Device Manufacturer Type Details
ActiGraph ActiGraph Activity monitor 24-hour high resolution raw activity data with flexible desktop and web-based software platforms. Bluetooth-enabled devices support wireless HR data capture and epoch-level uploads via mobile device. Optional display provides real-time subject feedback and IMU (gyroscope, magnetometer, secondary accelerometer) delivers advanced information about movement, rotation, and body position.
activPAL PAL Technologies Activity monitor activPAL provides an objective, accurate measurement of free-living sedentary, upright and ambulatory activities, providing evidence to link physical behaviours with chronic disease risk. By providing context to these activities in addition to intensity, it can quantify active (walking or cycling) travel to work versus car or other transportation, posture allocation and the pattern and intensity of these activities.
Actiwatch Philips Sleep/wake history Physical activity monitor for quantifiable activity data. Event marker records events of significance. Colored light sensor for full light exposure profile.
GeneActiv Activinsights Activity monitor

Designed for 24-hour wear in both free-living and clinical studies. Raw data output includes acceleration in 3 axes, physical activity intensity and sleep/wake measurements.

Open source analytics and open SDKs. Ambient light & temperature sensor.
Activinsights Band Activinsights Activity behavioural monitor

Designed to provide a much wider adoption of wearables by healthcare and other professionals. It runs low power algorithms on the device to determine behavioural events (sleep, sitting, walking etc.) with associated measures. It uses wireless data transmission, works with in a privacy-by-design data architecture and runs for a year without charging.

Hexoskin device Carre Technologies Heart monitor Heart rate, (variability and recovery), ECG, breathing rate, minute ventilation, activity intensity, peak acceleration, steps, cadence, sleep position
Lumo Back Lumo Bodytech Activity monitor

Worn around lower back and core, measures posture, how many steps you take, how long you sit, and how you sleep.

Shine Misfit Activity monitor Steps, calories burned, distance; and sleep quality and duration
Simband Samsung Activity monitor Provides data from six sensor inputs: electrocardiogram (ECG); photoplethysmogram (PPG); galvanic Skin Response (GSR); bio-Impedance (Bio-Z); accelerometer; and skin temperature. As an open developer platform, the device consists of a watch unit running Tizen and a wristband connector that holds a custom sensor module.


General  Sleep

Automated Sleep Staging

Processes a single channel of spontaneous EEG data from the differential-mastoid (A1-A2) location to determine the sleep stage of the user as either wake, light sleep (N1 & N2), deep sleep (N3) or REM. The sleep staging output is updated every 30 seconds throughout the recording. Contains an integrated low-noise, wide-bandwidth EEG amplifier, high-speed impedance measurement circuitry, and an on-board processor running the sleep staging algorithms.


How compositional data analysis can help us optimise our daily routine to be healthy. (n.d.). Retrieved March 09, 2016, from

Chastin, S., & Palarea-Albaladejo, J. (2015). Concise Guide to Compositional Data Analysis for Physical Activity, Sedentary Behavior and Sleep Research.

Chastin, S. F., Palarea-Albaladejo, J., Dontje, M. L., & Skelton, D. A. (2015). Combined Effects of Time Spent in Physical Activity, Sedentary Behaviors and Sleep on Obesity and Cardio-Metabolic Health Markers: A Novel Compositional Data Analysis Approach. PLOS ONE PLoS ONE, 10(10).

Chastin, S. F., Winkler, E. A., Eakin, E. G., Gardiner, P. A., Dunstan, D. W., Owen, N., & Healy, G. N. (2015). Sensitivity to Change of Objectively-Derived Measures of Sedentary Behavior. Measurement in Physical Education and Exercise Science, 19(3), 138-147.


Buman, M. P., Winkler, E. A., Kurka, J. M., Hekler, E. B., Baldwin, C. M., Owen, N., . . . Gardiner, P. A. (2013). Reallocating Time to Sleep, Sedentary Behaviors, or Active Behaviors: Associations With Cardiovascular Disease Risk Biomarkers, NHANES 2005-2006. American Journal of Epidemiology, 179(3), 323-334.

Buman, M., Kurka, J., Winkler, E., Gardiner, P., Hekler, E., Healy, G., . . . Ainsworth, B. (2012). Estimated replacement effects of accelerometer-derived physical activity and self-reported sleep duration on chronic disease biomarkers. Journal of Science and Medicine in Sport, 15.

Ekblom-Bak, E., Ekblom, O., Bergstro M, G., & Bo Rjesson, M. (2015). Isotemporal substitution of sedentary time by physical activity of different intensities and bout lengths, and its associations with metabolic risk. European Journal of Preventive Cardiology.

Fishman, E. I., Steeves, J. A., Zipunnikov, V., Koster, A., Berrigan, D., Harris, T. A., & Murphy, R. (2016). Association between Objectively Measured Physical Activity and Mortality in NHANES. Medicine & Science in Sports & Exercise, 1.

Healy, G. N., Winkler, E. A., Brakenridge, C. L., Reeves, M. M., & Eakin, E. G. (2015). Accelerometer-Derived Sedentary and Physical Activity Time in Overweight/Obese Adults with Type 2 Diabetes: Cross-Sectional Associations with Cardiometabolic Biomarkers. PLOS ONE PLoS ONE, 10(3).

Kim, M. (2015). Isotemporal Substitution Analysis of Accelerometer-derived Sedentary Behavior, Physical Activity Time, and Physical Function in Older Women: A Preliminary Study. Exercisescience Exercise Science, 24(4), 373-381.

Loprinzi, P. D., Cardinal, B. J., Lee, H., & Tudor-Locke, C. (2015). Markers of adiposity among children and adolescents: Implications of the isotemporal substitution paradigm with sedentary behavior and physical activity patterns. Journal of Diabetes & Metabolic Disorders J Diabetes Metab Disord, 14(1).

Stamatakis, E., Rogers, K., Ding, D., Berrigan, D., Chau, J., Hamer, M., & Bauman, A. (2015). All-cause mortality effects of replacing sedentary time with physical activity and sleeping using an isotemporal substitution model: A prospective study of 201,129 mid-aged and older adults. Int J Behav Nutr Phys Act International Journal of Behavioral Nutrition and Physical Activity, 12(1).

Measuring and Modeling Sedentary Behavior

Healy, G. N., Winkler, E. A., Owen, N., Anuradha, S., & Dunstan, D. W. (2015). Replacing sitting time with standing or stepping: Associations with cardio-metabolic risk biomarkers. Eur Heart J European Heart Journal, 36(39), 2643-2649.

Janssen, X., Basterfield, L., Parkinson, K. N., Pearce, M. S., Reilly, J. K., Adamson, A. J., & Reilly, J. J. (2015). Objective measurement of sedentary behavior: Impact of non-wear time rules on changes in sedentary time. BMC Public Health, 15(1).

Jefferis, B. J., Sartini, C., Shiroma, E., Whincup, P. H., Wannamethee, S. G., & Lee, I. (2014). Duration and breaks in sedentary behaviour: Accelerometer data from 1566 community-dwelling older men (British Regional Heart Study). British Journal of Sports Medicine Br J Sports Med, 49(24), 1591-1594.

Peterson, N. E., Sirard, J. R., Kulbok, P. A., Deboer, M. D., & Erickson, J. M. (2015). Validation of Accelerometer Thresholds and Inclinometry for Measurement of Sedentary Behavior in Young Adult University Students. Res Nurs Health Research in Nursing & Health, 38(6), 492-499.

Rowe, D. A., & Kang, M. (2015). “Don’t Just Sit There - Do Something!” - The Measurement of Sedentary Behavior. Measurement in Physical Education and Exercise Science, 19(3), 103-104.

Rowlands, A. V., Olds, T. S., Hillsdon, M., Pulsford, R., Hurst, T. L., Eston, R. G., . . . Langford, J. (2014). Assessing Sedentary Behavior with the GENEActiv. Medicine & Science in Sports & Exercise, 46(6), 1235-1247.

Rowlands, A. V., Yates, T., Olds, T. S., Davies, M., Khunti, K., & Edwardson, C. L. (2015). Sedentary Sphere. Medicine & Science in Sports & Exercise, 1.

Saunders, T., Chaput, J., Goldfield, G., Colley, R., Kenny, G., Doucet, E., & Tremblay, M. (2012). Effects of prolonged sitting and physical activity on markers of cardiometabolic risk in healthy children and youth: A pilot study. Journal of Science and Medicine in Sport, 15.


Content to come


Roenneberg, T., Kuehnle, T., Juda, M., Kantermann, T., Allebrandt, K., Gordijn, M., & Merrow, M. (2007). Epidemiology of the human circadian clock. Sleep Medicine Reviews, 11(6), 429-438. doi:10.1016/j.smrv.2007.07.005


Chaput, J., Carson, V., Gray, C., & Tremblay, M. (2014). Importance of All Movement Behaviors in a 24 Hour Period for Overall Health. International Journal of Environmental Research and Public Health IJERPH, 11(12), 12575-12581.


The ‘Data to Life’ book describes an open standard for classifying human behaviour which is now hosted by OASIS and supported by Fujitsu and Coelition. The open resource contains a hierarchical taxonomy of over 5,000 human behaviours alongside data exchange protocols and privacy governance processes. Coelition has done work with the Data Science Institute at Lancaster University to explore the use of sequence analysis techniques with event-based behaviour coding. Also, see an associated paper.

Barreira, T. V., Schuna, J. M., Mire, E. F., Katzmarzyk, P. T., Chaput, J., Leduc, G., & Tudor-Locke, C. (2015). Identifying Children’s Nocturnal Sleep Using 24-h Waist Accelerometry. Medicine & Science in Sports & Exercise, 47(5), 937-943.

Tudor-Locke, C., Barreira, T. V., Schuna, J. M., & Katzmarzyk, P. T. (2015). Unique contributions of ISCOLE to the advancement of accelerometry in large studies. International Journal of Obesity Supplements Int J Obes Supp Relat Metab Disord, 5.

Tudor-Locke, C., Mire, E. F., Barreira, T. V., Schuna, J. M., Chaput, J., Fogelholm, M., . . . Katzmarzyk, P. T. (2015). Nocturnal sleep-related variables from 24-h free-living waist-worn accelerometry: International Study of Childhood Obesity, Lifestyle and the Environment. International Journal of Obesity Supplements Int J Obes Supp Relat Metab Disord, 5.

Tudor-Locke, C., Mire, E. F., Dentro, K. N., Barreira, T. V., Schuna, J. M., Zhao, P., . . . Katzmarzyk, P. T. (2015). A model for presenting accelerometer paradata in large studies: ISCOLE. Int J Behav Nutr Phys Act International Journal of Behavioral Nutrition and Physical Activity, 12(1).

Tudor-Locke, C., Barreira, T. V., Schuna, J. M., Mire, E. F., Chaput, J., Fogelholm, M., . . . Katzmarzyk, P. T. (2015). Improving wear time compliance with a 24-hour waist-worn accelerometer protocol in the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE). Int J Behav Nutr Phys Act International Journal of Behavioral Nutrition and Physical Activity, 12(1), 11.