3.1 Table of Sociodemographic Overview of Respondents
No.
|
Variable
|
Frequency
|
Percentage (%)
|
Minimum
|
Maximum
|
Mean
|
Std.Deviation
|
1.
|
Gender
|
|
|
|
|
|
|
|
a. Male
|
30
|
75.00
|
|
|
|
|
|
b. Female
|
10
|
25.00
|
|
|
|
|
2.
|
Level of Education
|
|
|
|
|
|
|
|
a. High
|
29
|
72.5
|
|
|
|
|
|
b. Moderate
|
11
|
27.5
|
|
|
|
|
3.
|
Marital Status
|
|
|
|
|
|
|
|
a. Married
|
29
|
72.5
|
|
|
|
|
|
b. Unmarried
|
11
|
27.5
|
|
|
|
|
4.
|
Respondent Age
|
|
|
21
|
53
|
35.76
|
8.632
|
5.
|
Working Years
|
|
|
1
|
33
|
11.03
|
8.888
|
3.2 Table of sAA Concentration and ELISA Graph
Conc. matrix
|
|
|
|
|
|
|
|
|
|
|
|
|
1
|
2
|
3
|
4
|
5
|
6
|
7
|
8
|
9
|
10
|
11
|
12
|
A
|
4063.748
|
4063.748
|
681.938
|
681.938
|
1059.116
|
1059.116
|
1519.965
|
1519.965
|
1055.579
|
1055.579
|
1149.849
|
1149.849
|
B
|
1866.294
|
1866.294
|
1136.259
|
1136.259
|
1081.680
|
1081.680
|
1330.453
|
1330.453
|
1307.448
|
1307.448
|
1229.625
|
1229.625
|
C
|
1148.609
|
1148.609
|
984.011
|
984.011
|
1148.609
|
1148.609
|
1326.374
|
1326.374
|
676.324
|
676.324
|
540.996
|
540.996
|
D
|
425.889
|
425.889
|
9.704
|
9.704
|
967.125
|
967.125
|
1103.317
|
1103.317
|
1472.770
|
1472.770
|
1423.756
|
1423.756
|
E
|
273.392
|
273.392
|
1344.110
|
1344.110
|
507.506
|
507.506
|
1056.757
|
1056.757
|
1100.901
|
1100.901
|
1398.268
|
1398.268
|
F
|
103.968
|
103.968
|
805.948
|
805.948
|
1360.621
|
1360.621
|
1342.740
|
1342.740
|
1228.332
|
1228.332
|
1288.701
|
1288.701
|
G
|
76.605
|
76.605
|
1035.667
|
1035.667
|
1481.539
|
1481.539
|
938.243
|
938.243
|
1409.557
|
1409.557
|
1138.722
|
1138.722
|
H
|
0.000
|
0.000
|
1018.279
|
1018.279
|
1239.997
|
1239.997
|
1188.685
|
1188.685
|
1278.067
|
1278.067
|
900.122
|
900.122
|
< Plate layout >
|
|
|
|
|
|
|
|
|
|
|
|
|
1
|
2
|
3
|
4
|
5
|
6
|
7
|
8
|
9
|
10
|
11
|
12
|
A
|
STD07-1
|
STD07-2
|
SAM01-1
|
SAM01-2
|
SAM02-1
|
SAM02-2
|
SAM03-1
|
SAM03-2
|
SAM04-1
|
SAM04-2
|
SAM05-1
|
SAM05-2
|
B
|
STD06-1
|
STD06-2
|
SAM06-1
|
SAM06-2
|
SAM07-1
|
SAM07-2
|
SAM08-1
|
SAM08-2
|
SAM09-1
|
SAM09-2
|
SAM10-1
|
SAM10-2
|
C
|
STD05-1
|
STD05-2
|
SAM11-1
|
SAM11-2
|
SAM12-1
|
SAM12-2
|
SAM13-1
|
SAM13-2
|
SAM14-1
|
SAM14-2
|
SAM15-1
|
SAM15-2
|
D
|
STD04-1
|
STD04-2
|
SAM16-1
|
SAM16-2
|
SAM17-1
|
SAM17-2
|
SAM18-1
|
SAM18-2
|
SAM19-1
|
SAM19-2
|
SAM20-1
|
SAM20-2
|
E
|
STD03-1
|
STD03-2
|
SAM21-1
|
SAM21-2
|
SAM22-1
|
SAM22-2
|
SAM23-1
|
SAM23-2
|
SAM24-1
|
SAM24-2
|
SAM25-1
|
SAM25-2
|
F
|
STD02-1
|
STD02-2
|
SAM26-1
|
SAM26-2
|
SAM27-1
|
SAM27-2
|
SAM28-1
|
SAM28-2
|
SAM29-1
|
SAM29-2
|
SAM30-1
|
SAM30-2
|
G
|
STD01-1
|
STD01-2
|
SAM31-1
|
SAM31-2
|
SAM32-1
|
SAM32-2
|
SAM33-1
|
SAM33-2
|
SAM34-1
|
SAM34-2
|
SAM35-1
|
SAM35-2
|
H
|
BLK01-1
|
BLK01-2
|
SAM36-1
|
SAM36-2
|
SAM37-1
|
SAM37-2
|
SAM38-1
|
SAM38-2
|
SAM39-1
|
SAM39-2
|
SAM40-1
|
SAM40-2
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
< Raw abs. matrix >
|
|
|
|
|
|
|
|
|
|
|
|
P1
|
1
|
2
|
3
|
4
|
5
|
6
|
7
|
8
|
9
|
10
|
11
|
12
|
A
|
2.906
|
3.018
|
1.415
|
0.923
|
1.444
|
1.611
|
1.740
|
2.009
|
1.495
|
1.553
|
1.626
|
1.579
|
B
|
2.178
|
2.001
|
1.434
|
1.749
|
1.502
|
1.591
|
1.775
|
1.708
|
1.777
|
1.671
|
1.567
|
1.763
|
C
|
1.660
|
1.542
|
1.522
|
1.403
|
1.690
|
1.513
|
1.706
|
1.771
|
1.234
|
1.093
|
1.062
|
0.960
|
D
|
0.880
|
0.859
|
0.204
|
0.160
|
1.348
|
1.546
|
1.425
|
1.704
|
1.882
|
1.803
|
1.826
|
1.790
|
E
|
0.681
|
0.635
|
2.036
|
1.466
|
1.051
|
0.891
|
1.728
|
1.322
|
1.587
|
1.538
|
1.789
|
1.792
|
F
|
0.390
|
0.369
|
1.430
|
1.162
|
1.833
|
1.694
|
1.762
|
1.739
|
1.777
|
1.551
|
1.883
|
1.537
|
G
|
0.335
|
0.319
|
1.546
|
1.468
|
1.848
|
1.848
|
1.507
|
1.336
|
1.793
|
1.804
|
1.498
|
1.688
|
H
|
0.074
|
0.068
|
1.468
|
1.516
|
1.661
|
1.686
|
1.651
|
1.615
|
1.679
|
1.726
|
1.484
|
1.288
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
< Blanked abs. matrix >
|
|
|
|
|
|
|
|
|
|
|
P1
|
1
|
2
|
3
|
4
|
5
|
6
|
7
|
8
|
9
|
10
|
11
|
12
|
A
|
2.835
|
2.947
|
1.344
|
0.852
|
1.373
|
1.540
|
1.669
|
1.938
|
1.424
|
1.482
|
1.555
|
1.508
|
B
|
2.107
|
1.930
|
1.363
|
1.678
|
1.431
|
1.520
|
1.704
|
1.637
|
1.706
|
1.600
|
1.496
|
1.692
|
C
|
1.589
|
1.471
|
1.451
|
1.332
|
1.619
|
1.442
|
1.635
|
1.700
|
1.163
|
1.022
|
0.991
|
0.889
|
D
|
0.809
|
0.788
|
0.133
|
0.089
|
1.277
|
1.475
|
1.354
|
1.633
|
1.811
|
1.732
|
1.755
|
1.719
|
E
|
0.610
|
0.564
|
1.965
|
1.395
|
0.980
|
0.820
|
1.657
|
1.251
|
1.516
|
1.467
|
1.718
|
1.721
|
F
|
0.319
|
0.298
|
1.359
|
1.091
|
1.762
|
1.623
|
1.691
|
1.668
|
1.706
|
1.480
|
1.812
|
1.466
|
G
|
0.264
|
0.248
|
1.475
|
1.397
|
1.777
|
1.777
|
1.436
|
1.265
|
1.722
|
1.733
|
1.427
|
1.617
|
H
|
0.000
|
0.000
|
1.397
|
1.445
|
1.590
|
1.615
|
1.580
|
1.544
|
1.608
|
1.655
|
1.413
|
1.217
|
3.3. Figures ELISA Result Graph
3.4 Table of Factors Associated with [sAA]
Variables
|
Salivary Alpha-Amylase Concentration
|
|
|
Pearson Correlation
|
Sig.
|
Description
|
Fatigue (IFRC)
|
0,740
|
0.000*
|
Significantly Correlated
|
Sleep Quality (PSQI)
|
-0.721
|
0.000*
|
Significantly Correlated
|
Mental Workload
|
-0.211
|
0.192
|
Not Significantly Correlated
|
Age
|
-0.016
|
0.921
|
Not Significantly Correlated
|
Gender
|
0.102
|
0.530
|
Not Significantly Correlated
|
Level of education
|
-0.031
|
0.850
|
Not Significantly Correlated
|
Marital status
|
0.032
|
0.846
|
Not Significantly Correlated
|
Working years
|
0.089
|
0.587
|
Not Significantly Correlated
|
Abbreviations:
IFRC = Industrial Fatigue Research Committee
PSQI = Pittsburg Sleep Quality Index
MWL = Mental Workload
3.5 Table of Final Model Results of Multiple Linear Regression Analysis
Variabel
|
B
|
Std Error
|
Beta
|
t
|
Sig
|
Fatigue (IFRC)
|
15.901
|
3.973
|
0.525
|
4.003
|
0.000
|
Sleep Quality (PSQI)
|
-13.38
|
4.006
|
-0.443
|
-3.34
|
0.002
|
Mental Workload
|
14.698
|
22.236
|
0.075
|
0.661
|
0.513
|
Age
|
0.082
|
12.282
|
0.002
|
0.007
|
0.995
|
Gender
|
142.754
|
75.612
|
0.205
|
1.888
|
0.068
|
Level of education
|
-4.705
|
84.631
|
-0.007
|
-0.056
|
0.956
|
Marital status
|
31.994
|
81.342
|
0.047
|
0.393
|
0.697
|
Working years
|
-0.837
|
13.041
|
-0.024
|
-0.064
|
0.949
|
Note: Dependent Variable: α amylase Concentration (U/mL)
Based on the descriptive analysis results, the data showed that of the 40 worker respondents are dominated by males (75%) with the distribution of higher education levels and marital status of 72.5%. Based on the results of Mental Workload (MWL) scoring using the NASA-TLX instrument, the respondents had an average score of 70.91 which is categorized as a high workload category. The high workload is in line with the level of stress or fatigue, qualities experienced by workers.
Then based on the results of the bivariate analysis with the Pearson correlation, it showed that the variables of work fatigue and sleep quality had a significant correlation with the concentration of the salivary alpha-amylase (sAA) in workers. The relationship between sleep quality variables has a negative direction, so if the dependent variable increases, the independent variable decreases. This resulted in the finding that if the quality of sleep is getting better, the concentration of sAA will decrease and vice versa. Then the work fatigue variable is positive. This means that if it is positive, if the dependent variable increases, then the independent will also increase. This resulted in the finding that if the level of work fatigue increases, the concentration of sAA will also increase.
Based on the final model analysis, it was found that the factors that affect the salivary alpha-amylase (sAA) concentration in oil palm plantation office workers in Jambi Province are the work-related fatigue variable (p-value = 0.000) and sleep quality variable (p-value = 0.002). The work-related fatigue variable analyzed using the IFRC instrument showed that for every unit increase in work-related fatigue, the salivary alpha-amylase (U/mL) concentration will increase by 15,901 (U/mL). Meanwhile, the sleep quality variable showed that for every one-unit increase in sleep quality, salivary alpha-amylase (sAA) concentration will decrease by 13.38 (U/mL).
Salivary alpha-amylase concentration after waking up was not related to waking time, sleep duration, and sleep quality. However, participants who woke up without using an alarm clock in the morning showed a decrease in salivary alpha-amylase concentrations in the first hour after waking up (parameter=−0.4661, SE=0.2053, P=0.023). The mean salivary alpha-amylase concentration was reduced by 1.59 U/ml in the respondents. Comparison of salivary alpha-amylase concentrations in three measurements between alarm and non-alarm users showed that salivary alpha-amylase concentrations upon woke up were higher in alarm users (t=2.29, df=69, P=0.025), while the difference was weaker at 30 minutes after waking up (t=1.84, df=73, P=0.070) and disappearing 60 minutes after waking up (t=1.56, df=72, P=0.124) [9].
It is recommended that the diurnal profile of salivary alpha-amylase is relatively strong against transient effects and may therefore be useful in assessing sympathetic nervous system activity. In addition, there is a need to control time in studies using salivary alpha-amylase as the dependent variable [9]. To examine the independence of the effect of Body Mass Index, smoking status, and use of an alarm clock to wake up in the morning, the significant influence parameters of these variables were included in the combined model. The effect on salivary alpha-amylase is independent. Similar to the results of the separate models, the combined model showed that salivary alpha-amylase concentrations were significantly higher in respondents who used alarms, were negatively associated with BMI, and that salivary alpha-amylase concentrations after woke up decreased at a greater rate and had a clearer curve curvature in smokers than in nonsmokers.
This study used inclusion criteria, one of which is respondents who do not have a smoking history because smoking can affect the consistency and stability of salivary analytes [10,11]. Salivary alpha-amylase secreted by the salivary glands is a stress biomarker in humans whose concentrations will increase in stressful situations and conditions [12]. Although this study did not measure the stress level of the respondents, it did measure the level of fatigue and sleep quality of the respondents. Many studies have found a correlation between sleep quality and fatigue that occurs due to stress with salivary alpha-amylase concentrations.
Several factors can reduce the concentration of salivary alpha-amylase secretion in salivae such as smoking habits, alcoholic beverages, and drug consumption such as hypertension drugs, antidepressants, and drugs that interfere with salivary secretion which is regulated by the sympathetic and parasympathetic nervous systems [13]. According to [14,15] who conducted a study with samples in children showed the results that sAA activity could be influenced by sleep time variables. Sleep deprivation is predicted as a marker of changes in neuroendocrine function. The results of this study provide information about pathways that can link poor sleep quality with health problems. Sleep has been correlated with daily patterns of stress-responsive physiological systems, in particular the Hypothalamus-Pituitary-Adrenal (HPA) and autonomic nervous system (ANS) axes [16].
These results supported previous findings, that in addition to conditions of reported increased fatigue, basal activity concentrations of sAA were found to be higher in the sleep-restricted group than in the rested group. These findings also support data that increased sleep duration results in lower resting Salivary alpha-amylase concentrations [17]. The sAA becomes important in assessing sleepiness for the first time in a large population studied longitudinally. The sAA in the evaluation of sleep deprivation among the adolescent population. This suggests that the measurement of salivary alpha-amylase activity may be a suitable non-invasive biochemical parameter for the objective assessment of sleep deprivation among individuals as well as at the population level. However, studies on a larger population consisting of various sectors of society would be helpful for further validation [18].
The increase in salivary alpha-amylase concentrations in this study was in line with other studies which showed that stress caused by fatigue in Indonesian civil pilots resulted in higher salivary alpha-amylase concentrations compared to Indonesian civil pilots who did not experience stress due to work-related fatigue. There is a positive correlation between salivary amylase concentrations and burnout scores and women show a significant increase in sAA alertness after the presence of a stressor compared to men. In addition, women also had a more pronounced increase in sAA throughout the day than men [19,20,21,22].
Salivary alpha-amylase biomarkers can be measured exclusively in saliva. Over the last few years, there has been an increasing number of studies using salivary alpha-amylase as a biological marker as a non-invasive substitute for sympathetic nervous activity. Salivary alpha-amylase has been considered a sensitive candidate biomarker to detect stress-related changes in the body that reflect sympathetic nervous system activity, and more study is being conducted to support the validity and reliability of this biomarker parameter [23]. Salivary alpha-amylase (sAA) has emerged as a valid and reliable marker of ANS activity in stress studies and is therefore an important biomarker to consider. In addition, salivary fluid holds promise for the development of objective fatigue measurements applicable to a much wider population in the uncontrollable environment [24,4]. However, it is important to choose an appropriate study design when trying to decipher the clinically and biologically appropriate changes in the natural variation of diurnal cortisol and alpha-amylase [25]. According to the results of the study [26] in evaluating the effect of the firefighting activity simulation intervention on the parameters of salivary alpha-amylase (sAA), free cortisol, anxiety, and mood profiles, the results showed that at 30 minutes post-intervention there was an increase in sAA by 174% and sC by 109%. In addition, the results of this study indicate that sAA will increase concerning physical activity such as exercise and this reflects an increase in plasma catecholamines and thermal stress. According to studies [27-33], there are biochemical changes in respondents after mental and physical activities that cause fatigue, with subjective fatigue scores increasing. After mental exhaustion sessions, urine vanillylmandelic acid levels were higher and plasma valine levels lower than after relaxation sessions. In contrast, after a physical session that causes fatigue, serum citric acid, triacylglycerol, free fatty acids, ketone bodies, total carnitine, acylcarnitine, uric acid, creatine kinase, aspartate aminotransferase, lactate dehydrogenase, cortisol, dehydroepiandrosterone, dehydroepiandrosterone sulfate, transforming growth factor beta1 and beta 2, white blood cell and neutrophil counts, cortisol and salivary amylase, and urinary vanillylmandelic acid levels were higher and serum-free carnitine and plasma total amino acids and alanine levels were lower than after the relaxation session. Salivary cortisol (sCort) and salivary alpha-amylase (sAA) are proxy measures of the two main stress response systems. The sAA profile is not sensitive to daily variations in sleep but is an indicator of stress-induced ANS dysregulation over a long period.