1. INTRODUCTION
Stress-related problems are strongly associated with health and safety risks among blue-collar workers and are major contributors to high error, poor quality of work, absenteeism, high turn, and decreased productivity at work (Limm et al., 2010;Bubonya et al., 2017). Multi countries surveys revealed a high prevalence of mental health-related problems such as anxiety and depression in blue-collar workers (Sanne et al., 2003;International Labour Organization, 2016). In a smaller population, (Edimansyah et al., 2008) found that Malaysian assembly-line operators reported high levels of job-stress which were strongly correlated with psychological job demand, job insecurities, and hazardous conditions. Karasek and Theorell (1990) and Hämmig (2017) also emphasized that lack of control, low salaries, lack of support from co-workers and supervisors, as well as family conditions are among the main stressors.
Stress occurs when demands mismatch with an individual’s capability to cope with, resulting in a complex interaction between physiological and psychological that are mainly affected by the autonomic nervous system (ANS) (Lehrer, 2007). Studies showed that heart rate variability (HRV), a very vital measurement in assessing ANS function, is a powerful and objective psychophysiological indicator to assess workplace stress (Mauss et al., 2016;Järvelin-Pasanen et al., 2018). HRV represents the beat-to‐beat changes in the inter-beat interval (time between two successive R‐waves) which is affected by ANS activity (Malik, 1996). Research has shown that interaction at the heart is a reflection of ANS balance or imbalance (homeostasis) in the body in general (Lehrer et al., 2003). Higher levels of HRV are associated with the capability to emotional self-regulation and cognitive performance while low HRV is a marker of depression, anxiety, and chronic stress symptoms and predicts mortality and morbidity (Lehrer and Gevirtz, 2014). Thus, stress management intervention might benefit from the addition of HRV as a psychophysiological component through the biofeedback mechanism. HRV biofeedback is based on the concept that individuals who might gain control over their physiological can improve their performance and well-being (Yu et al., 2018;De Witte et al., 2019).
One of the main HRV indices in the context of stress management is psychophysiological or cardiac coherence (McCraty and Childre, 2011;Whited et al., 2014;Yu et al., 2018). The coherence is exhibited by the development of smooth, sinusoidal pattern in HRV waveform, oscillating at a frequency around 0.1 Hz and formulated as (peak power/ [total power – peak power]). Furthermore, a widely used biofeedback technique to achieve cardiac coherence involves paced breathing, i.e. breathing at the resonant frequency, approximately ~5–7 breaths per minute. Regulating the respiration rate is important because it drives the heart rhythm oscillation.
Both cardiac coherence and resonant breathing share similarities that a higher vagal tone – a marker of the parasympathetic activity of the ANS – can be obtained through breathing at slow frequency. Being aware to breath slower will increase HRV or respiratory sinus arrhythmia (RSA) amplitude, a dynamic, naturally occurring heart pattern when heart rate increases during inhalation and decreases during exhalation. Numerous studies find that the maximum effects of RSA usually are achieved when the breathing is at a rate of approximately 0.1 Hz (six breaths per minute). At this frequency, heart rate oscillates with breathing at a 0° phase relationship – a phase in which the highest amplitude of RSA and the most efficient gas exchange in alveoli occur simultaneously (Lehrer and Gevirtz, 2014). When a person is in this phase, he or she will also generate more coherence state, an increased heart-brain synchronization, and entrainment between diverse physiological systems. Higher RSA and coherence states are associated with reduced medical morbidity, improved sports performance, reduced mental health symptoms, enhanced cognitive functioning, and improved decision making (Sutarto et al., 2013;Shaffer et al., 2014;Morgan and Mora, 2017;De Couck et al., 2019). While conscious slower breathing may induce coherence for brief periods, people can sustain it longer by actively self-cultivating positive emotions.
HRV biofeedback is a behavioral intervention where real-time HRV is processed and displayed over a monitor while users attempt to breathe at slow a pace following the cues (Lehrer, 2007). By increasing control over their HRV physiological processes, individuals can improve their overall performance and well-being (Wheat and Larkin, 2010;Whited et al., 2014;van der Zwan et al., 2015;Goessl et al., 2017).
Meanwhile, an electronic manufacturing company in Kuantan, Malaysia conducted an Employee Engagement Survey (EES), a preliminary step in its organizational wellness program to promote workplace health and employee well-being. From 18 of 319 assembly line operators reported extreme levels of depression, anxiety, and stress. Depression, anxiety, and stress scales have been linked to mental health indicators in stressful working settings (Edimansyah et al., 2008;Cash and Whittingham, 2010). The relationship between mental health disorders and productivity loss has been well documented (Burton et al., 2008;Bubonya et al., 2017). Hence, there is a need to implement a stress management program to eliminate the risk factors and teach employees with knowledge and skills to alleviate the stress.
Of all stress management techniques, individual-focused using physiological approach has been effective to alleviate negative-emotional symptoms toward reducing excessive physiological arousal (Van der Klink et al., 2001;Tetrick and Winslow, 2015). Zheng et al. (2016) also found that the impacts of individual coping strategies were more meaningful than organizational well-being programs in helping employees attaining overall well-being. This study, therefore, extended the work of Sutarto et al. (2012) who used a similar psychophysiological approach method to reduce stress among industrial operators. As our target population was those individuals with self-perceived extremely severe negative emotional symptoms, to increase the effectiveness, we developed a mul-timodal approach based on biofeedback and the work-life balance (WLB) concept. This WLB concept emphasized the balance between work engagements and activities outside of work (e.g. family demands) and is associated with improved organizational performance (Beauregard and Henry, 2009)
Despite the acknowledged importance of individual intervention, little research has been conducted to assess its effect on multiple individual-level outcomes of individuals using either psychological (e.g. self-perceived stress, depression, anxiety), physiological (e.g. HRV), and worker's productivity measures. In a broad sense, productivity is expressed as the ratio of output to the input used in the production process. Sauermann (2016) recommends observing the productivity of each worker at each point in time (i.e. output) because it provides an objective measure than those usually being assessed such as self-reported productivity, absenteeism, presenteeism, and health care cost. Besides, Bommer et al. (1995) also suggests not using subjective and objective measures of employee’s performance interchangeably. Although for most types of occupations, calculating output for a reasonable cost is impractical, it may be suitable for assembly line workers who perform relatively similar work content.
To our knowledge, no studies have investigated the impact of multimodal stress management individual intervention on physiological, psychological, and productivity in an occupational setting such as those in the manufacturing industry. Our study aimed to evaluate whether HRV biofeedback and work-life balance stress management intervention would reduce negative emotional symptoms which in turn enhance assembly line workers’ productivity (Tetrick and Winslow, 2015;Zheng et al., 2016).
2. METHODS
2.1 Participants
The participants were selected from the company’s preliminary survey, an electronic manufacturing company, located in Kuantan, Malaysia who self-reported extreme severity level of depression, anxiety, and stress. Because HRV measures differ between gender groups, we included only female assembly-line workers to reduce bias. Figure 1 displayed the flow of participant selection.
Eighteen female assembly-line workers participated in this study (age: Mean = 35.1±9.13 years; work experience: Mean = 15.7 ± 9.20 years). The majority of participants were married (61%), had 1-3 children (50%) and with family income (in Malaysian Ringgit, Mean = MYR 2212±770.5) All participants were provided a brief description of the study and signed informed consent before participation in the study. Approval to conduct this study was granted by the Research Committee of the University of Malaysia Pahang, Malaysia. Participant's demographic characteristics are summarized in Table 1.
2.2 Instrument and Apparatus
2.2.1 Depression, Anxiety, Stress Scale (DASS)
The Depression Anxiety Stress Scales (DASS) ques-tionnaire was administered to measure the states of self-perceived negative emotional symptoms (Lovibond and Lovibond, 1996). The scale consists of three factors (depression, anxiety, and stress) with 14 items each and is suitable for the non-clinical population. Subjects were asked to rate using a five-point severity scale ranging from 0 (Did not apply to me at all) to 4 (Applied to me very much, or most of the time) about the extent to which they have felt each state in the past week. The total score of each scale is calculated by adding the scores for the relevant scale. A higher score indicates a higher level of depression, anxiety, or stress. Table 2 displays the interpretation of the DASS based on the use of cut-off (percentile) scores, presenting severity ratings from ‘normal’ to ‘extremely severe’. We delivered the DASS in Bahasa Malaysia which has been empirically validated and is reliable among the Malaysian population with Cronbach’s α 0.84, 0.74, and 0.79 for depression, anxiety, and stress, respectively (Musa et al., 2007).
2.2.2 Em-Wave for HRV Measurement
The emWave Pro is a computer software program that collects pulse data obtained through a Photoplethysmography (PPG) sensor which is an accurate and reliable method of gathering and quantifying HRV through the surrogate measurement of pulse rate variability (Institute of Heart Math). Using a small amount of infrared light, PPG sensors transmit blood pulses in peripheral tissue (finger or earlobe) into blood volume changes, represented by respective HRV waves. The emWave software then assesses the coherence ratio information derived from heart rhythm patterns and power spectral density analysis of HRV. The coherence ratio is formulated as: “identifying the maximum peak in the 0.04 to the 0.26-hertz range of the HRV power spectrum, calculating the integral in a window 0.030 hertz wide, centered on the highest peak in that region and then calculating the total power of the entire spectrum” (McCraty, 2015, p. 28). The higher degree of coherence is represented by the more stable of the HRV amplitude, frequency, and shape of the wave. The coherence ratio meter provides feedback on coherence levels consisting of red (low), blue (medium), and green (high) (Figure 2).
Many researchers have found the relationship be-tween coherence biofeedback training with the aid of emWave Pro tool and improvement in cognitive, psychological, and physical performance both in a laboratory and natural settings (Zauszniewski et al., 2013;Berry et al., 2014;Field et al., 2018;Burch et al., 2019).
2.2.3 Resonant Breathing App
With the rapid development of smartphone technology, a “Paced Breathing” android-based application was introduced to subjects so they could use it not only during a session at the factory but also remotely or at home. This app was available for free which provides visual, audio, and haptic (vibrate) breathing cues to assists users to improve their breathing. In this study, we set the breathing pacer at 6 - 7 breaths per minute. After clicking on “Press to Start” to begin a session, a participant will hear a sound and simply follow the instructions on the screen, when to start inhaling and exhaling.
2.2.4 Productivity Measurement
In this study, the worker’s productivity on the manufacturing floor has been formulated based on the internal company’s study and being used for five years (see Equation 1). The measure is a function of many features: man per hour unit, output (derived from the cycle time), and attendance hour. Thus, it provides a more objective indicator than those usually being assessed in other empirical studies such as self-reported productivity, absenteeism, presenteeism, and health care cost. The use of productivity measures rather than cycle time was mainly anchored to its standardized measure since participants were from different department. Within-subject analysis was then performed to measure the extent of productivity im-provement.
2.2.5 Intervention Module Protocol
We developed a six-week multimodal stress man-agement program based upon the principles and practices of biofeedback and work-life balance (WLB) concept. The module would be delivered by the performance team who acted as the intervention facilitator. They were made up of members from various departments and equipped with some knowledge and skills to deliver the intervention based on the choice theory reality therapy approach (Wubbolding et al., 2004). This approach views behaviors as choices so people have the power to control themselves. In addition to monitoring biofeedback sessions toward subjects' acquisition of HRV coherence and resonant breathing skills, the facilitator team aimed to assist subjects to take responsibility for their behaviors and make better choices to achieve work-life balance. The foundation in the practice of this approach is the self-evaluation by subjects.
The biofeedback component used in this study was developed to teach subjects how to practice breathing at their resonant breathing around six breaths per minute and induce the coherence rhythm. The second component was the work-life balance (WLB) concept which covered three main aspects: personal management, work management, and religious practices (Islamic devoutness prayers), developed based on Bloom et al. (2009) and Burton (2004). Personal management balanced various aspects of self-management such as physical need, mental health, emotional and internal strength. Work management covered issues such as goal setting, scale priority identification, and time management. Religious practices consisted of Muslim devoutness spiritual practices including daily obligatory (fard 'ain) and optional (sunnah) prayers, recitation of the holy book Quran or du'a, and self-reflection (muhasabah). Spiritual well-being has been connected to better health and well-being and positively affects individual job performance (Jurkiewicz and Giacalone, 2004;Bodla and Ali, 2012). We provided to do list templates comprising of daily activities related to all aforemen-tioned aspects. Participants were asked to log their activities into the list and later discuss with the performance team at the subsequent session.
2.3 Experimental Procedure
The experiment lasted eight weeks with the intervention being performed six weeks, about 60 minutes per session for each subject. Pre- and post-test assessment was conducted at the first- and eight-week. The experiment was conducted in a specially designated place, called Happy Room, a quiet and well-controlled environment conducive to relaxation. The flow of the experimental procedure was displayed in Figure 3.
Before starting the intervention, a bonding session was conducted to get subjects to feel comfortable and familiar with the sessions and the performance team. They were then taught to use the emWave Pro system as well as learn the coherence and resonant biofeedback technique. The emWave sensor was placed on the right earlobe and the subject was told to sit quietly for a five-minute resting state and breathe normally to achieve standardized measurement and adaptation to the biofeedback training environment (Laborde et al., 2017). Each subject then was encouraged to breathe deeper and slower than normal following a coherence breath-pacer to assist them in achieving more regular breathing patterns (see Figure 4 above). All participants were encouraged to achieve a higher coherence ratio by filling the blue and green bars as much as possible during the training session, the baseline, and the assessment.
2.3.1 Training Session
The flow of training session during week two to seven was depicted in Figure 3. A rest break of two minutes was provided before starting a new session. The coherence and resonant biofeedback training were conducted three times each session week so participants were able to master the technique and developed a habit to use such a technique when combatting stress in their daily lives. They will be able to self-regulate - induced by their more coherent physiological state - thus enhancing their phys-ical, psychosocial well-being, and cognitive function (McCraty and Zayas, 2014).
During the orientation session, subjects were en-couraged to describe as frankly as possible their problems. Members of the team facilitated a discussion on the participant's needs especially for power or inner control. This session was focused on how participants could evaluate their behaviors and make plans for accomplishing their needs in order to change their lives. In the work-life balance assessment session, the performance team followed up all checklists collected by participants to ensure all problems or issues encountered during the previous week were properly monitored and resolved (Figure 4 below). Each week’s session was closed by reciting Islamic prayer.
2.4 Study Design and Data Analysis
In this study, we used a within-subject or repeated measure design, following recent guidelines on the HRV biofeedback intervention studies (Laborde et al., 2017). The benefits of measuring individuals at pre- and post-intervention reduce the errors associated with individual differences and the complex interactions influencing HRV. The independent variable was time at two levels: pre-and post-intervention.
The effects of biofeedback and WLB intervention training were assessed on eight dependent variables: one productivity variable (percentage of productivity), four physiological variables (coherence ratio and three levels of physiological coherence), and three emotional symp-toms variables (depression, anxiety, and stress). The analysis was performed by paired t-test or Wilcoxon signed-rank test if further data was found to be not distributed normally. Moreover, to investigate the relationship between all dependent variables and explore the possible underlying mechanism of the intervention outcomes, we would conduct repeated measures correlation (rmcorr). This technique is used to determine the overall common within-individual relationship among paired measures on two or more occasions which is robust to independence assumption in common regression or Pearson correlation analysis (Bakdash and Marusich, 2017). We reported rmcorr quantitatively using the rrm (i.e. coefficient correlation value with error degrees of freedom in parentheses) and p-value. The description of the rmcorr plot was also provided.
Data analysis was conducted using R statistical package and p < 0.05 were considered statistically significant. We also reported effect sizes, the most important outcome of empirical studies, which are objective and standardized measures of the magnitude of differences found before and after the intervention (Field et al., 2012).
3. RESULT
3.1 Pre-Processing Data
Normality tests using the Shapiro-Wilk test was per-formed for each data before deciding to use a paired-sample t-test or non-parametric Wilcoxon-signed rank test. All of the data distribution deviated from normality except productivity (all p-value < 0.05) and were then log-transformed with natural logarithm as recommended by Laborde et al. (2017). Because the new transformation of DASS data did not improve their normality, the analyses of respective data were carried out using the Wilcoxon-signed rank test (V statistic), an alternative to paired t-test.
3.1.1 The Effect of Intervention on Physiological, Psychological, and Productivity Outcome Measures
As summarized in Table 3, participants showed sig-nificant changes in each outcome measure following the Biofeedback-WLB stress management except for medium physiological coherence. Our findings reveal a significant improvement of coherence ratio, after log-transformed, from pre to post-intervention (t(17) = -4.76, p < 0.01) with a large effect size (r=0.76). All participants were able to generate more coherence states significantly as indicated by their predominantly shifting from red (low level) to green (high level) area (see Figure 5). No significant differences were observed between pre and post on medium coherence level (p = 0.16, r = 0.33). They also reported lower scores on depression (V = 11.5, p < 0.01), anxiety (V = 5, p < 0.01), and stress (V = 4, p < 0.01). Moreover, paired t-test statistics showed subjects had significantly increased productivity (t(17) = 13.07, p < 0.01, r = 0.95), following the intervention.
3.2 Repeated Measures Correlation Analysis
The results of repeated measures correlation (rmcorr) analysis across all outcome variables have been presented in Figure 6. A positive relationship between physiological coherence and productivity at the intra-individual level (rrm (17) = 0.69, p < 0.001) (Figure 6A) was found, indicating a given subject’s improved coherence was in line with her increased productivity. A negative strong relationship between productivity and each psychological measure was also found: depression (rrm (17) = -0.75, p < 0.01), anxiety (rrm (17) = -0.81, p < 0.01), and stress (rrm (17) = -0.71, p < 0.01). These suggest that a lower level of negative emotional symptoms could predict greater productivity. Moreover, we also found the occurrence of coherence state was associated in the negative direction with the level of depression (rrm (17) = -0.53, p < 0.05), anxiety (rrm (17) = -0.64, p<0.01), and stress (rrm (17) = -0.58, p < 0.01).
4. DISCUSSION
We found that multimodal stress management intervention using HRV biofeedback and work-life balance approach had significant impacts on improving physiological response, reducing stress, and increasing worker’s productivity. Our large effect sizes (r > 0.5) result suggest that the effects are important in practical terms, accounting more than 25% of the variance Field et al. (2012). The productivity enhancement is also aligned with improved physiological coherence and reduced self-perceived negative emotional symptoms.
These psychological and physiological outcome measures are consistent with previous studies in clinical settings (Siepmann et al., 2008;Hallman et al., 2011;Gevirtz, 2013). This finding also supports a recent meta-analysis on the effect of HRV biofeedback training in treating people with stress and anxiety which also found a large effect size both in the within-group or between-group analysis. Goessl et al. (2017), suggesting the effi-cacy and its robustness across various treatment condi-tions and patient characteristics. The similar effects were also revealed among healthy individuals from a variety of professions such as athletes, university students, veterans, and military personnel (Whited et al., 2014;Chaló et al., 2017;Morgan and Mora, 2017;Jacobs, 2018). Because very few studies on biofeedback stress management intervention have been evaluated on productivity measures, we compared our findings with previous biofeedback studies that used rather similar-related work performance indices such as self-reported productivity measure, and cognitive functioning. The results of providing self-regulation skills combined with heart-rhythm coherence training showed significant improvement in self-report productivity and reduction of stress among health-care workers (McCraty, 2015), and correctional officers (McCraty et al., 2003). A prior study also found some significantly improved cognitive functions, indexed by response time and sustained attention in the treatment group of industrial workers after eight sessions of reso-nant biofeedback training (Sutarto et al., 2012).
A possible explanation for how increased HRV co-herence resulted from biofeedback training correlates with less depression, anxiety, and stress, and improved work performance has been explained by Lehrer and Gevirtz (2014) and McCraty (2015). The resonant breathing and psychophysiological coherence generated an increase of RSA and apparently normalized autonomic regulation due to the strengthening of homeostasis – internal stability and balance – in the baroreceptor (Vaschillo et al., 2006). The baroreceptors have effects on the brain, mediating cardiovascular influences on the central nervous system (CNS). The changes in baroreceptor, either activation or inhibition, involve modulation of CNS structures with implications for the brain’s information processing capabilities. These may have been responsible for the substantial enhancement in the subject’s cognitive performance related to work performance; such as focused attention, working memory, cognitive flexibility; along with better emotional self-regulation (Sutarto et al., 2013;McCraty and Zayas, 2014;McCraty, 2015).
Since researchers are still not in agreement in which stress-management intervention is the most effective due to the great variety of stress management programs and outcome variables (Tetrick and Winslow, 2015), hence, we developed a combination of multiple types of individual-level interventions to increase its effectiveness. Our research was one of the very few studies that examined the impact of stress intervention on multiple outcomes, thus allowing us to evaluate factors that may account for its effectiveness. This is also the first study that used repeated measures correlation analysis to explore the relationship between all the outcome measures within a participant. Our findings showed a positive strong relationship between physiological and productivity (rrm = 0.69) and negative medium to strong relationship between physiological and productivity as well as physiological and subjective psychological measures (0.53 < rrm < 0.81).
Nevertheless, these findings should be interpreted cautiously because there are very few studies of the effect of biofeedback-based stress management intervention on various subjective and objective measures thus the causal relationships between all outcome measures could not be inferred. A biofeedback study in grandmothers raising children reported a significant correlation between self-reported measures of stress, negative emotions, and the HRV coherence (Zauszniewski et al., 2013). On the opposite, Behrmann (2015) did not find an association between HRV parameters and self-reported productivity as measured by the work ability index. Furthermore, a recent systematic review (Yu et al., 2018) showed the effectiveness of biofeedback application in stress man-agement while De Witte et al. (2019) presented incon-sistent evidence about the association between psychological and physiological outcome domains following the biofeedback stress management intervention. Thus, it remains unclear whether physiological changes, evoked by biofeedback interventions, are required for psychological and productivity effects.
Limitations of this study include lack of an active control group, employing a single group with pre- and post-test study design in a particular work setting. The significant differences reported in all the outcomes variables, hence, might reflect to some sort the Hawthorne effect since individual differences and preferences can influence effectiveness through effects on motivation, engagement, and compliance. Such effects are difficult to avoid in commonly biofeedback or other behavioral intervention approaches (Shellenberger and Green, 1986;De Witte et al., 2019). To reduce the extent of such effects, at week 1, before the intervention, subjects were taught to learn the coherence and resonant breathing skills and use the device to familiarize themselves with the observers and the intervention environment. Moreover, we used a variety of outcome measures, not only a self-reported psychological measure but also objectively measures: physiology and productivity. When reporting the distress levels, the participants might be aware of being studied, however, manipulating their productivity was relatively difficult because the productivity formula has been developed based on several features (see Equation 1) and implemented for several years. Thus, real behaviors of subjects could be observed. As the target population of this study was those who had extreme levels of self-reported distress, our lack of sample size (18 subjects) hindered us from allocating subjects randomly into the treatment and control groups. We decided not to assign a control group from low-stress subjects because individuals with less severe problems might show less room for improvement on the outcome variables (e.g., HRV) when being compared to those with higher severe problems (De Witte et al., 2019). Further research needs to dichotomize each DASS sub-scale in normal/mild/moderate and severe/ extreme scores to gain a larger sample size (Pasqualucci et al., 2019). Therefore, a threat to internal validity (i.e. the Hawthorne effect) can be eliminated by a randomized control trial with repeated-measures assessment across all sessions and prospective design
Furthermore, there is also a possibility that well-controlled subjects might mediate our results because all subjects were able to acquire a specific breathing skill to some extent using a particular device at the end of the intervention. The main goal of biofeedback training is to teach subjects to increase their HRV (coherence ratio) using some strategies (e.g. inducing positive emotions and resonant breathing) to get the most psychological and other performance outcomes benefits. Without specific strategies, subjects are less likely to increase their HRV significantly (Paul and Garg, 2012;Whited et al., 2014). Therefore, well-controlled subjects, as measured by their increased physiological index, might intervene or mediate the interaction between their reduced negative emotional symptoms and improved productivity. However, we could not further evaluate this mediator model statistically because of our lack of sample and study design limitations. Future research is clearly needed to address this question.
Meanwhile, although there is good evidence that this intervention appears to be effective for improving productivity, its value (mean = 77%) remained below the average productivity of the organization which accounted for 80%, implying a need to refine the intervention module content. Upcoming research should address a more permanent effect by monitoring how the participants incorporate the skill they learned to their daily lives as well as conducting reassessment several weeks after the intervention termination.
Furthermore, in this study, we emphasized only the stress outcomes rather than possible stressors such as marital status, family size, and family income. Our experimental design with small sample size, however, restricted us to develop a model that explores to what extent the impact of stressors when being compared with the effect of stress outcomes on productivity (Donald et al., 2005).
Despite these limitations, our results suggest that a multimodal stress management approach using HRV biofeedback and work-life balance concept is a promising intervention for improving worker’s performance and well-being. This type of intervention requires support from all levels of management - particularly their supervisors (e.g. assembly-line leaders) and co-workers. Previous studies found that employees who had a higher risk of depressive symptoms reported high levels of stress in lacking social support from co-workers and supervisors (Edimansyah et al., 2008;Yang et al., 2016;Hämmig, 2017).
For the practical implications, it is worthwhile to implement a multimodal stress management intervention as a part of the organizational health and employee well-being program. Modifying the technique according to the type of organization as well as the organizational and social culture should be considered. While determining the productivity of line workers with a routine task is relatively straightforward but of knowledge-intensive or non-routine professions performance is more difficult to assess. The organiza-tion, in conjunction with researchers, should select the right performance measurement by considering the following set properties: objectivity, availability, comparability, and quality and controllability (Sauermann, 2016).
5. CONCLUSION
In summary, our six-week multimodal stress inter-vention based on biofeedback and work-life balance improved well-being and productivity in high-level stress female assembly line workers. The interrelationship among the cardiac coherence, psychological well-being, and productivity was also found to be significant that might explain the possible mechanism of the intervention in reducing negative-emotional symptoms and improving productivity. Further investigation should utilize a more rigorous experimental design: control group, larger sample size, and longitudinal observation, which would allow us to attribute changes to the intervention.
