Pilot study

One of the constraints of daylight research is the estimation of daylight levels which change rapidly with time with the change of cloud cover in the sky. In Walch et al. (2005) research, the measurements of daylight intensities were taken by a light meter twice daily in the observed patient rooms at approximately 9:30 AM and 3:30 PM. These measurements were multiplied by the number of AM and PM daylight exposure hours and summed to determine the cumulative daily daylight exposure in lux-hours. The measurement of daylight intensity only twice a day does not represent the actual daylight levels that the patients experienced during their stay time in hospital, because of the rapid change of daylight intensity throughout the day. A more continuous measurement of daylight intensities for patient rooms was necessary for reliable outcome. This measurement could be done by either installing several data loggers in each patient room, or using simulation software to identify the average daylight levels. As it was not possible and practical to fix several data loggers on the test plane of the each patient room in a running hospital environment to calculate the average daylight intensity of each room (for e.g. 60 data loggers will be required to place on 850mm height with 500mm interval/grid in each room which is not possible to continue under the presence or treatment of patients), application of daylight simulation programme was preferred for pilot study to calculate the average room illumination.

To analyse the daylighting environment Choi (2005) used lighting simulation program, RADIANCE, to identify the illuminance level in his study. To verify the output data from RADIANCE, the calculated data produced by RADIANCE were compared with the data from the site and scale model measurement. The discrepancy between RADIANCE and on-site measurements was 2% to 47% and RADIANCE and the scale model was 9% to 50%. Choi (2005) suggested that as daylight is very much sensitive to sky conditions, this dependency can result in large discrepancies due to the difference between CIE sky condition (defined by International Commission on Illumination (CIE)) and the actual sky condition. CIE intermediate sky condition does not cover the various amount of cloud on sky and it is not the same with the actual sky condition. Thus, the HEI of RADIANCE is not identical with the actual HEI values. One HEI value cannot cover the diversity of the intermediate sky that covers 30% to 70% of the sky with clouds. Figure 3.3 shows the variation of averaged HEI from 19 November 2008 to 21 January 2009 for Dhaka, Bangladesh. So, during pilot study, one outdoor

57 data logger was installed at the top of helipad above case hospital roof about 66m from ground level (Figure 3.4) to measure HEI with five minute interval. The output of data logger was used to simulate average interior daylight intensity of the studied rooms considering the CIE standard overcast sky model with a full progressive radiosity inter- reflection method using FlucsDL of IES (Virtual Environment 5.5).

0 5000 10000 15000 20000 25000 30000

19 Nov 26 Nov 03 Dec 10 Dec 17 Dec 24 Dec 31 Dec 07Jan 14 Jan 21Jan 2008 2008 2008 2008 2008 2008 2008 2009 2009 2009 Il lu m in a ti o n ( lu x )

Figure 3.3: Averaged HEI from five minute interval data recorded by outdoor datalogger for Dhaka, Bangladesh.

Figure 3. 4: Location of outdoor data logger.

During pilot study, the hospital building and its surroundings were surveyed (Section 4.4 and 4.5; and Figure 4.6 and 4.7), and as-built drawings and material specifications were collected from the Engineering Division of the hospital to use the information to

58 build 3D model for daylight simulation study. Acquired building information and HEI obtained from outdoor data logger were entered into an integrated whole building simulation program (i.e. IES). Instead of the daylight data of Typical Meteorological Year (TMY), actual outdoor HEI, measured from site was used to consider the unpredictable nature of outdoor daylight intensity. The final output of IES was the threshold tables for average interior daylight intensity for each of the studied room in Hyper Text Markup Language (HTML) format for each day, with respect to particular patient stay time. These indoor average daylight intensities from daylight simulation programs were correlated with clinical variables (e.g. LoS, blood pressure and heart rate) to predict about patient LoS in hospital rooms. The data collected during pilot study were used to develop a MLR model to explore the relationship between average daylight intensity of the in-patient room and patient LoS in hospitals. The coefficient estimates of MLR model showed that while holding the other explanatory variables (POV, mean arterial pressure, heart rate, diabetes mellitus, SPO2 and FBS) constant, the increase of 100 lx of average daylight inside in-patient room reduces patient LoS by, on average, 4 hours. The major limitation of pilot study was that it was based on simulated average indoor daylight data that could not accounted many aspects, such as patients‟ behaviour on blinds adjustment and overhead lighting control.

Considering the time limit of pilot study and probable risk associated with uncertainty of the output of the analysis of collected data, it was preferred to do a quick statistical analysis with simulated lighting data at the beginning. It was also planned that a successful completion of pilot study and statistical analysis of collected data with expected outcomes will lead to do an extensive principal study for one year with an updated methodology with a higher number of data loggers to cover the entire cardiac in-patient unit of Square Hospital, located at tenth floor.