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Measuring group and individual relationship between patterns in sedentary behaviour and glucose in type 2 diabetes adults

(2020) PRACTICAL DIABETES. 37(1). p.13-18
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Abstract
The aim of this study was to explore the relationship between patterns in sedentary behaviour and glucose in adults with type 2 diabetes. Thirty-seven adults with type 2 diabetes managed with diet and/or anti-diabetes medication (not insulin) were recruited. Participants wore an activPAL accelerometer and FreeStyle Libre flash glucose monitor for continuous measurement of activity and glucose for 24 hours/day for three to 14days while documenting sleep, food and medication. The relationship between daily patterns of sedentary behaviour and sedentary breaks with glucose was investigated using correlation analysis. Regression analysis was used to investigate these relationships at an individual level. Participants (mean age 62.810.5years, BMI 29.6 +/- 6.8kg/m(2)) spent 64% of their day sedentary, 44.7% of sedentary bouts were 30-60 minutes in duration and mean bout duration was 47.2 minutes. No association between mean glucose and sedentary proportion (total sedentary time) was identified. Mean glucose and glucose standard deviation were positively correlated with sedentary bout duration (both p<0.05). Individual regression analysis showed increased sedentary time is associated with increased mean glucose in 25 (68%) of the participants, with a negative association being shown in 12 (32%) of the participants. In analysis of the whole group, sedentary bout duration but not sedentary proportion was associated with mean glucose and glucose variability. Individual regression analysis identified a different relationship pattern for the majority of participants. This is the first study to identify an individualised response to activity behaviour and highlights the importance of conducting individual analysis when using continuous measurement methods.
Keywords
sedentary time, glycaemic control, type 2 diabetes, diabetes management, mHealth, digital solutions, continuous glucose monitoring, STRUCTURED EXERCISE, ASSOCIATION, BENEFITS, BREAKING, TIME

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MLA
McMillan, Kathryn, et al. “Measuring Group and Individual Relationship between Patterns in Sedentary Behaviour and Glucose in Type 2 Diabetes Adults.” PRACTICAL DIABETES, vol. 37, no. 1, 2020, pp. 13–18.
APA
McMillan, K., Paing, A., Kirk, A., Hewitt, A., MacRury, S., Collier, A., & Chastin, S. (2020). Measuring group and individual relationship between patterns in sedentary behaviour and glucose in type 2 diabetes adults. PRACTICAL DIABETES, 37(1), 13–18.
Chicago author-date
McMillan, Kathryn, Aye Paing, Alison Kirk, Allan Hewitt, Sandra MacRury, Andrew Collier, and Sebastien Chastin. 2020. “Measuring Group and Individual Relationship between Patterns in Sedentary Behaviour and Glucose in Type 2 Diabetes Adults.” PRACTICAL DIABETES 37 (1): 13–18.
Chicago author-date (all authors)
McMillan, Kathryn, Aye Paing, Alison Kirk, Allan Hewitt, Sandra MacRury, Andrew Collier, and Sebastien Chastin. 2020. “Measuring Group and Individual Relationship between Patterns in Sedentary Behaviour and Glucose in Type 2 Diabetes Adults.” PRACTICAL DIABETES 37 (1): 13–18.
Vancouver
1.
McMillan K, Paing A, Kirk A, Hewitt A, MacRury S, Collier A, et al. Measuring group and individual relationship between patterns in sedentary behaviour and glucose in type 2 diabetes adults. PRACTICAL DIABETES. 2020;37(1):13–8.
IEEE
[1]
K. McMillan et al., “Measuring group and individual relationship between patterns in sedentary behaviour and glucose in type 2 diabetes adults,” PRACTICAL DIABETES, vol. 37, no. 1, pp. 13–18, 2020.
@article{8651145,
  abstract     = {{The aim of this study was to explore the relationship between patterns in sedentary behaviour and glucose in adults with type 2 diabetes. Thirty-seven adults with type 2 diabetes managed with diet and/or anti-diabetes medication (not insulin) were recruited. Participants wore an activPAL accelerometer and FreeStyle Libre flash glucose monitor for continuous measurement of activity and glucose for 24 hours/day for three to 14days while documenting sleep, food and medication. The relationship between daily patterns of sedentary behaviour and sedentary breaks with glucose was investigated using correlation analysis. Regression analysis was used to investigate these relationships at an individual level. Participants (mean age 62.810.5years, BMI 29.6 +/- 6.8kg/m(2)) spent 64% of their day sedentary, 44.7% of sedentary bouts were 30-60 minutes in duration and mean bout duration was 47.2 minutes. No association between mean glucose and sedentary proportion (total sedentary time) was identified. Mean glucose and glucose standard deviation were positively correlated with sedentary bout duration (both p<0.05). Individual regression analysis showed increased sedentary time is associated with increased mean glucose in 25 (68%) of the participants, with a negative association being shown in 12 (32%) of the participants. In analysis of the whole group, sedentary bout duration but not sedentary proportion was associated with mean glucose and glucose variability. Individual regression analysis identified a different relationship pattern for the majority of participants. This is the first study to identify an individualised response to activity behaviour and highlights the importance of conducting individual analysis when using continuous measurement methods.}},
  author       = {{McMillan, Kathryn and Paing, Aye and Kirk, Alison and Hewitt, Allan and MacRury, Sandra and Collier, Andrew and Chastin, Sebastien}},
  issn         = {{2047-2897}},
  journal      = {{PRACTICAL DIABETES}},
  keywords     = {{sedentary time,glycaemic control,type 2 diabetes,diabetes management,mHealth,digital solutions,continuous glucose monitoring,STRUCTURED EXERCISE,ASSOCIATION,BENEFITS,BREAKING,TIME}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{13--18}},
  title        = {{Measuring group and individual relationship between patterns in sedentary behaviour and glucose in type 2 diabetes adults}},
  url          = {{http://dx.doi.org/10.1002/pdi.2254}},
  volume       = {{37}},
  year         = {{2020}},
}

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