The database search identified 1557 papers of which 21 met the inclusion criteria. Two additional studies were included following a reference list search of the articles which met the inclusion criteria, making a total of 23 studies. The primary reason for exclusion was the study did not include a health outcome. The PRISMA diagram outlines the screening process (see Fig. 1). The studies represented 10 countries (USA, UK, Australia, New Zealand, Japan, Belgium, South Africa, Brazil, Germany, The Netherlands), and varied in study design: 20 cross sectional, one cohort, one controlled before and after, and one combined cross sectional and cohort (refer Table 1). No randomised trials were identified. Studies included 19 quantitative, 3 qualitative and 1 mixed methods.
Table 1 Study characteristics Studies were conducted in the following industry sectors: government departments and agencies (five), financial services (three), technology (two), academia (one), telecommunications (one), logistics (one). Ten studies used data from surveys of the general public or did not focus on a particular industry sector. The number of hours and nature of WAH arrangements varied between studies; participants WAH either full time (two studies [21, 36] or part-time, and had access to a formal WAH policy or ad hoc WAH approval by managers. Only one study examined employees undertaking mandatory WAH [36]. Some studies did not specify the nature of the WAH arrangements. Due to the heterogenous nature of the studies, it was not possible to conduct a meta-analysis.
Health related outcomes
Physical health related outcomes (n = 3) identified in the studies included: pain, self-reported health and perceived safety. Mental health related outcomes (n = 7) identified included: well-being, stress, depression, fatigue, quality of life, strain and happiness. Seven studies undertook separate gender analysis (see Table 2).
Table 2 Summary of studies by health outcome Risk of bias
Following assessment of risk of bias, quantitative studies were rated as: four high risk, three moderate risk, and 13 low risk. For the qualitative studies (n = 3) the overall risk of bias for all studies was assessed as moderate. The four studies with high risk of bias included cross sectional surveys [18, 22, 26, 31]. For the cohort studies, quantitative [29], qualitative [36] and mixed methods [39] were utilised, with moderate and low risk of bias, respectively (see Tables 3 and 4).
Table 3 Quality assessment of quantitative studies Table 4 Quality assessment of qualitative studies Physical health-related impacts
Three studies explored the physical health impacts of WAH [22, 23, 32]; one of these will be discussed in the section on gender. Filardí [22] surveyed government employees who reported that ‘I feel safer working from home’, but the WAH arrangements were not clearly defined. In contrast, a study by Nijp et al. [32] found WAH had a negative impact on physical health. This study measured self-reported health in a control and an intervention group of finance company employees, before and after implementation of a policy to enable part-time WAH. Participants reported a small but statistically significant decrease in self-reported health which could not be explained as usual health indicators and job demands remained unchanged.
Mental health-related impacts
The majority of studies (21 studies) explored the effect of working at home on mental health. Fourteen are explored in this section and seven studies that included a gender analysis are presented separately.
The impacts of WAH on mental health were complex. Nine studies considered environmental, organisational, physical, or psychosocial factors in the relationship between WAH and mental health [18, 20, 21, 24, 25, 31, 33, 35, 38]. Working at home could have negative or positive impacts, depending on various systemic moderators such as: the demands of the home environment, level of organisational support, and social connections external to work.
Five studies [20, 25, 33, 35, 38] examined the influence of colleagues and organisational support on WAH. Suh & Less [35] compared the effect of technostress (defined as work overload, invasion of privacy, and role ambiguity) on IT company employees doing low intensity WAH (< 2.5 days per week), to those doing high intensity WAH (> 2.5 days per week). Low intensity WAH employees experienced higher strain associated with work overload and invasion of privacy, related to IT complexity, pace of IT change, lower job autonomy, and being constantly in electronic contact with work. Bentley et al. [20] explored the influence of organisational (social and manager) support on health outcomes of WAH employees and found a similar relationship between lower levels of organisational support and higher psychological strain. Sardeshmukh et al. [33] also examined the effects of organisational support (via job resources and demands) and found associations between WAH and less time pressure, less role conflict, and greater autonomy, resulting in less exhaustion. However, they also found WAH was associated with lower social support, lower feedback and greater role ambiguity which increased exhaustion; overall these negative effects did not outweigh the overall positive impact of WAH. Vander Elst et al. [38] found increased WAH hours were associated with less emotional exhaustion and cognitive stress which was mediated by support from colleagues. Those working more days at home experienced greater emotional exhaustion and cognitive stress associated with reduced social support from their colleagues. Grant et al. [25] interviewed employees WAH and identified colleagues’ support and communication as important influences on psychological well-being. Tietze et al. [36] interviewed seven employees WAH on a full-time basis as part of a three-month pilot scheme. Employees reported an improved sense of personal well-being as they were no longer in a stressful office environment.
Anderson [18] measured the effect of WAH on the mental well-being of government employees (all participants were WAH > 1 day per fortnight), and found WAH had a positive effect on well-being (feeling at ease, grateful, enthusiastic, happy, and proud) with less negative effect on well-being (bored, frustrated, angry, anxious, and fatigued). The study also found individual traits of openness to experience, lower rumination, and greater social connectedness moderated the relationship between WAH and positive well-being, and a strong level of social connectedness (outside of work) was related to a less negative effect on well-being.
Two studies explored the home environment as a mediator for the relationship between WAH and health related outcomes. Work-family conflict (WFC) occurs when the demands of work impinge on domestic and family commitments. Golden’s [24] study of computer company employees who were WAH for greater periods of time than in the office, found high levels of exhaustion when combined with a high level of WFC. When WFC was low the same employees experienced a low level of exhaustion compared to those WAH only occasionally. Another study [31], which surveyed employees with dependent-care responsibilities, found an association between WAH and increased energy levels, and decreased stress; WAH acted as a mediator between health-related outcomes and dependent care responsibilities.
Relationships between WAH and the following mental health-related outcomes were examined: stress [8, 19, 21,22,23, 26, 28,29,30,31, 34, 37, 38], quality of life [22, 27, 37], well-being [18, 19, 25, 36, 38], and depression [8]. Five studies [19, 26, 28, 31, 37], reported a decrease in stress levels of employees WAH on a part-time basis. One study [8] explored employees who were WAH either all or part of their work time and found no direct relationship between WAH and levels of stress. In contrast, VanderElst et al. [38] found WAH was associated with increased stress. Quality of life was enhanced through WAH in two surveys of employees [22, 37]. Filardí et al. [22] included public sector employees but did not report how long employees were WAH. Tustin [37] included university employees who were WAH for some of the week.
Bosua et al. [19] studied employees from government, education and private sectors WAH for some of their week and found a greater sense of well-being was reported compared to when working in the office. Of note, participants reported their preference was to combine WAH with some office time so they could connect with colleagues.
Henke et al. [8] conducted a study within a financial company and compared employees WAH to those not WAH; those WAH less than 8 h per month had statistically lower levels of depression than those not WAH. No statistically significant relationships were identified between depression and greater number of hours WAH.
Four studies examined the direct relationship impact of WAH on fatigue (including exhaustion, tiredness or changes in energy levels) with mixed results [28, 31, 32, 37]. Two studies [31, 37] concluded WAH resulted in decreased levels of fatigue. However, others [28, 32] concluded WAH had no effect on levels of fatigue.
The gender differences in health outcomes related to WAH
Seven studies examined outcomes by gender [21, 23, 27, 29, 30, 34, 39]. Three studies considered complex interactions when examining gender differences in the WAH and health related outcome relationship. Windelar et al. [39] examined the effect of interpersonal and external interactions on work exhaustion, using WAH as a moderator. They surveyed employees pre and post implementation of a formal WAH policy (study 1) and then compared employees WAH to those based in the office (study 2). Males had higher levels of work exhaustion following the commencement of telework (study 1). Both studies found WAH increased the negative effect of interactions external to the business on work exhaustion. Females WAH reported higher levels of work exhaustion compared to their colleagues who remained at the office (Study 2). Hornung et al. [27] examined the role of mediators on the relationship between WAH and mental health and gender differences; they surveyed public servants and found increased time WAH improved quality of life through increased autonomy (mediator). However, in a separate gender analysis the relationship was only significant for males. Eddleston & Mulki [21] reported an increase in job stress for employees WAH full-time. This was mediated by WFC; an inability to disengage from work, and the integration of work into home life, led to higher WFC which was associated with higher job stress. This relationship was moderated by gender with women experiencing greater WFC due to inability to disengage from work, and men experiencing greater WFC due to integration of work into the family domain.
The remaining four studies examined the direct relationship between WAH and health outcomes. Two studies, both using data from the American Time Use Survey, examined physical and mental health outcomes by gender. Gimenez-Nadal et al. [23] identified participants WAH as those who indicated non-commute days in a diary record. Diary records were followed by a well-being survey, where male teleworkers reported lower pain levels, lower stress, and lower tiredness (p < 0.05) compared to non-teleworkers; no differences were found between female teleworkers and non-teleworkers. Song & Gao [34] compared subjective pain when WAH to work at the office, by gender and parental status, and reported no differences. However, fathers who were WAH reported increased stress, and mothers WAH had decreased happiness.
Kim et al. [30] and Kazekami [29] examined the direct relationship between fatigue, stress and happiness. Kim et al. [30] reported males who were WAH regularly had lower levels of fatigue and stress compared to those who did not. For women, WAH was associated with lower stress levels but higher levels of fatigue compared to those not WAH. Kazekami [29] found that males WAH reported increased stress and happiness whilst no effect was found for females.