Systematic review and tools appraisal of prognostic factors of return to work in workers on sick leave due to musculoskeletal and common mental disorders

Abstract

With the overall objective of providing implication for clinical and research practices regarding the identification and measurement of modifiable predicting factors for return to work (RTW) in people with musculoskeletal disorders (MSDs) and common mental disorders (CMDs), this study 1) systematically examined and synthetized the research evidence available in the literature on the topic, and 2) critically evaluated the tools used to measure each identified factor. A systematic search of prognostic studies was conducted, considering four groups of keywords: 1) population (i.e., MSDs or CMDs), 2) study design (prospective), 3) modifiable factors, 4) outcomes of interest (i.e., RTW). Studies showing high risk of bias were eliminated. Tools used to measure prognostic factors were assessed using psychometric and usability criteria. From the 78 studies that met inclusion criteria, 19 (for MSDs) and 5 (for CMDs) factors reaching moderate or strong evidence were extracted. These factors included work accommodations, RTW expectations, job demands (physical), job demands (psychological), job strain, work ability, RTW self-efficacy, expectations of recovery, locus of control, referred pain (back pain), activities as assessed with disability questionnaires, pain catastrophizing, coping strategies, fears, illness behaviours, mental vitality, a positive health change, sleep quality, and participation. Measurement tools ranged from single-item tools to multi-item standardized questionnaires or subscales. The former generally showed low psychometric properties but excellent usability, whereas the later showed good to excellent psychometric properties and variable usability. The rigorous approach to the selection of eligible studies allowed the identification of a relatively small set of prognostic factors, but with a higher level of certainty. For each factor, the present tool assessment allows an informed choice to balance psychometric and usability criteria.

Introduction

Contemporary societies view employment as the standard and essential aspect of adulthood and full citizenship. Consequently, work is central to a person’s identity, social role, community status and overall wellbeing [1]. People with disabilities or decreased functional abilities, whether temporary or permanent, may face challenges in entering, staying in, or re-entering the workforce [2, 3]. In particular, musculoskeletal disorders (MSDs, such as low back and neck pain, joint pain, tendonitis, carpal tunnel syndrome) and common mental disorders (CMDs, such as depression, anxiety, mood disorders) are leading contributors to work disability and to the global need for rehabilitation services worldwide [4, 5]. This entails important economic implications, as well as hefty social and personal burdens [6, 7]. Return to work (RTW) is often used as an indicator of recovery, and it is consistently reported to be associated with improved mental and physical health, increased quality of life, and enhanced social functioning [8]. To identify the factors that contribute to RTW after the onset of a physical or mental disability has thus become a priority in all industrialized countries. Of particular interest are those factors promoting or hindering RTW that are modifiable throughout a targeted intervention. Modifiable factors can be modified or adjusted through RTW, disability prevention programs, or other clinical and occupational interventions that aim to increase or maintain individual health and capabilities [9–11].

According to the disability prevention management model [12], predictive factors of RTW can be divided, in theory, according to four systems, namely the personal, workplace, healthcare and compensation systems. Unfortunately, factors related to the healthcare and compensation systems have scarcely been studied using only longitudinal observational studies. Factors that have been more extensively studied can be divided into organizational factors, such as work accommodations, and personal factors that can be work-related, such as RTW expectations, or non-work-related, such as recovery expectations. These factors, also called contextual factors, have been identified and classified in categories that are relevant to the occupational rehabilitation domain, extending the well-known International Classification of Functioning (ICF) framework [13], as illustrated in Fig 1.

[Figure omitted. See PDF.]

Prognostic factors from the classification categories identified in red characters are environmental (here organizational) and personal work-related modifiable factors, while personal non-work-related modifiable factors are identified in green characters.

Prognostic factors contributing to RTW have been the subject of several reviews [10, 11, 14–23]. However, there are great variability in the identified factors, which can become problematic when designing interventions. This variability may primarily depend on the health condition [14, 18] and whether RTW or sustainable RTW are considered as outcomes. However, much variability may also be explained by the design and quality of the included studies and more specifically, by the way to manage the different risks of bias (RoB). Another challenge for researchers and practitioners is how the prognostic factors are evaluated/measured, because different tools exist to assess a same concept [17, 20].

A common strategy to cumulate the scientific evidence on a given factor is to merge concepts that are closely related. However, creating factors from the merging of concepts that are a little too distinct may potentially lead to misleading conclusions. Therefore, the merging of concepts requires to look at the items of each tool to make sure they tap on the same broader concept, at least in terms of face validity. In addition, the quality of tools used to measure the different concepts or factors might be influential regarding their ability to predict RTW outcomes. In fact, the prognostic factor measurement is one of the domains considered when assessing the RoB in studies of prognostic factors [24]. To the authors’ opinion, when the number of studies using different tools are numerous enough for a given concept, instead of only merging the evidence across all the tools available, it might also be relevant to verify whether the corresponding factor becomes predictive only for specific tools. This strategy might be relevant especially for non-work-related factors as they have been much more studied than organizational or personal work-related factors, considering that the disease treatment paradigm (or biomedical model) has been introduced much earlier than the disability prevention paradigm (or biopsychosocial model) [12]. Ideally, health care professionals and researchers should measure factors predictive of RTW using standardized tools. However, for each of these factors, the tools that have been used in studies showing a relationship with RTW outcomes are generally not explicitly presented. Assessing their psychometric qualities and usability would allow practitioners and researchers to make an informed choice of available tools based on their practice/research setting.

Two literature reviews and tool appraisals of factors predicting RTW in workers on sick leave due to MSDs and CMDs have been recently published, more precisely on organizational factors [17] and personal work-related factors [20]. Despite including only studies using a prognostic design in these reviews to establish a stronger predictive association with RTW, and conducting a systematic search across databases, there is a lack of quality assessment of the included prognostic studies. A more stringent approach by rejecting studies with a high RoB and then basing quality of evidence on the quantity of studies alone is thus needed. It is hypothesized that this would help reducing “noise” and therefore get a clearer picture of the prognostic factors of RTW.

With the overarching objective of providing implication for clinical and research practices regarding the identification and measurement of modifiable predicting factors for RTW in people with MSDs and CMDs, this study 1) systematically examined and synthetized the research evidence available in the literature on the topic, and 2) critically evaluated the tools used to measure each identified factor. A particular effort has been made to define distinct concepts related to predictive factors (e.g., coping can be separated into coping styles, coping cognitive strategies and coping behavioural strategies). Moreover, when possible, the evidence has been separated from specific tools measuring the same concepts (e.g., perceived disability can be measured with the Oswestry, Roland-Morris or SF-36 questionnaires).

Materials and methods

Study design

A systematic literature review was conducted to identify prognostic factors associated with RTW among workers on sick leave due to MSDs and CMDs. The identified prognostic factors were evaluated based on their level of evidence, categorized as strong, moderate, limited, inconsistent, insufficient, or non-significant. Subsequently, the tools used to assess each identified prognostic factor, which exhibited moderate to strong evidence, were systematically described and evaluated. This evaluation encompassed an assessment of the psychometric properties of the tools, including reliability and validity, as well as their usability criteria.

Phase 1 –Identification of the prognostic factors of RTW

Search strategy.

Under the supervision of a research librarian, three databases (i.e., PubMed, CIHAHL and PsycINFO) were searched from their inception to January 19^th, 2023, to retrieve articles on organizational, personal work-related, as well as personal non-work-related prognostic factors of RTW. Four groups of keywords served to identify potential articles: 1) disability condition; 2) outcome of interest; 3) prognostic factors; 4) study design. The search strategy is reported in S1 File. Additional articles were extracted from bibliographic references of included articles or from reviews on this topic.

In this review, RTW as an outcome was conceptualized either as the 1) being or not returned to work at the follow-up, or 2) the time taken to RTW or the duration of work absence. Other RTW measures, collected from employer/insurance databases, such as 1) being or not on wage replacement benefit, or 2) time to benefit suspension/time to claim closure, were also retained as acceptable surrogates of RTW in the present review. Sustainable RTW was not retained as a RTW outcome as the corresponding prognostic factors might be different.

Study selection.

Studies were included if they met the following criteria: 1) they had a prospective design; 2) participants were workers with MSD or CMD or, for mixed population studies, at least two thirds (67%) of the workers were suffering from MSDs and/or CMDs; 3) participants were fully or partly on sick leave at baseline; 4) RTW or duration of sickness absence was analysed as an outcome; 5) factors were measured as predictors of the RTW outcome in multivariate analyses controlling for age and sex; 6) were published in English or French. Articles displaying a high RoB were excluded. While it is common practice to assess the quality of evidence for a particular factor by considering the quantity (number, proportions) and the quality of included studies, we propose a more rigorous approach by excluding studies with a high RoB and subsequently basing the assessment of evidence quality on the quantity of studies alone.

Study selection was conducted by using COVIDENCE software. After removing duplicates, two trained evaluators (MSc or PhD students) independently screened each title and abstract. Two additional authors (PV and CL) double checked 30% of the references. All relevant full text articles were then obtained and screened by three independent authors (PV, A-C K, CL) to determine if they met the inclusion and exclusion criteria. Discussion among the three authors was undertaken to reach full agreement when the inclusion of an article was uncertain.

Methodological quality assessment of prognostic studies.

Risks of bias were evaluated with the Quality In Prognosis Studies (QUIPS) tool [24]. As outlined in Table 1, the QUIPS comprises six bias domains: 1) study participation, 2) study attrition, 3) prognostic factor measurement, 4) outcome measurement, 5) study confounding and 6) statistical analysis and reporting. To our knowledge, there is no guideline to determine the overall RoB of a given study, and the developers of QUIPS recommend against the use of a summated score for overall study quality. Other research groups have suggested different thresholds [19], based on the total score, which basically reflects the number of domains met. We thus determined that a study would have a low RoB when all 6 bias domains were rated as having low RoB, a moderate RoB when 5 out of 6 bias domains were rated as having low RoB, and a high RoB when less than five bias domains were rated as having low RoB. As mentioned, studies with a high RoB were excluded from this review, more specifically the ones showing a high RoB for at least one of four specific bias domains identified in Table 1.

[Figure omitted. See PDF.]

Methodological quality assessment of evidence on risk factors.

For the assessment of the quality of evidence regarding risk factors, we adapted an approach similar to GRADE [25], a widely recognized method for evaluating the quality of evidence pertaining to interventions. In GRADE, evidence derived from studies with the highest level of evidence (e.g., RCT for intervention studies) is initially classified as high-quality evidence. Nevertheless, confidence in the evidence may be downgraded from high to moderate, and subsequently to low and very low, for different reasons (e.g., inconsistency in relative effects). In the case of assessing the quality of evidence concerning risk factors, it was determined that prognostic studies using a prospective design represented the highest level of evidence.

Data extraction.

Trained evaluators (MSc or PhD students) first extracted the data for characteristics of the participant sample, type of outcome, time of outcome follow-ups, name of the factor, whether it predicts RTW or not, and the tools used to measure these factors. Because of the complexity of the procedure adopted to identify the different concepts/factors and the understanding of multivariate statistical analyses, the entire database was revised by three researchers (PV, A-C K, CL). The present review focused solely factors measured at baseline.

Data analysis.

To adopt a common language, factors were first labelled based on the work of Heerkens and colleagues [13], whom have extended the ICF terminology to better reflect contextual factors, more precisely work-related environmental and personal factors. Partly, we elaborated the terminology further to include factors that were not labelled in the paper of Heerkens and colleagues [13] (e.g., differentiation of social support from various sources like supervisors, coworkers, families or from all sources).

In some studies, outcomes were measured at multiple follow-up points, or different tools were employed to measure the same concept. In such instances, the tools with significant findings were initially selected. If multiple tools led to significant findings, the most psychometrically sound tool was selected. In cases with multiple follow-up times, a factor was considered predictive if it demonstrated statistical significance in at least one of the follow-up assessments.

The factors were classified as having a “strong”, “moderate”, “limited”, “inconsistent”, or “insufficient” level of evidence to predict RTW in MSD and CMD populations separately. The level of evidence was attributed by counting the number of multivariate effects tested that were statistically significant (p < .05) with a positive (+ = protection factor) or negative (– = risk factor) association with the outcome. The detailed evidence-synthesis rules are documented in Fig 2, from Villotti and colleagues [17, 20], allowing to set the level of evidentiary support as follows: 1) “strong”, when three or more studies were found statistically significant, or the ratio was between 80 and 99.9%; 2) “moderate”, when two effects were found, or the ratio was between 65 and 79.9%; 3) “limited”, when only one effect (positive or negative) was found, or the ratio among significant and non-significant evidences was between 60 and 64.9%; 4) “inconsistent”, when the studies did not meet the criteria for any level of evidence and there was no consistent agreement in reported outcomes; 5) “insufficient”, when information was not inconsistent but did not meet the criteria for limited evidence (Fig 2).

[Figure omitted. See PDF.]

Phase 2 –Identification and description of the measurement tools

Inventory of tools.

The inventory of tools was made for each prognostic factor of RTW reaching a moderate or strong level of evidence. The first article that ever validated the tool was first considered. We also considered reviews that summarized the psychometric properties of the tool. Thus, no systematic search was performed in the databases for all the studies substantiating the different psychometric properties of the tools.

Critical appraisal of the tools.

Tools were critically appraised according to six psychometric (scientific) and four usability (practical) criteria, the latter being the ones that a practitioner would also need to look at for their standard practice. The psychometric properties were as follows: 1) face validity; 2) construct validity; 3) convergent validity; 4) internal consistency; 5) test-retest reliability; 6) predictive validity. The usability criteria (practical relevance) were as follows: 1) time of assessment, 2) administrative burden, 3) ease of interpretation and 4) accessibility. These criteria were considered as sufficient based on a consensus involving 11 researchers and 12 RTW stakeholders, namely three representatives each from healthcare professionals, employers, unions and insurers [26]. The operationalization of these psychometric and usability criteria is detailed in S1 File, and were used by three authors (PV, A-C K, CL) to evaluate the tools retained from prognostic RTW factors reaching moderate to strong evidence in the review. An overall evaluation of the tool being “excellent”, “good” or “questionable” was obtained by crossing the psychometric score with the practical one (Table 2). At least two attempts were made to contact some of the authors to obtain more specific information about some of the tools used (item wording, scales, scoring) but these attempts were unsuccessful in the majority of cases.

[Figure omitted. See PDF.]

The overall rating (Excellent, Good, Questionable) is depending on the number of psychometric (n = 6) and usability (n = 4) criteria that were assessed positively.

Results

A full breakdown of article identification and selection procedure for this review is outlined in Fig 3. A total of 10981 articles were identified through the database searches. Following the removal of duplicates, 6223 references remained. A further 5364 were excluded based on title and abstract. Full texts for the remaining 877 articles were retrieved. After full text review, 78 articles remained for data extraction. The main characteristics of these 78 included studies are reported in S2 File. At full text revision stage, articles were excluded for several reasons, such as language (i.e., other than English or French), study design (e.g., qualitative study), publication type (e.g., review, dissertation thesis), population (i.e., other than MSDs or CMDs), statistics (e.g., not controlling for age and sex), outcome (i.e., RTW measures other than those stated in our inclusion criteria), predictor (e.g., not measured at baseline, not modifiable).

[Figure omitted. See PDF.]

The same publication can investigate organizational, personal work-related and personal non-work-related factors; therefore, the sum of the publications on these different categories of factors is higher than the number of publications that met eligibility criteria.

Table 3 provides a general indication of the amount of research on which the level of evidence for each factor is based. More particularly, it reports the number of factors measured for MSDs and CMDs, as well as the proportion of these factors that were measured in only 1, 2, 3 to 5, or more than 5 studies. A total of 90 factors were identified for MSDs, and a total of 46 for CMDs. It was conceptually acceptable to merge some concepts to produce 19 and 7 additional factors, respectively. These additional factors either combine closely related concepts (e.g., All work accommodations factors, combining work accommodations offer/availability/feasibility, worksite visit, workload, RTW plan, and work accommodations general; All CMD symptoms, namely anxiety, cognitive difficulties, depression, stress; or All participation factors, combining the importance to participate to family, leisure, and work activities), or were measured with different tools (e.g., All activities disability questionnaires, using the Oswestry Disability Index (ODI), Quebec back pain disability scale (QBPDS), Roland-Morris disability questionnaire (RMDQ), and other disability questionnaires). For both MSDs and CMDs, the majority of factors were measured in just one study each (i.e., 59% and 65% respectively), while a smaller proportion (29% and 7%) were measured in more than five studies (Table 3).

[Figure omitted. See PDF.]

Table 4 reports the prognostic factors that have reached strong, moderate, and limited levels of evidence for MSDs and CMDs (more detailed in the S3 File). For MSDs, seven factors reached strong evidence, namely Work accommodations (offer/availability/feasibility), All work accommodations factors which is a merged factor, Expectation (RTW), Fear (Fear Avoidance Questionnaire, work subscale), All coping strategies factors, Expectations (recovery) and Locus of control. Thirteen factors reached moderate evidence, namely Job demands (physical), Job strain, Work ability, and Self-efficacy (RTW), Referred pain (back pain), Activities (disability/ODI), Activities (disability/SF-36), Catastrophizing (pain), All fear factors, Illness behaviour, Mental vitality, Positive health change and Sleep quality. For CMDs, Expectation (RTW) emerged as the only factor reaching strong evidence, while Job strain, Job demands (psychological), Sleep quality and All participation factors reached moderate evidence. Factors that reached a limited, inconsistent, or insufficient level of evidence for MSDs and CMDs are reported in S4 File.

[Figure omitted. See PDF.]

Measurement tools for the prognostic factors reaching strong, moderate, and limited levels of evidence for MSDs and CMDs ranged from single-item tools to multi-item standardized questionnaires or subscales. Table 5 summarize this information. The detailed description (i.e., the title and number of items, scales and scoring, accessibility) of the tools used to measure the factors that reached strong and moderate evidence is available in S5 File. S5 File provides the information about the psychometric properties of the tools (6 criteria), their usability (4 criteria) and their global appraisal (i.e., excellent, good, questionable). They are ordered according to their level of evidence (strong evidence first) and then in a sequence allowing to make relationships between some concepts, as presented in the discussion section. For a given factor, the measurement tools ranged from single-item to multi-item tools (questionnaires) or interviews to direct measures during the clinical physical examination.

[Figure omitted. See PDF.]

Discussion

The main results of this study cover three areas: (1) more prognostic factors reached moderate or strong evidence for MSDs (n = 19) than CMDs (n = 5); (2) each of these factors were measured with tools (between one and 14) having different psychometric properties and usability; and (3) limited or insufficient evidence was obtained for a large proportion of prognostic factors that were seldom studied.

As discussed in the “Strengths and limitations” section, considering specific methodological aspects (e.g., inclusion criteria) and the assessment of study quality (RoB), only the factors that have reached moderate or strong evidence will be discussed hereafter. It was also elected not to compare our results to other reviews as large methodological differences (e.g., workers with MSDs or CMDs; inclusion of prognostic studies only; specific RTW outcomes; adjustment for age and sex in multivariate analyses) make these comparisons irrelevant.

Overall trends about prognostic factors of RTW and corresponding measurement tools

In the current state of research, more attention was paid to personal factors rather than organisational ones, which could lead to an ascription of the responsibility to the individual rather than the workplace [98]. However, as concluded in a systematic review on RTW interventions for MSDs and CMDs, successful RTW solutions are made of intervention components from three domains, namely healthcare provision (worker focused), service coordination (e.g., return to work coordinator) and work accommodations (workplace-focused) [99].

There were more prognostic factors reaching moderate or strong evidence for MSDs than CMDs, which can be attributed to the much lower number of prognostic studies on CMDs. A review of reviews observed that most of the literature about factors influencing the RTW in relation to specific diseases (MSDs, CMDs, cardiovascular diseases and cancers) concerned MSDs [18]. Even if nowadays, CMDs are one of the leading causes of disability, it has been difficult to have them recognised and compensated as an occupational disease. Consequently, research on RTW after a CMD was initiated latter and call for more high-quality studies.

Except for Referred pain (back pain) and Sleep quality, none of the numerous candidate factors (e.g., MSD or CMD symptoms, reflexes, muscle strength/endurance, joint mobility/flexibility) of the ICF category of body functions and structures reached moderate or strong evidence, which concurs with analyses (structural equation modeling) showing no direct link between body functions/structures and participation (here work participation) [100].

Many tools described in the present review were single items taken from a standardized questionnaire or were simply self-developed by researchers. Single-item tools might be adequate to measure simple constructs but were automatically (and possibly erroneously) downgraded here in terms of psychometric properties as the assessment of construct validity required a factor analysis applied on several items and internal consistency can be assessed only on two item-tools and over. Consequently, the reader is advised not to judge the psychometric qualities of these tools too severely, as they can still be sensitive enough, especially if the scales comprise several points of discrimination and their inter-rater and/or intra-rater (test-retest) reliability is demonstrated (https://measuringu.com/single-multi-items/). Although these tools do not appear psychometrically sound at first sight, they are admittedly attractive to clinicians in terms of usability as this is clearly a matter of time and supporting resources [101]. Physicians and occupational therapists prefer discussing factors with the help of a topic list instead of using standardized tools [102]. These single items could thus be part of this topic list as they have at least shown their predictive validity, which is better than any other single item that have not been subjected to any form of validation. The information available in the S5 File allows for an informed choice to balance psychometric and usability criteria, when more than one tool is available.

Organizational factors

Work accommodations (offer/availability/feasibility) and All work accommodations factors (for MSDs).

In total, only one organizational factor reached strong evidence, and only for workers on sick leave due to MSDs. The offer or availability of work accommodations (e.g., light duties, less working hours [63]) was evaluated as facilitating RTW, which applied specifically for employees with MSDs. Results are in line with the current state of research underlining work accommodations as an important pillar of disability management at work depending on company specific policies and programmes [103, 104] and represent one of the three main intervention domains (healthcare provision; service coordination; work accommodations) that makes RTW interventions successful for both MSDs and CMDs [99]. When summarizing all types of work accommodations (by adding for instance the workload or worksite visits), a strong level of evidence was achieved for employees with MSDs, which further support these reviews.

Nevertheless, when interpreting the results on work accommodations, it should be taken into account that included studies mainly relied on single items with poor psychometric characteristics. Therefore, upcoming research should strengthen the development of new tools or adapting the existing ones, like the Work Accommodation and Natural Support Scale (WANSS; [105]) or the Job Demands and Accommodation Planning Tool (JDAPT; [106]).

Job demands (physical), job demands (psychological), and job strain.

In terms of other organisational factors like task contents, physical job demands for employees with MSDs and psychological job demands for employees with CMDs reached moderate evidence. This was further substantiated in a recent prospective study of 55467 employees as an exposure to a combination of different types of job demands (e.g., high quantitative, unclear and contradictory demands) that was linked to an increased risk of long-term sickness absence, manifested through additive or super additive effects [107]. Given certain preconditions, psychological job demands may also result in increased motivation at work, personal learning, or development [108]. However, during the RTW process, high perceived physical and/or psychological job demands may lead to adverse health complaints, cause fears of relapse or worsening of symptoms, affecting the employees way back to work indirectly [56, 109]. The present review also underlined that high job strain, or mentally strenuous work, which combines high psychological (or psychosocial) job demands and low job control (Job-Demand-Control-Model, [110]), showed moderate evidence for both employees with MSDs and employees with CMDs. As discussed elsewhere [111], work stress can either boost behaviours as smoking or lack of exercise or can involve various mechanisms generating imbalance in various stress-sensitive systems, negatively impacting various diseases, including MSDs and CMDs.

The examination of physical job demands was conducted with both single items and subscales of validated questionnaires. Unfortunately, in two studies, it was not possible to find specific information on which items were selected from the Dutch Musculoskeletal Questionnaire to make up certain tools, which calls for more precise description to inform future work (new research or reviews). Both psychological job demands, and job strain were assessed using some subscales of the Job Content Questionnaire, which shows excellent psychometric properties but has some usability issues (e.g. accessibility). However, those approaches to assess job strain or job demands may introduce biased results due to self-reported answers, wherefore objective approaches were requested in the current state of research [48].

Personal factors

Expectations (RTW) (for MSDs and CMDs) and expectations (recovery) (for MSDs).

In the present review, rather than merging them as a broader concept as previously reviewed [112, 113], RTW expectations, referring to work participation, were differentiated from recovery expectations, referring to health and healing [114]. RTW expectations reached a strong level of evidence for both MSDs and CMDs, indicating that employees having optimistic expectations towards RTW reached positive outcomes. Recovery expectations are also thought to impact the clinical outcome of several health conditions via behavioural and physiological mechanisms [115]. RTW expectations is the most studied prognostic factor (here 14+/15 for MSDs and 6+/7 for CMDs), also showing highly consistent predictive findings. Consequently, exchanging whether an employee is expecting to RTW irrespective of the health condition, may recognise those at risk for delayed RTW, especially at the beginning of sick leave. However, RTW expectations being a very complex construct (as discussed in [116] and in [112]), clinicians should be trained to recognise the factors influencing RTW, especially in view of ongoing stigma and discrimination of employees with CMDs and their capabilities of RTW [14].

Both concepts (RTW expectations, recovery expectations) are considered as complex constructs [112], thus calling for measurement tools comprising several dimensions and items, as recommended [117]. Yet, most studies used single items to assess RTW expectations with little consistency on the measurement (e.g., on terminology, timeframes for RTW or return to different types of duties) leading to limited comparability of results [118]. Likewise, as in the review of Ebrahim, Malachowski [112] on measures of patients’ expectations about recovery, tools to measure recovery expectations were numerous in the present review, but including primarily single items (4 studies) or a non-standardized questionnaire without scoring information (2 studies), all leading to a low psychometric score of 2/6. Interestingly, single-item tools measuring RTW expectations were shown as predictive of RTW outcomes as multi-item tools [113]. Unfortunately, this was not shown for recovery expectations as there were no corresponding multi-item tools for this concept in Carriere, Pimentel [113]’ review. However, another review identified the Brief Illness Perceptions Questionnaire as a good potential candidate [114].

Most of the studies did not assess the internal consistency based on a variation of items covering multiple domains of an underlying construct [112]. The only RTW expectations scale proposed in the current state of research was the Work-Related Recovery Expectations Questionnaire [68, 74, 119] with some limitations on psychometric characteristics. In conclusion, considering the highly consistent predictive value of RTW expectations and recovery expectations [112, 113], the development of a multi-dimensional/item tool seems justified, especially to help better identifying intervention targets within these complex constructs. For example, different aspects of RTW expectations should be considered, like the modification of work, hierarchies, expectations to deal with job demands or those about pre-absence levels of productivity [118].

All fear factors and fear (Fear-Avoidance Beliefs Questionnaire—Work subscale), both for MSDs.

All fear factors combine fears about work-related and non-work-related activities (e.g., physical activities) and it is part of the fear-avoidance model. Fear of relapse or movement can lead to a maladaptive strategy of activity avoidance, which reduces anxiety in the short term (related to activities likely to cause pain), but maintains the fear (as not exposed to these activities), which results in disability over time [120]. Moreover, fear of pain has been shown as more hindering than pain intensity itself, leading to adaptions in behaviour like limitations while moving or when fulfilling certain activities [30, 31]. Therefore, interventions aiming at decreasing fear-avoidance, involving gradual exposure to feared activities at work or a reduction of job demands were described as appropriate strategies for patients with MSDs [71, 80], as well as cognitive behavior therapies and psychoeducation strategies designed to target patient-specific fears [121].

The studies supporting the All fear factors (10 out of 15) either used one item (Tampa Scale for Kinesiophobia, Graded Reduced Work Ability Scale) or one subscale (4-item Fear of relapse subscale of the Return-to-Work Obstacles and Self-Efficacy Scale; 7-item Work subscale of the Fear-Avoidance Beliefs Questionnaire or FABQ-W) of standardized questionnaires. Interestingly, seven out of the 10 studies showing a significant negative relationship with RTW used the FABQ-W, enabling the conduct of this tool-specific review, which demonstrated strong evidence for employees with MSDs. The Fear-Avoidance Beliefs Questionnaire (FABQ) was rated as excellent, with good psychometric and practical characteristics. Interestingly, among four questionnaires measuring fear-avoidance constructs, it was shown that the FABQ and the Pain Catastrophizing Scale (further discussed below) provide complementary and relevant information [122].

Work ability (for MSDs).

Work ability was broadly defined as the workers capacity (including physical, psychological, and social capabilities) to perform their work while also meeting their job demands [123]. Initially developed in the 1990s in Scandinavia, no common definition was available in the current state of research resulting in varying theoretical concepts [124]. As a result, different viewpoints on work ability are influencing available results and unclear defined concepts cause heterogeneity in measurement approaches impeding interpretation and comparison [124]. Work ability was predominantly measured by two questionnaires, namely the Work Ability Index (WAI) and the Graded Reduced Work Ability Scale, either by the full scale or single items. While the WAI considers the employees job demands, current health status and available resources, the Graded Reduced Work Ability Scale addresses other dimensions including the ability to carry out ordinary or other work in light of the complaints, the amount in which activities and well-being are affected as well as the effect on complaints when continuing to work. Additionally, other broader symptoms affecting well-being and health are assessed [82].

Overall, the WAI and the Graded Reduced Work Ability Scale were evaluated as having excellent and good psychometric properties, respectively. However, the WAI was criticized for its broad variety of questions like the number of diagnoses adding weight for those not being related to work ability [125]. Due to theoretical and practical reasons, the WAI was often replaced by a single item, which was validated by comparing it to the full version of the WAI [125].

Self-efficacy (RTW) for MSDs.

In the context of RTW, self-efficacy refers to the perception of obstacles at work and beliefs in own abilities to overcome them. Consequently, employees reporting higher levels of RTW self-efficacy are more likely to deal with job demands and to fulfil their role at work [66, 126]. The moderate level of evidence for workers with MSDs on RTW self-efficacy was also replicated in a review on sustained RTW when staying for a period of at least 3 months [16]. Hence, assessing RTW self-efficacy as a measure of expectancies about the work and health situation could be used as a proxy for RTW during therapy [127] or in RTW interventions as Lagerveld and colleagues [128] underlined a predictive value of self-efficacy change in terms of RTW after receiving cognitive behavioral therapy (CBT). Though, focussing on achievable objectives, workplace accommodations and gradual RTW might be necessary in the first place for patients with reasons for low levels of RTW self-efficacy, e.g., when goals were set outside their abilities [128].

The measurement of RTW self-efficacy was conducted by using either the Return-to-work Self-Efficacy Questionnaire (11 items, [126]) or the Return-to-Work Obstacles and Self-Efficacy Scale (ROSES) (46 items, [66]). Both questionnaires scored high on psychometric criteria but differed in length as the ROSES includes ten conceptual dimensions with possible RTW barriers following an assessment of capabilities in dealing with them [66].

All coping strategies factors (for MSDs) and Catastrophizing (pain) (for MSDs).

Coping refers to “cognitive and behavioural efforts to master, reduce, or tolerate the internal and/or external demands that are created by the stressful transaction” [129]. However, coping was a difficult factor to assess/define as there are apparently over 100 coping taxonomies and 400 ways of coping [130]. Following the main tools that were used in the present prognostic studies (e.g., Utrecht Coping List—UCL, Coping Strategies Questionnaire—CSQ, Chronic Pain Coping Inventory—CPCI), coping was separated in three factors: 1. Coping (style) using the UCL, 2. Coping (cognitive strategies) and 3. Coping (behavioural strategies), the latter two being measured in the different subscales of the CSQ and CPCI. Strong evidence was reached when combining the evidence from the latter two factors, considering all coping strategies (cognitive and behavioural). Unfortunately, although the CSQ and CPCI questionnaires show good psychometric properties, they are not freely accessible, which represents a non-negligible barrier. Catastrophizing (pain) is comprised in the CSQ as this can be considered as a maladaptive cognitive coping strategy that may elicit assistance or empathic responses from others. It is currently defined as “an exaggerated negative mental set brought to bear during actual or anticipated painful experience” [131]. However, we elected to keep this factor apart as there is a debate about whether catastrophizing should be viewed as a communal coping strategy, a cognitive schemata or a personality trait [132]. Nevertheless, pain catastrophizing is considered as one of the most important psychological correlates of pain chronicity and disability and is also associated with neurophysiological processes [132]. According to the fear-avoidance model [120], pain catastrophizing leads to pain-related fear (previously discussed), which in turn leads to avoidance behaviours (or Illness behaviour) and then to disability. It was measured with the 13-item Pain Catastrophizing Scale (rated as excellent) or the mean of three items from this standardized questionnaire (rated as questionable).

Locus of control (for MSDs).

Rotter [133] defines locus of control as the degree to which a person perceives an outcome as being dependent on their own actions or those of external forces, existing along a continuum from a more internalized orientation to a more externalized orientation. Bandura [134] more recently explains that locus of control should be distinguished from other constructs such as perceived self-efficacy (e.g., RTW self-efficacy), self-esteem, and outcome expectancies (e.g., Recovery expectations or RTW expectations): “Perceived efficacy is a judgment of capability; self-esteem is a judgment of selfworth. They are entirely different phenomena. Locus of control is concerned, not with perceived capability, but with belief about outcome contingencies—whether outcomes are determined by one’s actions or by forces outside one’s control. High locus of control does not necessarily signify a sense of enablement and well-being.” Three tools, all classified as excellent (Table 4), were shown as predictive of RTW. Two subscales of the Multidimensional Health Locus of Control questionnaire can be used independently, namely the 6-item “change externality” [37] or the 6-item “internality” [82] subscale. The third tool is the 3-item “internal locus of control” subscale of a modified version of another standardized questionnaire, namely the Wallston’s Health Locus of Control scale [92]. This modified version is justified by the fact that as for the perceived self-efficacy concept (e.g., RTW self-efficacy), locus of control should also be measured with regard to a specific context to make it a better predictor of a given behaviour, namely RTW here [92].

Activities (disability/SF-36) and Activities (disability/ODI) (for MSDs).

Although activities and participation concepts are not well differentiated in the ICF framework, the activities domain was operationalized as simple tasks/actions (e.g., mobility and daily activities of domestic life and self-care domains) while participation was defined as the ability to perform more complex social roles (e.g., leisure/social/work life situations) within a sociocultural and physical environment [135]. Consequently, even if disability can be interpreted as activities or participation [135], “disability” questionnaires are generally more in line with activities than participation. Activities were predictive of RTW only when specific tools were used such as the generic (all health conditions) physical functioning scale of the RAND-36 or SF-36 questionnaires or the more specific (low back pain) Oswestry disability index (ODI), both having an excellent overall rating. Interestingly, the Roland-Morris disability questionnaire (Activities (RMDQ), 2-/5 studies), which is as well recognized as the ODI [136], have shown insufficient evidence for predicting RTW. The items of SF-36 physical functioning scale and RMDQ are all activities while the 10-item ODI contains two participation items (8. Sex life; 9. Social life), which may help predicting RTW.

All participation factors (for CMDs).

This factor could also be called social functioning, which refers to individual’s interactions with their environment and the ability to fulfill their role within such environments as work, social activities, and relationships with partners and family [137]. It has been examined with a single-item tool measuring participation in general, including social and occupational activities (Social and Occupational Functioning Assessment Scale—SOFAS) and the two-item social functioning subscale (2 items of the SF-36) standardized questionnaire measuring participation to social life activities with family, friends, neighbors, or groups. The SOFAS, including occupational activities, is expected to be predictive of RTW outcomes but the fact that the SF-36 functioning subscale was also predictive indicates that once a worker is able to participate to social activities, it is more likely to be able to reintegrates work activities.

Self-rated health was predictive of RTW as detected by two prognostic factors, more specifically Positive health change (for MSDs) and Mental vitality (for MSDs)

A positive health change, compared to one year ago, can be measured at the baseline assessment, and this “relative” health status was shown as a more powerful and consistent predictor of RTW than perceived pre-injury health [87]. Interestingly, in the three studies showing an association with RTW outcomes, two out of three showed a positive correlation (protective factor) when a positive health change was observed [43, 87], while one study showed a negative correlation (risk factor) when a negative health change was observed [28]. Mental vitality is also an indicator of good health, as it is composed of three dimensions (energy, motivation, and resilience) and has been associated with economic (including work), societal and social participation outcomes [138]. Interestingly, these two factors can be measured with the same questionnaire (RAND-36 or SF-36), using either a single item (for Positive health change) or multi-item subscale (Vitality: 4 items), but only the multi-item subscale showed an excellent overall rating, the single-item tool being questionable psychometrically.

Sleep quality (for MSDs and CMDs).

Sleep quality refers to ease in falling asleep at bedtime and staying asleep during the night and is a significant component of physical and mental health, as well as overall well-being. Interestingly, working conditions (schedules and workload) can act as precipitating and perpetuating sleep disturbances [139], which in turn can interfere with activities of daily living [140] and work occupational functioning [141]. Single-item tools (overall rating: questionable) were used in two studies, one for MSDs [34] and the other for CMDs [35], measuring overall sleep quality. A standardized 7-item questionnaire (Insomnia Severity Index) was used in one study [32] comprising both populations (MSDs and CMDs), with an excellent overall rating, also measuring the impact of sleep disturbances on daily functioning and quality of life.

Two prognostic factors of RTW for MSDs were specific to workers with back pain or sciatica, namely Referred pain (back pain) and Illness behaviour. They were also the sole factors that were not assessed using questionnaires. Referred pain (back pain) below the knee is well-known as a poor prognostic factor of pain disability in workers with back pain [142]. The corresponding clinical tests are rapid, which also explains why it is part of the regular clinical investigations. Illness behaviour is defined as "observable and potentially measurable actions and conduct which express and communicate the individual’s own perception of disturbed health" [143]. These “pain” behaviours are interpreted as out of proportion to the underlying physical disease and related more to associated psychological disturbances. This may explain why they were predictive of RTW. While there are different questionnaires to measure illness behaviours [144], only measures from the physical examination (n = 3) were used in the studies substantiating this prognostic factor in the present review. Waddell’s signs are inappropriate (exaggerated) responses to clinical physical examination [145] while Waddell’s symptoms, collected during the clinical interview, are described as not fitting general clinical experience [146]. Finally, the pain behaviour observation system [147, 148] allows to directly observe “guarding” among five pain behaviours. While the later protocol is more standardized, requiring to systematically score the different observations across various physical tests (training required), it is lengthy and not more reliable than Waddell’s signs. Waddell symptoms have the advantage of being part of the history, which reduces worker assessment time, but some psychometric qualities (e.g., internal consistency, interrater and test-retest reliability) have not been tested adequately.

Strengths and limitations

This review focused on studies with a prospective longitudinal design only, which represent the best study design to identify prognostic factors. A particular feature of this study is the rejection of studies with a high RoB, which increases the confidence in the results and thus reduces the number (or proportion) of studies that are required to reach the strong, moderate, or limited levels of evidence. On the other hand, for most of these factors, a new study will inevitably upgrade or downgrade this evidence, which emphasize the need of new high-quality prospective studies. Limited factors should be considered as valuable potential candidates in future studies, as well as factors for which only one study was available, but showing non-significant findings. This may already involve several candidate factors as up to 59% (for MSDs) or 65% (for CMDs) of the factors identified in the present review were measured in only one study. On the opposite, other factors obtained only non-significant results from more than three studies. Unless these studies were poorly conducted for any other reason, these factors might be disregarded, especially when the participant burden is of concern.

No study was rejected based on two out of the six criteria considered for the assessment of study quality (RoB), namely the loss to follow-up and the measurement of the prognostic factor. It was deemed adequate not rejecting studies based on the quality of measurement tools as one objective of this study was to identify the variety of tools that have shown at least some (statistically significant) RTW predictive validity. This paper evaluated the tools that were shown as predictive, providing not only table on psychometric criteria, but also on practical relevance. However, a systematic search of all studies substantiating all the psychometric properties was not carried out, leading only to a first appraisal of their quality.

Other limitations also need to be acknowledged. Only factors measured at baseline were considered as a small amount of research have looked at factors measured further in the disability/recovery process. Future studies should consider measures of factors that may appear further (e.g., psychosocial factors) as disability is prolonged or measures of change (from baseline) of a given factor after an amount of time or after a given treatment, as these factors might have some additional prognostic value. The same holds true for the measurement of the RTW outcomes as all follow-ups were combined, not allowing to determine the factors that were predictive of early (e.g., 0–6 months), mid (e.g., 6–12 months) and late RTW (e.g., >12 months). Furthermore, work-related environmental and personal factors influencing RTW were assessed by using self-administered questionnaires being subject to limited objectivity. Finally, only studies published in English or French were included.

Conclusions

In view of MSDs and CMDs as the leading causes for work disability, several environmental and personal influencing factors were identified. In the current state of research, much more prognostic factors reached moderate or strong evidence for MSDs than CMDs, reflecting the delay in CMD research.

For each factor, the corresponding measurement tools were assessed to allow an informed choice to balance psychometric and usability criteria. Among the 68 tools tested, 20 (29%) were graded as excellent, 8 (12%) as good, and 36 (53%) as questionable (4 were not graded). This review also allowed identifying factors and tools for which more research is needed, especially for CMDs. As a result, information on applied tools will be useful not only for practice when facilitating RTW but also for upcoming research, when filling gaps in the current state of research and for policymakers in developing RTW strategies.

Supporting information

S1 File. Tool appraisal criteria and syntax strategy.

https://doi.org/10.1371/journal.pone.0307284.s001

(DOCX)

S2 File. Characteristics of included studies.

https://doi.org/10.1371/journal.pone.0307284.s002

(DOCX)

S3 File. Detailed evidentiary tables for factors reaching strong, moderate or limited level of evidence.

https://doi.org/10.1371/journal.pone.0307284.s003

(DOCX)

S4 File. Summary of evidentiary support for factors reaching inconsistent or insufficient level of evidence or showing only non-significant results.

https://doi.org/10.1371/journal.pone.0307284.s004

(DOCX)

S5 File. Tools appraisal.

https://doi.org/10.1371/journal.pone.0307284.s005

(DOCX)

Acknowledgments

We acknowledge the valuable contributions of Maryse Gagnon (IRSST) and Marie-Claude Laferrière (Université Laval) for their bibliometric work, and Amir Chitour, Émilie Champagne, Joëlle Cossette, Annabelle Fortin, Karolane Groulx, Benjamin Hack, Charles Plourde, Anna Ta as research assistants.

Citation: Villotti P, Kordsmeyer A-C, Roy J-S, Corbière M, Negrini A, Larivière C (2024) Systematic review and tools appraisal of prognostic factors of return to work in workers on sick leave due to musculoskeletal and common mental disorders. PLoS ONE 19(7): e0307284. https://doi.org/10.1371/journal.pone.0307284

About the Authors:

Patrizia Villotti

Contributed equally to this work with: Patrizia Villotti, Christian Larivière

Roles: Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Writing – original draft

E-mail: [email protected]

Affiliations: Department of Education and Pedagogy–Career Counseling, Université du Québec à Montréal, Montréal, Canada, Centre de recherche de l’Institut universitaire en santé mentale de Montréal, Montréal, Canada

ORICD: https://orcid.org/0000-0003-4528-6340

Ann-Christin Kordsmeyer

Roles: Data curation, Formal analysis, Writing – original draft

Affiliation: Institute for Occupational and Maritime Medicine (ZfAM), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany

Jean-Sébastien Roy

Roles: Conceptualization, Data curation, Funding acquisition, Methodology, Writing – review & editing

Affiliations: School of Rehabilitation Sciences, Faculty of Medicine, Université Laval, Quebec City, Canada, Centre for Interdisciplinary Research in Rehabilitation and Social Integration, Quebec, Rehabilitation Institute, Quebec City, Canada

ORICD: https://orcid.org/0000-0003-2853-9940

Marc Corbière

Roles: Conceptualization, Funding acquisition, Methodology, Writing – review & editing

Affiliations: Department of Education and Pedagogy–Career Counseling, Université du Québec à Montréal, Montréal, Canada, Centre de recherche de l’Institut universitaire en santé mentale de Montréal, Montréal, Canada

Alessia Negrini

Roles: Conceptualization, Funding acquisition, Writing – review & editing

Affiliation: Institut de recherche Robert-Sauvé en santé et en sécurité du travail, Montréal, Canada

Christian Larivière

Contributed equally to this work with: Patrizia Villotti, Christian Larivière

Roles: Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Writing – original draft

Affiliation: Institut de recherche Robert-Sauvé en santé et en sécurité du travail, Montréal, Canada

[/RAW_REF_TEXT]

[/RAW_REF_TEXT]

[/RAW_REF_TEXT]

[/RAW_REF_TEXT]

[/RAW_REF_TEXT]

References

1. Corbière M, Villotti P, Pachoud B. Chapitre 12. Maintien en emploi avec un trouble psychique. Une synthèse des écrits. Psychologie et carrières. Louvain-la-Neuve: De Boeck Supérieur; 2022. p. 221–40.

2. Dewa CS, Loong D, Bonato S, Hees H. Incidence rates of sickness absence related to mental disorders: a systematic literature review. BMC Public Health. 2014;14(1):205. pmid:24571641

3. Bellotti L, Zaniboni S, Langlois I, Villotti P. 6 Age, Mental Disorders and Work Design Factors. In: Joy B, Sophie H, Mukta K, editors. De Gruyter Handbook of Disability and Management. Berlin, Boston: De Gruyter; 2023. p. 105–24.

4. Cieza A, Causey K, Kamenov K, Hanson SW, Chatterji S, Vos T. Global estimates of the need for rehabilitation based on the Global Burden of Disease study 2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet. 2020;396(10267):2006–17. pmid:33275908

5. James SL, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet. 2018;392(10159):1789–858. pmid:30496104

6. Dewa CS, Loong D, Bonato S. Work outcomes of sickness absence related to mental disorders: a systematic literature review. BMJ open. 2014;4(7):e005533. pmid:25023133

7. Sultan-Taïeb H, Parent-Lamarche A, Gaillard A, Stock S, Nicolakakis N, Hong QN, et al. Economic evaluations of ergonomic interventions preventing work-related musculoskeletal disorders: a systematic review of organizational-level interventions. BMC Public Health. 2017;17(1):935. pmid:29216849

8. Figueredo JM, García-Ael C, Gragnano A, Topa G. Well-Being at Work after Return to Work (RTW): A Systematic Review. Int J Environ Res Public Health. 2020;17(20). Epub 2020/10/21. pmid:33076302; PubMed Central PMCID: PMC7602369.

9. Lagerveld SE, Bultmann U, Franche RL, van Dijk FJ, Vlasveld MC, van der Feltz-Cornelis CM, et al. Factors associated with work participation and work functioning in depressed workers: a systematic review. J Occup Rehabil. 2010;20(3):275–92. pmid:20091105; PubMed Central PMCID: PMC2923705.

10. White M, Wagner S, Schultz IZ, Murray E, Bradley SM, Hsu V, et al. Modifiable workplace risk factors contributing to workplace absence across health conditions: A stakeholder-centered best-evidence synthesis of systematic reviews. Work. 2013;45(4):475–92. Epub 2013/03/28. pmid:23531590.

11. Wagner S, White M, Schultz I, Murray E, Bradley SM, Hsu V, et al. Modifiable worker risk factors contributing to workplace absence: a stakeholder-centred best-evidence synthesis of systematic reviews. Work. 2014;49(4):541–58. pmid:24004777.

12. Loisel P, Durand MJ, Berthelette D, Vézina N, Baril R, Gagnon D, et al. Disability prevention. New paradigm for the management of occupational back pain. Disease Management and Health Outcomes. 2001;9(7):351–60.

13. Heerkens YF, de Brouwer CPM, Engels JA, van der Gulden JWJ, Kant I. Elaboration of the contextual factors of the ICF for Occupational Health Care. Work. 2017;57(2):187–204. pmid:28582939.

14. Cancelliere C, Donovan J, Stochkendahl MJ, Biscardi M, Ammendolia C, Myburgh C, et al. Factors affecting return to work after injury or illness: best evidence synthesis of systematic reviews. Chiropr Man Therap. 2016;24(1):32. Epub 2016/09/10. pmid:27610218; PubMed Central PMCID: PMC5015229.

15. Detaille SI, Heerkens YF, Engels JA, van der Gulden JW, van Dijk FJ. Common prognostic factors of work disability among employees with a chronic somatic disease: a systematic review of cohort studies. Scand J Work Environ Health. 2009;35(4):261–81. pmid:19562236.

16. Etuknwa A, Daniels K, Eib C. Sustainable Return to Work: A Systematic Review Focusing on Personal and Social Factors. J Occup Rehabil. 2019;29(4):679–700. Epub 2019/02/16. pmid:30767151; PubMed Central PMCID: PMC6838034.

17. Gragnano A, Villotti P, Larivière C, Negrini A, Corbière M. A Systematic Search and Review of Questionnaires Measuring Individual psychosocial Factors Predicting Return to Work After Musculoskeletal and Common Mental Disorders. J Occup Rehabil. 2021;31(3):491–511. Epub 2020/12/29. pmid:33355911; PubMed Central PMCID: PMC8298352.

18. Gragnano A, Negrini A, Miglioretti M, Corbiere M. Common Psychosocial Factors Predicting Return to Work After Common Mental Disorders, Cardiovascular Diseases, and Cancers: A Review of Reviews Supporting a Cross-Disease Approach. J Occup Rehabil. 2017. pmid:28589524.

19. Rashid M, Kristofferzon ML, Nilsson A, Heiden M. Factors associated with return to work among people on work absence due to long-term neck or back pain: a narrative systematic review. BMJ Open. 2017;7(6):e014939. Epub 2017/07/05. pmid:28674139; PubMed Central PMCID: PMC5734441.

20. Villotti P, Gragnano A, Lariviere C, Negrini A, Dionne CE, Corbiere M. Tools Appraisal of Organizational Factors Associated with Return-to-Work in Workers on Sick Leave Due to Musculoskeletal and Common Mental Disorders: A Systematic Search and Review. J Occup Rehabil. 2021;31:7–25. pmid:32440855.

21. White MI, Wagner SL, Schultz IZ, Murray E, Bradley SM, Hsu V, et al. Non-modifiable worker and workplace risk factors contributing to workplace absence: A stakeholder-centred synthesis of systematic reviews. Work. 2015;52(2):353–73. pmid:26409377.

22. de Wit M, Wind H, Hulshof CTJ, Frings-Dresen MHW. Person-related factors associated with work participation in employees with health problems: a systematic review. Int Arch Occup Environ Health. 2018;91(5):497–512. Epub 2018/04/28. pmid:29700608; PubMed Central PMCID: PMC6002456.

23. van Hees SG, Carlier BE, Vossen E, Blonk RW, Oomens S. Towards a better understanding of work participation among employees with common mental health problems: a systematic realist review. Scand J Work Environ Health. 2022;48(3):173–89. Epub 2021/12/09. pmid:34878557.

24. Hayden JA, van der Windt DA, Cartwright JL, Cote P, Bombardier C. Assessing bias in studies of prognostic factors. Ann Intern Med. 2013;158(4):280–6. pmid:23420236.

25. Guyatt G, Oxman AD, Akl EA, Kunz R, Vist G, Brozek J, et al. GRADE guidelines: 1. Introduction-GRADE evidence profiles and summary of findings tables. J Clin Epidemiol. 2011;64(4):383–94. Epub 2011/01/05. pmid:21195583.

26. Larivière C, Nastasia I, Corbière M, Truchon M, Côté D, Mathieu A, et al. Élaboration du contenu d’un site Web portant sur l’incapacité et le retour au travail. Montréal, Québec: Institut de recherche Robert-Sauvé en santé et en sécurité du travail du Québec (IRSST), 2021 R-997.

27. Du Bois M, Donceel P. A screening questionnaire to predict no return to work within 3 months for low back pain claimants. European Spine Journal. 2008;17(3):380–5. Epub 2008/01/04. pmid:18172698; PubMed Central PMCID: PMC2270393.

28. Schultz IZ, Crook JM, Berkowitz J, Meloche GR, Milner R, Zuberbier OA, et al. Biopsychosocial multivariate predictive model of occupational low back disability. Spine. 2002;27(23):2720–5. pmid:12461399

29. Turner J, Franklin G, Fulton-Kehoe D, Sheppard L, Stover B, Wu R, et al. Early predictors of chronic work disability: a prospective, population-based study of workers with back injuries. Spine. 2008;33(25):2809–18.

30. Grøvle L, Haugen AJ, Keller A, Ntvig B, Brox JI, Grotle M. Prognostic factors for return to work in patients with sciatica. The Spine Journal: Official Journal of the North American Spine Society. 2013;13(12):1849–57. Epub 2013/09/26. pmid:24060231.

31. Storheim K, Brox JI, Holm I, Bo K. Predictors of return to work in patients sick listed for sub-acute low back pain: a 12-month follow-up study. J Rehabil Med. 2005;37(6):365–71. pmid:16287668

32. Hara KW, Bjorngaard JH, Jacobsen HB, Borchgrevink PC, Johnsen R, Stiles TC, et al. Biopsychosocial predictors and trajectories of work participation after transdiagnostic occupational rehabilitation of participants with mental and somatic disorders: a cohort study. BMC Public Health. 2018;18(1):1014. pmid:30111291; PubMed Central PMCID: PMC6094579.

33. Nicholas MK, Costa DSJ, Linton SJ, Main CJ, Shaw WS, Pearce R, et al. Predicting Return to Work in a Heterogeneous Sample of Recently Injured Workers Using the Brief ÖMPSQ-SF. J Occup Rehabil. 2019;29(2):295–302. Epub 2018/05/26. pmid:29796980.

34. Reme SE, Hagen EM, Eriksen HR. Expectations, perceptions, and physiotherapy predict prolonged sick leave in subacute low back pain. BMC Musculoskelet Disord. 2009;10:139. pmid:19912626; PubMed Central PMCID: PMC2780378.

35. Gustafsson K, Lundh G, Svedberg P, Linder J, Alexanderson K, Marklund S. Psychological factors are related to return to work among long-term sickness absentees who have undergone a multidisciplinary medical assessment. J Rehabil Med. 2013;45(2):186–91. pmid:23138390.

36. Bontoux L, Roquelaure Y, Billabert C, Dubus V, Sancho PO, Colin D, et al. [Prospective study of the outcome at one year of patients with chronic low back pain in a program of intensive functional restoration and ergonomic intervention. Factors predicting their return to work]. Annales de réadaptation et de médecine physique. 2004;47(8):563–72. pmid:15465161

37. Hagen EM, Svensen E, Eriksen HR. Predictors and modifiers of treatment effect influencing sick leave in subacute low back pain patients. Spine (Phila Pa 1976). 2005;30(24):2717–23. pmid:16371893.

38. Faber E, Burdorf A, Bierma-Zeinstra SM, Miedema HS, Koes BW. Determinants for improvement in different back pain measures and their influence on the duration of sickness absence. Spine. 2006;31(13):1477–83. pmid:16741458

39. Asher AL, Devin CJ, Archer KR, Chotai S, Parker S, Bydon M, et al. An analysis from the Quality Outcomes Database, Part 2. Predictive model for return to work after elective surgery for lumbar degenerative disease. Journal of Neurosurgery Spine. 2017:1–12. pmid:28498069

40. Opsahl J, Eriksen HR, Tveito TH. Do expectancies of return to work and Job satisfaction predict actual return to work in workers with long lasting LBP? BMC Musculoskeletal Disorders. 2016;17(1):481. Epub 2016/11/20. pmid:27855684; PubMed Central PMCID: PMC5114779.

41. Gross DP, Battie MC, Cassidy JD. The prognostic value of functional capacity evaluation in patients with chronic low back pain: part 1: timely return to work. Spine. 2004;29(8):914–9. pmid:15082996

42. Kvam L, Vik K, Eide AH. Importance of participation in major life areas matters for return to work. J Occup Rehabil. 2015;25(2):368–77. pmid:25319539; PubMed Central PMCID: PMC4436658.

43. Post M, Krol B, Groothoff JW. Self-rated health as a predictor of return to work among employees on long-term sickness absence. Disabil Rehabil. 2006;28(5):289–97. Epub 2006/02/24. pmid:16492623.

44. Schultz IZ, Crook J, Berkowitz J, Milner R, Meloche GR. Predicting Return to Work After Low Back Injury Using the Psychosocial Risk for Occupational Disability Instrument: A Validation Study. Journal of Occupational Rehabilitation. 2005;15(3):365–76. pmid:16119227

45. Laukkala T, Heikinheimo S, Vuokko A, Junttila IS, Tuisku K. Subjective and objective measures of function and return to work: an observational study with a clinical psychiatric cohort. Soc Psychiatry Psychiatr Epidemiol. 2018;53(5):537–40. Epub 2017/12/25. pmid:29275503.

46. Lötters F, Burdorf A. Prognostic factors for duration of sickness absence due to musculoskeletal disorders. Clin J Pain. 2006;22(2):212–21. Epub 2006/01/24. pmid:16428958.

47. Steenstra IA, Busse JW, Tolusso D, Davilmar A, Lee H, Furlan AD, et al. Predicting time on prolonged benefits for injured workers with acute back pain. J Occup Rehabil. 2015;25(2):267–78. Epub 2014/08/29. pmid:25164779; PubMed Central PMCID: PMC4436678.

48. Bosman LC, Twisk JWR, Geraedts AS, Heymans MW. Development of Prediction Model for the Prognosis of Sick Leave Due to Low Back Pain. J Occup Environ Med. 2019;61(12):1065–71. Epub 2019/10/28. pmid:31651601.

49. Hansson TH, Hansson EK. The effects of common medical interventions on pain, back function, and work resumption in patients with chronic low back pain: A prospective 2-year cohort study in six countries. Spine (Phila Pa 1976). 2000;25(23):3055–64. Epub 2001/01/06. pmid:11145817.

50. Amick BC 3rd, Lee H, Hogg-Johnson S, Katz JN, Brouwer S, Franche RL, et al. How Do Organizational Policies and Practices Affect Return to Work and Work Role Functioning Following a Musculoskeletal Injury? J Occup Rehabil. 2017;27(3):393–404. Epub 2016/09/23. pmid:27654622.

51. Huijs JJ, Koppes LL, Taris TW, Blonk RW. Differences in predictors of return to work among long-term sick-listed employees with different self-reported reasons for sick leave. J Occup Rehabil. 2012;22(3):301–11. Epub 2012/02/04. pmid:22302668.

52. Storheim K, Brox J. I., Holm I., & Bo K. Predictors of return to work in patients sick listed for sub-acute low back pain: a 12-month follow-up study. Journal of rehabilitation medicine. 2005;37(6):365–71. pmid:16287668

53. Abásolo L, Carmona L, Lajas C, Candelas G, Blanco M, Loza E, et al. Prognostic factors in short-term disability due to musculoskeletal disorders. Arthritis Rheum. 2008;59(4):489–96. Epub 2008/04/03. pmid:18383421.

54. Gaines WG Jr., Hegmann KT. Effectiveness of Waddell’s nonorganic signs in predicting a delayed return to regular work in patients experiencing acute occupational low back pain. Spine (Phila Pa 1976). 1999;24(4):396–400; discussion 1. Epub 1999/03/05. pmid:10065525.

55. Haveraaen LA, Skarpaas LS, Berg JE, Aas RW. Do psychological job demands, decision control and social support predictreturn to work three months after a return-to-work (RTW) programme? The rapid-RTW cohort study. Work. 2015;53 1:61–71. pmid:26684705

56. Haveraaen LA, Skarpaas LS, Aas RW. Job demands and decision control predicted return to work: the rapid-RTW cohort study. BMC Public Health. 2017;17(1):154. pmid:28152995

57. Norder G, Roelen CAM, van der Klink JJL, Bültmann U, Sluiter JK, Nieuwenhuijsen K. External Validation and Update of a Prediction Rule for the Duration of Sickness Absence Due to Common Mental Disorders. J Occup Rehabil. 2017;27(2):202–9. Epub 2016/06/05. pmid:27260170; PubMed Central PMCID: PMC5405096.

58. Soucy I, Truchon M, Côté D. Work-related factors contributing to chronic disability in low back pain. Work. 2006;26(3):313–26. Epub 2006/05/25. pmid:16720972.

59. Beemster TT, van Bennekom CAM, van Velzen JM, Frings-Dresen MHW, Reneman MF. Vocational Rehabilitation with or without Work Module for Patients with Chronic Musculoskeletal Pain and Sick Leave from Work: Longitudinal Impact on Work Participation. J Occup Rehabil. 2021;31(1):72–83. Epub 2020/05/08. pmid:32378023; PubMed Central PMCID: PMC7954725.

60. Hogg-Johnson S, Cole DC. Early prognostic factors for duration on temporary total benefits in the first year among workers with compensated occupational soft tissue injuries. Occup Environ Med. 2003;60(4):244–53. Epub 2003/03/28. pmid:12660372; PubMed Central PMCID: PMC1740514.

61. Franche R-L, Severin CN, Hogg-Johnson S, Côté P, Vidmar M, Lee H. The impact of early workplace-based return-to-work strategies on work absence duration: a 6-month longitudinal study following an occupational musculoskeletal injury. Journal of occupational and environmental medicine. 2007;49(9):960–74. pmid:17848852.

62. Iles RA, Sheehan LR, Gosling CM. Assessment of a new tool to improve case manager identification of delayed return to work in the first two weeks of a workers’ compensation claim. Clin Rehabil. 2020;34(5):656–66. Epub 2020/03/19. pmid:32183561.

63. Turner JA, Franklin G, Fulton-Kehoe D, Sheppard L, Stover B, Wu R, et al. ISSLS prize winner: early predictors of chronic work disability: a prospective, population-based study of workers with back injuries. Spine (Phila Pa 1976). 2008;33(25):2809–18. Epub 2008/12/04. pmid:19050587.

64. van Duijn M, Lotters F, Burdorf A. Influence of modified work on return to work for employees on sick leave due to musculoskeletal complaints. J Rehabil Med. 2005;37(3):172–9. pmid:16040475

65. Nieuwenhuijsen K, Verbeek JH, de Boer AG, Blonk RW, van Dijk FJ. Supervisory behaviour as a predictor of return to work in employees absent from work due to mental health problems. Occup Environ Med. 2004;61(10):817–23. Epub 2004/09/21. pmid:15377767; PubMed Central PMCID: PMC1740675.

66. Corbiere M, Negrini A, Durand MJ, St-Arnaud L, Briand C, Fassier JB, et al. Development of the Return-to-Work Obstacles and Self-Efficacy Scale (ROSES) and Validation with Workers Suffering from a Common Mental Disorder or Musculoskeletal Disorder. J Occup Rehabil. 2017;27(3):329–41. pmid:27562583.

67. Du Bois M, Szpalski M, Donceel P. Patients at risk for long-term sick leave because of low back pain. Spine J. 2009;9(5):350–9. pmid:18790677.

68. Gross DP, Battié MC. Recovery expectations predict recovery in workers with back pain but not other musculoskeletal conditions. J Spinal Disord Tech. 2010;23(7):451–6. Epub 2010/04/24. pmid:20414134.

69. Sampere M, Gimeno D, Serra C, Plana M, López JC, Martínez JM, et al. Return to work expectations of workers on long-term non-work-related sick leave. J Occup Rehabil. 2012;22(1):15–26. Epub 2011/06/28. pmid:21701951.

70. Steenstra IA, Koopman FS, Knol DL, Kat E, Bongers PM, de Vet HC, et al. Prognostic factors for duration of sick leave due to low-back pain in dutch health care professionals. J Occup Rehabil. 2005;15(4):591–605. pmid:16254758.

71. Turner JA, Franklin G, Fulton-Kehoe D, Sheppard L, Wickizer TM, Wu R, et al. Worker recovery expectations and fear-avoidance predict work disability in a population-based workers’ compensation back pain sample. Spine (Phila Pa 1976). 2006;31(6):682–9. Epub 2006/03/17. pmid:16540874.

72. Wåhlin C, Ekberg K, Persson J, Bernfort L, Oberg B. Association between clinical and work-related interventions and return-to-work for patients with musculoskeletal or mental disorders. J Rehabil Med. 2012;44(4):355–62. Epub 2012/03/22. pmid:22434378.

73. Fishbain DA, Cutler RB, Rosomoff HL, Khalil T, Steele-Rosomoff R. Impact of chronic pain patients’ job perception variables on actual return to work. Clin J Pain. 1997;13(3):197–206. Epub 1997/09/26. pmid:9303251.

74. Gross DP, Battié MC. Work-related recovery expectations and the prognosis of chronic low back pain within a workers’ compensation setting. J Occup Environ Med. 2005;47(4):428–33. Epub 2005/04/13. pmid:15824635.

75. Løvvik C, Shaw W, Overland S, Reme SE. Expectations and illness perceptions as predictors of benefit recipiency among workers with common mental disorders: secondary analysis from a randomised controlled trial. BMJ Open. 2014;4(3):e004321. Epub 2014/03/05. pmid:24589824; PubMed Central PMCID: PMC3948454.

76. Nielsen MB, Madsen IE, Bultmann U, Christensen U, Diderichsen F, Rugulies R. Predictors of return to work in employees sick-listed with mental health problems: findings from a longitudinal study. Eur J Public Health. 2011;21(6):806–11. Epub 2010/12/04. pmid:21126986.

77. Nieuwenhuijsen K, Verbeek JH, de Boer AG, Blonk RW, van Dijk FJ. Predicting the duration of sickness absence for patients with common mental disorders in occupational health care. Scand J Work Environ Health. 2006;32(1):67–74. pmid:16539174.

78. Hedlund Å, Nilsson A, Boman E, Kristofferzon ML. Predictors of return to work and psychological well-being among women during/after long-term sick leave due to common mental disorders—a prospective cohort study based on the theory of planned behaviour. Health Soc Care Community. 2022;30(6):e5245–e58. Epub 2022/07/28. pmid:35894151; PubMed Central PMCID: PMC10087653.

79. Okurowski L, Pransky G, Webster B, Shaw WS, Verma S. Prediction of prolonged work disability in occupational low-back pain based on nurse case management data. J Occup Environ Med. 2003;45(7):763–70. Epub 2003/07/12. pmid:12855916.

80. Oyeflaten I, Hysing M, Eriksen HR. Prognostic factors associated with return to work following multidisciplinary vocational rehabilitation. J Rehabil Med. 2008;40(7):548–54. pmid:18758672.

81. Cougot B, Petit A, Paget C, Roedlich C, Fleury-Bahi G, Fouquet M, et al. Chronic low back pain among French healthcare workers and prognostic factors of return to work (RTW): a non-randomized controlled trial. J Occup Med Toxicol. 2015;10:40. pmid:26516339; PubMed Central PMCID: PMC4625968.

82. Haldorsen EM, Indahl A, Ursin H. Patients with low back pain not returning to work. A 12-month follow-up study. Spine (Phila Pa 1976). 1998;23(11):1202–7; discussion 8. Epub 1998/06/24. pmid:9636972.

83. Brouwer S, Reneman MF, Bültmann U, van der Klink JJ, Groothoff JW. A prospective study of return to work across health conditions: perceived work attitude, self-efficacy and perceived social support. J Occup Rehabil. 2010;20(1):104–12. Epub 2009/11/07. pmid:19894106; PubMed Central PMCID: PMC2832875.

84. Victor M, Lau B, Ruud T. Predictors of Return to Work 6 Months After the End of Treatment in Patients with Common Mental Disorders: A Cohort Study. J Occup Rehabil. 2018;28(3):548–58. Epub 2017/12/14. pmid:29234955; PubMed Central PMCID: PMC6096513.

85. Cole DC, Mondloch MV, Hogg-Johnson S. Listening to injured workers: how recovery expectations predict outcomes—a prospective study. CMAJ. 2002;166(6):749–54. pmid:11944761

86. Hogg-Johnson S, Cole D. Early prognostic factors for duration on temporary total benefits in the first year among workers with compensated occupational soft tissue injuries. Occupational and Environmental Medicine. 2003;60(4):244–53. pmid:12660372

87. Schultz I Z., Crook J, Meloche GR, Berkowitz J, Milner R, Zuberbier OA, et al. Psychosocial factors predictive of occupational low back disability: towards development of a return-to-work model. Pain. 2004;107(1–2):77–85. pmid:14715392

88. Gauthier N, Sullivan MJ, Adams H, Stanish WD, Thibault P. Investigating risk factors for chronicity: the importance of distinguishing between return-to-work status and self-report measures of disability. JOccupEnvironMed. 2006;48(3):312–8. pmid:16531836

89. Koopman FS, Edelaar M, Slikker R, Reynders K, van der Woude LH, Hoozemans MJ. Effectiveness of a multidisciplinary occupational training program for chronic low back pain: a prospective cohort study. Am J Phys Med Rehabil. 2004;83(2):94–103. pmid:14758295.

90. Truchon M, Côté D. Predictive validity of the Chronic Pain Coping Inventory in subacute low back pain. Pain. 2005;116(3):205–12. pmid:15927382

91. Rashid M, Kristofferzon ML, Nilsson A. Predictors of return to work among women with long-term neck/shoulder and/or back pain: A 1-year prospective study. PLoS One. 2021;16(11):e0260490. Epub 2021/11/24. pmid:34813601; PubMed Central PMCID: PMC8610267.

92. Selander J, Marnetoft SU, Asell M. Predictors for successful vocational rehabilitation for clients with back pain problems. Disabil Rehabil. 2007;29(3):215–20. pmid:17364772.

93. Gaines WG Jr, Hegmann KT. Effectiveness of Waddell’s nonorganic signs in predicting a delayed return to regular work in patients experiencing acute occupational low back pain. Spine. 1999;24(4):396–400. pmid:10065525

94. Dersh J, Mayer TG, Gatchel RJ, Polatin PB, Theodore BR, Mayer EA. Prescription opioid dependence is associated with poorer outcomes in disabling spinal disorders. Spine (Phila Pa 1976). 2008;33(20):2219–27. Epub 2008/08/30. pmid:18725868.

95. Nielsen MB, Bultmann U, Madsen IE, Martin M, Christensen U, Diderichsen F, et al. Health, work, and personal-related predictors of time to return to work among employees with mental health problems. Disabil Rehabil. 2012;34(15):1311–6. Epub 2011/12/28. pmid:22200251.

96. Hedlund A, Kristofferzon ML, Boman E, Nilsson A. Are return to work beliefs, psychological well-being and perceived health related to return-to-work intentions among women on long-term sick leave for common mental disorders? A cross-sectional study based on the theory of planned behaviour. BMC Public Health. 2021;21(1):535. Epub 20210319. pmid:33740921; PubMed Central PMCID: PMC7977300.

97. Momsen A-MH, Jensen OK, Nielsen CV, Jensen C. Multiple somatic symptoms in employees participating in a randomized controlled trial associated with sickness absence because of nonspecific low back pain. The Spine Journal: Official Journal of the North American Spine Society. 2014;14(12):2868–76. pmid:24743062

98. van Hees SGM, Carlier BE, Blonk RWB, Oomens S. Strengthening supervisor support for employees with common mental health problems: developing a workplace intervention using intervention mapping. BMC Public Health. 2022;22(1):1146. pmid:35676640

99. Cullen KL, Irvin E, Collie A, Clay F, Gensby U, Jennings PA, et al. Effectiveness of Workplace Interventions in Return-to-Work for Musculoskeletal, Pain-Related and Mental Health Conditions: An Update of the Evidence and Messages for Practitioners. J Occup Rehabil. 2018;28(1):1–15. Epub 2017/02/23. pmid:28224415; PubMed Central PMCID: PMC5820404.

100. Rouquette A, Badley EM, Falissard B, Dub T, Leplege A, Coste J. Moderators, mediators, and bidirectional relationships in the International Classification of Functioning, Disability and Health (ICF) framework: An empirical investigation using a longitudinal design and Structural Equation Modeling (SEM). Soc Sci Med. 2015;135:133–42. Epub 2015/05/13. pmid:25965894.

101. Briggs MS, Rethman KK, Crookes J, Cheek F, Pottkotter K, McGrath S, et al. Implementing Patient-Reported Outcome Measures in Outpatient Rehabilitation Settings: A Systematic Review of Facilitators and Barriers Using the Consolidated Framework for Implementation Research. Arch Phys Med Rehabil. 2020;101(10):1796–812. pmid:32416149.

102. de Wit M, Wind H, Snippen NC, Sluiter JK, Hulshof CTJ, Frings-Dresen MHW. Physicians’ Perspectives on Person-Related Factors Associated With Work Participation and Methods Used to Obtain Information About These Factors. J Occup Environ Med. 2019;61(6):499–504. Epub 2019/06/06. pmid:31167222.

103. Health IfWa. Seven “principles” for successful return to work. Available at: https://wwwiwhonca/tools-and-guides/seven-principles-for-successful-return-to-work. 2014.

104. Franche RL, Cullen K, Clarke J, Irvin E, Sinclair S, Frank J. Workplace-based return-to-work interventions: a systematic review of the quantitative literature. J Occup Rehabil. 2005;15(4):607–31. Epub 2005/10/29. pmid:16254759.

105. Corbière M, Villotti P, Lecomte T, Bond GR, Lesage A, Goldner EM. Work accommodations and natural supports for maintaining employment. Psychiatr Rehabil J. 2014;37(2):90–8. Epub 2014/02/12. pmid:24512481.

106. Gignac MAM, Bowring J, Tonima S, Franche RL, Thompson A, Jetha A, et al. A Sensibility Assessment of the Job Demands and Accommodation Planning Tool (JDAPT): A Tool to Help Workers with an Episodic Disability Plan Workplace Support. J Occup Rehabil. 2023;33(1):145–59. Epub 2022/07/15. pmid:35835885; PubMed Central PMCID: PMC9282615.

107. Thomas C, Jeppe Karl S, Louise D, Iben Louise K, Jesper K. Do different job demands interact as predictors of long-term sickness absence? A register-based follow-up on 55 467 Danish workers. Occupational and Environmental Medicine. 2023;80(1):7. pmid:36270798

108. Siegrist J, Starke D, Chandola T, Godin I, Marmot M, Niedhammer I, et al. The measurement of effort–reward imbalance at work: European comparisons. Social Science & Medicine. 2004;58(8):1483–99. pmid:14759692

109. Janssen N, van den Heuvel WPM, Beurskens AJHM, Nijhuis FJN, Schröer CAP, van Eijk JTM. The Demand–Control–Support model as a predictor of return to work. International Journal of Rehabilitation Research. 2003;26(1):1–9. 00004356-200303000-00001.

110. Karasek R, Brisson C, Kawakami N, Houtman I, Bongers P, Amick B. The Job Content Questionnaire (JCQ): an instrument for internationally comparative assessments of psychosocial job characteristics. J Occup Health Psychol. 1998;3(4):322–55. pmid:9805280.

111. Canivet C, Choi B, Karasek R, Moghaddassi M, Staland-Nyman C, Östergren PO. Can high psychological job demands, low decision latitude, and high job strain predict disability pensions? A 12-year follow-up of middle-aged Swedish workers. Int Arch Occup Environ Health. 2013;86(3):307–19. Epub 2012/04/06. pmid:22476722.

112. Ebrahim S, Malachowski C, Kamal El Din M, Mulla SM, Montoya L, Bance S, et al. Measures of patients’ expectations about recovery: a systematic review. J Occup Rehabil. 2015;25(1):240–55. Epub 2014/08/08. pmid:25100443.

113. Carriere JS, Pimentel SD, Bou-Saba S, Boehme B, Berbiche D, Coutu MF, et al. Recovery expectations can be measured with single-item measures: findings of a systematic review and meta-analysis on the role of recovery expectations on return-to-work outcomes following musculoskeletal pain conditions. Pain. 2022. pmid:36155605.

114. Laferton JAC, Kube T, Salzmann S, Auer CJ, Shedden-Mora MC. Patients’ Expectations Regarding Medical Treatment: A Critical Review of Concepts and Their Assessment. Frontiers in Psychology. 2017;8. pmid:28270786

115. Mondloch MV, Cole DC, Frank JW. Does how you do depend on how you think you’ll do? A systematic review of the evidence for a relation between patients’ recovery expectations and health outcomes. Canadian Medical Association Journal. 2001;165(2):174–9. pmid:11501456

116. Carrière JS, Donayre Pimentel S, Bou Saba S, Boehme B, Berbiche D, Coutu MF, et al. Recovery expectations can be assessed with single-item measures: findings of a systematic review and meta-analysis on the role of recovery expectations on return-to-work outcomes after musculoskeletal pain conditions. Pain. 2023;164(4):e190–e206. Epub 2022/09/27. pmid:36155605; PubMed Central PMCID: PMC10026834 interests that may be relevant to content are disclosed at the end of this article.

117. Corbière M, Fraccaroli F. La conception, la validation, la traduction et l’adaptation transculturelle d’outils de mesure: Exemples dans le domaine de la santé mentale. In: Corbière M, Larivière N, editors. Méthodes qualitatives, quantitatives et mixtes dans la recherche en sciences humaines, sociales et de la santé. 2e ed. Québec, QC: Presses de l’Université du Québec (PUQ); 2020. p. 703–52.

118. Young AE, Besen E, Choi Y. The importance, measurement and practical implications of worker’s expectations for return to work. Disabil Rehabil. 2015;37(20):1808–16. Epub 2014/11/07. pmid:25374043.

119. Gross DP, Battié MC. Factors influencing results of functional capacity evaluations in workers’ compensation claimants with low back pain. Physical therapy. 2005;85(4):315–22. pmid:15794702

120. Vlaeyen JWS, Linton SJ. Fear-avoidance and its consequences in chronic musculoskeletal pain: a state of the art. Pain. 2000;85(3):317–32. pmid:10781906

121. Baez S, Hoch MC, Hoch JM. Evaluation of Cognitive Behavioral Interventions and Psychoeducation Implemented by Rehabilitation Specialists to Treat Fear-Avoidance Beliefs in Patients With Low Back Pain: A Systematic Review. Arch Phys Med Rehabil. 2018;99(11):2287–98. Epub 2017/12/17. pmid:29247627.

122. George SZ, Valencia C, Beneciuk JM. A psychometric investigation of fear-avoidance model measures in patients with chronic low back pain. J Orthop Sports Phys Ther. 2010;40(4):197–205. Epub 2010/04/02. pmid:20357418.

123. International Symposium on Work A, Ilmarinen J, Lehtinen S, Työterveyslaitos. Past, present, and future of work ability: proceedings of the 1st International Symposium on Work Ability, 5–6 September 2001, Tampere, Finland. Helsinki: Finnish Institute of Occupational Health Helsinki; 2004.

124. Lederer V, Loisel P, Rivard M, Champagne F. Exploring the diversity of conceptualizations of work (dis)ability: a scoping review of published definitions. J Occup Rehabil. 2014;24(2):242–67. Epub 2013/07/26. pmid:23884716.

125. Ahlstrom L, Grimby-Ekman A, Hagberg M, Dellve L. The work ability index and single-item question: associations with sick leave, symptoms, and health—a prospective study of women on long-term sick leave. Scand J Work Environ Health. 2010;36(5):404–12. Epub 2010/04/08. pmid:20372766.

126. Lagerveld SE, Blonk RWB, Brenninkmeijer V, Schaufeli WB. Return to work among employees with mental health problems: Development and validation of a self-efficacy questionnaire. Work & Stress. 2010;24(4):359–75.

127. Gjengedal RGH, Lagerveld SE, Reme SE, Osnes K, Sandin K, Hjemdal O. The Return-to-Work Self-efficacy Questionnaire (RTW-SE): A Validation Study of Predictive Abilities and Cut-off Values for Patients on Sick Leave Due to Anxiety or Depression. J Occup Rehabil. 2021;31(3):664–73. Epub 2021/02/26. pmid:33630238; PubMed Central PMCID: PMC8298338.

128. Lagerveld SE, Brenninkmeijer V, Blonk RW, Twisk J, Schaufeli WB. Predictive value of work-related self-efficacy change on RTW for employees with common mental disorders. Occup Environ Med. 2017;74(5):381–3. Epub 2016/12/23. pmid:28007760.

129. Lazarus RS, Folkman S. Stress, appraisal, and coping: Springer publishing company; 1984.

130. Skinner EA, Edge K, Altman J, Sherwood H. Searching for the structure of coping: a review and critique of category systems for classifying ways of coping. Psychol Bull. 2003;129(2):216–69. pmid:12696840.

131. Sullivan MJ, Thorn B, Haythornthwaite JA, Keefe F, Martin M, Bradley LA, et al. Theoretical perspectives on the relation between catastrophizing and pain. The Clinical journal of pain. 2001;17(1):52–64. pmid:11289089

132. Petrini L, Arendt-Nielsen L. Understanding Pain Catastrophizing: Putting Pieces Together. Front Psychol. 2020;11:603420. pmid:33391121; PubMed Central PMCID: PMC7772183.

133. Rotter JB. Generalized expectancies for internal versus external control of reinforcement. Psychol Monogr. 1966;80(1):1–28. pmid:5340840.

134. Bandura A. Guide for constructing self-efficacy scales. Self-efficacy beliefs of adolescents. 52006. p. 307–37.

135. Jette AM. Toward a common language for function, disability, and health. Phys Ther. 2006;86(5):726–34. pmid:16649895.

136. Chiarotto A, Maxwell LJ, Terwee CB, Wells GA, Tugwell P, Ostelo RW. Roland-Morris Disability Questionnaire and Oswestry Disability Index: Which Has Better Measurement Properties for Measuring Physical Functioning in Nonspecific Low Back Pain? Systematic Review and Meta-Analysis. Phys Ther. 2016;96(10):1620–37. pmid:27081203.

137. Bosc M. Assessment of social functioning in depression. Compr Psychiatry. 2000;41(1):63–9. pmid:10646621.

138. van Steenbergen E, van Dongen JM, Wendel-Vos GC, Hildebrandt VH, Strijk JE. Insights into the concept of vitality: associations with participation and societal costs. Eur J Public Health. 2016;26(2):354–9. pmid:26578664.

139. Linton SJ, Kecklund G, Franklin KA, Leissner LC, Sivertsen B, Lindberg E, et al. The effect of the work environment on future sleep disturbances: a systematic review. Sleep Med Rev. 2015;23:10–9. pmid:25645126.

140. Duclos C, Beauregard MP, Bottari C, Ouellet MC, Gosselin N. The impact of poor sleep on cognition and activities of daily living after traumatic brain injury: a review. Aust Occup Ther J. 2015;62(1):2–12. pmid:25331353.

141. Kucharczyk ER, Morgan K, Hall AP. The occupational impact of sleep quality and insomnia symptoms. Sleep Med Rev. 2012;16(6):547–59. pmid:22401983.

142. Hartvigsen J, Hancock MJ, Kongsted A, Louw Q, Ferreira ML, Genevay S, et al. What low back pain is and why we need to pay attention. The Lancet. 2018;391(10137):2356–67. pmid:29573870

143. Waddell G, Bircher M, Finlayson D, Main CJ. Symptoms and signs: physical disease or illness behaviour? Br Med J (Clin Res Ed). 1984;289(6447):739–41. pmid:6236867

144. Sirri L, Fava GA, Sonino N. The unifying concept of illness behavior. Psychother Psychosom. 2013;82(2):74–81. pmid:23295460.

145. Waddell G, McCulloch JA, Kummel E, Venner RM. Nonorganic physical signs in low-back pain. Spine. 1980;5(2):117–25. pmid:6446157

146. Waddell G, Main CJ, Morris EW, Di Paola M, Gray IC. Chronic low-back pain, psychologic distress, and illness behavior. Spine. 1984;9(2):209–13. pmid:6233714

147. Prkachin KM, Hughes E, Schultz I, Joy P, Hunt D. Real-time assessment of pain behavior during clinical assessment of low back pain patients. Pain. 2002;95(1–2):23–30. pmid:11790464

148. Prkachin KM, Schultz I, Berkowitz J, Hughes E, Hunt D. Assessing pain behaviour of low-back pain patients in real time: concurrent validity and examiner sensitivity. BehavResTher. 2002;40(5):595–607. pmid:12038651