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30 September 2026

22 Pages

Sustainable Remote Work Practices: Examining Engagement, Technological Demands, and Work–Family Conflict

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Department of Human Sciences, European University of Rome, 00163 Rome, Italy
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Business@Health Laboratory, European University of Rome, 00163 Rome, Italy
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Author to whom correspondence should be addressed.
Sustainability2026, 18(19), 9985;https://doi.org/10.3390/su18199985
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Abstract

The widespread adoption of remote and hybrid work has profoundly reshaped organizational processes, offering greater flexibility and autonomy while simultaneously introducing new psychosocial challenges. Drawing on the Job Demands–Resources (JD-R) model, the present study examined the relationship between employees’ perceived disadvantages of remote work and work engagement, considering the parallel indirect associations involving technology-related demands and work–family conflict. Data were collected through a cross-sectional survey involving 3762 hybrid workers employed by an Italian IT company. Primary analyses were conducted on 3028 participants with complete observed data on the four focal constructs. Participants completed an adapted measure of he Remote Working Benefits and Disadvantages Scale, together with the Utrecht Work Engagement Scale (UWES-3), the Emerging Technology-related Stressors Scale, and the Work–Family Conflict Scale. Greater perceived disadvantages were associated with higher technology-related demands and work–family conflict, both of which were in turn associated with lower work engagement. The standardized indirect associations were β = −0.086 through technology-related demands and β = −0.044 through work–family conflict, while the remaining direct association was small (β = −0.058). The overall model explained 8.3% of the variance in work engagement, indicating modest explanatory power. These findings support an appraisal-based interpretation of remote and hybrid work, suggesting that employees’ subjective evaluations of their work arrangements may represent one relevant correlate of engagement. The study underscores the importance of implementing sustainable remote work practices that minimize perceived disadvantages, support effective boundary management, and reduce technology-related demands to foster employee engagement and well-being.

1. Introduction

Digitalization, widespread access to information and communication technologies (ICTs), and evolving employee expectations have progressively transformed how work is organized. Although working outside the employer’s premises is not a new phenomenon, its scale and organizational relevance changed substantially following the COVID-19 pandemic. In the European Union, the proportion of employees usually working from home remained relatively stable at around 5% for almost a decade before increasing sharply to 12.3% in 2020 and 13.5% in 2021. Although this proportion declined after the pandemic peak, it has remained well above pre-pandemic levels, with 8.9% of employees usually working from home in 2025, suggesting that remote and hybrid work have become enduring features of the European labor market [1]. Many European organizations continued to adopt flexible work arrangements rather than returning to exclusively office-based practices [2].Throughout this study, remote work is used as the overarching term to refer to work performed outside the traditional workplace through ICTs, whereas hybrid work refers to work arrangements combining remote and on-site work [3,4]. The diffusion of remote and hybrid work has been accompanied by a growing body of evidence on its potential benefits. Among its most consistently reported advantages is greater flexibility in organizing work activities. Compared with traditional office-based work, employees working remotely generally report higher levels of autonomy and perceived control over their work schedules, facilitating self-regulation and allowing them to adapt work demands to individual productivity rhythms [5,6]. Likewise, eliminating or reducing daily commuting generates substantial savings in both time and financial resources while increasing opportunities for recovery, leisure activities, and family responsibilities [7,8]. Several studies have consequently documented improvements in work–life balance, lower perceived stress, greater job satisfaction, and, under appropriate organizational conditions, higher levels of productivity and work engagement [5,9,10]. At the organizational level, remote and hybrid work have also been associated with greater workforce flexibility, improved talent attraction and retention, reduced operating costs, and enhanced organizational resilience [1]. Despite these advantages, an increasing number of studies have highlighted that remote work may also generate significant psychosocial challenges. The extensive reliance on ICTs, combined with reduced face-to-face interactions and the absence of clear spatial and temporal boundaries, has introduced new demands that may compromise employee well-being and work experiences [11,12] one of the most frequently reported disadvantages concerns the progressive blurring of work and non-work boundaries, which may increase work intensification, prolong working hours, and make psychological detachment from work more difficult [13,14]. At the same time, remote workers may experience greater social isolation, a weaker sense of organizational belonging, reduced informal interactions with colleagues, and fewer opportunities for collaboration and knowledge sharing [15,16]. Additional challenges include reduced visibility toward supervisors, concerns regarding career progression and professional development, difficulties accessing organizational information and support, perceptions of increased monitoring and control, and greater difficulty concentrating due to domestic distractions or technology-related interruptions [17,18,19]. Taken together, this body of evidence suggests that remote work cannot be considered either inherently beneficial or detrimental. Rather, the same characteristics that are often regarded as advantages, such as flexibility and autonomy, may under certain circumstances become sources of strain, leading to markedly different occupational outcomes across employees and organizational contexts. This raises an important question: what happens if remote work and hybrid work are ultimately experienced not as a resource that promotes well-being but as a demand that undermines it? Previous reviews have extensively documented the benefits, challenges, and occupational consequences associated with remote and hybrid work [4,20]. Nevertheless, considerably less attention has been devoted to employees’ perceptions of the disadvantages associated with remote work and to the psychological processes through which these perceptions may influence occupational outcomes. Building on this gap, the present study investigates whether employees’ perceived disadvantages of remote work are associated with work engagement and whether this relationship is explained by technology-related demands and work–family conflict. The relevance of this perspective extends to the broader debate on sustainable work and organizational practices. Sustainability involves the creation of enduring value while balancing economic prosperity, social well-being, and environmental responsibility, in line with the broader principles of sustainable development [21]. From this perspective, the sustainability of remote and hybrid work cannot be evaluated solely in terms of flexibility, productivity, or organizational efficiency, but also in terms of their capacity to support employee well-being and motivation over time. This view is consistent with broader sustainability frameworks emphasizing the need to integrate social outcomes into organizational performance and strategy [22,23]. As digital and remote-working practices become increasingly embedded in contemporary organizations, understanding how employees experience their associated demands is therefore relevant to the development of work arrangements that are both organizationally viable and psychologically sustainable. Employee engagement is particularly relevant in this regard, as supportive work environments and effective management of technology-related stressors have been linked to engagement and other outcomes that contribute to sustainable organizational functioning [24,25]. Accordingly, the present study addresses the following research question: How are employees’ perceived disadvantages of remote and hybrid work associated with work engagement, and to what extent are technology-related demands and work–family conflict involved in these indirect associations?

1.1. Theoretical Background and Hypothesis Development

Work engagement represents one of the most widely studied indicators of employee motivation and occupational well-being [26,27]. Originally conceptualized by [28] as the investment of individuals’ physical, cognitive, and emotional energies into their work roles, work engagement has subsequently been defined as a positive, fulfilling, work-related motivational state characterized by vigor, dedication, and absorption [29,30]. Unlike transient affective states, work engagement is considered a relatively persistent and pervasive psychological state that reflects the extent to which employees invest themselves cognitively, emotionally, and behaviorally in their work [26,31,32]. Because engaged employees display higher levels of energy, persistence, learning, creativity, job performance, and organizational commitment, work engagement has become a central indicator of healthy and sustainable employee functioning as well as a key organizational outcome [33,34,35].
Despite the extensive literature on work engagement, research examining its antecedents in remote and hybrid work contexts remains relatively limited, with most evidence originating from traditional office settings. Moreover, existing research has primarily focused on identifying the resources that promote engagement among remote workers, whereas considerably less attention has been devoted to understanding how employees’ negative appraisal of remote work may undermine the motivational process leading to work engagement [36,37]. Within the Job Demands–Resources (JD-R) model [38,39], every work environment is characterized by a combination of job demands and job resources, whose balance shapes employee well-being and motivation. Consequently, engagement is fostered when employees perceive that their work environment provides adequate resources to accomplish their tasks, whereas it tends to decline when job demands become excessive or resources are perceived as insufficient [40]. Within this framework, remote and hybrid work represent particularly complex ways of working because they may simultaneously provide valuable job resources while introducing new job demands [6,41,42]. Consequently, remote work and hybrid work cannot be conceptualized as either positive or negative per se; rather, their consequences depend on which characteristics become more salient in employees’ work experience. Importantly, the present study does not treat subjective appraisal as a simple perceptual substitute for objective characteristics of remote work. Rather, appraisal is conceptualized as the process through which employees integrate multiple features of their remote-work experience into an overall evaluation of whether their work arrangement is predominantly experienced as resource-supportive or demand-laden. This perspective may help explain why employees exposed to broadly similar formal work arrangements nevertheless report markedly different experiences and motivational outcomes. Characteristics such as flexibility, extensive technology use, or reduced direct supervision may, for example, be experienced as enabling resources by some employees but as additional demands by others. Accordingly, an appraisal-based perspective complements exposure-based approaches by focusing on how the configuration of remote-work characteristics is subjectively interpreted, rather than assuming that a given feature has a uniform psychological meaning across employees. Drawing on the multidimensional conceptual framework proposed by [43], the present study conceptualizes perceived disadvantages of remote work as employees’ overall subjective evaluation of the extent to which remote work is experienced as characterized by social, organizational, and technological challenges. Specifically, this dimension captures employees’ perceptions of interpersonal, organizational, and work-related disadvantages, including social isolation, reduced visibility and recognition, limited access to organizational resources and information, fewer career and training opportunities, stricter monitoring, and concentration difficulties associated with the home working environment. Rather than referring to isolated features of remote work, this construct captures employees’ integrated perception of how remote working conditions may constrain or hinder their work experience across multiple domains of the work environment. To date, with a few exceptions (e.g., [44], these disadvantages have largely been investigated as separate characteristics of remote work rather than as part of employees’ overall subjective appraisal of their remote work experience. The present study adopts an employee-centered perspective by focusing on how these disadvantages are perceived and integrated into an overall evaluation of the remote work experience. This perspective is also consistent with the Transactional Theory of Stress [45,46], which posits that individuals’ responses to environmental conditions are determined not simply by the objective characteristics of those conditions, but by the cognitive appraisal they make of their significance for their own functioning and well-being. A global appraisal is theoretically relevant because remote-work characteristics are rarely experienced in isolation. Employees are simultaneously exposed to multiple social, organizational, and technological features whose psychological meaning may depend on how they combine within the broader work arrangement. Assessing these characteristics separately may therefore overlook the cumulative meaning employees attribute to the remote-work experience as a whole. By contrast, an overall appraisal captures the extent to which multiple potentially disadvantageous features are integrated into a broader evaluation of the work environment. This is important because motivational responses may depend less on the presence of any single characteristic than on the overall meaning employees assign to the configuration of demands and resources they experience. Accordingly, the extent to which employees perceive remote/hybrid work as characterized by disadvantages is expected to shape how they experience their work environment. Perceiving greater disadvantages associated with remote work reflects an appraisal of the work environment in which job demands outweigh available job resources, thereby weakening the motivational process underlying work engagement. Accordingly, employees perceiving greater disadvantages associated with remote work are expected to report lower levels of work engagement.
H1. 
Perceived disadvantages of remote work are negatively associated with work engagement.

1.2. Technology-Related Demands and Work–Family Conflict

While perceived disadvantages of remote work are expected to be directly associated with work engagement, examining the indirect associations involving technology-related demands and work–family conflict may provide a more comprehensive understanding of how employees’ appraisal of remote work is related to motivational outcomes. One mechanism that may explain this relationship is techno-overload, one of the core dimensions of technostress [47,48]. Techno-overload refers to situations in which the use of ICTs compels employees to work faster, process increasing amounts of information, and respond to multiple work demands simultaneously, thereby intensifying both the pace and volume of work [49,50]. Although different theoretical models propose partially different taxonomies of techno-stressors, they consistently identify technology-induced workload and work intensification as a central source of technology-related strain [51,52]. In remote and hybrid work settings, where communication, coordination, information exchange, and task accomplishment rely almost entirely on digital technologies, these demands become an integral component of employees’ daily work activities [5]. However, the extent to which employees experience technology-related demands is unlikely to depend solely on the objective use of ICTs. Following the transactional perspective [46], employees who perceive their remote/hybrid work environment as characterized by multiple social, organizational, and work-related disadvantages are likely to interpret ICT-mediated work demands as more excessive, intrusive, and difficult to manage. When remote work is experienced as limiting access to organizational resources, reducing interpersonal support, increasing monitoring, or creating coordination difficulties, digital technologies may no longer function primarily as enabling work tools but instead become an additional source of workload, requiring greater cognitive effort, sustained attention, and continuous responsiveness [43,53,54].Consequently, standard technological input, such as a continuous stream of emails, virtual meetings, or instant messages, can be perceived as amplified, overwhelming, and unmanageable [55]. In turn, technology-related overload is expected to be associated with lower levels of work engagement. When ICT-related demands become excessive, employees may increasingly perceive digital technologies as obstacles to accomplishing their work. Consequently, a growing proportion of their efforts is devoted to coping with technology itself instead of pursuing meaningful work goals, undermining the sense of energy, enthusiasm, and involvement that characterizes work engagement [38]. In line with this, Pham et al. [56] found that techno-overload significantly reduced work engagement among ICT employees. Similarly, Orhan et al. [57] theorized that work engagement depends on employees’ psychological availability and that technology-related interruptions may disrupt this availability, thereby weakening vigor and dedication. Applied to techno-overload, excessive ICT-related demands may make it more difficult for employees to remain fully immersed in their work activities, ultimately reducing work engagement.
Beyond technology-related demands, remote work and hybrid work also fundamentally change the boundaries between work and family domains. Accordingly, employees reporting greater perceived disadvantages of remote work may also report higher levels of work–family conflict (WFC), which may represent an additional indirect association with work engagement. Remote work has frequently been associated with greater flexibility and autonomy in organizing work, potentially facilitating the integration of work and family responsibilities [5,6]. However, growing evidence suggests that these same characteristics may simultaneously blur the temporal and spatial boundaries separating work and family domains, making employees continuously reachable beyond formal working hours [58,59,60]. As a consequence, employees may experience greater difficulty psychologically detaching from work, increasing the likelihood that work demands spill over into their private lives [59,61]. According to Boundary Theory [62] and inter-role conflict theory [63], work-family conflict (WFC) emerges when the boundaries separating work and family become increasingly permeable, allowing pressures originating in the work domain to interfere with family responsibilities. Although extensive research has identified numerous antecedents of WFC (e.g., job demands, organizational characteristics, and boundary management practices) [64], considerably less attention has been devoted to understanding whether employees’ overall appraisal of their remote and hybrid work experience contributes to the development of WFC. Employees who perceive remote and hybrid work as characterized by greater disadvantages may be less likely to view these work arrangements as facilitating the integration of work and family roles. Rather, they may perceive the boundaries between work and family as increasingly blurred, making work more likely to interfere with family responsibilities. For example, perceptions of excessive monitoring, coordination difficulties, reduced organizational support, and persistent technology-mediated interruptions may reinforce the perception that work remains continuously present, making psychological detachment from work more difficult and increasing perceived work–family conflict [59,60,61]. Consistent with the transactional perspective, [65] conceptualized WFC as a form of perceptual stress, arguing that conflict between work and family arises from employees’ appraisal of technology-related work demands rather than from technology use itself. Extending this, the present study focuses on employees’ primary appraisal of their remote work experience. Specifically, we examine whether employees who evaluate remote work as being characterized by greater disadvantages are more likely to experience WFC. In turn, WFC is expected to be negatively associated with work engagement because the strain associated with managing competing work and family demands may gradually weaken employees’ motivational processes. This is in line with previous research showing that higher levels of WFC are consistently associated with lower work engagement [66,67,68,69]. More recently, Kobayashi et al. [70] showed in a one-year longitudinal study that higher levels of work interference with family were associated with lower subsequent work engagement, suggesting that persistent conflict between work and family roles may gradually undermine employees’ enthusiasm and energy at work. Taken together, these theoretical perspectives serve complementary functions within the proposed framework. The JD-R model provides the overarching motivational framework, linking employees’ experience of job demands and resources to work engagement. The Transactional Theory of Stress complements this perspective by explaining how work characteristics acquire psychological meaning through employees’ appraisal, helping to account for why similar remote-work arrangements may be experienced differently across individuals. Boundary Theory provides a more specific account of the work–family interface, explaining how the permeability of work and family boundaries may be associated with work–family conflict. Accordingly, these perspectives jointly support the examination of technology-related demands and work–family conflict as two distinct demand-related correlates of employees’ appraisal of remote work, operating respectively within the work domain and across the work–family interface. Technology-related demands and work–family conflict were modeled in parallel in the primary analysis because the central aim was to examine whether each variable showed a specific indirect association between perceived disadvantages of remote work and work engagement when considered simultaneously. Although previous research provides grounds for expecting technology-related demands and work–family conflict to be related, the available theory does not provide a sufficiently strong basis for privileging a specific directional ordering between them in the present context. In particular, technology-related demands may contribute to work–family conflict, but alternative or reciprocal relationships are also theoretically plausible.
H2a. 
A negative indirect association between perceived disadvantages of remote work and work engagement is expected through technology-related demands: greater perceived disadvantages are expected to be associated with higher technology-related demands, which are in turn expected to be associated with lower work engagement.
H2b. 
A negative indirect association between perceived disadvantages of remote work and work engagement is expected through work–family conflict: greater perceived disadvantages are expected to be associated with higher work–family conflict, which is in turn expected to be associated with lower work engagement.

2. Materials and Methods

2.1. Participants

Data were collected between July and October 2023 through an anonymous online questionnaire administered via SurveyMonkey. The research team provided the survey link to the participating organization, and management distributed it to the company workforce through the corporate intranet. Eligibility was based on being employed by the participating organization and working under its hybrid-work arrangement; no additional study-specific exclusion criteria were applied. Participation was voluntary, and employees were allowed to complete the questionnaire during working hours. Before accessing the survey, participants were informed about the aims of the study, the anonymous and confidential treatment of their responses, and their right to withdraw at any time without consequences, and informed consent was obtained. The organization did not have access to individual-level responses and received only aggregated results. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki, the General Data Protection Regulation (GDPR), and the Ethical Principles of Psychologists and Code of Conduct of the American Psychological Association. The initial dataset comprised 3762 hybrid workers employed by a single Italian company operating in the IT sector. At the time of data collection, employees were required to attend the workplace approximately one day per month. The total number of employees who received or viewed the survey invitation was not available to the research team; therefore, a formal participation rate could not be calculated, and individual-level comparisons between respondents and nonrespondents were not possible. Participants belonged to different organizational departments and locations, and potential clustering was therefore examined in the sensitivity analyses. Complete socio-demographic information was available for 2968 participants (78.9%). Among participants with available demographic data, 70.0% were men and 30.0% were women. Regarding age, 24.3% were between 20 and 30 years old, 28.1% between 31 and 40 years, 25.6% between 41 and 50 years, and 22.0% were older than 50 years. Concerning organizational tenure, 18.6% had worked in the organization for less than one year, 37.7% between one and five years, 14.3% between six and ten years, 14.4% between 11 and 20 years, and 15.0% for more than 20 years.

2.2. Measures

Perceived disadvantages of remote and hybrid work were assessed using an adapted six-item version derived from the Disadvantages dimension of the Remote Working Benefits and Disadvantages Scale, developed by [43]. The measure was adapted prior to data collection in consultation with the participating organization to align the instrument with the scope and requirements of the broader organizational survey. As part of this adaptation, one of the seven original items was not administered. Responses were provided on a five-point Likert scale ranging from 1 (“completely disagree”) to 5 (“extremely agree”). No “Not applicable” response option was included in the administered questionnaire. A mean score across the six administered items was calculated, with higher scores indicating greater perceived disadvantages; possible scores therefore ranged from 1 to 5. In the present study, the scale showed good internal consistency (Cronbach’s α = 0.865).
Technology-related demands were measured using the subdimension of the Emerging Technology-related Stressors Scale [71]. The six-item scale assesses the extent to which employees perceive digital technologies as increasing workload, work pace, interruptions, and pressure during work activities. Responses were provided on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Higher scores indicate greater perceived technology-related demands. Internal consistency in the present study showed a good value (Cronbach’s α = 0.874).
Work–family conflict was assessed using the Italian version of the Work-Family Conflict Scale [72]. The scale consists of five items assessing the extent to which work demands interfere with family life. Participants indicated their agreement with each statement on a seven-point Likert scale ranging from 1 (completely disagree) to 7 (completely agree). Higher scores indicate greater work–family conflict. In the present study, the scale showed excellent internal consistency (Cronbach’s α = 0.927).
Work engagement was measured using the Ultra-Short Utrecht Work Engagement Scale (UWES-3), Italian validation by [73]. The scale includes three items assessing the core dimensions of work engagement (vigor, dedication, and absorption). Responses were recorded on a seven-point Likert scale ranging from 0 (never) to 6 (always/every day). Higher scores indicate greater work engagement. Internal consistency in the present study was acceptable (Cronbach’s α = 0.696), which is consistent with previous studies employing the three-item version of the instrument.

2.3. Data Analysis

Data analyses were conducted using R 4.4.2. Descriptive statistics, Pearson’s correlation coefficients, and reliability estimates were computed for all study variables. Missing-data patterns were examined using the original observed item responses. Primary indirect-association analyses were conducted using complete observed cases on the four focal constructs (N = 3028), without replacing missing observations through mean imputation. Analyses involving socio-demographic covariates were conducted on the 2968 participants with complete demographic information. Confirmatory factor analyses were performed to evaluate the measurement structure and construct distinctiveness of the study variables. The hypothesized parallel model was examined as a cross-sectional indirect-association model, with perceived disadvantages of remote and hybrid work specified as the independent variable, work engagement as the outcome, and technology-related demands and work–family conflict as parallel intervening variables. Indirect associations were estimated using 5000 bootstrap resamples and evaluated using 95% bootstrap confidence intervals. Because all variables were measured at the same time, these estimates were interpreted as statistical associations and not as evidence of temporal or causal mediation. Additional analyses included missing-data diagnostics, effect-size estimation, regression diagnostics, covariate-adjusted sensitivity analyses, cluster-robust standard errors, and alternative model specifications to assess the robustness of the observed pattern of associations.

3. Results

3.1. Sample Characteristics and Missing Data

The initial dataset comprised 3762 hybrid workers employed by a single Italian company operating in the IT sector. Complete socio-demographic information was available for 2968 participants (78.9%), whereas the remaining 794 participants (21.1%) had missing information across the socio-demographic variables. The socio-demographic characteristics of participants with complete data are reported in Table 1. Among participants with complete socio-demographic data, 2077 (70.0%) were men and 891 (30.0%) were women. Regarding age, 720 participants (24.3%) were between 20 and 30 years old, 834 (28.1%) were between 31 and 40 years, 760 (25.6%) were between 41 and 50 years, and 654 (22.0%) were older than 50 years. With respect to organizational tenure, 552 participants (18.6%) had worked in the organization for less than one year, 1118 (37.7%) for 1–5 years, 425 (14.3%) for 6–10 years, 427 (14.4%) for 11–20 years, and 446 (15.0%) for more than 20 years.
Table 1. Sociodemographic Characteristics of Participants With Complete Demographic Data.
Inspection of the raw dataset showed that socio-demographic missingness occurred as a complete block: all 794 participants with incomplete socio-demographic information were missing the full set of available demographic variables. Missingness in the focal study variables also occurred primarily at the scale-block level. At the variable level, observed data were available for 3084 participants for perceived disadvantages of remote work, 3147 for technology-related demands, 3028 for work–family conflict, and 3132 for work engagement, corresponding to 678, 615, 734, and 630 missing observations, respectively. Of the full sample, 3028 participants (80.49%) had complete observed data across perceived disadvantages of remote work, technology-related demands, work–family conflict, and work engagement. Fifty-six participants (1.49%) were missing only work–family conflict data; 48 (1.28%) were missing perceived disadvantages and work–family conflict; 15 (0.40%) were missing perceived disadvantages, work–family conflict, and work engagement; and 615 (16.35%) had no observed responses on any of the four focal constructs.
Little’s MCAR test conducted on the original item-level data did not provide sufficient evidence to reject the MCAR hypothesis, χ2(30) = 40.90, p = 0.089. However, because a nonsignificant MCAR test does not establish that missingness is definitively random, the pattern of missingness was also examined descriptively.
Participants with and without complete socio-demographic information were compared on the available focal study variables. No statistically significant differences emerged for perceived disadvantages of remote work, technology-related demands, work–family conflict, or work engagement. Standardized mean differences were small in magnitude, with absolute Cohen’s d values ranging from 0.06 to 0.15.
Inspection of the original analytic dataset further indicated that missing item blocks had previously been replaced using item-level means. To avoid relying on single mean imputation, the primary analyses reported below were therefore re-estimated using the original observed responses. The main indirect-association model was based on the 3028 participants with complete observed data across all four focal constructs, whereas analyses involving socio-demographic covariates were conducted on the 2968 participants with complete demographic information.
Inspection of the raw dataset showed that the 794 participants without socio-demographic information were missing the demographic variables as a complete block. Missingness in the focal study variables also occurred predominantly at the scale-block level. Of the 3762 respondents, 3028 (80.49%) had complete observed data across perceived disadvantages of remote work, technology-related demands, work–family conflict, and work engagement. Fifty-six participants (1.49%) were missing only work–family conflict data, 48 (1.28%) were missing perceived disadvantages and work–family conflict, 15 (0.40%) were missing perceived disadvantages, work–family conflict, and work engagement, and 615 (16.35%) had no observed responses on any of the four focal constructs.
Little’s MCAR test conducted on the original item-level responses did not provide sufficient evidence to reject the hypothesis that the data were missing completely at random, χ2(30) = 40.90, p = 0.089. Given that a nonsignificant MCAR test does not establish that missingness is definitively random, missingness was additionally examined descriptively.
Participants with and without complete socio-demographic information were compared on the available focal variables. No statistically significant differences emerged for perceived disadvantages of remote work, technology-related demands, work–family conflict, or work engagement. Standardized mean differences were small, with absolute Cohen’s d values ranging from 0.06 to 0.15.
Inspection of the original analytic dataset further showed that missing item blocks had previously been replaced using item-level means. To avoid relying on single mean imputation, the analyses reported below were re-estimated using the original observed responses. Accordingly, the main indirect-association model was estimated in the 3028 participants with complete observed scores on all four focal constructs, whereas sensitivity analyses involving demographic covariates were conducted in the 2968 participants with complete socio-demographic information.

3.2. Descriptive Statistics and Distributional Characteristics

Descriptive analyses were conducted on the original, non-imputed composite scores. Mean levels were 2.33 (SD = 0.84) for perceived disadvantages of remote work, 2.54 (SD = 0.80) for technology-related demands, 3.75 (SD = 1.52) for work–family conflict, and 5.43 (SD = 0.88) for work engagement. The full set of descriptive statistics, distributional indices, and reliability estimates for the four variables is reported in Table 2.
Table 2. Descriptive Statistics and Reliability Estimates.
Perceived disadvantages of remote work, technology-related demands, and work–family conflict showed relatively limited departures from symmetry, with skewness values of 0.31, 0.35, and −0.01, respectively. Work engagement showed a more pronounced negative skew (−1.71) and positive excess kurtosis (2.65), consistent with a concentration of responses toward the upper end of the engagement scale.
Cronbach’s alpha coefficients ranged from 0.696 for work engagement to 0.927 for work–family conflict. The alpha coefficient for the three-item engagement measure was borderline, α = 0.696, 95% CI [0.68, 0.71]. Model-based McDonald’s omega, however, indicated stronger reliability for the engagement construct, ω = 0.812.

3.3. Measurement Model and Construct Validity

A confirmatory factor analysis was conducted to examine the measurement structure of the four focal constructs. The hypothesized four-factor model included separate latent factors for perceived disadvantages of remote work, technology-related demands, work–family conflict, and work engagement. The model was estimated using the original non-imputed items, robust maximum-likelihood estimation, and full-information maximum likelihood for available item-level responses.
The four-factor model showed adequate fit to the data, robust CFI = 0.951, robust TLI = 0.943, robust RMSEA = 0.058, 90% CI [0.056, 0.061], and SRMR = 0.049.
Standardized factor loadings for perceived disadvantages ranged from 0.588 to 0.804. Loadings for technology-related demands ranged from 0.630 to 0.837, whereas those for work–family conflict ranged from 0.799 to 0.903. For engagement, standardized loadings were 0.805 for UWES1, 0.915 for UWES2, and 0.280 for UWES3. Thus, the third engagement indicator showed substantially weaker performance than the other two indicators. The standardized factor loadings for all indicators are reported in Table 3.
Table 3. Standardized Factor Loadings.
Convergent validity was supported by AVE values above 0.50 for each construct: 0.524 for perceived disadvantages, 0.546 for technology-related demands, 0.719 for work–family conflict, and 0.521 for work engagement.
Discriminant validity was assessed using the heterotrait–monotrait ratio. All HTMT values were well below conventional thresholds. The largest coefficient was 0.636, between perceived disadvantages and technology-related demands. The remaining coefficients ranged from 0.166 to 0.616, supporting empirical differentiation among the focal constructs.
To examine whether the covariance among the self-report measures could be adequately represented by a single common factor, the four-factor model was compared with a single-factor alternative. The single-factor model showed poor fit, robust CFI = 0.584, robust TLI = 0.535, robust RMSEA = 0.166, and SRMR = 0.119. The four-factor model fit the data substantially better than the single-factor solution, Δχ2(6) = 8101.90, p < 0.001. Thus, the data did not support a single common-factor representation of the study measures. This post hoc diagnostic, however, cannot rule out the possibility of common-method variance.

3.4. Primary Cross-Sectional Indirect-Association Model

The hypothesized parallel model was re-estimated using the 3028 participants with complete observed data on all four focal constructs. Perceived disadvantages of remote work were specified as the independent variable, work engagement as the outcome, and technology-related demands and work–family conflict as parallel intervening variables. The unstandardized and standardized path coefficients, with their 95% confidence intervals, are reported in Table 4.
Table 4. Path Coefficients for the Primary Parallel Indirect-Association Model.
Because all variables were assessed cross-sectionally, coefficients are interpreted as direct and indirect statistical associations and not as evidence of temporal or causal mediation.
Perceived disadvantages of remote work were positively associated with technology-related demands, B = 0.524, 95% CI [0.493, 0.555], β = 0.546, p < 0.001, and with work–family conflict, B = 0.609, 95% CI [0.542, 0.672], β = 0.334, p < 0.001.
When perceived disadvantages, technology-related demands, and work–family conflict were considered simultaneously in relation to work engagement, technology-related demands were negatively associated with engagement, B = −0.170, 95% CI [−0.227, −0.112], β = −0.157, p < 0.001. Work–family conflict was also negatively associated with engagement, B = −0.075, 95% CI [−0.102, −0.048], β = −0.132, p < 0.001.
The direct association between perceived disadvantages and work engagement remained statistically significant after accounting for the two intervening variables, B = −0.060, 95% CI [−0.112, −0.010], β = −0.058, p = 0.020.
The model explained 29.9% of the variance in technology-related demands, 11.2% of the variance in work–family conflict, and 8.3% of the variance in work engagement. Thus, although the associations were statistically reliable, the explanatory power of the outcome model was modest.
Bootstrap analyses based on 5000 resamples indicated that both specific indirect associations were statistically significant. The indirect association through technology-related demands was B = −0.089, 95% bootstrap CI [−0.119, −0.059], with a standardized estimate of β = −0.086, 95% CI [−0.115, −0.057]. The indirect association through work–family conflict was B = −0.046, 95% bootstrap CI [−0.063, −0.029], β = −0.044, 95% CI [−0.060, −0.028].
The total indirect association was B = −0.135, 95% bootstrap CI [−0.162, −0.107], β = −0.130, 95% CI [−0.155, −0.104]. The total association between perceived disadvantages and work engagement was B = −0.195, 95% bootstrap CI [−0.239, −0.153], β = −0.188, 95% CI [−0.228, −0.148]. The specific, total indirect, direct, and total associations, with their bootstrap confidence intervals, are summarized in Table 5.
Table 5. Indirect and Total Associations.
Because the direct association remained statistically significant after inclusion of the two intervening variables, the results are consistent with partial statistical mediation, rather than full mediation.
The two specific indirect associations were additionally compared. The contrast was explicitly defined as the indirect association through technology-related demands minus the indirect association through work–family conflict. The resulting contrast was B = −0.043, 95% bootstrap CI [−0.084, −0.002], p = 0.039. The negative contrast indicates that the indirect association through technology-related demands was statistically more negative in magnitude than the indirect association through work–family conflict. However, the magnitude of this difference was small and does not, by itself, indicate greater practical importance.

3.5. Effect Magnitude and Regression Diagnostics

The primary outcome model explained 8.3% of the variance in work engagement, R2 = 0.083. The corresponding overall Cohen’s f2 was 0.091, indicating modest explanatory power.
Incremental variance analyses further indicated that the unique contribution of perceived disadvantages to engagement was small, ΔR2 = 0.002. The unique contributions of technology-related demands and work–family conflict were ΔR2 = 0.013 and 0.012, respectively. Accordingly, statistical significance should be interpreted separately from substantive magnitude.
Potential multicollinearity was low, with variance inflation factors ranging from 1.43 to 1.84.
Multivariate outlier screening using Mahalanobis distance identified 30 complete observations, approximately 1.0% of the analytic sample, above the p < 0.001 criterion. Cook’s distance did not identify highly influential individual observations, with a maximum Cook’s D of 0.033.
The Breusch–Pagan test indicated heteroskedasticity, BP(3) = 62.14, p < 0.001. Accordingly, heteroskedasticity-consistent HC3 standard errors were examined. The substantive conclusions remained unchanged. Perceived disadvantages remained negatively associated with work engagement, B = −0.060, HC3 SE = 0.026, p = 0.020; technology-related demands remained negatively associated with engagement, B = −0.170, HC3 SE = 0.029, p < 0.001; and work–family conflict remained negatively associated with engagement, B = −0.075, HC3 SE = 0.014, p < 0.001.
Residual diagnostics indicated departures from normality, particularly in the lower tail, consistent with the negatively skewed and bounded engagement outcome. Some curvature was also evident in residual plots.
To assess potential nonlinearity, a quadratic model including squared terms for perceived disadvantages, technology-related demands, and work–family conflict was compared with the linear specification. The quadratic model provided a statistically significant improvement, F(3, 3021) = 16.29, p < 0.001, but the incremental variance explained was modest, ΔR2 = 0.015.
When only a quadratic term for perceived disadvantages was added to the primary linear model, explained variance increased from R2 = 0.083 to R2 = 0.089, ΔR2 = 0.006. Thus, some nonlinearity was statistically detectable in the large sample, but the improvement in explanatory power was small and the associations involving technology-related demands and work–family conflict remained substantively unchanged.

3.6. Covariate and Clustering Sensitivity Analyses

A covariate-adjusted sensitivity analysis was conducted using the 2968 participants with complete demographic information. Gender, age, organizational tenure, and organizational grade were included as covariates in both mediator equations and in the work-engagement equation.
The substantive pattern remained highly consistent with the unadjusted model. Perceived disadvantages remained positively associated with technology-related demands, B = 0.508, 95% CI [0.476, 0.539], β = 0.531, and with work–family conflict, B = 0.573, 95% CI [0.508, 0.636], β = 0.313.
Technology-related demands remained negatively associated with engagement, B = −0.180, 95% CI [−0.237, −0.123], β = −0.165, and work–family conflict remained negatively associated with engagement, B = −0.082, 95% CI [−0.110, −0.054], β = −0.143. The direct association between perceived disadvantages and engagement also remained statistically significant, B = −0.065, 95% CI [−0.115, −0.015], β = −0.062, p = 0.011.
The adjusted indirect association through technology-related demands was B = −0.092, 95% CI [−0.121, −0.063], β = −0.088, whereas the adjusted indirect association through work–family conflict was B = −0.047, 95% CI [−0.064, −0.031], β = −0.045. The total indirect association was β = −0.133 and the total association was β = −0.195. The adjusted model explained 10.0% of the variance in work engagement.
Potential clustering was also examined. For work engagement, the intraclass correlation was 0.014 across organizational departments and 0.043 across locations, indicating relatively limited between-cluster variance.
Because the location-level ICC was larger, cluster-robust CR2 standard errors were estimated using location as the clustering variable. The associations remained statistically significant: perceived disadvantages, B = −0.063, CR2 SE = 0.017, p = 0.018; technology-related demands, B = −0.174, CR2 SE = 0.024, p = 0.001; and work–family conflict, B = −0.075, CR2 SE = 0.009, p < 0.001.
Thus, the main findings were not materially altered by adjustment for the available socio-demographic covariates or by accounting for organizational clustering.

3.7. Alternative Model Specifications

To examine whether the hypothesized ordering was empirically distinctive, alternative specifications were tested that reversed the direction between engagement and the proposed intervening variables and that introduced a sequential association between technology-related demands and work–family conflict.
First, a reverse-order model was estimated in which work engagement was specified as the predictor of technology-related demands and work–family conflict, which were subsequently associated with perceived disadvantages of remote work.
Work engagement was negatively associated with technology-related demands, β = −0.262, and with work–family conflict, β = −0.239. Technology-related demands were, in turn, positively associated with perceived disadvantages, β = 0.514, whereas the corresponding association between work–family conflict and perceived disadvantages was comparatively weak, β = 0.038, and did not reach conventional statistical significance.
The standardized indirect association between engagement and perceived disadvantages through technology-related demands was β = −0.135 and was statistically significant. The indirect association through work–family conflict was β = −0.009 and was not statistically significant.
Thus, the reverse specification received partial empirical support, particularly for the ordering involving engagement, technology-related demands, and perceived disadvantages.
A second alternative model tested a serial specification in which perceived disadvantages were associated with technology-related demands, technology-related demands were associated with work–family conflict, and work–family conflict was subsequently associated with engagement.
Perceived disadvantages were positively associated with technology-related demands, β = 0.546, while technology-related demands were positively associated with work–family conflict, β = 0.533. Work–family conflict was negatively associated with engagement, β = −0.132.
The serial indirect association through technology-related demands and work–family conflict was statistically significant, β = −0.038. The indirect association through technology-related demands alone also remained significant, β = −0.086. The total standardized indirect association was β = −0.130.
Because the primary parallel model and the alternative path specifications were saturated, their global fit indices cannot be used to determine which directional ordering is superior. These analyses therefore do not establish an alternative causal sequence. Rather, they demonstrate that multiple directional arrangements are compatible with the observed cross-sectional covariance structure.
Accordingly, the hypothesized parallel model should be interpreted as theoretically motivated and consistent with the observed associations, but not as demonstrating temporal or causal ordering.
Taken together, the analyses showed that greater perceived disadvantages of remote work were associated with lower work engagement both directly and indirectly through higher technology-related demands and greater work–family conflict. The pattern was robust to the exclusion of previously mean-imputed observations, adjustment for available socio-demographic characteristics, heteroskedasticity-consistent estimation, and organizational clustering. However, the modest variance explained, the presence of plausible alternative specifications, and the cross-sectional nature of the data warrant a cautious, non-causal interpretation of these associations.

4. Discussion

The widespread adoption of remote work has profoundly transformed the way employees perform their work, prompting growing interest in understanding its implications for employee well-being and motivation. Although remote work has often been associated with increased flexibility, autonomy, and improved work–life integration, empirical findings remain inconsistent, with studies reporting both beneficial and detrimental effects on employee well-being and work engagement [5,52,67]. One factor that may contribute to employees’ heterogeneous experiences of remote work is how they subjectively evaluate its characteristics [43,46]. Building on this perspective, the present study examined whether employees’ perceived disadvantages of remote work were associated with work engagement and whether this relationship was mediated by two complementary mechanisms: technology-related demands and work–family conflict. Overall, the findings supported the proposed model. Employees reporting greater perceived disadvantages associated with remote work also reported lower work engagement, both directly and indirectly through higher technology-related demands and greater work–family conflict. However, the alternative-model analyses further indicated that the hypothesized parallel ordering was not empirically unique, as both reverse and serial specifications received partial support. These findings reinforce the need to interpret the primary model as theoretically motivated rather than as evidence of a unique temporal sequence. The first contribution of the present study lies not only in showing that perceived disadvantages of remote work are associated with work engagement, but in adopting an appraisal-based perspective on heterogeneous remote-work experiences. Rather than assuming that particular remote-work characteristics operate uniformly as demands or resources, the findings are consistent with the view that their psychological relevance depends partly on how employees interpret the broader configuration of their work arrangement. This perspective may help explain why employees working under comparable formal remote-work conditions can nevertheless report different levels of technology-related demands, work–family conflict, and engagement. Although previous studies have yielded inconsistent findings regarding the effects of remote work on employee well-being [6,74], our findings suggest that employees’ perceived disadvantages represent an important, yet still relatively overlooked, factor associated with work engagement. While previous reviews have extensively documented both the benefits and challenges associated with remote work, considerably less attention has been devoted to understanding how employees’ subjective perceptions of these disadvantages relate to motivational outcomes. In this regard, our findings are consistent with those of [44], who reported that employees’ perceived disadvantages of remote work were negatively associated with subjective well-being, highlighting the importance of considering employees’ evaluations of their remote work experience. The second contribution of the present study concerns the mechanisms linking perceived disadvantages of remote work to work engagement. Although previous research has identified several challenges associated with remote and hybrid work, including technology-related stressors, social isolation, blurred work–family boundaries, and reduced organizational support [8,17,18], these factors have largely been investigated independently. By contrast, the present study simultaneously examined two complementary pathways reflecting different facets of the remote work experience. Specifically, technology-related demands capture challenges arising within the work domain, whereas work–family conflict reflects the spillover of work demands into employees’ private lives. Regarding technology-related demands, our findings indicate that employees who perceived greater disadvantages associated with remote work also reported higher levels of technology-related demands. This finding extends previous technostress research by suggesting that employees’ experience of technology-related demands depends not only on the intensity of ICT use but also on how the broader remote work environment is cognitively appraised. In other words, employees who evaluate remote work more negatively may be more likely to experience technology-mediated work activities as cognitively demanding and effortful [5,47,51,54,65]. Employees reporting greater perceived disadvantages were also more likely to perceive interference between their work and family roles. This finding suggests that employees who evaluate their remote work experience more negatively may be less likely to perceive the flexibility offered by remote work as a resource for managing work and family responsibilities. Instead, they may be more likely to perceive work demands as extending into the family domain. This interpretation is consistent with previous research indicating that remote work may simultaneously increase flexibility and blur work–family boundaries, thereby facilitating work-to-family spillover [6,59,60,64,65]. Nevertheless, although the observed associations were statistically reliable, their magnitude was modest. The model accounted for 8.3% of the variance in work engagement, with an overall Cohen’s f2 of 0.091, and the unique contribution of each predictor was limited. Perceived disadvantages uniquely accounted for 0.2% of additional variance in engagement, while technology-related demands and work–family conflict accounted for 1.3% and 1.2%, respectively. Accordingly, the findings should be interpreted as evidence of small but consistent associations rather than as indicating strong explanatory or practical effects. Taken together, these findings suggest that employees’ perceived disadvantages of remote work do not influence work engagement through a single mechanism. Rather, they appear to activate complementary processes operating both within the work domain (through technology-related demands) and across the work–family interface (through work–family conflict). The present findings reinforce the growing view that remote and hybrid work should not be conceptualized as either inherently beneficial or detrimental. Rather, many of its defining characteristics, such as flexibility, autonomy, and extensive ICT use, appear to be inherently ambivalent, functioning as resources under some circumstances and as demands under others. From a cognitive perspective, individuals tend to interpret new work experiences through broader evaluative schemas rather than as isolated events [75]. Accordingly, a generally disadvantageous appraisal of remote or hybrid work may be associated with more negative evaluations of specific aspects of the work experience, including technology-related demands and interference between work and family domains. From a sustainability perspective, the findings suggest that the long-term viability of hybrid work should be considered not only in terms of flexibility and organizational efficiency, but also in relation to employees’ appraisal of the work arrangement and the psychosocial demands associated with it. Although the observed associations were modest, they highlight the relevance of considering employee experience as one component of psychologically sustainable hybrid-work practices.

5. Limitations and Future Perspectives

The present study should be interpreted in light of several limitations. First, the cross-sectional design precludes drawing causal conclusions regarding the relationships among the study variables. Although the proposed model was theoretically grounded and supported by previous research, future longitudinal and experimental studies are needed to examine the temporal dynamics linking employees’ perceived disadvantages of remote work, technology-related demands, work–family conflict, and work engagement. Second, all focal variables were assessed through self-report questionnaires administered to the same respondents at the same time, raising the possibility of common-method variance and response biases. Although the theoretically specified four-factor model showed substantially better fit than a single-factor model, this post hoc diagnostic cannot rule out common-method variance. Shared measurement characteristics may therefore have inflated the magnitude of some of the observed associations. Future studies should combine multiple sources and methods, for example by integrating self-reports with supervisor-rated, behavioral, or objective indicators where appropriate. A further measurement limitation concerns the use of an adapted six-item version of the perceived-disadvantages measure rather than the complete seven-item instrument. The adaptation was established prior to data collection in consultation with the participating organization as part of the broader organizational survey. Although the resulting six-item measure showed satisfactory reliability and construct validity in the present sample, it should not be regarded as fully equivalent to the original validated instrument. Furthermore, the study was conducted within a single Italian organization operating in the ICT sector. Although this context represents an appropriate setting for examining remote and hybrid work, it also represents an important boundary condition of the findings. Employees working in a highly digitalized ICT environment are likely to have relatively high familiarity with digital technologies and may experience technology-related demands differently from employees in less digitalized occupations or sectors in which remote work is less established. Accordingly, the observed associations should not be assumed to generalize uniformly across industries, occupations, countries, or levels of remote-work intensity. Future research should therefore replicate the proposed model across different occupational contexts and cultural settings. Although participants worked predominantly remotely, they were formally employed under a hybrid work model. Consequently, the present study drew on evidence from both the remote and hybrid work literature, given the substantial overlap in the psychosocial mechanisms underlying these work arrangements. Nevertheless, the extent of remote work exposure may vary considerably across hybrid workers. Future research should examine whether the proposed relationships differ between fully remote and hybrid workers, as varying levels of remote work exposure may shape employees’ perceptions of work-related disadvantages, technology-related demands, work–family conflict, and, ultimately, work engagement. Finally, the present study focused specifically on employees’ perceived disadvantages of remote work. While this choice was consistent with the study’s objective of examining the health-impairment pathway, future research could simultaneously consider both perceived advantages and disadvantages to provide a more balanced understanding of employees’ evaluations of remote work and their respective contributions to occupational outcomes.

6. Practical Implications

The present findings offer several practical implications for organizations adopting remote and hybrid work. Given the modest explanatory power of the model (R2 = 0.083) and the small unique contributions of the individual predictors, these implications should be interpreted cautiously and as complementary to, rather than determinative of, broader organizational factors. First, the results suggest that remote and hybrid work should not be treated as a uniform experience, as employees may respond differently to comparable working conditions. A key practical implication therefore concerns the value of considering employees’ subjective appraisal of remote work alongside objective indicators of exposure, such as the number of days worked remotely or the intensity of technology use. Periodic surveys and psychosocial risk assessments may provide useful information on how employees experience remote and hybrid work and help organizations identify recurring areas of perceived difficulty [76]. From a primary prevention perspective, organizations should proactively design remote and hybrid work policies and practices that minimize employees’ perceptions of remote work disadvantages. This may involve establishing clear communication norms, ensuring equitable access to information, training and career opportunities, fostering opportunities for social interaction and collaboration, and promoting supportive leadership behaviors that strengthen employees’ sense of inclusion and connectedness [6,77,78,79]. From a secondary prevention perspective, organizations may benefit from monitoring employees’ experiences of remote work and use these data to identify individuals or groups at greater risk of experiencing work–family conflict and reduced work engagement. Such information ay inform the refinement of remote-work policies and organizational practices [5,80]. At the individual level, organizations may also provide interventions aimed at strengthening employees’ personal resources, including training on boundary management, recovery strategies, time management, and digital communication practices. In parallel, managers should be trained to adopt supportive and trust-based leadership behaviors that facilitate communication, recognition, and team cohesion in remote and hybrid work settings [8,76,77].

7. Conclusions

The present study contributes to the remote- and hybrid-work literature by focusing on employees’ broader appraisal of the disadvantages associated with these work arrangements and examining its associations with technology-related demands, work–family conflict, and work engagement. Greater perceived disadvantages were associated with lower engagement both directly and indirectly through higher technology-related demands and work–family conflict, although the magnitude of these associations and the overall explanatory power of the model were modest.
These findings should be interpreted as cross-sectional statistical associations rather than evidence of temporal or causal mediation. Moreover, the study assessed employees’ perceived disadvantages of remote and hybrid work rather than objectively measured characteristics of the work arrangement. The results therefore concern how employees evaluate their work experience and should not be generalized directly to objective remote-work conditions.
Given that the study was conducted within a single Italian organization operating in the ICT sector, the generalizability of the findings is limited. Future research should replicate these associations across different organizational, occupational, and cultural contexts and use longitudinal and multi-source designs to test temporal ordering, reduce reliance on common-method self-report data, and examine the extent to which subjective appraisals correspond to objectively measured features of remote and hybrid work.

Author Contributions

Conceptualization, G.L.F., C.G. and G.G.; methodology, G.L.F., C.G. and G.G.; software, G.L.F.; validation, G.L.F., C.G., F.A. and G.G.; formal analysis, G.L.F.; investigation, G.L.F., C.G., F.A., P.C., G.F., N.C. and V.V.; resources, C.G. and G.G.; data curation, G.L.F., C.G., F.A., P.C., G.F., N.C. and V.V.; writing—original draft preparation, G.L.F.; writing—review and editing, G.L.F., C.G., F.A., P.C., G.F., N.C., G.G. and V.V.; visualization, G.L.F.; supervision, C.G. and G.G.; project administration, C.G. and G.G.; funding acquisition, G.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in line with the Hel-sinki Declaration, as well as the European data protection treatment defined by the General Data Protection Regulation (GDPR) and Italian privacy law (Law Decree DL- 196/2003 and art. 89 of EU Regulation 2016/679). Because of the observational nature of the study, and in the absence of any involvement of therapeutic medication, no formal approval of the Institutional Review Board of the local Ethics Committee was required.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

Conflicts of Interest

The authors declare no conflicts of interest.

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