ABSTRACT
The SenseCam data can be used to estimate time spent in specific episodes of sedentary behaviors, as well as some dimensions of sedentary behaviors. However, it is unknown whether SenseCam data can be aggregated to provide an objective estimate of total sedentary time accumulated during a single day. We compared SenseCam-derived day-level estimates to self-report estimates of time spent in sedentary behaviors using 39 days of concurrent SenseCam and self-report data from a sample of university employed adults (age 18--70 years). We also examined whether SenseCam data can be used to compute day-level estimates of specific dimensions of sedentary behavior (e.g., co-occurring sedentary behaviors and social context). Twenty-four percent of the days of SenseCam image data collected did not have enough image data (i.e., ≥8 hours of data) to generate day-level estimates. Further, the day-level agreement between the SenseCam and self-report estimates of time spent in sedentary behaviors varied considerably by device wear time. In terms of dimensions of sedentary behaviors measured by the SenseCam, over one-third of the total sedentary time involved a social interaction and the majority (71%) of the estimated sedentary time was spent in one behavior. Overall, SenseCam data can be used to compute day-level estimates of time spent in specific episodes of sedentary behaviors and the images provide data on critical dimensions of these behaviors; however, device wear-time significantly influences the accuracy of day-level estimates.
- Ainswort BE, Haskell WL, Herrmann SD, Meckes N, Bassett DR, Tudor-Locke C, et al. Compendium of Physical Activities: a second update of codes and MET values. Medicine & Science in Sports & Exercise (2011), 43: 1575--81.Google Scholar
- Atkin AJ, Gorely T, Clemes SA, Yates T, Edwardson C, Brage S, et al. Methods of Measurement in epidemiology: Sedentary Behaviour. International Journal of Epidemiology (2012) 41 (5): 1460--1471.Google Scholar
- Clark BK, Sugiyama T, Healy GN, Salmon J, Dunstan DW, Owen N. Validity and reliability of measures of television viewing time and other non-occupational sedentary behaviour of adults: a review. Obesity Reviews (2009), 10(1): 7--16.Google Scholar
- Clark BK, Winkler E, Healy GN, Gardiner PG, Dunstan DW, Owen N, Reeves MM. Adults' Past-Day Recall of Sedentary Time: Reliability, Validity, and Responsiveness. Medicine & Science in Sports & Exercise, 2013. 45(6): p. 1198--1207.Google Scholar
- Doherty AR, Caprani N, O Conaire C, Kalnikaite V, Gurrin C, O'Connor NE, et al. Passively recognising human activities through lifelogging. Computers in Human Behavior (2011), 27 (5): 1948--1958 Google ScholarCross Ref
- Doherty AR, Kelly P, Brenden O, Curran P, Smeaton AF, O'Mathuna C, et al. Effects of environmental colour on mood: a wearable LifeColour capture device. In Proc. ICM, 2010, Computers in Human Behavior (2010), 10: 1655--8. Google ScholarDigital Library
- Doherty AR, Moulin CJA, Smeaton AF. Automatically assisting human memory: a SenseCam browser. Memory (2011), 19: 785--95.Google Scholar
- Edwardson CL, Gorely T, Davies MJ, Gray LJ, Khunti K, Wilmot EG, et al. Association of sedentary behaviour with metabolic syndrome: a meta-analysis. PloS one (2012), 7 (4): e34916--e34916.Google Scholar
- Gore SA, Foster JA, DiLillo VG, Kirk K, Smith West D. Television viewing and snacking. Eating behaviors (2003), 4(4): 399--405.Google Scholar
- Grant PM, Ryan CG, Tigbe, WW, Granat MH. The validation of a novel activity monitor in the measurement of posture and motion during everyday activities. British Journal of Sports Medicine (2006), 40: 992--997.Google Scholar
- Healy GN, Clark BK, Winkler EAH, Gardiner PA, Brown WJ, Matthews CE. Measurement of Adults' Sedentary Time in Population-Based Studies. American journal of preventive medicine (2011), 41(2): 216--227.Google Scholar
- Healy GN, Dunstan DW, Salmon J, Cerin E, Shaw JE, Zimmet PZ, et al. Breaks in sedentary time: beneficial associations with metabolic risk. Diabetes Care (2008), 31(4): 661--6.Google Scholar
- Kelly P, Doherty A, Berry E, Hodges S, Batterham AM, Foster C. Can we use digital life-log images to investigate active and sedentary travel behaviour? Results from a pilot study. The international journal of behavioral nutrition and physical activity (2011), 8: 44.Google Scholar
- Kelly P, Doherty AR, Hamilton A, Matthews A, Batterham AM, Nelson M, et al. Evaluating the feasibility of measuring travel to school using a wearable camera. American journal of preventive medicine (2012), 43: 546--50.Google Scholar
- Kelly P, Marshall SJ, Badland H, Kerr J, Oliver M, Doherty AR, Foster C. An Ethical framework for automated, wearable cameras in health behavior research. American journal of preventive medicine (2013), 44: 314--9.Google Scholar
- Kerr J, Marshall SJ, Godbole S, Chen J, Legge A, Doherty AR, et al. Using the SenseCam to improve classifications of sedentary behavior in free-living settings. American journal of preventive medicine (2013), 44(3): 290--6.Google Scholar
- Matthews CE, Chen KY, Freedson PS, Buchowski MS, Beech BM, Pate RR, et al. Amount of time spent in sedentary behaviors in the United States, 2003--2004. American journal of epidemiology (2008), 167: 875--81.Google Scholar
- Proper KI, Singh AS, van Mechelen W, Chinapaw MJM. Sedentary behaviors and health outcomes among adults: a systematic review of prospective studies. American journal of preventive medicine (2011), 40: 174--82.Google Scholar
- Tremblay, M. Letter to the editor: standardized use of the terms "sedentary" and "sedentary behaviours". Sedentary Behaviour Research Network. NRC Research Press (2012), 37(3): 540--2.Google Scholar
- Tudor-Locke C, Camhi SM, Troiano RP. A Catalog of Rules, Variables, and Definitions Applied to Accelerometer Data in the National Health and Nutrition Examination Survey, 2003--2006. CDC-Preventing Chronic Disease (2012), 9: 110332.Google Scholar
- Tudor-Locke C, Johnson WD, Katzmarzyk PT. U.S. population profile of time-stamped accelerometer outputs: impact of wear time. Journal of physical activity & health (2011), 8(5): 693--8.Google Scholar
Index Terms
- The feasibility of using SenseCams to measure the type and context of daily sedentary behaviors
Recommendations
Measuring time spent outdoors using a wearable camera and GPS
SenseCam '13: Proceedings of the 4th International SenseCam & Pervasive Imaging ConferenceNumerous studies have demonstrated multiple health benefits of being outside and exposure to natural environments. It is essential to accurately measure the amount of time individuals spend outdoors to assess the impact of exposure to outdoor time on ...
Using the SenseCam as an objective tool for evaluating eating patterns
SenseCam '13: Proceedings of the 4th International SenseCam & Pervasive Imaging ConferenceObesity is a major public health concern in the United States. Eating while doing other activities, including watching television can increase energy intake. However, to our knowledge, no studies have quantified and examined the eating context among ...
IdleStripes shirt - wearable display of sedentary time
PerDis '20: Proceedings of the 9TH ACM International Symposium on Pervasive DisplaysPrior work on clothing with integrated displays has typically presented catwalk-style garments, with light output from hundreds of LEDs. In contrast, we present the IdleStripes shirt, a garment for normal daily office wear, that encourages the wearer to ...
Comments