The impact of walking in different urban environments on brain activity in older people

Figures & data

Figure 2. Map of the walking routes undertaken by participants (walking in one of six possible scenarios). UQ – Urban quiet; UB – Urban busy; UG – Urban green.

Figure 3. Street views of the three walking environments; (a) Urban green (b) Urban busy and (c) Urban quiet (Photo credit: OPENspace Research Centre).

Table 1. Descriptions of the EEG signals of interest.

Frequency	Working description
Alpha (9–13 Hz)	Increases associated with relaxed states and shown to be affected by natural spaces (Chang et al. 2007; Ulrich 1981)
Low Beta (13–19 Hz) High Beta (21–27 Hz)	Associated with alert states, increases associated with increases in directed attention (Bonnet and Arand 2001; Dehzangi and Williams 2015)

Table 2. Logistic CCR outputs for the UB and UG routes.

Model Fit	Training	Cross validation	Standard error
R^2	.084	.011	.015
AUC	.618	.482	.054
Accuracy	.575	.492	.053
Predictors in rank order	Standardised Coefficient	Out of Sample frequency (n = 500 runs)
Alpha	1.3318	493
Low Beta	−2.3218	422

AUC = Area under the curve.

Figure 4. Alpha and low beta difference scores for each walking context.

Note: A positive value above zero indicates levels for that parameter are greater in the first part of the walk and a negative value below zero indicates levels for that parameter are greater in the second part of the walk.

Table 3. Logistic CCR outputs for the UB and UQ routes.

Model Fit	Training	Cross validation	Standard error
R^2	.29	.156	.033
AUC	.977	.864	.059
Accuracy	.869	.807	.058
Predictors	Standardised Coefficient	Out of Sample frequency (n = 500 runs)
Alpha	9.297	370

AUC = Area under the curve.

Figure 5. Alpha difference scores for each walking condition showing that alpha is greater in the UB setting than the UQ setting.

Note: A positive value above zero indicates levels for that parameter are greater in the first part of the walk and a negative value below zero indicates levels for that parameter are greater in the second part of the walk.

Table 4. Logistic CCR outputs for the UG and UQ routes.

Model Fit	Training	Cross validation	Standard error
R^2	.355	.289	.047
AUC	.941	.937	.028
Accuracy	.909	.894	.043
Predictors
None

AUC = Area under the curve.

Figure 1. A demonstration of the headset being worn.

Chang, C.-Y., et al. 2007. Psychophysiological responses and restorative values of wilderness environments. In: A. Watson, et al., eds. Science and Stewardship to Protect and Sustain Wilderness Values: Eighth World Wilderness Congress Symposium Proceedings RMRS-P-49. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, 479–484, 049. Available from: https://www.fs.usda.gov/treesearch/pubs/all/31072 September 30-October 6, 2005; Anchorage, AK.

 Google Scholar

Ulrich, R.S., 1981. Natural versus urban scenes: some psychophysiological effects. Environment and behavior, 13 (5), 523–556. doi:10.1177/0013916581135001.

 Google Scholar

Bonnet, M.H. and Arand, D.L., 2001. Impact of activity and arousal upon spectral EEG parameters. Physiology & behavior, 74 (3), 291–298. doi:10.1016/S0031-9384(01)00581-9.

 Google Scholar

Dehzangi, O. and Williams, C., 2015. Towards multi-modal wearable driver monitoring: impact of road condition on driver distraction. In: 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 1–6. doi:10.1109/BSN.2015.7299408.

 Google Scholar