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23. Ottaviano, M., et al.: Empowering citizens through perceptual sensing of urban environmental and health data following a participative citizen science approach. Sensors 19(13),
2940 (2019)
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons
license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter’s Creative
Commons license, unless indicated otherwise in a credit line to the material. If material is not
included in the chapter’s Creative Commons license and your intended use is not permitted by
statutory regulation or exceeds the permitted use, you will need to obtain permission directly
from the copyright holder.
Baseline Modelling and Composite Representation
167
many steps/day are enough? For adults. Int. J. Behav. Nutr. Phys. Act. 8, 79 (2011)
16. Uth, N., Sorensen, H., Overgaard, K., Pedersen, P.: Estimation of VO2max from the ratio
between HRmax and HRrest - the heart rate ratio method. Eur. J. Appl. Physiol. 91(1), 111–
115 (2004). https://doi.org/10.1007/s00421-003-0988-y
17. Sambo, F., et al.: A Bayesian Network analysis of the probabilistic relations between risk
factors in the predisposition to type 2 diabetes. In: Conference Proceedings IEEE
Engineering Medicine Biology Society, pp. 2119–2122 (2015). https://doi.org/10.1109/
embc.2015.7318807
18. Shan Z., et al.: Sleep duration and risk of type 2 diabetes: a meta-analysis of prospective
studies. Diabetes Care 38(3), 529–537 (2015). https://doi.org/10.2337/dc14-2073
19. Smith, L., Smith,, H., Case, J., Harwell, L., Summers, J., Wade, C.: Indicators and methods
for constructing a US human well-being index (HWBI) for ecosystem services research. US
Environmental Protection Agency, Report #EPA/600/R-12/023 (2012)
20. Freeberg, K.A., Baughman, B.R., Vickey, T., Sullivan, J.A., Sawyer, B.J.: Assessing the
ability of the fitbit charge 2 to accurately predict VO 2 max. mHealth 5, 39 (2019). https://doi.
org/10.21037/mhealth.2019.09.07
21. Li, W., et al.: Sleep duration and risk of stroke events and stroke mortality: a systematic
review and meta-analysis of prospective cohort studies. Int. J. Cardiol. 223, 870–876 (2016).
https://doi.org/10.1016/j.ijcard.2016.08.302
22. Ricevuti, G., Venturini, L., Copelli, S., Mercalli, F., Nicolardi, G.: Data driven MCI and
frailty prevention: geriatric modelling in the City4Age project. In: IEEE 3rd International
Forum on Research and Technologies for Society and Industry (RTSI), Modena, pp. 1–6
(2017)
23. Ottaviano, M., et al.: Empowering citizens through perceptual sensing of urban environmental and health data following a participative citizen science approach. Sensors 19(13),
2940 (2019)
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons
license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter’s Creative
Commons license, unless indicated otherwise in a credit line to the material. If material is not
included in the chapter’s Creative Commons license and your intended use is not permitted by
statutory regulation or exceeds the permitted use, you will need to obtain permission directly
from the copyright holder.
Baseline Modelling and Composite Representation
167