Introduction

Work-related activity during non-work time is a type of demand that consume personal energy resources and consequently influences fatigue recovery [1]. Fatigue and recovery are closely related concepts [2, 3]. On one hand, fatigue refers to the state of reduced capacity, either physically or psychologically, to perform work-related activity as a result of depleted personal energy resources [2, 3]. On the other hand, recovery refers to the unwinding of fatigue through energy restoration to its pre-demand level in the absence of demand such as work-related activity [2–6]. Central to the concept of recovery is the idea that employees need breaks from the work demand to recover from effort-driven work-related fatigue in order to function optimally on the next working day [7, 8]. Based on the effort-recovery model and conservation of resources theory, inadequate recovery will cause fatigue accumulation and subsequently commence a cycle of accumulated fatigue which eventually lead to irreversible fatigue [3, 9, 10].

Intershift activity, or activity during non-work period, influences recovery [11]. Generally, intershift activities were classified into work-related activity and other non-work activities such as physical exercise, social activity, household chores, social interaction with family, hobby or creative activity. Sonnentag conceptualized work-related activity during non-work time as high duty, that is, activity that consumes personal energy resources and impedes recovery [11]. Demerouti et al. listed work-related activity as one of the intershift activities that potentially inhibits recovery [12]. Matthews et al. extend the discussion on work-related activities during non-work time by conceptualizing it as interdomain transition, that is, home-to-work or home-to-family transition [13]. It refers to the frequency of behavioral action of role shifting made from home domain to work domain during non-work period [13]. For example, during non-work period, individual should assumes family role at home domain; however, due to some circumstances, he/she has shifts his/her role to employee role at work domain during non-work time. Higher frequency of work-related activities exerts more efforts [9] and drain more energy resources which consequently hinder recovery [14]. Apart from that, multiple studies specifically focus on the use of communication technology such as smartphones for work-related purposes during non-work time [15–19]. In those studies, the measures include the usage, duration, and frequency of work-related smartphone use during non-work time [15–19]. As far as we concern, there is limited study on work-related activities during non-work time specifically among doctors population despite multiple available studies on fatigue and its consequences among this fatigue-prone population.

Doctors are among the main workforce in health service delivery; and their nature of work exposed them to various fatigue-prone factors at work such as demanding job and long work hours [20]. It is thus not surprising that the prevalence of fatigue among doctors is worrying. For instance, in Saudi Arabia, Arab & Khayyat found that 54% of 102 Anesthesia residents were at risk of fatigue [21]. In the United Kingdom (UK), 84.2% of 2,231 Anesthesia trainees were in fatigue state during post-night shift [22]. Recently, a national survey of consultant anesthetists and pediatric intensivists in the UK and Ireland found that 91% of them reported having work-related fatigue [23]. Additionally, in China, Tang et al. found that 78.8% of 1,729 full-time doctors at 24 tertiary hospitals had high level of occupational fatigue [24]. A study among intensive care unit doctors providing 24-hour resident cover on a 12-hour day and 12-hour night shift roster found that they experience high level of fatigue in both day and night shift; however, the night shift lead to worsening fatigue [25].

High prevalence of fatigue among doctors is a major occupational issue that adversely affect the doctors, patients, and organization. Fatigue doctor is at high risk of commuting accident [26] and work-related accident such as needlestick injury [27]. Fatigue doctors may jeopardize patients’ care such as decision fatigue [28] and serious diagnostic errors [29]. Other fatigue consequences include medication errors, adverse health and wellbeing, work-life dissatisfaction, low quality of life, job dissatisfaction, poor cognitive and poor skill performance [30]. The inadequate unwinding of post-work fatigue, or lack of fatigue recovery, during non-work period cause the doctors to work on the subsequent shift with fatigue residue [31] and put them at risk of fatigue consequences [30]. Moreover, if the recovery process during non-work time is persistently inadequate, fatigue may accumulate, and consequently progresses into non-reversible chronic fatigue [32].

It is therefore crucial to ensure adequate recovery during non-work period as it is significantly related with fatigue. Recovery can be classified into process and outcome [6, 33]. Recovery process refers to the activities (such as work-related activities, household activities, social activities) and experiences (such as psychological detachment from work, relaxation, mastery) [6, 11, 33] that may lead to a change in functioning and strain level. Recovery as an outcome implies an employee’s psychophysiological state that is reached after a recovery period such as state of recovery, work engagement, cortisol level, and proactive behavior [6, 33]. We will focus on recovery as a process, and specifically on the activities during non-work period. In general, studies shown that work-related activities hamper recovery process [6, 33].

The spillover of the work-related demand into home domain during non-work time potentially disrupts recovery experiences of psychological detachment from work [11]. A two-wave panel study among 230 healthcare workers at three general hospitals in Netherland found that those who were involved in work-related activities during off-job hours reported being less able to psychologically detach from work [32]. Recent study found that work-related smartphones use during non-work time was negatively associated with psychological detachment from work [15]. This was also supported by previous researches which found significant negative relationship between the usage, duration and frequency of work-related smartphone use during non-work time with psychological detachment from work [16–19]. However, limited study was found to specify the type and context of work-related smartphone use, whether it is an unanticipated command from superior, or unwelcoming informal discussion with colleague. Work-related ruminative thoughts, such as thoughts on the incident of being violated at work, mistakes being done at work, patient-related conditions, and task-related aspects may as well hamper the recovery process during non-work time [20, 34–36]. Adverse events at work such as violence is highly prevalent among healthcare workers [37–39] and is associated with intrusive thoughts [40]. Other work-related activities include pressures related to unfinished or forthcoming task [20].

Measurement for work-related activities during non-work time is mostly unspecific. Most of the available instruments measure time spent for work-related activity or frequency of involvement in work-related activity during non-work time by cumulating it as one single indicator. For instance, Sonnentag measures work-related activity by asking the participants to report in diary the time they start and end the activity such as finishing or preparing for work duties and use the cumulative time spent as an indicator [11]. Similarly, Garrick et al. measures work-related activities during non-work time by defining it as hours spent on school-related tasks outside of paid working hours such as lesson preparation and assignment marking [41]. In contrast, Matthews et al. develop and tested a sub-scale of home-to-work transition among heterogenous group of workers (i.e. management, business, financial operations, administrative, etc.) that measure the frequency of work-related activities during non-work time using 6-item 7-point Likert scale such as receiving calls from co-workers while at home or changing plans with family to meet work-related responsibilities activities [13]. Nevertheless, the existing instruments did not examine the dimensions of each work-related activities which may exert different effect against recovery process. In addition, despite extensive research on the work-related activities during non-work time among employees, little is known about work-related activities being done by healthcare workers, particularly doctors.

As doctors’ duty involved high-intensity job demand and fatigue-prone schedule with potential adverse fatigue consequences, it is crucial to examine their work-related activities during non-work time. Therefore, this study aims to develop and validate the questionnaires that measures the frequency of work-related activities during non-work time among doctors in Malaysia.

Materials and methods

Results

Content validation

Initial pool consisted of 30 items were content-validated by three occupational health experts. 23 items had I-CVI and CVR of 1.00. The I-CVI for another 7 items ranged between 0.00 and 0.67, while their CVR ranged between -1.00 to 0.33. The S-CVI for relevance of this 30-item pool were 0.844. Initial pool of 30 items was then reduced to 23 items which demonstrate I-CVI and CVR of 1.00 and resulting in S-CVI of 1.00. Following interview with fourteen medical doctors, re-wording for some items based on feedback were done to ensure comprehensibility. Table 1 presented initial scales containing 23 items which were classified based on the types of activity (work-related thoughts, task, communication), medium (psychological, physical, virtual) and interaction groups (superior, colleague, clients, family, self).

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Table 1. Initial scale with 23 items.

https://doi.org/10.1371/journal.pone.0241577.t001

Construct validation

Descriptive analysis.

The response rate was 83.04% (n = 382). The mean age of participants was 31.21 (SD = 3.488) years with average work tenure as doctors of 4.60 (SD = 3.075) years. The majority of them were female (n = 242, 63.4%) and married (n = 253, 66.2%). Most of the participants had no children (n = 206, 53.9%), followed by one children (n = 89, 23.3%), and two children (n = 62, 16.2%). All of them reported using WhatsApp as digital work-related communication medium. 38.2% (n = 146) also used e-mail as second medium for work-related digital communication.

Table 2 listed 23 work-related activities during non-work time, which were ranked based on the mean score. Thought-related activities during non-work time dominated the top sixth ranking, which indicates frequency of at least once a week.

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Table 2. Work-related activities during non-work time.

https://doi.org/10.1371/journal.pone.0241577.t002

Exploratory factor analysis.

Bivariate correlation matrix showed none of the pairs had bivariate correlation scores greater than 0.8; thus, no item was removed. An EFA was then conducted on this initial 23-item scale by using a PAF technique with a Varimax rotation. First observation showed that KMO test was 0.803 with significant Bartlett’s Test, and one item with communality less than 0.2 (i.e. ‘locum at private health facilities’) was removed. Re-observation showed that KMO test was 0.806 with significant Bartlett’s Test, and one item with communality less than 0.2 (i.e. ‘locum at government health facilities’) was removed. Next re-observation showed that KMO test was 0.809 with significant Bartlett’s Test, and one item with communality less than 0.2 (i.e. ‘attending work-related upskill training at workplace’) was removed. Another re-observation showed that KMO was 0.808 with significant Bartlett’s Test, and no item had communality less than 0.2. This led to a solution comprising of six factors, in which three factors have loadings of 0.4 on at least 3 items, while another three factors have only two items with loadings of 0.4, and several items in the rotated factor matrix cross loaded on more than one factor. These cross loaded items were removed in turn, starting with the item with the highest ratio of loadings on the most variables with the lowest highest loading. First item being removed was ‘doing official task at home’ which loaded on three factors with factor loading of 0.334, 0.383 and 0.354. Re-observation led to six factors solution. Items with highest factor components much less than 0.5 were then removed in turn starting with lowest factor loading i.e. ‘virtual communication with patients/clients’ (factor loading: 0.365). Re-observation was done, and another factor component with factor less than 0.5 was removed i.e. ‘handling work-related email / website / social media’ (factor loading: 0.470). This eventually yielded a solution with 17 variables with six factors (Table 3). Two factors have at least three items with a loading greater than 0.5, but another four factors only have two items.

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Table 3. The improved six-factor model.

https://doi.org/10.1371/journal.pone.0241577.t003

Table 4 showed the result of further testing for different fixed-factor model using forced-factor extraction method, and four-factor model was identified as the most stable.

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Table 4. n-factor model.

https://doi.org/10.1371/journal.pone.0241577.t004

We decided to use the four-factor model. Subsequent PCA without rotation on each single factor was done (Table 5). PCA for each single factor fulfilled the following criteria: (a) correlation matrix determinant (<0.0001), (b) KMO statistic (> 0.5), (c) Bartlett’s test of sphericity (significant), (d) communality <0.2 (none), (e) total variance explained (>60.00%), (f) variables loading factors >0.4 (all items), and (g) number of items with loading >0.4 per factors (all factors have >3 items with loading >0.4).

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Table 5. PCA analysis on each single factor of four-factor model with factor loadings.

https://doi.org/10.1371/journal.pone.0241577.t005

A reliability analysis showed that all four factors were considered acceptable as each had at least three component scores greater than 0.7 (Table 6).

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Table 6. Final four-factor model with reliability analysis.

https://doi.org/10.1371/journal.pone.0241577.t006

Discussion

We established a valid and reliable 17-items four-factor scale specifically for doctors. The top 13 activities, based on ranking that was set according to mean, were statistically-selected by EFA into these 17-items scale, while bottom two items were statistically-removed. All these 17 items were statistically-grouped into four latent constructs, which are labelled as ‘work-related thought’, ‘work-to-home conversation’, ‘task spillover’, and ‘superior-subordinate communication’. By identifying latent construct of WANTS, researchers may be able to quantify frequency for each construct activities, determine their association with health outcome and planning the intervention.

The items were generated by combining inductive and deductive technique, and involving target population, expert judgment and authors’ own experiences. These ensure optimum coverage with appropriate relevance of context [44]. The use of EFA for exploratory construct validation which assumes that any indicator or variable may be associated with any factor is appropriate [44, 58]. The use of EFA to assess the underlying factor structure and refine the item pool is recommended as a precursor to CFA [44, 46, 58, 59]. As the name suggested, it is not based on any prior theory, but solely depends on the statistical test. Thus, it may or may not been fully explainable by current available theory. Based on EFA, we able to obtain four latent construct which is fully explainable.

‘Work-related thought’ is broadly defined as repetitive and intrusive thinking in response to negative moods or life situations related to work [66]. As far as our knowledge, there is limited similar construct of work-related thought existed in the current prevailing literature. We acknowledged the presence of construct measuring work rumination as psychological activity [67–70], however, it does not cover all type of work-related activities aspects during non-work time such as work-related conversation or physical work task. Most of thought, or rumination, measurement instruments also mostly developed to measure its association with affect such as depression, anxiety, and phobia [67–70], but very limited on fatigue and recovery. This gap in the work-related thought literature limits the ability of researchers to understand the role that work-related thinking plays during intershift period in fatigue recovery particularly among doctors. Through the development of a valid and reliable measure of WANTS, the research in this area can be extended to provide greater clarity on the ways work-related thought is related to recovery.

‘Work-to-home conversation’ is not uncommon, in which people often bring their work-related issues from one domain into the other [71]. It is a type of work-to-family spillover, which generally can occurs when behaviors, moods, stress, and emotions from work are transferred to the family domain [72]. Grzywacz & Marks indicate that there are four distinct work-family spillover experiences: negative and positive spillover from work to family and family to work [73]. There is limited study to indicate whether work-to-home conversation is a negative or positive work-related spillover, particularly among doctors. The role of content or context or conversation may play a role, however we did not specify them into this scale; instead, we just generalized it as work-to-home conversation. Thus, it may be necessary to reassess the indication to specify the content and context, however it need trade off with lengthy questionnaires.

‘Task spillover’ refers to work-related task spillover from work hour’s domain into off-job hour’s domain, either at workplace or at home, either physically present or through virtual means, and either involve directly or indirectly person at work such as colleague and clients or patients [74–76]. For example, ‘doing work task at workplace’ during off-job hours is not uncommon. The scheduling system with lack of overlapping hours for shift handover push the doctors to work extra hours unpaid to ensure proper shift handover and safety of the patients. In addition, doctors are expected to welcome all the patients or clients until end of shift without any buffer hour to settle the pending task. For example, supposed the doctors finish their job on-schedule at 1700H, but the clients came at 1650H; the doctors have to attend to this patient comprehensively from history taking, physical examination, lab or imaging investigation, and others, due to lack of overlapping hours for shift handover. Similarly, after handover the patients including the newly-came patients, their colleague may call or text them to clarify the cases or update the cases. Consequently, the doctors are doing extra unpaid job either physically or virtually, which consume their internal resources, and put them at high risk of fatigue [10, 77]. Additionally, their off-job hours are being cut indirectly, and put them at risk of lack of recovery [10, 77].

‘Superior-subordinate communication’ refers to the interactions between organizational leaders such as medical specialist or administrative people with their subordinates, either virtually or physically present at workplace, to achieve personal and organizational goals [78]. Communication can be solely transmission, solely reception, or exchange of messages between superior and subordinate [79]. This downward communication type is used to coordinate efforts and activities, to instruct, to direct, or to explain company decisions [79]. Subordinates react most effectively to those matters that they judge to be of greatest personal interest to the boss [80]. Thus, it is not uncommon that subordinate responds to their superior during off-job hours and predispose them to work-related activities.

This newly-developed instrument has several similarity and differences with previously-developed instruments. This instrument quantifies the work-related activities during non-work time based on the frequency, which is similar with multiple instruments in previous studies [11, 13, 41]. In addition, this instrument covers both activities related to psychological and behavioral aspect of recovery process during non-work time [13]. This coverage is also consistent with the concept of interdomain transition that highlighted the transition of physical and cognitive from home domain to work domain during non-work time [13]. Despite some similarities, the advantage of this instrument is on its differences with previously-developed instruments. First, this instrument combines three aspects of work-related activities namely types of activity (i.e. work-related thoughts [12, 34–36, 63], task [11, 12, 20, 41, 64], communication [15–19, 63]), medium (i.e. psychological [12, 34–36, 63], physical [11, 12, 20, 41, 64], virtual [15–19, 63]) and interaction groups (i.e. superior, colleague, clients, family, self) before statistically determine and validate its latent construct. Second, this instrument distinguished four latent constructs of work-related activities during non-work time i.e. work-related thoughts, work-to-home conversation, task spillover, and superior-subordinate communication. Third, this instrument is developed specifically for medical doctors whom their intershift recovery is rarely explored despite high risk of having fatigue from multiple work exposure. Nevertheless, this instrument still needs further refinement and validation among target population in different workplace and geographical setting.

There are several limitations in our study. First, this is the first and only exploratory validation conducted on this newly-developed instrument; thus, it is not yet ready to be used to test a theory. Qualitative studies involving focus group discussion, in-depth interview or diary documentation among target population should be considered to ensure exhaustive list of work-related activities during non-work time is established. Second, study location involved hospitals in urban setting, which may significantly differ from rural or suburban setting which may influence type of work-related activities during non-work time. This scale should be tested on similar target population, but different work and home environment, including suburban and rural setting. Third, the conduct of item generation among medical doctors only from one public hospital may limit the number of unique items generated as participants from same centre may experience similar work-related activities during non-work time. However, effort to ensure heterogeneity was done by purposively selecting different gender and seniority from each clinical discipline from that one hospital.

Conclusions

The development of WANTS enabled the measurement of work-related activities during non-work time among doctors using a single close-ended Likert scale that includes four identifiable domains, namely work-related thoughts, work-to-home conversation, task spillover, and superior-subordinate communication. WANTS is found to be content- and constructively valid and reliable. This custom-designed scale for measuring work-related activities during non-work time is beneficial for research purposes and organizational practices. On one hand, researcher could quantify work-related activities during non-work time based on specific latent construct instead of using a single-item general work-related intershift activity involvement or listing each item independently which cause redundancy. On the other hand, organization could determine which aspect of work factors that spillover into the home domain and manifested as work-related activities during non-work time, and further planning the organizational intervention on work-home interface. However, further assessment is still needed to refine the construct among medical doctors in different setting.

Supporting information

Acknowledgments

This study was carried out in collaboration with Occupational & Environmental Health Unit, Selangor State Health Department and Department of Community Health, Faculty of Medicine, Universiti Kebangsaan Malaysia Medical Centre. We would like to thank them for their support and assistance in this study.