outdoors Ambient temperature Physical activity 



Special thanks to the participants and community partners Ethel Johnson, Sheryl-Threadgill Matthews, Sheila Tyson, Keisha Brown, Clarice Davis, and Emily Ingram. All authors certify that they have participated sufficiently in the work. Funding was provided by National Institute of Environmental Health Sciences (Grant No. R01ES023029).

Compliance with ethical standards

Conflict of interest

Molly B. Richardson, Courtney Chmielewski, Connor Y. H. Wu, Mary B. Evans, Leslie A. McClure, Kathryn W. Hosig and Julia M. Gohlke declare that they have no conflict of interest.

Human and animal rights and Informed consent

All procedures followed were in accordance with ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. Informed consent was obtained from all participants prior to inclusion in the study.

Supplementary material

10865_2019_113_MOESM1_ESM.doc (46 kb)
Supplementary Fig. 1. Flow diagram (adapted from the CONSORT flow diagram) (DOC 46 kb)
10865_2019_113_MOESM2_ESM.pdf (1.2 mb)
Supplementary material 2 (PDF 1233 kb)
10865_2019_113_MOESM3_ESM.pdf (50 kb)
Supplementary Fig. 2. Decision Tree for Inclusive and Restrictive Criteria for Pedometer Step Data. 1—“Day prior”–“day of” rule applied to all days (i.e. Day2–Day1); 2—Individually evaluate days identified with original daily log (322-48 = 14.9% of person-days modified in the Inclusive Dataset; n = 59, 18.3% in the Restrictive Dataset); 3—Baseline day 2 missing so repeated day 1 values; 4—Intervention days as last day or multiple intervention last days were missing so applied average of existing intervention days were imputed (i.e. Days 6 and 7 missing then used average of Days 3,4,5); 5—Days surrounding were reset so the average of intervention days was imputed; 6—Missing day was followed by the same type of day (i.e. intervention missing and intervention day following known) then the missing day was replaced with ½ of the following day and the day following was ½ as well (i.e. If Day 3 = Missing, Day 4 = 4818, then Day 3 = 2409, Day 4 = 2409) (PDF 49 kb)
10865_2019_113_MOESM4_ESM.docx (14 kb)
Supplementary Table 1. Results of linear mixed effects models describing the relationship between the intervention and personal temperature (daily mean hourly) or steps (inclusive criteria) adjusting for weather variables (precipitation, weather station maximum and minimum temperatures). *0.047611 (DOCX 14 kb)
10865_2019_113_MOESM5_ESM.docx (16 kb)
Supplementary Table 2. Results of linear mixed effects models testing to screen for partial mediation by steps day prior (inclusive and restrictive criteria) (DOCX 15 kb)
10865_2019_113_MOESM6_ESM.docx (16 kb)
Supplementary Table 3. Results of linear mixed effects models testing to screen for partial mediation by personal temperature (daily mean average and daily max average) (DOCX 15 kb)
10865_2019_113_MOESM7_ESM.docx (17 kb)
Supplementary Table 4. Model stratified by glycemic thresholds and adjusting for individual steps and/or personal temperature (Models 1-3 Poor Glycemic Control, Models 4-6 Good Glycemic Control). *0.05548 (DOCX 16 kb)


  1. American Diabetes Association. (2003). Physical activity/exercise and diabetes mellitus. Diabetes Care, 26, S73–S77. CrossRefGoogle Scholar
  2. American Diabetes Association. (2007). Standards of medical care for patients with diabetes mellitus. Diabetes Care. CrossRefGoogle Scholar
  3. American Diabetes Association. (2016). 5. Glycemic targets: Standards of medical care in diabetes-2016. Diabetes Care, 39(Suppl. 1), S39–S46.Google Scholar
  4. Arno, A., & Thomas, S. (2016). The efficacy of nudge theory strategies in influencing adult dietary behaviour: a systematic review and meta-analysis. BMC Public Health, 16, 676. CrossRefPubMedPubMedCentralGoogle Scholar
  5. Bai, L., Li, Q., Wang, J., Lavinge, E., Gasparrini, A., et al. (2016). Hospitalizations from hypertensive diseases, diabetes, and arrhythmia in relation to low and high temperatures: population-based study. Scientific Reports, 6, 3028. CrossRefGoogle Scholar
  6. Bernhard, M. C., Kent, S. T., Sloan, M. E., Evans, M. B., McClure, L. A., & Gohlke, J. M. (2015). Measuring personal heat exposure in an urban and rural environment. Environmental Research, 137, 410–418. CrossRefPubMedPubMedCentralGoogle Scholar
  7. Beyer, K. M. M., Szabo, A., Hoormann, K., & Stolley, M. (2018). Time spent outdoors, activity levels, and chronic disease among American adults. Journal of Behavioral Medicine, 41, 494–503. CrossRefPubMedPubMedCentralGoogle Scholar
  8. Chen, J., Alemao, E., Yin, D., & Cook, J. (2008). Development of a diabetes treatment simulation model: With application to assessing alternative treatment intensification strategies on survival and diabetes-related complications. Diabetes, Obesity & Metabolism, 10, 33–42. CrossRefGoogle Scholar
  9. Colberg, S. R., Sigal, R. J., Fernhall, B., Regensteiner, J. G., Blissmer, B. J., Rubin, R. R., et al. (2010). Exercise and type 2 diabetes: the American College of Sports Medicine and the American Diabetes Association: Joint position statement executive summary. Diabetes Care, 3, 2692–2696. CrossRefGoogle Scholar
  10. de Lissovoy, G., Ganoczy, D. A., & Ray, N. F. (2000). Relationship of hemoglobin A1c, age of diabetes diagnosis, and ethnicity to clinical outcomes and medical costs in a computer-simulated cohort of persons with type 2 diabetes. The American Journal of Managed Care, 6, 573–584.PubMedGoogle Scholar
  11. Dumke, C. L., Silvaka, D. R., Cuddy, J. S., Hailes, W. S., Rose, S. M., & Ruby, B. C. (2015). The effect of environmental temperature on glucose and insulin after an oral glucose tolerance test in healthy young men. Wilderness & Environmental Medicine, 26, 335–342. CrossRefGoogle Scholar
  12. Felício, K. M., de Souza, A. C. C. B., Neto, J. F. A., de Melo, F. T. C., Carvalho, C. T., Arbage, T. P., et al. (2018). Glycemic variability and insulin needs in patients with type 1 diabetes mellitus supplemented with vitamin D: A pilot study using continuous glucose monitoring system. Current Diabetes Reviews, 14, 395–403.CrossRefGoogle Scholar
  13. Golay, A., & Ybarra, J. (2005). Link between obesity and type 2 diabetes. Best Practice & Research Clinical Endocrinology & Metabolism, 19, 649–663. CrossRefGoogle Scholar
  14. Gothe, N. P., & Kendall, B. J. (2016). Barriers, motivations, and preferences for physical activity among female african american older adults. Gerontology and Geriatric Medicine, 2, 2333721416677399. CrossRefPubMedPubMedCentralGoogle Scholar
  15. Gray, C., Gibbons, R., Larouche, R., Sandseter, E. B., Bienenstock, A., Brussoni, M., et al. (2015). What is the relationship between outdoor time and physical activity, sedentary behaviour, and physical fitness in children? A systematic review. International Journal of Environmental Research and Public Health., 12, 6455–6474. CrossRefPubMedPubMedCentralGoogle Scholar
  16. Hamasaki, H. (2016). Daily physical activity and type 2 diabetes: A review. World J Diabetes, 7, 243–251. CrossRefPubMedPubMedCentralGoogle Scholar
  17. Hyatt, T. C., Phadke, R. P., Hunter, G. R., Bush, N. C., Muñoz, A. J., & Gower, B. A. (2009). Insulin sensitivity in African–American and white women: Association with inflammation. Obesity (Silver Spring), 17, 276–282. CrossRefGoogle Scholar
  18. Kautzky-Willer, A., Harreiter, J., & Pacini, G. (2016). Sex and gender differences in risk, pathophysiology and complications of type 2 diabetes mellitus. Endocrine Reviews, 37, 278–316. CrossRefPubMedPubMedCentralGoogle Scholar
  19. Kenny, G. P., Sigal, R. J., & McGinn, R. (2016). Body temperature regulation in diabetes. Temperature (Austin), 3, 119–145. CrossRefGoogle Scholar
  20. Klein, R., Klein, B. E., Moss, S. E., Davis, M. D., & DeMets, D. L. (1988). Glycosylated hemoglobin predicts the incidence and progression of diabetic retinopathy. JAMA, 260, 2864–2871.CrossRefGoogle Scholar
  21. Li, Xinyi, Liu, Yan, Zheng, Yingdong, Wang, Peiyu, & Zhang, Yumei. (2018). The effect of vitamin D supplementation on glycemic control in type 2 diabetes patients: A systematic review and meta-analysis. Nutrients, 10, 375.CrossRefGoogle Scholar
  22. MacKinnon, D. P., Fairchild, A. J., & Fritz, M. S. (2007). Mediation analysis. Annual Review of Psychology, 58, 593–614. CrossRefPubMedPubMedCentralGoogle Scholar
  23. Manley, S. (2003). Haemoglobin A1c–a marker for complications of type 2 diabetes: The experience from the UK Prospective Diabetes Study (UKPDS). Clinical Chemistry and Laboratory Medicine, 41, 1182–1190. CrossRefPubMedGoogle Scholar
  24. Maxim Integrated. DS1922L: iBUTTON temperature loggers with 8 KB data-log memory. [Web page] 2018 23 Aug 2018; Retrieved September 10, 2018 from:
  25. Minshall, M. E., Roze, P., Palmer, A. J., Valentine, W. J., Foos, V., et al. (2005). Treating diabetes to accepted standards of care: A 10-year projection of the estimated economic and health impact in patients with type 1 and type 2 diabetes mellitus in the United States. Clinical Therapeutics, 27, 940–950. CrossRefPubMedGoogle Scholar
  26. National Oceanic Atmospheric Administration. Climate Data Online Surface Data Hourly Global (DS3505), US Department of Commerce. National Climatic Data Center. Available from:
  27. Negri, C., Bacchi, E., Morgante, S., Soave, D., Marques, A., Menghini, E., et al. (2010). Supervised walking groups to increase physical activity in type 2 diabetic patients. Diabetes Care, 33, 2333–2335. CrossRefPubMedPubMedCentralGoogle Scholar
  28. Rivas, E., Newmire, D. E., Crandall, C. G., Hooper, P. L., & Ben-Ezra, V. (2016). An acute bout of whole body passive hyperthermia increases plasma leptin, but does not alter glucose or insulin responses in obese type 2 diabetics and healthy adults. Journal of Thermal Biology, 59, 26–33. CrossRefPubMedGoogle Scholar
  29. Rouyard, T., Leal, J., Baskervill, R., Velardo, C., Salvi, D., & Gray, A. (2018). Nudging people with type 2 diabetes towards better self-management through personalized risk communication: A pilot randomized controlled trial in primary care. Endocrinology, Diabetes & Metabolism, 1, e22. CrossRefGoogle Scholar
  30. Sanderson, B., Littleton, M. A., & Pulley, L. V. (2002). Environmental, policy, and cultural factors related to physical activity among rural, African American women. Women and Health, 36, 75–90.CrossRefGoogle Scholar
  31. Schmitt, A., Gahr, A., Hermanns, N., Kulzer, B., Huber, J., & Haak, T. (2013). The Diabetes Self-Management Questionnaire (DSMQ): Development and evaluation of an instrument to assess diabetes self-care activities associated with glycaemic control. Health and Quality of Life Outcomes, 11, 138. CrossRefPubMedPubMedCentralGoogle Scholar
  32. Shetty, S., Secnik, K., & Oglesby, A. K. (2005). Relationship of glycemic control to total diabetes-related costs for managed care health plan members with type 2 diabetes. Journal of Managed Care & Specialty Pharmacy, 11, 559–564. CrossRefGoogle Scholar
  33. Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. New Haven: Yale University Press.Google Scholar
  34. Tudor-Locke, C. E., & Myers, A. M. (2001). Methodological considerations for researchers and practitioners using pedometers to measure physical (ambulatory) activity. Research Quarterly for Exercise and Sport, 72, 1–12. CrossRefPubMedGoogle Scholar
  35. United States Census Bureau. Quick Facts. U.S. Department of Commerce. [Webpage] Retrieved March 3, 2019 from:
  36. Valentine, W. J., Palmer, A. J., Nicklasson, L., & Cobden, D. (2006). Improving life expectancy and decreasing the incidence of complications associated with type 2 diabetes: A modelling study of HbA1c targets. International Journal of Clinical Practice, 60, 1138–1145. CrossRefPubMedGoogle Scholar
  37. Wagner, A. L., Keusch, F., Yan, T., & Clark, P. J. (2016). The impact of weather on summer and winter exercise behaviors. Journal of Sport and Health Science.. CrossRefPubMedPubMedCentralGoogle Scholar
  38. Wells, J. C. K., & Fewtrell, M. S. (2006). Measuring body composition. Archives of Disease in Childhood, 91, 612–617. CrossRefPubMedPubMedCentralGoogle Scholar
  39. Yardley, J. E., Stepleton, J. M., Sigal, R. J., & Kenny, G. P. (2013). Do heat events pose a greater health risk for individuals with type 2 diabetes? Diabetes Technology & Therapeutics, 15, 520–529. CrossRefGoogle Scholar
  40. Zhao, G., Ford, E. S., Li, C., & Mokdad, A. H. (2008). Compliance with physical activity recommendations in US adults with diabetes. Diabetic Medicine, 25, 221–227. CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Population Health SciencesVirginia TechBlacksburgUSA
  2. 2.Physiology and BiophysicsVirginia Commonwealth UniversityRichmondUSA
  3. 3.Department of Geospatial InformaticsTroy UniversityTroyUSA
  4. 4.Center for the Study of Community HealthUniversity of Alabama at BirminghamBirminghamUSA
  5. 5.Department of Epidemiology and BiostatisticsDrexel UniversityPhiladelphiaUSA
  6. 6.Center for Public Health Practice and ResearchVirginia TechBlacksburgUSA

Personalised recommendations