# Effect of Exposure Time on Thermal Behaviour: A Psychophysiological Approach
## 4 Data Analysis and Findings
### Temporal Change
The data are not normalized and presented in absolute values. However, on two separate occasions, the participants did not even leave the space for lunch and decided to sustain on smaller snacks throughout the day, not walking for longer than the distance to the bathroom. The clusters were very similar. Additionally, while the majority of participants had their cluster divisions very close to lunch breaks (for these four participants, \"afternoon\" cluster starts 00:08 \(\pm\) 00:14 after the lunch break) Participant 2, who started her day an average 2 h later than the other participants, has her cluster division time 01:23 \(\pm\) 01:18 after her lunch break. And finally, for Participant 3, on the days of 27 \({}^{\circ}\)C and 28 \({}^{\circ}\)C tests, the division starts an hour before the lunch break ends (see Table 4). 2. The digestion of lunch could have raised the metabolic rate and the body temperature. The explanation of the above exceptions apply in this case as well. 3. The EDA was affected by the circadian rhythm of the body and with the natural rise in body temperature in the afternoon, the skin was less capable of handling heat and started accumulating stress, which was also the reason of the higher correlation between temperature and EDA levels in the afternoon. The rise in the tonic EDA is less impacted by the rise in temperature in the morning clusters, hence the lower correlation. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Daily Schedule** & \
\begin{tabular}{c} **Time after Start** \\ **(Mean \(\pm\) Std)** \\ \end{tabular} &
\begin{tabular}{c} **Time after Lunch** \\ **(Mean \(\pm\) Std)** \\ \end{tabular} \\ \hline Participant 1 & 09:00–16:00 & 04:04:44 \(\pm\) 00:21:57 & 00:01:44 \(\pm\) 00:01:01 \\ Participant 2 & 11:00–18:00 & 03:26:12 \(\pm\) 00:15:08 & 01:23:37 \(\pm\) 01:18:31 \\ Participant 3 & 08:00–15:00 & 04:06:51 \(\pm\) 00:43:29 & \(-\)00:20:15 \(\pm\) 00:35:33 \\ Participant 4 & 09:30–16:30 & 03:49:03 \(\pm\) 00:30:13 & 00:09:54 \(\pm\) 00:19:44 \\ Participant 5 & 09:00–16:00 & 03:57:36 \(\pm\) 00:33:01 & 00:01:12 \(\pm\) 00:01:06 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The afternoon cluster start after experiment start time and lunch break end. Figure 11: Temperature and EDA tonic level correlations, clustered. Note that most participants (1, 3, 4 and 5) had starting times between 8.15-9.30, while Participant 2 started at 11.00 by her own choice. Her clustering places not so differently from others, with nearly 3.5 h after her starting time. This raises the question whether her natural circadian rhythm has shifted accordingly, or the main determinant is the amount of time spent in the Sense Lab, or a balance of the two. The daily oscillation of the body temperature over the full 24 h cycle is nearly 1 \({}^{\circ}\)C [53], and can be even higher based on gender, age and other factors. The temperature is at its lowest in the morning (bathyphes), and highest in the afternoon (acrophase), mirroring the Midline Estimating Statistic of Rhythm (MESOR) line. While for a person that is awake between 7:00 and 23:00 o'clock, the bathyphes is between 03:00 and 06:00 o'clock, and the acrophase is observed between 18:00 and 21:00 o'clock, a more universal rule of thumb can be generalised as the acrophase happening 12 h after waking [54]. In a recent research, [PERSON] et al. [55] ran a 2-day long study in a hospital environment with 14 healthy adults, and found that EDA showed significant similarity to the circadian rhythm, by differing from mean value by 15% around 16:00 o'clock. This correlation and its implications need further exploring, however such a temporal clustering of the relationship of the human and its environment can be a promising start in handling the spaces more efficiently and sufficiently. Another study from 2021 by [PERSON] et al. [56] collect data from 119 patients and model the EDA level curve over the 24 h period, with an oscillation of 2 uS in the individual means, and around 0.3 uS in the best fitting pattern (Figure 12).