The Integration of Nature in the Elderly Care Work Environment as a Buffer for Psychological Distress: A Quasi-Experimental Study
Daphne Meuwese
10344977
University of Amsterdam
Department of Clinical Psychology External supervisor: Dr. Jolanda Maas UvA supervisor: Dr. Marieke Effting August 30, 2016
Number of words: 9025
Table of Contents Abstract ... 2 Introduction ... 3 Methods ... 8 Study Location ... 8 Green Walls ... 9 Participants ... 11 Measures ... 12 Work stress. ... 12 Psychological distress...13
Medication errors and Sick Leave. ... 13
Physical symptoms related to poor air quality. ... 14
Attention ... 14
Emotional States. ... 15
Appreciation of the work environment. ... 15
Procedure ... 16
Statistical Analysis ... 16
Results ... 18
Comparison of Groups With and Without a Green Wall ... 18
Evaluation of the Green Walls ... 29
Discussion ... 31
References ... 36
Abstract
The aim was to evaluate the impact of green walls on psychological distress, attention and work performance measures for employees in the elderly care work environment using a quasi-experimental design with one experimental (n = 29) and one control group (n = 29). The participants were assessed at pre-, post-, and follow up. Green walls were placed at the experimental facility. The control facility (without plants) was selected to match certain relevant characteristics of the experimental facility. Results showed an overall decline in psychological distress, negative affect and physical symptoms related to air quality and an increase in attention capacity. No differences were found between the groups. This could possibly b0e explained by the control group implementing their own plants, which seriously compromised the manipulation.
The Integration of Nature in the Workplace as a Buffer for Psychological Distress: A Controlled Evaluation Study
Introduction
Work stress for employees in the elderly care sector is increasing due to, among other things, budget cuts and increasing numbers of clients that need to be admitted (Dietrich, Rösler, Bellmann, Scharfe, & Kirch, 2014; Wildenborg, 2015;). This increase of work stress has resulted in attention fatigue, a higher prevalence of mental illness and a rise in medication errors and needed sick leave in this sector (Ackerstedt et al., 2004; Boksem, Meijman, & Lorist, 2005; Wilkins, 2007; Dollarhide et al., 2014; CBS, 2015). To decrease work stress among employees, elderly home facilities may create buffers for work stress (Karantzas et al., 2011). Increasing the interaction with nature might be a promising intervention to create such a buffer, since several studies have shown that nature can reduce stress and enhance attention restoration (Ulrich, 1991; Hartig, Mitchell, De Vries, & Frumkin, 2014; Kuo, 2015).
Nature‟s stress reduction and attention restoration effects have been studied thoroughly in the last three decennia (Hartig et al., 2014). For example by monitoring the blood pressure and cortisol levels of people who either walked in natural or urban
surroundings after completing a stressful task (Park, Tsunetsugu, Kasetani, Kagawa, & Miyazaki, 2010). It was found that the participants who walked in natural surroundings recovered more quickly and more profoundly from the stressful task, compared to the
participants who walked in urban surroundings. In the study of Hartig, Evans, Jamner, Davis, and Gärling (2003) psychophysiological stress recovery and directed attention restoration were measured in natural and urban surroundings using repeated measures of blood pressure and attention. Sitting in a room with views of trees initiated a more rapid decline in blood
pressure than sitting in a viewless room. Attention capacity increased slightly in the subsequent walk in a nature reserve compared to a walk in the urban setting.
A meta-analysis of such studies by Bowler, Buyung Ali, Knights, and Pullin (2010) pleads for caution when stating that nature restores attention however, due to issues with the generalizability of the studies that have been done. The authors state that it is possible that the impact of nature on attention is not as strong as was conveyed by most publications, because there were methodological issues, like not incorporating baseline measurements. When baseline measurements were assessed, several effects were not found. Small sample sizes and the use of many different attention tasks were also aspects that complicated generalizability. Thus, attention restoration by nature has to be stated tentatively.
A more recent meta-analysis of beneficial effects of nature has included several other outcome measures beyond attention restoration (Hartig, et al., 2014). The authors state that there is a reliable evidence base for a positive relationship between nature exposure and the reduction of fatigue, self-reported anger, anxiety and sadness and an increase in feelings of energy. Reduction of negative emotions is an especially promising link to clinical psychology, since negative emotional states are often part of the symptomatology of mental disorders (Diagnostic and Statistical Manual of Mental Disorders, 5th ed.; American Psychiatric Organization, 2014).
Underlying these so called restorative effects of being exposed to nature are two possible psychological mechanisms, as described by the Attention Restoration Theory (ART) (Kaplan & Kaplan, 1989; Kaplan & Berman, 2010) and the Stress Recovery Theory (SRT) (Ulrich et al., 1991). According to the ART, intrinsically interesting natural elements engage attention in an effortless manner. This enables rest for the directed attention system, because directing attention requires effort to be able to inhibit distracting stimuli during the (work) day. It gets depleted when used intensively and henceforth reduces the capacity for
higher-order executive functions, resulting in fatigue. In addition to effortless attention, Kaplan and Kaplan (1989) state that nature can make a person feel as if they are away from their daily bothers („sense of being away‟) and part of something greater („sense of extent‟). Moreover natural features such as a leaf moving in the wind can hold a person‟s attention effortlessly („soft fascination‟) and there is a feeling of belonging because we were surrounded by nature for a long period of time. This makes the environment closely compatible with our purposes („compatibility‟).
The SRT is based on the fact that we were surrounded by nature for a long period of time during evolution (Ulrich et al., 1991). The SRT posits that non-harmful natural elements and features evoke a rapid positive affective response due to a physiologically engrained, instinct like, recognition process that was important for survival. This positive affective response blocks negative thoughts and mood and helps to reduce physiological activation and henceforth stress (Fredrickson, 2000; Ong, Bergeman, Bisconti, & Wallace, 2006). This process is also known as „biophilia‟ (Kellert & Wilson, 1993). „Biophilia‟ entails a primal need to affiliate or connect with natural elements including plants and animals. If this need is satisfied a positive affective response is elicited and stress is reduced (Fredrickson, 2000; Ong et al, 2006).
These theories and former research suggest that nature is not merely restorative outside, since they describe the impact of natural elements. Henceforth it is implied that natural elements like plants could be restorative indoors as well. Restorative effects of indoor plants have been studied in office settings, since stress and its adverse consequences are often high in these settings thus providing a naturalistic setting for research (Virtanen, et al., 2007) A meta-analysis of such studies showed mixed results (Bringslimark, Hartig, & Patil, 2009), suggesting that plants in the work environment can reduce stress, but whether plants are also beneficial with respect to task performance and mood is unclear. It appears that
beneficial effects of nature are potentially dependent on the context in which the plants are experienced (in the view from a window vs. plants at the work station) and on characteristics of the people encoutering them (sometimes effects were only found for men, not for females and vice versa).
Recently a review by Korpela, De Bloom and Konnunen (2015) postulated that plants in the workplace enhance the solution of creative tasks, but have a reversed effect for the simple tasks. When productivity was measured with self-report measures consistent positive effects were found. Moreover an association between more indoor plants that can be viewed from the work station and less sick leave was stated. These analyses were controlled for numerous relevant characteristics, like gender, age and physical workplace factors.
In spite of the mixed results former research consistently indicates that implementing plants in the work environment could decrease work stress, enhance creative task performance and could be associated with less sick leave for employees. However, these aspects have not been studied to date in the elderly care work environment, while it is a promising field of study due to the high levels of work stress (Wilkins, 2007; Dietrich, Rösler, Bellmann, Scharfe, & Kirch, 2014). In addition, the elderly care sector has the highest rate of sick leave in the Netherlands (CBS, 2015).
Another problem for elderly care facilities is maintaining a proper level of air quality (Aguiar et al., 2014). Air quality is often poor due to a lack of ventilation systems resulting in high levels of CO2 and low levels of humidity. These aspects have adverse health
consequences such as headache, a dry throat or an itchy skin (Gezondheidsnet, 29-06-2015). Moreover high CO2 levels are known to negatively affect attention capacity and productivity (Freeman, 2008). Certain plants have purifying properties and produce high amounts of oxygen as well (Wolverton, Douglas, & Bound, 1989). Henceforth implementing plants in the
elderly care work environment could improve air quality and ameliorate the negative effects associated with poor air quality.
Implementing plants in the elderly care work environment could also improve the quality of care for the patients in terms of reducing medication errors by the restoration of directed attention for the employees, since medication errors occur more often if care staff is either stressed, fatigued, and/or their work load is excessive (Dollarhide et al., 2014). These circumstances lead to difficulties in attention capacity (Boksem et al., 2005). Attention capacity is needed to make sure that patients receive the right (dosage of) medicaments (Dollarhide et al., 2014). Medication errors in this study are everything from forgetting to administer medicaments to making mistakes in the type or dosage of the medicaments. It can have immense consequences when employees can‟t remember if they have already given medication to patients or when wrong (dosages of) medicaments are administered, especially in settings where patients can‟t register themselves when they receive wrong medicaments or dosages (IGZ, 2015).
The Present Research
Aim of this study was to evaluate the restorative impacts of the implementation of green walls in the elderly care work environment, using a quasi-experimental design with one experimental and one control group. Green walls were placed at the experimental facility. The control facility (without green walls) was selected to match certain relevant characteristics of the experimental facility. Employees were assessed before (pre), one week after (post), and three weeks after (follow-up) the placement of the green walls. This study was carried out in the context of a Dutch national project, which aims at increasing contact with nature for the elderly.
Based on former research it was hypothesized that at one and three weeks after the placement of the green walls, employees in the experimental group, as compared to
employees in the control group without green walls, would display a decline in work stress, psychological distress and health issues related to air quality. With respect to medication errors and sick leave it was expected that placement of the green walls would be associated with fewer medication errors and less sick leave. Based on the ART and the SRT it was expected that the direct relationship between the implementation of the green walls and a decline in work stress would be mediated by attention restoration (ART) and/or positive emotions (SRT). Appreciation of the work environment was measured explorative as a potential mediator as well, since people have a strong preference for natural elements as compared to neutral or urban elements (Jacobs, 2006).
Methods Study Location
This study took place at two elderly care facilities (the experimental facility and the control facility) in Zaandam, the Netherlands. Zaandam is a medium sized municipality close to Amsterdam. The experimental facility was selected from the Dutch program “Grijs Groen en Gelukkig” (Grey, Green and Happy), which is project with a duration of five years that aims to provide daily contact with nature for 10 000 elderly throughout their old age. One of the aspects of providing contact with nature is the implementation of green walls at closed wards of 100 Dutch facilities for elderly care, whose patients suffer from severe dementia. When this study started “Stichting Hervormd Centrum Pennemes” (Pennemes) was the first to sign up for this particular aspect of the “Grijs Groen en Gelukkig” project and was thereby chosen as the experimental facility. Pennemes is one of the two remaining independent elderly care facilities in Zaandam and provides care for approximately 250 patients at
different levels of care intensity. The four wards that comprised the experimental group were situated at the first floor of Pennemes and were connected through one corridor. The corridor
is a closed system, which means that someone can only exit if they know the code to the doors. The wards situated alongside this corridor are all similar with respect to size
(maximum of 8-10 patients per ward), diagnosis of the patients (severe dementia), number of employees (approximately 25 for the four wards) and view from the wards (urban elements). The control facility was selected to match Pennemes on these characteristics to attempt to form a reliable control group. The elderly care facility across the street from Pennemes, called “het Mennistenerf” matched these characteristics best and agreed to partake in this study. The Mennistenerf is the other remaining independent elderly care facility in Zaandam and provides care for approximately 100 patients. Pennemes and the Mennistenerf work together intensively and both have closed wards the size of approximately 8-10 patients per ward. The Mennistenerf has a total of four closed wards situated at four different floors. Approximately 25 employees work for these wards. Furthermore, at both facilities the patients at the closed wards suffer from severe dementia and the view from the facilities is similar as well with urban elements like roads, parking spaces and houses.
Green Walls
The green walls are a closed system, which consists of a metal frame and root cloth filled with small amounts of fertile soil for the plants. Once every two weeks, water is poured into a tank at the bottom of the frame by staff of the maintenance company. Hereafter an irrigation system ensures that the water is properly distributed over the different plants until the next refill. The plants for the walls were especially selected to not be poisonous (in case patients would eat them) and for a high production of oxygen to improve the air quality. In total four green walls, two large and two small, were distributed over the four closed wards of Pennemes that were connected through a corridor at the first floor. The large green walls were placed at two sides of the same wall at the first ward. They were 1.25 m wide and 2.00 m high and comprised of eight plant species (Figure 1). One of the small green
walls was placed in the corridor close to the third ward and the other was placed at the fourth ward. These small green walls were 1.00 m wide and 0.50 m high and comprised of one single species (Figure 2). Thus two of the four wards had green walls placed and one small green wall was placed in the corridor. The green walls were placed in the second week of January 2016. The other two closed wards of Pennemes, at the ground floor, were intended as part of the control group, but these were excluded from the study as they viewed the green courtyard. No green walls were placed at the four closed wards of the Mennistenerf.
As the researcher visited the Mennistenerf for the second and third measurement moment it was found that the employees of the control group had brought their own plants, for pictures see Figure 20 - 24. Before the study both facilities only had fake plants to decorate the wards. This means that the control group unintentionally had a relevant change instead of the planned no change, which seriously compromised the manipulation and complicates the interpretation of the results.
Figure 1. Example of the large plant walls, which were both placed at the first ward.
Figure 2. The two small green walls. The left wall was placed in the corridor and the right wall was placed at the fourth ward.
Participants
A total of 58 employees participated in this study (29 from Pennemes and 29 from the Mennistenerf). Due to revolving of night shifts, illness and other circumstances, 2 employees of the experimental group and 4 employees of the control group were absent during one or more moments of measurement. Furthermore, survey data were not obtained for every
participant during the three moments of measurement (response rates varied between 66% and 72%). The total sample for which data was available for all the variables as well as for all three measurement moments consisted of 26 participants (11 from the experimental group and 15 from the control group). This suggested there was substantial missing data. The technique multiple imputation was used to be able to include the participants who provided data for less than three measurement moments or less than all variables. This technique will be
exemplified in the section on statistical analysis.
For the total sample of 58 participants, 54 (93%) were female. This distribution was skewed, with the only four male participants in the control group. The age of the participants was for 31% in the first age category (18-30 years old), for 18.9% in the second age category (30-45 years old), for 44.8% in the third age category (46-60 years old) and for 5.2% in the fourth age category (older than 60 years old). Most of the participants were on the payroll of the facilities (74.1%), others were volunteers1 or otherwise intensively involved with care of the patients. Volunteers were included in the sample to provide a representative sample of the people who work for the facilities. With respect to the number of hours that the participants worked weekly, 18.9 % worked less than 16 hours each week, 39.6% worked between 16 and 24 hours each week, 36.2% worked between 24 and 32 hours each week and only 5.2%
1
The main analysis were computed with and without the inclusion of the volunteers, no differences in significance were found between these analyzes
worked more than 32 hours each week. The experimental group and the control group did not differ significantly (p < .05) for these variables, except for gender t (56) = - 2.12, p = .04.
Participants were given a small present after completing the third measurement moment of the attention task. It was a map to search for insects and a magnifying glass. No other form of compensation was awarded for partaking in this research.
Measures
Work stress. Work stress was measured through the seven statements phrased by De Vries, Roe and Taillieu (2002). Participants were asked how applicable the statements are to their work situation, with five possible responses ranging from 1 (strongly disagree) to 5 (strongly agree). Examples of the statements are; “I often have to hurry to complete my tasks in time” and “Pressure is high in this line of work”. No information about the validity is known, but the reliability is sufficient with a Cronbach‟s alpha of .77 (De Vries et al., 2002). In the present study Cronbach‟s alpha ranged between .85 and .91.
Psychological distress. To measure psychological distress, the General Health Questionnaire (GHQ-12) and the Brief Symptom Inventory (BSI-18) were administered. This combination is commonly used in clinical settings to assess psychological distress (Van Beljouw, & Verhaak, 2010) The GHQ-12 is a self-report inventory that measures psychologic distress through questions about changes in psychological functioning (Koeter & Ormel, 1995). It consists of 12 items with answers being scored on a four-point Likert scale, ranging from 0 (less than usual) to 3 (much more than usual). Examples of questions are; “Have you been feeling unhappy and depressed recently?” and “Have you been able to concentrate on whatever you‟re doing recently?”. The psychometric qualities of the GHQ are generally positive (Koeter, & Ormel, 1995). Criterion validity is good; the GHQ-12 can discriminate properly between people with and without a DSM diagnosis. The construct validity fluctuates between moderate and strong correlations of r =.37 - .73. Reliability as conveyed by Cronbach‟s alpha
was good ranging from good ranging from .76 to .94. In the present study it ranged from.82 to .85.
The BSI-18 is a “self-report symptom inventory designed to serve as a screening for psychological distress and psychiatric disorders” (De Beurs & Zitman, 2006; Derogatis, 1993). It is the shortest version available of the BSI which is originally based on the Symptom Check List (SCL-90-R). The BSI-18 was designed for broad use within society and not just for clinical populations. The BSI-18 contains 18 items for which people are asked to rate the extent to which they experienced certain issues in the past week, including today. Answers can be scored on a five-point Likert scale, ranging from 0 (not at all) to 4 (very much) (De Beurs, & Zitman, 2006). Examples of issues are; “anxiousness” and “trouble with
recollection”. The psychometric qualities of the BSI-18 are also good (De Beurs, & Zitman, 2006). The construct validity of the BSI-18 was assessed through comparison with the SCL-90-R scales and resulted in a sufficient validity of correlations between r = .70 to r = .79 (De Beurs, & Zitman, 2006). The construct validity of the English versions was higher, which is probably caused by the translation of the BSI-18 items from the English SCL-90-R instead of using the items from the Dutch SCL-90-R. Cronbach‟s alpha for the BSI-18 was very good ranging from 74 - .89. In the present study it ranged from .84 to .93.
Medication errors and Sick Leave. The software system for the administration of care related events and the database of human resources for personnel characteristics were used to compare the number of medication errors and sick leave between the wards of the experimental group and the control group. No data of individuals was provided for these measures, henceforth the total scores of the wards were used for the analysis. Since practically all the employees of the wards were participants in this study it was assumed that these data were comparable. The total scores comprised only of employees however, thus volunteers were not included in the analysis. The trend in average frequencies of medication errors and
sick leave for the months; December 2014, January 2015 and February 2015 was compared with the trend in average frequencies for the months; December 2015, January 2016 and February 2016. These months were chosen to represent the measurement moments and the same months a year before, so that if the years would differ greatly the results could be interpreted with greater caution.
Physical symptoms related to poor air quality. Questions about health issues related to poor air quality comprised of several known symptoms of poor air quality like a dry skin, dry throat, dry or irritated eyes, headaches and coughs (Gezondheidsnet, 29-06-2015). Cronbach‟s alpha was sufficient for the first and second measurement moment (.76 and .77). It was poor for the third measurement moment (.50).
Attention. To measure attention capacity the D2 was assessed (Brickenkamp, 2007). The D2 is primarily used in neuropsychological and clinical settings. However, it is also used in work related settings. The D2 comprises of 14 lines of d‟s and p‟s. There are stripes above and or below these letters. Participants are asked to underline the d‟s with a total of two stripes. This means there are three options, two stripes above a d, two stripes below a d and 1 stripe above and below a d. For an example see Appendix 1. Participants have 20 seconds per line to underline the d‟s that have two stripes surrounding them. The number of mistakes they make (i.e., unchecked d‟s or wrongly checked d‟s/p‟s) are counted and averaged which results in a number that is representative for a person‟s general attention capacity. The stability of the D2 is good with values between .72 and .92 (Brickenkamp, 2007). The validity of the D2 as measured with the intercorrelations of the different scales is good as well with correlations ranging from r = .72 to r = .97 (Brickenkamp, 2007). Furthermore the D2 has been found reliable with a Cronbach‟s alpha of .93 as described by the manual and .82 to .84 in the present study.
Emotional States. Emotional states were measured with the Positive Affect and Negative Affect Scale (PANAS) which consists of 20 items (Watson, Clark, & Tellegen, 1988). The items of the PANAS refer to emotional states and participants are asked how applicable they are for the participants‟ emotional state in the past week including today. Answers are scored on a five-point scale and can range from 1 (not at all) to 5 (extremely). Examples of the emotional states questioned by this version of the PANAS are; “interested” and “irritable”. The convergent and discriminant validity are good (Watson et al., 1988). The reliability was also sufficient with Cronbach‟s alpha for the Positive Affect scale of .88 and .85 for the Negative Affect scale. In the present research these values were .77 and .81. Appreciation of the work environment. Questions about the evaluation of the environment were asked with the environment rating scale, to assess if the participants reported significant differences in their environment. The format was created by Scannel and Gifford (2010) and comprised of 10 statements about the environment, such as: I find this environment “pleasant” or “gloomy”. The answer possibilities range from 1 (not at all) to 4 (very much). These questions have and a proper construct and convergent validity and the reliability of this scale was sufficient with a Cronbach‟s alpha of .79 (Scannel, & Gifford, 2010). In the present research it ranged from .78 to .81.
Evaluation of the green walls. At the final measurement moment the participants of the experimental group were asked some additional questions at the questionnaire about their experience with the green walls. They were asked to rate if the walls were an improvement of certain aspects, like atmosphere and friendliness. They were given the opportunity to give their opinion in a qualitative manner as well, by means of an open space at the end of the questionnaire.
Procedure
In November 2015 the managers of the closed wards for both groups were approached to ask for their assistance in recruiting participants for the study. With their endorsement the first measurements of the combined questionnaire (general questions, questions about work stress, GHQ-12, BSI-18, PANAS, environment rating scale and symptoms of poor air quality) and the D2 task for attention were administered in the week of 07-12-2015, to provide data for the situation before the intervention. In the week of 11-01-2016 the plant walls were placed on Monday and the data for second measurement moment was collected later that week to assess the short term effects. In the last week of January 2016 the final measurement moment was completed to assess the long(er) term effects.
The combined questionnaire was provided through a personal email with a link to an online survey, which the employees could complete at home. The D2 task was administered face to face during the working hours of the employees. Informed consent was completed at the first measurement moment of the D2 task. The D2 was handed out on paper and took ca. 8 minutes to complete. The survey was administered through the use of the program Qualtrics and took ca. 15 minutes to complete.
Statistical Analysis
As was mentioned earlier, there were missing data due to reduced response rates for the online survey and also due to some drop out. Figure 3 shows the outcome of the missing data analysis with respect to the percentage of variables, cases and values for which data was missing. Since a total sample of 26 complete cases would dismiss a lot of data and would result in very low power, a multiple imputation technique (Rubin, 2004) was applied to be able to include the cases that had only provided data for one or two measurement moments or for example just for the attention variables. This resulted in the imputation of all the missing data, without further specified conditions.
Firstly, the dataset was analyzed for certain patterns in the missing data with the Multiple Imputation; Analyze Patterns command (Rubin, 2004). This did not appear to be the case. There are different ways to impute data, thus to select the best analysis it important to see it the missing data are random (Rubin, 2004). To be sure of this the missing data a MCAR test was computed, which was not significant, Chi-Square (385) = 391.52, p = .40. This means that the assumption of random missing data was met and the multiple imputation technique was applied as usual. Hereafter it was checked if the dataset met the assumptions for a repeated measures ANOVA. There was one outlier that deviated more than two standard deviations from the mean for the BSI variable, this value was deleted for participant. The other assumptions were met, except for the assumption of normality and for some cases the assumption of sphericity. With respect to normality, a 3 measurement moment x 2 group repeated measures ANOVA was still computed to test the main hypothesis, since it is a reasonably robust test. Non-parametric tests to compare (in)dependent means were computed however, if a repeated measures ANOVA resulted in a significant main- or interaction effect to ensure no false-positive effects. If the assumption of sphericity was violated the
Greenhouse-Geisser correction was used when interpreting the results.
For all the repeated measures ANOVA described, measurement (pre, post and follow-Figure 3. The outcome of the missing data analysis.
up) was the within subjects variable and group (yes or no placement of green walls) was the between subjects variable.
Results
Comparison of Groups With and Without a Green Wall
There were no significant differences (p < .05) for the baseline measurement moment between the experimental group and the control group for any of the outcome measures (See Table 2 for an overview of the test statistics).
Work stress. It was expected that work stress would decline for the participants in the experimental group compared to the participants in the control group. The repeated measures ANOVA did not show a main effect of measurement however F (1.79, 100.47) = 0.17, p = .82, η2 = .003. This means that there was no significant change in work stress during the three measurement moments. There was also no main effect of group F (1, 56) = .87, p = .874, η2 = .015, indicating that work stress scores for participants in the experimental and control group were similar. In contrast to the expectations there was no interaction effect either F (1.79, 100.47) = 1.91, p = .37, η2 = .03, which means that the pattern of work stress did not differ between the experimental and the control group. For a schematic representation see Figure 4.
Psychological distress. It was expected that psychological distress as measured by the BSI-18 and GHQ-12 would decline for the participants in the experimental group compared to the participants in the control group.
For the BSI-18, the repeated measures ANOVA showed a main effect of measurement: F (1.44, 79.27) = 3.715, p = .042, η2 = .06. This means that there was a significant change in psychological distress during the three measurement moments. There was no main effect of group F (1,55) = 2.80, p = .10, η2 = .05, indicating that the BSI-18 scores for the experimental and control group were similar. No interaction effect was found either: F (1.44, 79.24) = 1.709, p = .194, η2 = .03, which was in contrast with the expectations since it means that the pattern of distress did not differ between the experimental and control group. For a schematic representation see Figure 5.
0 5 10 15 20 25 1 2 3 T ot al W or k S tr ess S cor e Measurement Moment Experimental group Control group
Figure 4. The total score of work stress for the three measurement moments for the experimental and control group.
To account for a possible false positive due to the violation of the assumption of normality, two non-parametric Wilcoxon Signed Rank Tests were computed to see if the main effect for measurement remained. When the first and second measurement moments were compared there was no significant effect: W (57) = 523.00, p = .059, r = - 0.25. The effect remained non-significant when the first and third measurement moments were compared: W (57) = 488.00, p = .101, r = - 0.22. In conclusion when correcting for the violation of
normality the effect turned non-significant, but a trend remained for psychological distress to be lower at the second measurement moment, compared with the first measurement moment.
For the GHQ-12, the repeated measures ANOVA did not show a main effect: F (2, 112) = 1.206, p = .303, η2 = .02. This means that there was no significant change in
psychological distress during the three measurement moments. There was also no main effect of group F (1, 56) = .25, p = .62, η2 = .004, indicating that work stress scores for participants in the experimental and control group were similar. No interaction effect was found either F (2, 112) = 0.094, p = .910, η2 = .002, which was in contrast with the expectations since it
0 2 4 6 8 10 12 14 1 2 3 T ot al B S I-18 S cor e Measurement Moment Experimental group Control group
Figure 5. Total score on the Brief Symptom Inventory for the three measurement moments for the experimental and control group.
means that the pattern of distress did not differ between the experimental and control group. For a schematic representation see Figure 6.
Medication Errors. It was expected that the placement of the green walls would be associated with fewer medication errors. Figure 7 shows the medications errors for both groups for the months of the measurement moments (pre = December 2015, post = January 2016, Follow-up = February 2016). The same months of the year before the study are also shown so that if the years would differ greatly the results could be interpreted with greater caution.
A decline in medication errors is seen after the placement of the green walls, but it is doubtful if this trend can be attributed to the impact of the green walls due to the contrast with the same months the year before and due to other factors that influence medication errors (Dollarhide et al., 2014). Moreover the experimental group has changed their administration of medication errors in January 2016, which complicates the interpretation of the results even more. 0 2 4 6 8 10 12 14 1 2 3 T ot al G HQ -12 S cor e Measurement Moment Experimental group Control group
Figure 6. Total score on the General Health Questionnaire for the three measurement moments for the experimental and control group.
Sick Leave. It was expected that the placement of green walls would be associated with less sick leave. Figure 8 shows the sick leave for both groups for the months of the measurement moments (pre = December 2015, post = January 2016, Follow-up = February 2016). The same months of the year before the study are also shown so that if the years would differ greatly the results could be interpreted with greater caution.
Figure 8 shows that the experimental group has an overall higher percentage of sick leave compared to the control group. Statements about the association between the placement of green walls and sick leave are difficult to make, since there is a large contrast between the year before the study and the months associated with the present study, which complicates the interpretation of the data.
0 2 4 6 8 10 12 T ot al N umber of Me dic at ion E rr ors Control group Experimental group
Figure 7. Medications errors for both groups for the months of the measurement moments (pre = December 2015, post = January 2016, Follow-up = February 2016) and the same months of the year before the study.
Physical symptoms related to poor air quality. For this measure a repeated
measures ANOVA was computed with only the second and third measurement moment, since the first and second measurement moments could not be compared due to the addition of an extra item in the questionnaire for the second measurement moment that resulted in an artificial rise of symptoms. It was expected that physical symptoms of poor air quality would decline for the participants in the experimental group compared to the participants in the control group from post measurement to follow-up. The repeated measures ANOVA showed a main effect of measurement moment F (1, 56) = 6.18, p = .02, η2 =.18. This means there was a significant change in symptoms during the measurement moments. There was no main effect of group F (1, 56) = 1.87, p = .18, η2 = .03, indicating that the symptoms for the experimental and the control group were similar. In contrast to the hypothesis, no interaction effect was found: F (1, 56) = 1.83, p = .18, η2 = .03, which means that there was no difference between the experimental and control group for this effect. For a schematic representation see Figure 9. 0 2 4 6 8 10 12 14 16 18 T ot al P er ce ntage of Worked H ours C onsume d by Sic k L eave Control group Experimental group
Figure 8. Medications errors for both groups for the months of the measurement moments (pre = December 2015, post = January 2016, Follow-up = February 2016) and the same months of the year before the study.
To account for a possible false positive due to the violation of the assumption of normality, a non-parametric Wilcoxon Signed Rank Tests was computed to see if the main effect remained when the third and second measurement moment were compared. A significant effect was found: W (58) = 523.00, p = .02, r = - 0.30, which means that there were significantly less symptoms related to poor air quality at the third measurement moment, compared to the second measurement moment.
Attention. It was expected that attention capacity as measured by the D2 F% and D2 Cp scores would increase for the participants in the experimental group compared to the participants in the control group.
The D2 Cp score was computed through subtracting the number of mistakes from the number of correct responses. An increase in attention capacity is hereby operationalized by an increase in the D2 Cp score. The D2 F% score was computed by dividing the number of items that were missed by the amount of processed items. An increase in attention capacity is hereby operationalized by a decline in the D2 F% score. The repeated measures ANOVA showed a main effect of measurement for both the D2 Cp and the D2 F% scores FCp (1.69,
0 2 4 6 8 10 12 2 3 T ot al S cor e of S ym p tom s Measurement Moment Experimental group Control group
Figure 9. Total score of the symptoms of reduced air quality for the post and follow-up measurement moments for the experimental and control group.
0 1 2 3 4 5 6 7 8 1 2 3 D2 F % S cor e Measurement Moment Experimental group Control group
Figure 11. Percentage of items missed on the D2 (D2 F% Score) for the three measurement moments for the experimental and control group.
94.87) = 49.51, p < .001, η2 = .47 and FF% (1.72, 96.05) = 17.38, p < .001, η2 = .24.This means that there was a significant change in attention capacity during the three measurement moments. No significant main effects of group were found FCp (1, 56) = 0,86, p = .36, η2 = .02 and FF% (1, 56) = 0,18, p = .67, η2= .003, indicating that attention scores were similar for participants in the experimental and control group. There were no significant interaction effects either: FCp (1.69, 94.87) = 1.68, p = .20, η2 = .03 and FF% (1.72, 96.05) = 1.84, p = .17, η2 = .03, which was in contrast with the expectations since it means that the pattern of attention did not differ between the experimental and control group. For a schematic overview see Figure 10 and Figure 11.
Figure 10. Total number of correct responses minus number of mistakes (Cp score) on the D2 for the three measurement moments for the experimental and control group. 0 50 100 150 200 250 1 2 3 D2 C p S cor e Measurement Moment Experimental group Control group
To account for a possible false positive for the main effect of measurement due to the violation of the assumption of normality, two non-parametric Wilcoxon Signed Rank Tests for each scoring method were computed. When the first and second measurement moments were compared a significant effect was found: WCp (58) = 1384.00, p < .001, r = 0.54, WF% (58) = 323.50, p < .001, r = - 0.54. This effect remained when the first and third measurement moments were compared: WCp (58) = 1638.00, p < .001 r = 0.80, WF% (58) = 291.00, p < .001, r = - 0.57. In conclusion when correcting for the violation of normality the effect remained significant, which means that attention capacity increased directly after placement of the green walls and this effect remained at follow-up.
Emotional states. It was expected that negative affect would decline and positive affect would increase as was measured by the PANAS, for the participants in the experimental group compared to the participants in the control group.
For negative affect, the repeated measures ANOVA showed a main effect of measurement F (1.72, 96.13) = 8.78, p = .001, η2 = .14. This means that there was a
significant change in negative affect during the three measurement moments. No main effect of group was found F (1, 56) = 3.37, p = .07, η2 = .06, indicating that the negative affect scores were similar for the experimental and control group. There was no interaction effect either: F (1.72, 96.13) = 1.70, p = .193, η2 = .03, which means that the pattern of negative affect did not differ between the experimental and control group. This was not conform the expectations, for a schematic overview see Figure 12.
To account for a possible false positive for the main effect of measurement due to the violation of the assumption of normality, two non-parametric Wilcoxon Signed Rank Tests were computed. When the first and second measurement moment were compared a significant effect was found: W (58) = 462.00, p = .02, r = - 0.32. The effect remained significant when the first and third measurement moments were compared: W (58) = 365.00, p = .003, r = - 0.39. In conclusion when correcting for the violation of normality the effect remained significant, which means that negative affect declined directly after placement of the green walls and this effect remained at follow-up.
For positive affect the repeated measures ANOVA did not show a main effect of measurement F (2, 112) = 1.32, p = .27, η2 = .02. This means that there was no significant change in positive affect during the three measurement moments. No main effect of group was found either F (1, 56) = 1.90, p = .17, η2 = .03, indicating that the positive affect scores were similar for the experimental and the control group. Finally there was no significant interaction effect F (2, 112) = 1.00, p = .37, η2 = .02, which means that the pattern of positive
0 5 10 15 20 25 1 2 3 T ot al S cor e of Ne gative Af fe ct Measurement Moment Experimental group Control group
Figure 12. Total score of negative affect for the three measurement moments for the experimental and control group.
affect did not differ between the experimental and control group. These results were not conform the expectations, for a schematic overview see Figure 13.
Appreciation of the work environment. It was expected that the appreciation of the work environment would increase for the participants in the experimental group compared to the participants in the control group.
The repeated measures ANOVA did not show a main effect however F (1.68, 93.96) = 1.21, p = .30, η2 = .02. This means that there was no significant change in appreciation of the work environment during the three measurement moments. No main effect of group was found either F (1, 56) = 2.30, p = .14, η2 = .04, indicating that the appreciation of the work environment was similar for the experimental and control group. There was also no significant interaction effect F (1.68, 93.96) = 0,96, p = .37, η2 = .02 which means that the pattern of appreciation of the work environment did not differ between the experimental and control group. These results were not conform the expectations, for a schematic representation see Figure 14. 0 5 10 15 20 25 30 35 40 1 2 3 T ot al P ositive Af fe ct Sco re Measurement Moment Experimental group Control group
Figure 13. Total score of positive affect for the three measurement moments for the experimental and control group.
Evaluation of the Green Walls
In total 20 (69%) participants of the experimental group responded to questions about the evaluation of the green walls. It turns out that 90% of these participants were very positive about the overall placement of the green walls (Figure 15 and Figure 16).
However, there were some negative aspects as well. For instance the bright lamps that were placed to ensure that plants in the large walls had enough daylight for photosynthesis
0 5 10 15 20 25 30 35 1 2 3 T ot al W or k E n vir on m en t R at in g Measurement Moment Experimental group Control group Yes 90% No 10% Yes 25% No 70% Non respons e 5%
Figure 15. Do you like it that the plant walls have been placed?
Figure 16. Did you experience any negative side effects of the plant walls?
Figure 14. Total score on the work environment rating scale for the three measurement moments for the experimental and control group.
were something to improve according to three participants (10%) who worked at the ward with the large plant walls. These lamps were very bright and placed in a way that bothered the employees and the clients. It took several weeks before the lamps were adjusted.
Another aspect of feedback was that the participants of the experimental group
expected more plant walls, as was conveyed by three participants (10%). Now two of the four wards did not have any plant walls at all, since the second small wall was placed in the
corridor, and only one ward had large plant walls. This could have compromised the manipulation.
The small walls were filled with one single species. The participants would have liked it better if more and a larger variety of plants would have been placed. During the visits of the researcher three participants (10%) also conveyed that the plant walls were not as accessible as anticipated, because the design of the plant walls suggested “just look, don‟t touch”. This was especially applicable for the small plant walls, which looked very much like a sort of painting and were placed high up on the wall. These employees would have liked it better to have the larger plant walls at all the wards, so that clients could sit next to them and touch them because they saw that the clients from the first ward liked this very much.
When asked about the impact of the green walls as an improvement of the work atmosphere and air quality, participants reported mostly neutral or slightly favorable
responses (Figure 17 and Figure 19. They were convinced however that the green walls made the work environment look friendlier (Figure 18). The participants noted with these questions that for an improvement in atmosphere and air quality more plant walls were probably
Discussion
The aim of this study was to evaluate the restorative impacts of the implementation of green walls in the elderly care work environment, using a quasi-experimental design with one
Strongly disagree 2% Disagree 12% Neither agree n or disagree 43% Agree 30% Strongly agree 10%
Does not apply 0% Non response 3% Strongly disagree 2% Disagree 0% Neither agree n or disagree 13% Agree 66% Strongly agree 13% Does not apply
3% Non response 3% Disagree 10% Neither agree no r disagree 45% Agree 30% Strongly agree 5%
Does not apply 10%
Figure 17. The plant walls have improved the atmosphere.
Figure 18. The plant walls make the work environment friendlier.
experimental and one control group and three measurement moments (pre, post and follow-up). The results showed a general decline in psychological distress, negative affect, physical symptoms related to poor air quality and an increase in attention capacity during the three measurement moments. However, in contrast to the hypotheses, there were no differences between the experimental group and the control group for these measures.
These results are not in line with former research about the stress reducing effect of nature, since there was no difference between the groups (Hartig et al., 2014).
A possible explanation for not finding a difference between the experimental group and the control group is that the manipulation was probably compromised because the employees of the control group had implemented their own real plants when the researcher visited the institutions for the second and third measurement moment (for examples see Figure 20 – Figure 24). Prior to this study both facilities only had fake plants to decorate the wards. The placement of real plants meant that there was a relevant change instead of no change for the control group. Since the facilities work closely together and since there was widespread local media coverage of the placement of the green walls at Pennemes it is likely that the
participants of the control group got inspired by their colleagues of the experimental group. An aspect that could possibly be relevant for further research is that the plants brought by the employees of the control group were very approachable for the clients and the
employees as well. This was not the case for the small plant walls in the experimental groups, since the design of the walls suggested “just look, don‟t touch”. The importance of being able to interact with the plants is an interesting topic to study in further research.
The presence of flowers is also something to consider in further research of green walls. The plants in the control group had flowers, the green walls of the experimental group did not. Former research shows that flowers are associated with a higher appreciation of the
Contrary to the expectations, no beneficial impact of the green walls was found on employee‟s work stress, positive affect, appreciation of the work environment, medication errors and sick leave. Former research also showed mixed results about the effects of indoor plants, but a stress reduction effect was found consistently (Bringslimark, Hartig, & Patil, 2009). Maybe the participants were not as stressed as assumed, since their scores on the work stress measurements were not particularly high (De Vries et al., 2002). The mean item score on the work stress measure was 2.81 (SD = 0.08) for the experimental group and 2.98 (SD = 0.12) for the control group. In the study of De Vries (et al., 2002) a mean of 3.02 (SD = 0.73) was found. These three means are very similar, which was not expected since the elderly care sector is one of the most stressful sectors of the Dutch workforce (Wilkins, 2007; Dietrich et al., 2014) and the study of De Vries (et al., 2002) represented the overall Dutch workforce. Thus in theory the mean item scores of the participants in the present study should have been be higher than those found for the overall Dutch workforce.
It is possible that the mean item scores for work stress were lower than expected due to seasonal effects, since the baseline measurement was administered just before the Christmas holiday period and the post- and follow-up measurements were administered just after this period. It could be that the participants experienced fewer work stress due to the holiday spirit and/ or due to being relaxed after a few days off during this period in which people often take time for a vacation.
Another aspect for discussion is is that the amount of green might not have been sufficient for a successful manipulation. The participants reported that they had expected more green walls, since only one of the four researched wards had large green walls that were prominently present in the line of sight. Former research suggests that a small amount of plants (for instance 5 plants in containers from 10 to 20 centimeter in size,) can be effective (Bringslimark, et al., 2009). However, these studies have not been done in a very demanding
environment like the elderly care work environment is. A higher dose of plants may be needed to compensate for the number of distractions employees in this sector encounter.
When the participants of the experimental group were asked to evaluate the placement of the green walls they were positive about the placement in general. They were also
convinced that the plant walls made the work environment look friendlier. When asked about the green walls as an improvement of the work environment and air quality most participants answered neutral or slightly in favor of these statements. These findings confirm former research about plant walls as a possible improvement of the work environment (Bringslimark, et al., 2009). The air quality was also measured with objective measures by Fytagoras (Van Duijn, Holtman, & Smit, 2016). The results showed that the wards with green walls had a smaller increase of CO2 levels and a smaller decline of humidity during the day compared to
the wards without green walls. Ventilation was still not optimal, but these results could have health benefits.
Besides the question if there were enough green walls for a sufficient manipulation it can be stated that the number of participants of the study was probably too low for sufficient power. There was simply too much missing data due to reduced response rates for the online survey and also due to drop out associated with the second and third measurement moment. The multiple imputation technique was used to ameliorate the negative effect of the missing data, but with this technique values are estimated which is never as accurate as complete data cases (Rubin, 2004). The reduced response rate for the online survey was not as severe as could have been though, partly due to the personal contact of the researcher with the employees when administering the attention task and due to the reminder emails that were sent.
Finally the operationalization of the attention task is also something to think about when doing this kind of research. It is possible that there are learning effects, since this task
has not been used for longitudinal research much before. In the environmental psychology discipline many different tasks (all with learning effects) are used (Hartig et al., 2014). Besides the learning effects they probably could all measure different aspects of attention as well. For further research it is important to specify and test the hypothesis when it comes to the effect of nature at different aspects of attention. It would also possibly be helpful to compose a series of attention tasks that measure the same aspect of attention, so that different tasks can be administered at different measurement moments in order to account for the learning effects.
Priorities for Further Research
Throughout this study a few recommendations for further research have been made. To recapitulate;
First and foremost it is essential to make sure that the management of the included facilities clearly knows what group they represent and what actions would compromise the manipulation. Moreover a clear line of communication with the employees about these matters for the duration of the study is essential as well. For example the study could be mentioned in the facilities‟ newsletter and pamphlets could be posted at the wards for the duration of the study.
Second, the operationalization of the green walls should be similar for all the wards in the experimental group to ensure a consistent manipulation. It would also be interesting to see if it would make a difference for the participants to be able to interact with the plants in the walls. Moreover is would be interesting to see if the amount of flowers in the walls would make a difference.
Third, with respect to the operationalization of (work) stress it could be helpful to include an objective stress measure, like blood pressure or cortisol level. This would make comparison with national means and with other studies easier. Another addition to the
measures could be the Perceived Restoration Scale (Korpela et al., 2015). This measure has been created based on the Attention Restoration Theory. It is mostly used for landscapes, but it would be interesting to test the assumption that natural elements like plants are perceived to be restorative as well.
Forth, to ensure sufficient power multiple facilities that provide comparable elderly care could be included in such a study, provided that the experimental and control wards are properly matched.
Fifth, a follow-up measurement at three or six months after the placement of the green walls would make more sense when one is interested in long term effects (Hartig et al., 2014). The three week follow-up for this study was not chosen based on former research, but based on practical reasons.
Finally, it is important to specify the impact of nature at different aspects of attention. It would also possibly be helpful to compose a series of attention tasks that measure the same aspect of attention, so that different tasks can be administered at different measurement moments in order to account for the learning effects.
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Tables & Figures Table 1
Test Statistics, p-values and Effect Sizes for the Significance Tests of the Variables; Age, Type of Employment and Work Hours Between the Experimental and the Control group
Variables Test-statistic U p-value Effect size r
Age 414.00 .92 0.02
Type of Employment 469.00 .36 - 0.12
Work Hours 417.50 .96 0.01
Table 2
Test Statistics, p-values and Effect Sizes for the Significance Tests of the Baseline Measurement of the Outcome Measures Between the Experimental and the Control group
Variables Test-statistic U p-value Effect size r
Workstress 478.00 .37 - 0.13
GHQ-12 444.50 .71 - 0.06
BSI-18 340.00 .29 0.19
PANAS Positive Affect 405.50 .82 0.04
PANAS Negative Affect 428.50 .90 - 0.02
Appreciation of the Work Environment
416.50 .95 0.01
Figure 20, Figure 21, Figure 22, Figure 23 and Figure 24. Examples of plants implemented by the control group.
