Next Article in Journal
Trajectories of Hospitalization Cost Among Patients of End-Stage Lung Cancer: A Retrospective Study in China
Next Article in Special Issue
“In Initiative Overload”: Australian Perspectives on Promoting Physical Activity in the Workplace from Diverse Industries
Previous Article in Journal
Chronic Environmental and Occupational Lead Exposure and Kidney Function among African Americans: Dallas Lead Project II
Previous Article in Special Issue
Breaking up Sedentary Time in Overweight/Obese Adults on Work Days and Non-Work Days: Results from a Feasibility Study
Article Menu
Issue 12 (December) cover image

Export Article

Int. J. Environ. Res. Public Health 2018, 15(12), 2876; https://doi.org/10.3390/ijerph15122876

Review
An Integrative, Systematic Review Exploring the Research, Effectiveness, Adoption, Implementation, and Maintenance of Interventions to Reduce Sedentary Behaviour in Office Workers
School of Psychological Sciences and Health, University of Strathclyde, 16 Richmond Street, Glasgow G1 1XQ, UK
*
Author to whom correspondence should be addressed.
Received: 2 November 2018 / Accepted: 27 November 2018 / Published: 15 December 2018

Abstract

:
Sedentary behaviour is associated with poor health outcomes, and office-based workers are at significant health risk, as they accumulate large proportions of their overall sitting time at work. The aim of this integrated systematic review was to collate and synthesize published research on sedentary behaviour interventions in the workplace that have reported on at least one an aspect of the reach, effectiveness, adoption, implementation, and maintenance (RE-AIM) framework. Studies were included if they involved adult office workers, were conducted in an office setting, and changes in sedentary behaviour had been measured as a primary outcome. Five electronic databases were searched yielding 7234 articles, with 75 articles (61 individual interventions) meeting the inclusion criteria. Reach indicators were the most frequently reported RE-AIM dimensions, which were reported on average 59% of the time. Efficacy/effectiveness was the second most reported dimension at 49% reporting across all of the indicators. Implementation indicators were reported an average of 44% of the time, with indicators of adoption and maintenance reported as the lowest of all indicators at 13% and 8%, respectively. Recommendations are provided to improve reporting across all RE-AIM dimensions, which is an important first step to enable the effective translation of interventions into real world settings.
Keywords:
sitting time; sedentary; occupational; office workers; RE-AIM; translation; evaluation; review

1. Introduction

Sedentary behaviour (SB), or sitting time, is associated with an increased risk of chronic diseases, such as metabolic syndrome, cardiovascular disease, and diabetes mellitus, in addition to increased all-cause mortality in adults [1,2,3]. Despite the health risk, representative samples indicate that the prevalence of sedentary behaviour is high in Western adults (between 6.8 and 11.2 h/day) [4,5,6]. Research suggests that office-based workers are at significant health risk, as they accumulate large proportions of their overall sitting time at work [7,8,9]. The global prevalence of occupational sitting will likely continue to rise as the labour market continues to shift towards computerised employment [10]. Consequently, the United Kingdom has developed guidance for employers in order to promote the avoidance of prolonged periods of sedentary work [11].
There has been an increase in interventions targeting sedentary office workers [12,13,14,15], and a number of reviews of the intervention work have followed [16,17,18,19]. The majority of these reviews have provided an evaluation of these interventions in relation to indicators of “efficacy” [16,17,18,19]. However, there has been growing critique suggesting that, although indicators of efficacy are important to assess, there is little understanding of the additional indicators, which may help to understand the potential for successful translation and future real-world implementation [20]. Critics argue that other indicators that facilitate an understanding of generalisability and translation are equally important to evaluate, particularly if these additional indicators impact the success of future implementation, and consequently the potential public health impact of a given intervention [20,21].
The RE-AIM evaluation framework is one of several existing methods used to evaluate or report on the additional indicators that could influence the future external validity of an intervention. Glasgow et al. (1999) [22] proposed five dimensions in which these indicators sit—reach, efficacy/effectiveness, adoption, implementation, and maintenance. Reach is defined as the absolute number, proportion, and representativeness of individuals who are willing to participate in a given initiative. Efficacy/effectiveness refers to the impact of an intervention on the relevant outcomes, including potential adverse effects, quality of life, and economic outcomes. Adoption, within RE-AIM, is the absolute number, proportion, and representativeness of the settings and intervention agents who are willing to initiate a program. Implementation refers to the intervention agents’ (e.g., research teams) fidelity to the various elements of an intervention’s protocol. This includes consistency of delivery as intended, and the time and cost of the intervention. The maintenance dimension is concerned with both the setting and individual level. At the setting level, maintenance is the extent to which a program or policy becomes institutionalised or part of organisational practices and policies. At the individual level, maintenance has been defined as the long-term effects of a program on outcomes from six months onwards from the most recent contact [22,23].
Glasgow et al. (2004) [23] further explains that evaluating interventions over the five dimensions of the RE-AIM framework will help to facilitate an understanding of the potential external validity and public health impact of an intervention. This type of reporting is critically important as we move on a continuum from understanding an intervention effect produced under controlled conditions, towards implementation under real world conditions [21]. To date, no systematic reviews on sedentary behaviour interventions in office workers have been conducted using the RE-AIM framework. Therefore, the aim of the current study is to conduct a systematic review of sedentary behaviour interventions in the workplace focusing on the RE-AIM dimensions (reach, effectiveness, adoption, implementation, and maintenance). The review aims to gain an understanding of the proportion of RE-AIM indicators that are reported in the literature so as to identify whether gaps in reporting exist, which indicators are underreported, and which existing methods may be useful in collecting data on underreported indicators.

2. Methods

In order to capture published literature reporting on any dimension of the RE-AIM framework, an integrative, systematic review approach was used. The integrative methodology is specifically designed to facilitate the inclusion of a broad range of research designs, both qualitative and quantitative, so as to comprehensively understand a given phenomenon [24].

2.1. Search Strategy

Studies were included if they involved adult office workers, were conducted in an office setting, and if changes in sedentary behaviour had been measured (objectively or subjectively) as a primary outcome of the study. No limitations were placed on the design of the study. The inclusion/exclusion criteria and search terms were developed through scoping searches. The review team used PICOS criteria (population, intervention, comparators, outcome, and setting) to facilitate this process (Table 1). The search terms were used to search five electronic databases (MEDLINE (Ovid platform), PsycINFO, SPORTDiscus, Business Source Complete, and OPEN Grey), and searching was completed on 7 December 2017.

2.2. Screening Process

The retrieved articles (n = 7234) were exported into EndNote (Clarivate Analytics, Philadelphia, PA, USA) so as to remove duplicates. After the removal of the duplicates, a total of 5533 articles were left. These articles were then exported into Covidence (Covidence, Melbourne, Australia) for screening. Covidence is an online platform that is designed to enhance the reliability of systematic reviews by facilitating organisational that which enhance the rigour within the screening process. The platform also facilitates the blinding of the screening process between reviewers. Double screening of the studies was carried out at two stages, namely: title/abstract and full text. At the end of each stage, two reviewers (B.M. and M.P.) met to discuss the disagreements. Cohen’s Kappa calculations were done for the title and abstract (0.96), and for the full text (0.97). The studies that could not be agreed upon were brought to a third member of the review team (X.J.) and were discussed. On all occasions, a final decision was agreed upon by all parties. Figure 1 highlights this process.

2.3. Data Extraction

The data was extracted using a combination of two validated RE-AIM coding sheets [23,25,26,27]. The combination of the two sheets facilitated in the coding of information across all five dimensions of the RE-AIM framework, looking at 28 individual indicators from each intervention. The alignment of these indicators to each dimension of the RE-AIM framework is noted below.

2.3.1. Reach

The items from the extraction tool that facilitated in reporting on the potential reach of an intervention included the following: the method used to identify the target population, inclusion criteria and exclusion criteria, use of qualitative methods to understand reach or recruitment, sample size, participation rate, and sample representatives. The participation rate was calculated based on the reported number of participants, divided by the number of eligible participants exposed to recruitment. The sample representativeness information was extracted if an intervention reported the demographics of both the participants and eligible non-participants.

2.3.2. Efficacy/Effectiveness

The efficacy and effectiveness items included the following: assessment of the effect on outcomes at shortest assessment point, imputation procedures reported, the presence of quality of life measure, effects at longest follow-up, use of qualitative methods to understand outcomes, and percent attrition or dropout rate. If the attrition rate was not directly reported, it was calculated based on the participant numbers at randomization, as compared to the participant numbers at shortest assessment point.

2.3.3. Adoption

The items that were extracted for adoption related to both the setting and participants. Specifically, the extent to which a study reported; the method of identifying target agent—an agent should be identified regardless of the type of intervention (e.g., device-based or consultation approach); level of expertise of delivery agents (e.g., was specific training or level of understanding or influence reported for different intervention agents)—may be less relevant in device based interventions; inclusion and exclusion criteria for target agent—relevant for all intervention types; the adoption rate (e.g., number of companies who took part/number of companies who were approached)—relevant for all intervention types; comparison of settings/participants of adoption vs. non-adoption settings (e.g., demographic or environmental differences between adoption of program/intervention vs. non-adoption)—relevant for all intervention types; and use of qualitative methods to understand either adoption at setting level and staff participation—relevant for all intervention types.

2.3.4. Implementation

Information relating to the implementation that was extracted and reported on. Specifically, the intervention type (e.g., individual component vs. multi-component) and intensity. With no specific guidance on a measure of intensity, the review team judged the reporting of intensity based on the reporting of the length of the intervention, as well as components of the intervention. Further items included the following: the extent the protocol was delivered as intended (e.g., did the intervention achieve its intended implementation goal or did protocol need to be adapted); a measure of cost (e.g., monetary or time commitment); and use of qualitative methods to understand the implementation of the study.

2.3.5. Maintenance

Maintenance was assessed using the following three items: was an individual’s behaviour assessed at least six months following the completion of the intervention; is the program still in place, was the program modified, and use of qualitative methods to understand long-term effects.
All of the relevant information was extracted and coded in an excel spreadsheet by two reviewers (B.M. and M.P.), with each researcher extracting half of the papers. Upon the completion of the extraction, each of the 28 items were colour coded green if the information was presented, or red if the information was not presented. All of the data extraction was then double checked by a third member of the review team (X.J.) so as to enhance reliability.

2.4. Quality Assessment

Because of the broad range of study designs and the use of the RE-AIM reporting item for data extraction and reporting, no further assessment of the study quality was performed.

3. Results

3.1. Study Selection

The initial searches identified 7234 articles, and after title and abstract screening, 303 full text articles were screened. Of these, 75 articles representing 61 individual interventions were included in the review (Figure 1).

3.2. Characteristics of Identified Articles

Table 2 describes the characteristics of the identified articles. It is important to understand the distinction between the articles and interventions from this point forward in the review. The results of 10 interventions were reported in more than one article. This information has been brought together in order to understand the reporting of all of the indicators across the dimensions of the RE-AIM framework. This method has been used in other RE-AIM reviews for the same purpose [26,28]. In total, there were 75 included articles in the review, representing 61 individual interventions. Table 2 identifies which articles are from the same intervention. Of the 61 interventions, 23 interventions were completed in North America, 22 in Europe, 15 in Australia, and 1 in South America. The integrated review approach facilitated a large variety in both the study design and outcome measurement method. Of the 75 published articles, 39 reported controlled designs (both randomised and non-randomised), which was the most frequent. A total of fifteen articles reported pre- and post-test experimental designs; seven reported qualitative designs, six of which were reported as natural experiments; five reported quasi experimental designs, one of which was a cross sectional design; one reported mixed methods design; and one reported descriptive design. The duration of the interventions that were included ranged from one day to 12 months, with 20 interventions reporting less than 7 weeks, 25 interventions reporting 2–4 months, nine interventions reporting 4–9 months, and five interventions reporting 12 months. Two interventions did not report an intervention duration. In total, 17 individual data collection methods were used to measure sedentary behaviour (SB). Objective measures of SB were used in 39 interventions, with the most common being ActivPAL (n = 20). Other objective measures included accelerometery, video analysis, and objective proxy measures. Subjective measures of SB were used in 31 interventions, with the most common type being a questionnaire (n = 23). Other subjective methods included interview, focus group, diary/log, and open-ended questions. It should be noted that the number of SB outcome measures does not exactly equal the number of included interventions, as a result of nine of the 61 interventions using both objective and subjective measures of SB.

3.3. Percentage Reporting across RE-AIM Dimensions

The total percentage of reporting across all of the indicators within the individual RE-AIM dimension is represented in Figure 2. Reach indicators were reported on average 59% of the time. Efficacy/effectiveness was reported at 49% across all of the indicators. Implementation indicators were reported an average of 44% of the time. The overall percentage of interventions reporting on the indicators of adoption and maintenance indicators were 13% and 8%, respectively. A full break down of reporting across all of the indicators for individual studies is available in Supplementary Tables S1 and S2.

3.4. Reach

There was a significant variation between the reach indicators (Figure 3), with a high reporting of three indicators, namely, identifying target population (n = 57, 93%), inclusion criteria (n = 50, 82%), and sample size (n = 61, 100%). The reporting of exclusion criteria and participation rate were lower, with both being reported at 61% (n = 37). There was low reporting for the characteristics of participants vs. non-participants (n = 6, 10%), and for the use of qualitative methods to understand reach (n = 4, 7%).

3.5. Efficacy/Effectiveness

Figure 4 illustrates the percentage of reporting for individual efficacy/effectiveness indicators. High reporting was noted across several indicators, including the following: the measure of primary outcome at the shortest assessment point (n = 61, 100%), and the percent attrition rate (n = 47, 77%). The measurement of the primary outcome at extra follow up points was reported for 39 interventions (64%). The reporting dropped significantly for the remaining three indicators, with 15 interventions (25%) reporting on quality of life measurement, nine interventions (15%) reporting imputation or intention to treat analysis, and seven interventions (11%) reporting use of qualitative methods to understand outcomes.

3.6. Adoption

Figure 5 illustrates the percentage reporting for individual adoption indicators. In total, 16 interventions (26%) reported methods to identify delivery target agent, 11 interventions (18%) reported the level of expertise of the delivery agents, and five interventions (8%) provided inclusion/exclusion criteria concerning adoption at the setting level. Furthermore, five interventions (8%) reported a rate of adoption at the setting level, two interventions (3%) reported the use of qualitative methods to understand adoption, and six interventions (10%) reported differences in characteristics (either participant or setting) of adoption vs. non-adoption.

3.7. Implementation

Figure 6 illustrates the reporting for implementation. The most commonly reported indicator was the intervention type and intensity (n = 60, 98%). In total, 36 (59%) interventions reported on the extent the protocol was delivered as intended, and eight interventions (13%) used qualitative methods to understand implementation. Finally, a measure of cost (protocol) was reported in three interventions (5%).

3.8. Maintenance

Concerning individual indicators of maintenance (Figure 7), five interventions (8%) reported on an individual behaviour assessment at least six months following the completion of the intervention; five interventions (8%) reported whether the program is still in place, six interventions (10%) reported the use of qualitative methods to understand setting level institutionalization, and four interventions (7%) reported if the program was modified.

4. Discussion

The purpose of this review is to provide an understanding of the depth of reporting of indicators across the RE-AIM dimensions. Previous systematic reviews have investigated the effectiveness of workplace SB interventions [16,17,18]. However, to the authors’ knowledge, this is the first systematic review focusing on RE-AIM reporting in office-based SB interventions. This review is the first to synthesise a breadth of the evidence in the field, with a focus on the reporting of indicators important to the future implementation and translation of interventions.
The reach indicators were the most frequently reported of all of the RE-AIM dimensions; reported on average 59% of the time. Efficacy/effectiveness was the second most reported dimension at 49% reporting across all of the indicators. The implementation indicators were reported an average of 44% of the time. The overall percentage of studies reporting on the indicators of adoption and maintenance were the lowest of all of the RE-AIM framework indicators at 13% and 8%, respectively. The results revealed that 10 of the 28 indicators were reported more than 50% of the time however, and the remaining 18 indicators were reported less than 30% of the time, revealing a distinct contrast in the indicators that are routinely reported in interventions. In light of this result, the research team has focused the discussion primarily on the indicators or indeed the whole dimensions that have been “under-reported” or have been reported for less than 30% of the interventions. The discussion firstly presents specific methods used to capture the data from underreported indicators of RE-AIM; and secondly, provides future considerations and recommendations for collecting the data of under reported RE-AIM indicators. This is done in order to facilitate improved reporting (success and failure) across the RE-AIM dimensions, so as to improve our evaluation of generalisability and potential translation of interventions, as well as the potential for the public health impact of interventions [20,21,101].

4.1. Reach

The distinct contrast in reporting is evident in reach (Figure 2). Some indicators of reach are well reported across the included interventions, such as, a method to identify the target population (n = 57, 93%) or inclusion criteria (n = 50, 82%). However, reach indicators such as representativeness of participants vs. non-participants (n = 6, 10%), and use of qualitative methods (n = 4, 7%) are underreported. Nevertheless, interventions such as those of De Cocker et al. (2016, 2017) [55,56,57] and Bort-Roig et al. (2014) [33] highlight the methods for reporting on these indicators specifically.
De Cocker et al. (2016) delivered computer-tailored advice to influence sitting behaviour [56,57]. To report on the representativeness of participants vs. non-participants, the authors utilised the already available health information of the office employees that did not participate, and did a comparative analysis to the demographics of the workers who participated [55,56,57]. In De Cocker’s intervention, the office workers who were less educated were less likely to participate, therefore, an educational element may be critical in order to engage less educated office workers [56,57]. This example highlights how information on representativeness can provide further insight into how to best target intervention strategies.
Additionally, the data collected by Bort-Roig et al. (2014) used a qualitative methodology to facilitate an understanding of the participant uptake [33]. In the study, they interviewed the implementation team regarding their perceptions of factors that impacted on uptake within the study. They then triangulated the interview results with the participant surveys that rated the extent to which the uptake strategies were used [33]. This triangulation process facilitated understanding of reach, giving context to the factors that influenced the study population.
These two studies highlight methods that can be used to improve on the reporting of indicators of reach. Each method improved the understanding of the factors, which may impact on the future implementation and translation of the studies, and therefore, have a potential public health impact.

4.2. Efficacy/Effectiveness

As with reach, there are distinct differences in the indicators of efficacy/effectiveness that are routinely reported (Figure 3). The reporting of measure/results (at shortest assessment) (n = 61, 100%), effects at longest (extra follow up) (n = 39, 64%), and the percent attrition rate (dropout rate) (n = 47, 77%) were significantly higher than the quality of life measurement (n = 15, 25%) and use of qualitative methods or data to understand outcomes (n = 7, 11%), both of which were underreported.
SB is associated with the additional health related outcomes that may affect the “quality of life” of the participants, including, back, shoulder, and neck pain [102,103,104], and a variety of psychological issues, for example, depression [105], distress [106], and anxiety [107]. Therefore, these outcomes are also important to measure so as to improve our understanding of the association, and to monitor negative unintended outcomes. Importantly, the measurement of additional quality of life outcomes has the potential to strengthen the arguments for the importance of reducing office-based SB. For example, the methods utilised in the Pronk et al. (2012) [89] intervention “take a stand” provided an example of reporting quality of life measurement [89]. In the intervention, the research team administered validated questionnaires to collect data related to additional work-related outcomes (pre- and post-intervention), which facilitated reporting in relation to the quality of life indicator. The results showed that reductions in the sitting time were significantly associated with reductions in upper back and neck pain, fatigue, confusion, and total mood disturbance [89]. In this example, the measurement of the additional outcomes provided evidence that the intervention was not negatively affecting other related health conditions. This type of measurement may help to increase our understanding of other additional benefits of reducing office-based SB.
Hardgraft et al. (2017) [47] used interviews and focus groups to facilitate in understanding how additional factors impacted on the effectiveness of the strategies used in the study [47]. The authors found that specific at work “job tasks” were barriers to behaviour change, however “social support” was a facilitator [47]. Using qualitative methods improved how Hardgraft et al. (2017) understood how behaviour change occurred, and may be critical for improving efficacy/effectiveness in future iterations of the study [47].
It is clear that reporting on additional indicators of RE-AIM fostered a more holistic understanding of the real impact of the interventions. This information may now be used to help improve the future implementation and translation of the research into different settings.

4.3. Adoption

This review has highlighted the underreporting of all of the indicators of the adoption dimension (Figure 4). This is an interesting finding that, on face value, appears to give evidence of poor reporting on setting level indicators. However, a limited number of interventions were implemented across multiple settings (n = 16, 26%) in this review. Most of the included interventions were implemented in one setting only and on a relatively small scale (67%, <50 participants); this illustrates a clear gap in the literature.
This review gives further evidence that there is a barrier to translating research from small scale SB interventions to larger scale effectiveness trials [16,101]. The result of this review suggests that one barrier to translation may be the under reporting of indicators that would facilitate effective translation. However, resources, for example time and money, are also significant barriers that often result in pragmatic decision making with respect to the scale of implementation [20,21,101]. The solution to these significant barriers may lie in our engagement with additional stakeholders in workplace health. Companies continue to increase resources in order to improve employee health and wellbeing, as they increasingly understand the relationship between productivity and health status [108,109,110]. However, workplace health promotion programs are often not informed by evidence, and a recent review suggests that programs that are informed by research have more potential to yield positive results [111]. Therefore, a more “practice based” [21] approach, in which researchers work directly with workplace health promotion stakeholders, would bring together both the evidence-based knowledge and resources needed to effectively translate on a larger scale [21]. For this approach to be successful, understanding and addressing the potential barriers to working directly with companies would be important. For example, with new data protection regulations being implemented, one barrier to overcome may be the companies’ willingness to share/collect the health data of employees, with potential concerns that, if misused, it may bring harm to their employees [112,113]. However, if the relationship is nurtured, and concerns are mediated, the approach could help embed public–private partnerships at earlier stages of research. This will help to build stronger practice-based relationships as projects develop [114]. The approach could also circumvent funding bodies, which can be reluctant to fund scaled up trials, which are seen as less “scientifically pure” [115]. Although trade-offs in experimental design may be made, this more pragmatic “practice-based” [21] approach would produce evidence that more accurately reflects the conditions in which it is expected to be applied [20,21,116,117].
Of the 26% of the interventions implemented across multiple settings, there are none that reported all of the adoption indicators. However, there are examples of quality reporting of some individual indicators. For example, Brakenridge et al. (2016, 2017), who had the highest reporting in the review (21 of 28 indicators), reported four of the seven indicators of adoption [36,37]. In Brakenridge et al. (2017), the researchers interviewed members of the implementation team and conducted focus groups with participants in order to understand the differences in implementation across settings [37]. Qualitative findings revealed that there were differences in the role model influence and management engagement across settings, and this may have impacted on variations in the intervention effects across settings [36,37]. Collecting this information may help to improve future translations of this type of intervention. Additionally, when reporting the level of expertise of the delivery agent, Aittasalo et al. (2017) [29] explained the training process of the delivery agents, including the number of hours spent training face to face [29].
These two examples highlight that, when implementation across settings is done in office-based SB interventions, the collection and dissemination of the indicators of adoption enhances our understanding of the translational issues critical to the improvement of future implementation.

4.4. Implementation

The reporting of the indicators relating to the implementation dimension was mixed (Figure 5). Nearly all of the studies included in this review (n = 60, 98%) reported on the type of intervention and intensity by explaining the intervention activities in detail, and many studies (n = 36, 59%) reported on the extent the protocol was delivered as intended (development of a protocol). There was minimal reporting on the indicators which that are important for obtaining similar effects in future iterations of the study. These would include indicators that, for example, question whether the protocol was delivered by the implementation team as the intended? What aspects of the intervention were more or less effective than others? What was the cost (e.g., time commitment or monetary) to implement the intervention? Reporting on these indicators is critical to understanding which specific behaviour change strategies were successfully implemented and caused an effect within a study, and which were less successful. For example, Bort-Roig et al. (2014) [33] found, using both questionnaire and focus group data, that walk–talk meetings and lunch walking groups were rarely utilised within the intervention, and sitting time and step count logging were the most critical enabler of behaviour change. These results would be important to consider for the future implementation of this intervention, and may even trigger adaptations to the less successful strategies, potentially improving the potential public health impact of the study [33].

4.5. Maintenance

There was under-reporting of all of the indicators related to the maintenance dimension of RE-AIM (Figure 6), averaging just 8% overall (Figure 1). Two of the indicators assessed whether studies report on (a) if the program is still in place and (b) if the program was modified. These two indicators were only reported 8% (n = 5) and 7% (n = 4), respectively; however, Parry et al. (2013) [87] exemplified how this type of information could easily be reported, explaining, “The trial was ended due to the lack of further organisations willing to participate within the two-year data collection period” [87]. A third indicator looked for reporting on the follow up measurement six months post intervention. This indicator was also underreported (n = 5, 8%). This result is indicative of the fact that 41 of included studies were less than four months in length. From this analysis, it is clear there is a need for longer follow up periods. Interestingly, all of the studies that reported six-month follow-up data did so using self-report methods. Although self-report has its limitations, these results indicate that it may be best placed to pragmatically evaluate the long-term effect, which is vital to understand if long term public health impact is the objective. The six studies that reported on the final indicator of maintenance utilised qualitative methods in order to understand the setting level institutionalisation. For example, in Cifuentes et al. (2015) [44], the reporting highlighted significant barriers to maintaining change in the long term and highlighted areas, which would need to be adapted for the successful future uptake of the intervention [44].

4.6. Indicators of Cost

There were two indicators of cost within RE-AIM. Both referred to a measure of cost of implementation either at the individual level (implementation) or at setting level (adoption). Both of the indicators were under-reported, with measure of cost within implementation reported in just two interventions (3%), and measure of cost within adoption reported in 11 interventions (18%). These studies did report elements of cost, however, there was no clear example of a robust method used to fully understand the “cost” of an intervention. There is the potential to measure cost, however gaining transparency may require the development of methodology specific to office-based health promotion, which can articulate the costs incurred balanced with the benefits gained.

4.7. Recommendations for Future Reporting

In light of the significant gaps in reporting, the research team have created specific recommendations for the improved future reporting of office-based SB interventions (Table 3). Process evaluation is a critical part of any intervention study, however our review highlights a clear gap in the reporting of indicators that informs this practice [20]. The recommendations highlight that the RE-AIM framework may prove useful in providing a framework for collecting this breadth of process data or information. Additionally, it is clear from the recommendations that this process would require a mixed methods approach [118,119]. Using appropriate methods to capture the necessary data is the first step to both, improved translation, and population level impact.
Reporting on this breadth of indicators would often lead to the publication of a process evaluation, and this would be recommended in order to provide the capacity for reporting over so many indicators. The collection of data on under reported indicators can be done retrospectively [120]. However, it would be seen as best practice to imbed the necessary data collection methods in the initial study design, so as to inform the process evaluation [20]. Both retrospective and embedded process evaluation take careful and considered planning, however the RE-AIM recommendations would prove useful in both cases.

5. Strengths and Limitations

A key strength of the review is that it is the first review to look at a large proportion of published interventions that have been done targeting office based sedentary behaviour, in order to understand the state of reporting for effective future translation. This may be crucial to understand, as future population level impact relies on successful translation. Additionally, using the RE-AIM framework enabled an in-depth and critical analysis of the individual papers. This critical approach has facilitated the creation of specific and considered recommendations to enhance future intervention reporting within office-based sedentary interventions. Furthermore, the use of software tailored for reviews enabled quality assurance through the blinded double screening process. The study is not without limitations. Because of the focus on the quality of reporting across the RE-AIM dimensions, we did not include a quality assurance tool, which would be typically seen in an efficacy-based review. It could be the case that interventions that rate low across RE-AIM in this review rate high in other reviews, or vice versa. The review could also be limited by the number of databases (five) searched and the focus on workplace interventions that measure SB as a primary outcome.

6. Conclusions

The results of this review indicate that there is an imbalance in the reporting of indicators across the RE-AIM framework. The improvement of reporting across all interventions, designed to reduce sedentary behavior in office workers, will be an important first step in the effective translation of interventions into real world conditions [23]. Minimal studies have been implemented at scale with substantial follow up periods, suggesting that significant barriers exist, and this fuels arguments for a more pragmatic “practice-based” approach to intervention design, in which researchers work alongside delivery agents of workplace health [20,21,121]. Regardless of the intervention design or approach, the results and subsequent recommendations of this review would provide a useful starting point for researchers in the evaluation of important, often overlooked, indicators. Improved reporting may ultimately improve the translation of research on a large scale, and have impacts on public health as intended.

Supplementary Materials

Supplementary materials are available online at https://www.mdpi.com/1660-4601/15/12/2876/s1. Table S1: reporting of indicators of reach and effectiveness, across all of the included interventions, Table S2: reporting of indicators of adoption, implementation, and maintenance, across all of the included interventions.

Author Contributions

All of the authors contributed to the conceptualisations of the review. Searches and screening were done by B.M., M.P., and X.J. Data extraction was done by B.M., M.P., and X.J. Data analysis was done by B.M., X.J., and M.P. The paper was written by B.M., and all of the authors provided feedback on the draft manuscript and approved the final version.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Wilmot, E.G.; Edwardson, C.L.; Achana, F.A.; Davies, M.J.; Gorely, T.; Gray, L.J. Sedentary time in adults and the association with diabetes; cardiovascular disease and death: Systematic review and meta-analysis. Diabetologia 2012, 55, 2895–2905. [Google Scholar] [CrossRef] [PubMed]
  2. De Rezende, L.F.M.; Lopes, M.R.; Rey-López, J.P.; Matsudo, V.K.R.; do Carmo, L.O.J. Sedentary behavior and health outcomes: An overview of systematic reviews. PLoS ONE 2014, 9, e105620. [Google Scholar] [CrossRef] [PubMed]
  3. Owen, N.; Healy, G.N.; Matthews, C.E.; Dunstan, D.W. Too much sitting: The population-health science of sedentary behavior. Exerc. Sport Sci. Rev. 2010, 38, 105–113. [Google Scholar] [CrossRef] [PubMed]
  4. Colley, R.C.; Garriguet, D.; Janssen, I.; Craig, C.L.; Clarke, J.; Tremblay, M. Physical activity of Canadian adults: Accelerometer results from the 2007 to 2009 Canadian Health Measures Survey. Heath Rep. 2011, 22, 7–14. [Google Scholar]
  5. Bennie, J.A.; Chau, J.Y.; van der Ploeg, H.P.; Stamatakis, E.; Do, A.; Bauman, A. The prevalence and correlates of sitting in European adults—A comparison of 32 Eurobarometer-participating countries. Int. J. Behav. Nutr. Phys. Act. 2013, 10, 107. [Google Scholar] [CrossRef] [PubMed]
  6. Matthews, C.E.; Chen, K.Y.; Freedson, P.S.; Buchowski, M.S.; Beech, B.M.; Pate, R. Amount of time spent in sedentary behaviors in the United States; 2003–2004. Am. J. Epidemiol. 2008, 167, 875–881. [Google Scholar] [CrossRef] [PubMed]
  7. Parry, S.; Straker, L. The contribution of office work to sedentary behaviour associated risk. BMC Public Health 2013, 13, 296. [Google Scholar] [CrossRef]
  8. Thorp, A.A.; Healy, G.N.; Winkler, E.; Clark, B.K.; Gardiner, P.A.; Owen, N. Prolonged sedentary time and physical activity in workplace and non-work contexts: A cross-sectional study of office; customer service and call centre employees. Int. J. Behav. Nutr. Phys. Act. 2012, 9, 128. [Google Scholar] [CrossRef]
  9. Toomingas, A.; Forsman, M.; Mathiassen, S.E.; Heiden, M.; Nilsson, T. Variation between seated and standing/walking postures among male and female call centre operators. BMC Public Helath 2012, 12, 154. [Google Scholar] [CrossRef]
  10. Frey, C.B.; Osborne, M.A. The future of employment: How susceptible are jobs to computerisation? Technol. Forecast. Soc. Chang. 2017, 114, 254–280. [Google Scholar] [CrossRef][Green Version]
  11. Buckley, J.P.; Hedge, A.; Yates, T.; Copeland, R.J.; Loosemore, M.; Hamer, M. The sedentary office: A growing case for change towards better health and productivity. Expert statement commissioned by Public Health England and the Active Working Community Interest Company. Br. J. Sports Med. 2015, 49, 094618. [Google Scholar] [CrossRef] [PubMed]
  12. Barbieri, D.F.; Srinivasan, D.; Mathiassen, S.E.; Oliveira, A.B. Comparison of sedentary behaviors in office workers using sit-stand tables with and without semiautomated position changes. Hum. Factors 2017, 59, 782–795. [Google Scholar] [CrossRef] [PubMed]
  13. Hutchinson, J.; Headley, S.; Matthews, T.; Spicer, G.; Dempsey, K.; Wooley, S.; Janssen, X. Changes in sitting time and sitting fragmentation after a workplace sedentary behaviour intervention. Int. J. Environ. Res. Public Health 2018, 15, 1148. [Google Scholar] [CrossRef] [PubMed]
  14. Cooley, D.; Pedersen, S.; Mainsbridge, C.J. Assessment of the impact of a workplace intervention to reduce prolonged occupational sitting time. J. Qual. Health Res. 2014, 24, 90–101. [Google Scholar] [CrossRef] [PubMed]
  15. Neuhaus, M.; Healy, G.N.; Dunstan, D.W.; Owen, N.; Eakin, E.G. Workplace sitting and height-adjustable workstations: A randomized controlled trial. Am. J. Prev. Med. 2014, 46, 30–40. [Google Scholar] [CrossRef] [PubMed]
  16. Shrestha, N.; Kukkonen-Harjula, K.T.; Verbeek, J.H.; Ijaz, S.; Hermans, V.; Pedisic, Z. Workplace interventions for reducing sitting at work. Cochrane Database Syst. Rev. 2018, 6, CD010912. [Google Scholar] [CrossRef] [PubMed]
  17. Tew, G.; Posso, M.; Arundel, C.; McDaid, C. Systematic review: Height-adjustable workstations to reduce sedentary behaviour in office-based workers. Occup. Med. 2015, 65, 357–366. [Google Scholar] [CrossRef]
  18. Neuhaus, M.; Eakin, E.G.; Straker, L.; Owen, N.; Dunstan, D.W.; Reid, N. Reducing occupational sedentary time: A systematic review and meta-analysis of evidence on activity-permissive workstations. Obes. Rev. 2014, 15, 822–838. [Google Scholar] [CrossRef]
  19. Chau, J.Y.; van der Ploeg, H.P.; Van Uffelen, J.G.; Wong, J.; Riphagen, I.; Healy, G.N. Are workplace interventions to reduce sitting effective? A systematic review. Br. J. Sports Med. 2010, 51, 352–356. [Google Scholar] [CrossRef]
  20. Bauman, A.; Nutbeam, D. Evaluation in a Nutshell: A Practical Guide to the Evaluation of Health Promotion Programs, 2nd ed.; Mcgraw Hill: Sydney, Austrailia, 2013. [Google Scholar]
  21. Green, L.W.; Glasgow, R.E. Evaluating the relevance; generalization; and applicability of research: Issues in external validation and translation methodology. Eval. Health Prof. 2006, 29, 126–153. [Google Scholar] [CrossRef]
  22. Glasgow, R.E.; Vogt, T.M.; Boles, S.M. Evaluating the public health impact of health promotion interventions: The RE-AIM framework. Am. J. Prev. Med. 1999, 89, 1322–1327. [Google Scholar] [CrossRef]
  23. Glasgow, R.E.; Klesges, L.M.; Dzewaltowski, D.A.; Bull, S.S.; Estabrooks, P. The future of health behavior change research: What is needed to improve translation of research into health promotion practice? Annu. Behav. Med. 2004, 27, 3–12. [Google Scholar] [CrossRef] [PubMed]
  24. Whittemore, R.; Knafl, K.J. The integrative review: Updated methodology. J. Adv. Nurs. 2005, 52, 546–553. [Google Scholar] [CrossRef] [PubMed]
  25. Harden, S.M.; Gaglio, B.; Shoup, J.A.; Kinney, K.A.; Johnson, S.; Brito, F.; Blackman, K.C.A.; Zoellner, J.M.; Hill, J.L.; Almeida, F.A.; et al. Fidelity to and comparative results across behavioral interventions evaluated through the RE-AIM framework: A systematic review. Syst. Rev. 2015, 4, 155. [Google Scholar] [CrossRef] [PubMed]
  26. Allen, K.; Zoellner, J.; Motley, M.; Estabrooks, P.A. Understanding the internal and external validity of health literacy interventions: A systematic literature review using the RE-AIM framework. J. Health Commun. 2011, 16, 55–72. [Google Scholar] [CrossRef] [PubMed]
  27. Estabrooks, P.; Dzewaltowski, D.; Glasgow, R.; Klesges, L. School-based health promotion: Issues related to translating research into practice. J. Sch. Health 2002, 73, 21–28. [Google Scholar] [CrossRef]
  28. McGoey, T.; Root, Z.; Bruner, M.W.; Law, B. Evaluation of physical activity interventions in youth via the Reach, Efficacy/Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework: A systematic review of randomised and non-randomised trials. Prev. Med. 2015, 76, 58–67. [Google Scholar] [CrossRef] [PubMed]
  29. Aittasalo, M.; Livson, M.; Lusa, S.; Romo, A.; Vähä-Ypyä, H.; Tokola, K. Moving to business–changes in physical activity and sedentary behavior after multilevel intervention in small and medium-size workplaces. BMC Public Health 2017, 17, 319. [Google Scholar] [CrossRef]
  30. Alkhajah, T.A.; Reeves, M.M.; Eakin, E.G.; Winkler, E.A.; Owen, N.; Healy, G.N. Sit–stand workstations: A pilot intervention to reduce office sitting time. Am. J. Prev. Med. 2012, 43, 298–303. [Google Scholar] [CrossRef]
  31. Arrogi, A.; Schotte, A.; Bogaerts, A.; Boen, F.; Seghers, J. Short-and long-term effectiveness of a three-month individualized need-supportive physical activity counseling intervention at the workplace. BMC Public Health 2017, 17, 52. [Google Scholar] [CrossRef]
  32. Ben-Ner, A.; Hamann, D.J.; Koepp, G.; Manohar, C.U.; Levine, J. Treadmill workstations: The effects of walking while working on physical activity and work performance. PLoS ONE 2014, 9, e88620. [Google Scholar] [CrossRef] [PubMed]
  33. Bort-Roig, J.; Martin, M.; Puig-Ribera, A.; González-Suárez, Á.M.; Martínez-Lemos, I.; Martori, J.C. Uptake and factors that influence the use of ‘sit less; move more’occupational intervention strategies in Spanish office employees. Int. J. Behav. Nutr. Phys. Act. 2014, 11, 152. [Google Scholar] [CrossRef] [PubMed]
  34. Puig-Ribera, A.; Bort-Roig, J.; Giné-Garriga, M.; González-Suárez, A.M.; Martínez-Lemos, I.; Fortuño, J.; Martori, J.C.; Munoz-Ortiz, L.; Mila, R.; Gilson, N.; et al. Impact of a workplace ‘sit less; move more’program on efficiency-related outcomes of office employees. BMC Public Health 2017, 17, 455. [Google Scholar] [CrossRef] [PubMed]
  35. Puig-Ribera, A.; Bort-Roig, J.; González-Suárez, A.M.; Martínez-Lemos, I.; Giné-Garriga, M.; Fortuño, J.; Martori, J.C.; Munoz-Ortiz, L.; Mila, R.; McKenna, J.; et al. Patterns of impact resulting from a ‘sit less; move more’web-based program in sedentary office employees. PLoS ONE 2015, 10, e0122474. [Google Scholar] [CrossRef] [PubMed]
  36. Brakenridge, C.L.; Fjeldsoe, B.; Young, D.; Winkler, E.; Dunstan, D.; Straker, L. Evaluating the effectiveness of organisational-level strategies with or without an activity tracker to reduce office workers’ sitting time: A cluster-randomised trial. Int. J. Behav. Nutr. Phys. Act. 2016, 13, 115. [Google Scholar] [CrossRef] [PubMed]
  37. Brakenridge, C.L.; Healy, G.N.; Hadgraft, N.T.; Young, D.C.; Fjeldsoe, B.S. Australian employee perceptions of an organizational-level intervention to reduce sitting. Health Promot. Int. 2017. [Google Scholar] [CrossRef] [PubMed]
  38. Carr, L.J.; Leonhard, C.; Tucker, S.; Fethke, N.; Benzo, R.; Gerr, F. Total worker health intervention increases activity of sedentary workers. Am. J. Prev. Med. 2016, 50, 9–17. [Google Scholar] [CrossRef] [PubMed]
  39. Carr, L.J.; Karvinen, K.; Peavler, M.; Smith, R.; Cangelosi, K. Multicomponent intervention to reduce daily sedentary time: A randomised controlled trial. BMJ Open 2013, 3, e003261. [Google Scholar] [CrossRef]
  40. Carr, L.J.; Walaska, K.A.; Marcus, B.H. Feasibility of a portable pedal exercise machine for reducing sedentary time in the workplace. Br. J. Sports Med. 2012, 46, 430–435. [Google Scholar] [CrossRef] [PubMed]
  41. Chau, J.Y.; Daley, M.; Srinivasan, A.; Dunn, S.; Bauman, A.E.; van der Ploeg, H.P. Desk-based workers’ perspectives on using sit-stand workstations: A qualitative analysis of the [email protected] Work study. BMC Public Health 2014, 14, 752. [Google Scholar] [CrossRef] [PubMed]
  42. Chau, J.Y.; Daley, M.; Dunn, S.; Srinivasan, A.; Do, A.; Bauman, A.E. The effectiveness of sit-stand workstations for changing office workers’ sitting time: Results from the [email protected] Work randomized controlled trial pilot. Int. J. Behav. Nutr. Phys. Act. 2014, 11, 127. [Google Scholar] [CrossRef] [PubMed]
  43. Chau, J.Y.; Sukala, W.; Fedel, K.; Do, A.; Engelen, L.; Kingham, M. More standing and just as productive: Effects of a sit-stand desk intervention on call center workers’ sitting; standing; and productivity at work in the Opt to Stand pilot study. Prev. Med. 2016, 3, 68–74. [Google Scholar] [CrossRef] [PubMed]
  44. Cifuentes, M.; Qin, J.; Fulmer, S.; Bello, A. Facilitators and barriers to using treadmill workstations under real working conditions: A qualitative study in female office workers. Am. J. Prev. Med. 2015, 30, 93–100. [Google Scholar] [CrossRef] [PubMed]
  45. Coenen, P.; Healy, G.N.; Winkler, E.A.; Dunstan, D.W.; Owen, N.; Moodie, M.; LaMontagne, A.D.; Eakin, E.A.; Straker, L.M. Pre-existing low-back symptoms impact adversely on sitting time reduction in office workers. Int. Arch. Occop. Environ. Health 2017, 90, 609–618. [Google Scholar] [CrossRef] [PubMed]
  46. Hadgraft, N.T.; Winkler, E.A.; Healy, G.N.; Lynch, B.M.; Neuhaus, M.; Eakin, E.G.; Dunstan, D.W.; Owen, N.; Fjeldoe, B.S. Intervening to reduce workplace sitting: Mediating role of social-cognitive constructs during a cluster randomised controlled trial. Int. J. Behav. Nutr. Phys. Act. 2017, 14, 27. [Google Scholar] [CrossRef]
  47. Hadgraft, N.T.; Willenberg, L.; LaMontagne, A.D.; Malkoski, K.; Dunstan, D.W.; Healy, G.N.; Moodie, M.; Eakin, E.G.; Owen, N.; Lawler, P.S. Reducing occupational sitting: Workers’ perspectives on participation in a multi-component intervention. Int. J. Behav. Nutr. Phys. Act. 2017, 14, 73. [Google Scholar] [CrossRef][Green Version]
  48. Healy, G.N.; Winkler, E.A.; Eakin, E.G.; Owen, N.; Lamontagne, A.D.; Moodie, M.; Dunstan, D.W. A Cluster RCT to Reduce Workers’ Sitting Time: Impact on Cardiometabolic Biomarkers. Med. Sci. Sport Exerc. 2017, 49, 2032–2039. [Google Scholar] [CrossRef]
  49. Healy, G.N.; Eakin, E.G.; Owen, N.; Lamontagne, A.D.; Moodie, M.; Winkler, E.; Fjeldsoe, B.S.; Wiesner, G.; Willenberg, L.; Dunstan, D.W. A Cluster Randomized Controlled Trial to Reduce Office Workers’ Sitting Time: Effect on Activity Outcomes. Med. Sci. Sport Exerc. 2016, 48, 1787–1797. [Google Scholar] [CrossRef]
  50. Coffeng, J.K.; Boot, C.R.; Duijts, S.F.; Twisk, J.W.; van Mechelen, W.; Hendriksen, I.J. Effectiveness of a worksite social & physical environment intervention on need for recovery; physical activity and relaxation; results of a randomized controlled trial. PLoS ONE 2014, 9, e114860. [Google Scholar]
  51. Pedersen, S.J.; Cooley, P.D.; Mainsbridge, C.J. An e-health intervention designed to increase workday energy expenditure by reducing prolonged occupational sitting habits. Work 2014, 49, 289–295. [Google Scholar]
  52. Danquah, I.H.; Kloster, S.; Holtermann, A.; Aadahl, M.; Tolstrup, J.S. Effects on musculoskeletal pain from “Take a Stand!”—A cluster-randomized controlled trial reducing sitting time among office workers. Scand. J. Work Environ. Health 2017, 43, 350–357. [Google Scholar] [CrossRef] [PubMed]
  53. Danquah, I.H.; Kloster, S.; Holtermann, A.; Aadahl, M.; Bauman, A.; Ersbøll, A.K.; Tolstrup, J.S. Take a Stand!—A multi-component intervention aimed at reducing sitting time among office workers–a cluster randomized trial. Int. J. Epidemiol. 2017, 46, 128–140. [Google Scholar] [CrossRef] [PubMed]
  54. Davis, K.G.; Kotowski, S.E. Postural variability: An effective way to reduce musculoskeletal discomfort in office work. Hum. Fact. 2014, 56, 1249–1261. [Google Scholar] [CrossRef]
  55. De Cocker, K.; De Bourdeaudhuij, I.; Cardon, G.; Vandelanotte, C. Theory-driven; web-based; computer-tailored advice to reduce and interrupt sitting at work: Development; feasibility and acceptability testing among employees. BMC Public Health 2015, 15, 959. [Google Scholar] [CrossRef] [PubMed]
  56. De Cocker, K.; De Bourdeaudhuij, I.; Cardon, G.; Vandelanotte, C. The effectiveness of a web-based computer-tailored intervention on workplace sitting: A randomized controlled trial. J. Med. Internet Res. 2016, 8, 5. [Google Scholar] [CrossRef] [PubMed]
  57. De Cocker, K.; De Bourdeaudhuij, I.; Cardon, G.; Vandelanotte, C. What are the working mechanisms of a web-based workplace sitting intervention targeting psychosocial factors and action planning? BMC Public Health 2017, 17, 382. [Google Scholar] [CrossRef]
  58. Dewa, C.S.; deRuiter, W.; Chau, N.; Karioja, K.J. Walking for wellness: Using pedometers to decrease sedentary behaviour and promote mental health. Int. J. Ment. Health Promot. 2009, 11, 24–28. [Google Scholar] [CrossRef]
  59. Donath, L.; Faude, O.; Schefer, Y.; Roth, R.; Zahner, L. Repetitive daily point of choice prompts and occupational sit-stand transfers; concentration and neuromuscular performance in office workers: An RCT. Int. J. Environ. Res. Public Health 2015, 12, 4340–4353. [Google Scholar] [CrossRef] [PubMed]
  60. Ellegast, R.; Weber, B.; Mahlberg, R. Method inventory for assessment of physical activity at VDU workplaces. Work 2012, 41, 2355–2359. [Google Scholar]
  61. Engelen, L.; Dhillon, H.M.; Chau, J.Y.; Hespe, D.; Bauman, A.E. Do active design buildings change health behaviour and workplace perceptions? Occup. Med. 2016, 66, 408–411. [Google Scholar] [CrossRef][Green Version]
  62. Evans, R.E.; Fawole, H.O.; Sheriff, S.A.; Dall, P.M.; Grant, P.M.; Ryan, C.G. Point-of-choice prompts to reduce sitting time at work: A randomized trial. Am. J. Prev. Med. 2012, 43, 293–297. [Google Scholar] [CrossRef] [PubMed]
  63. Fennell, C.G. The Effects of a 16-Week Exercise Program and Cell Phone Use on Physical Activity; Sedentary Behavior; and Health-related Outcomes. Ph.D. Thesis, Kent State University, Kent, OH, USA, 2016. Available online: http://rave.ohiolink.edu/etdc/view?acc_num = kent1468331801 (accessed on 14 March 2018). [Google Scholar]
  64. Ganesan, A.N.; Louise, J.; Horsfall, M.; Bilsborough, S.A.; Hendriks, J.; McGavigan, A.D.; Selvanayagam, J.B.; Chew, D.P. International mobile-health intervention on physical activity; sitting; and weight: The Stepathlon cardiovascular health study. J. Am. Coll. Cardiol. 2016, 67, 2453–2463. [Google Scholar] [CrossRef]
  65. Gao, Y.; Nevala, N.; Cronin, N.J.; Finni, T. Effects of environmental intervention on sedentary time; musculoskeletal comfort and work ability in office workers. Eur. J. Sport Sci. 2016, 16, 747–754. [Google Scholar] [CrossRef] [PubMed]
  66. Gilson, N.D.; Puig-Ribera, A.; McKenna, J.; Brown, W.J.; Burton, N.W.; Cooke, C.B. Do walking strategies to increase physical activity reduce reported sitting in workplaces: A randomized control trial. Int. J. Behav. Nutr. Phys. Act. 2009, 6, 43. [Google Scholar] [CrossRef] [PubMed]
  67. Gilson, N.D.; Ng, N.; Pavey, T.G.; Ryde, G.C.; Straker, L.; Brown, W. Project Energise: Using participatory approaches and real time computer prompts to reduce occupational sitting and increase work time physical activity in office workers. J. Sci. Med. Sport 2016, 19, 926–930. [Google Scholar] [CrossRef]
  68. Gorman, E.; Ashe, M.C.; Dunstan, D.W.; Hanson, H.M.; Madden, K.; Winkler, E.A. Does an ‘activity-permissive’ workplace change office workers’ sitting and activity time? PLoS ONE 2013, 8, e76723. [Google Scholar] [CrossRef]
  69. Graves, L.; Murphy, R.; Shepherd, S.O.; Cabot, J.; Hopkins, N.D. Evaluation of sit-stand workstations in an office setting: A randomised controlled trial. PMC Public Health 2015, 15, 1145. [Google Scholar] [CrossRef]
  70. Green, N.; Sigurdsson, S.; Wilder, D.A. Decreasing bouts of prolonged sitting among office workers. J. Appl. Behav. Anal. 2016, 49, 717–722. [Google Scholar] [CrossRef]
  71. Healy, G.N.; Eakin, E.G.; LaMontagne, A.D.; Owen, N.; Winkler, E.A.; Wiesner, G.; Gunning, L.; Neuhaus, M.; Lawler, S.; Fieldsoe, B.S.; et al. Reducing sitting time in office workers: Short-term efficacy of a multicomponent intervention. Prev. Med. 2013, 57, 43–48. [Google Scholar] [CrossRef][Green Version]
  72. Stephens, S.K.; Winkler, E.A.; Trost, S.G.; Dunstan, D.W.; Eakin, E.G.; Chastin, S.F.; Healy, N.G. Intervening to reduce workplace sitting time: How and when do changes to sitting time occur? Br. J. Sport Med. 2014, 48, e93524. [Google Scholar] [CrossRef]
  73. Hendriksen, I.J.; Bernaards, C.M.; Steijn, W.M.; Hildebrandt, V.H. Longitudinal relationship between sitting time on a working day and vitality; work performance; presenteeism; and sickness absence. J. Occup. Environ. Med. 2016, 58, 784–789. [Google Scholar] [CrossRef] [PubMed]
  74. Jancey, J.M.; McGann, S.; Creagh, R.; Blackford, K.D.; Howat, P.; Tye, M. Workplace building design and office-based workers’ activity: A study of a natural experiment. Aust. N. Z. J. Public Health 2016, 40, 78–82. [Google Scholar] [CrossRef] [PubMed]
  75. John, D.; Thompson, D.L.; Raynor, H.; Bielak, K.; Rider, B.; Bassett, D.R. Treadmill workstations: A worksite physical activity intervention in overweight and obese office workers. J. Phys. Act. Health 2011, 8, 1034–1043. [Google Scholar] [CrossRef]
  76. Jones, C.A. Examining the Efficacy and Feasibility of Digital Activity Monitors and Shared Active Desks to Reduce Employee Sedentary Behavior. Ph.D. Thesis, The University of North Carolina, Chapel Hill, NC, USA, 2016. [Google Scholar]
  77. Júdice, P.B.; Hamilton, M.T.; Sardinha, L.B.; Silva, A.M. Randomized controlled pilot of an intervention to reduce and break-up overweight/obese adults’ overall sitting-time. Trials 2015, 16, 490. [Google Scholar] [CrossRef] [PubMed]
  78. Kerr, J.; Takemoto, M.; Bolling, K.; Atkin, A.; Carlson, J.; Rosenberg, D.; Crist, K.; Godbole, S.; Lewars, B.; Pena, C.; et al. Two-arm randomized pilot intervention trial to decrease sitting time and increase sit-to-stand transitions in working and non-working older adults. PLoS ONE 2016, 11, e0145427. [Google Scholar] [CrossRef] [PubMed]
  79. Kozey-Keadle, S.; Libertine, A.; Staudenmayer, J.; Freedson, P. The feasibility of reducing and measuring sedentary time among overweight; non-exercising office workers. J. Obes. 2012, 2012, 282303. [Google Scholar] [CrossRef] [PubMed]
  80. Kress, M.M. The Use of Stand-Capable Workstations for Reducing Sedentary Time in Office Employees. Ph.D. Thesis, Texas A & M University, College Station, TX, USA, 2015. [Google Scholar]
  81. Li, I.; Mackey, M.G.; Foley, B.; Pappas, E.; Edwards, K.; Chau, J.Y.; Engelen, L.; Voukelatos, A.; Whelan, A.; Bauman, A.; et al. Reducing office workers’ sitting time at work using sit-stand protocols: Results from a pilot randomized controlled trial. J. Occup. Envirno. Med. 2017, 59, 543–549. [Google Scholar] [CrossRef]
  82. MacEwen, B.T.; Saunders, T.J.; MacDonald, D.J.; Burr, J. Sit-stand desks to reduce workplace sitting time in office workers with abdominal obesity: A randomized controlled trial. J. Phys. Act. Health 2017, 14, 710–715. [Google Scholar] [CrossRef]
  83. Mackenzie, K.; Goyder, E.; Eves, F. Acceptability and feasibility of a low-cost; theory-based and co-produced intervention to reduce workplace sitting time in desk-based university employees. BMC Public Health 2015, 15, 1294. [Google Scholar] [CrossRef]
  84. Mailey, E.L.; Rosenkranz, S.K.; Casey, K.; Swank, A. Comparing the effects of two different break strategies on occupational sedentary behavior in a real world setting: A randomized trial. Prev. Med. 2016, 4, 423–428. [Google Scholar] [CrossRef]
  85. Mailey, E.L.; Rosenkranz, S.K.; Ablah, E.; Swank, A.; Casey, K. Effects of an Intervention to Reduce Sitting at Work on Arousal; Fatigue; and Mood Among Sedentary Female Employees. J. Occup. Environ. Med. 2017, 59, 1166–1171. [Google Scholar] [CrossRef] [PubMed]
  86. Mansoubi, M.; Pearson, N.; Biddle, S.J.; Clemes, S.A. Using sit-to-stand workstations in offices: Is there a compensation effect? Med. Sci. Sports Exerc. 2016, 48, 720–725. [Google Scholar] [CrossRef] [PubMed]
  87. Parry, S.; Straker, L.; Gilson, N.D.; Smith, A. Participatory workplace interventions can reduce sedentary time for office workers—A randomised controlled trial. PLoS ONE 2013, 8, e78957. [Google Scholar] [CrossRef] [PubMed]
  88. Priebe, C.S.; Spink, K.S. Less sitting and more moving in the office: Using descriptive norm messages to decrease sedentary behavior and increase light physical activity at work. Psychol. Sport Exerc. 2015, 19, 76–84. [Google Scholar] [CrossRef]
  89. Pronk, N.P.; Katz, A.S.; Lowry, M.; Payfer, J.R. Reducing occupational sitting time and improving worker health: The take-a-stand project. Prev. Chronic Dis. 2012, 9, e154. [Google Scholar] [CrossRef] [PubMed]
  90. Reece, J.D. Reduce Your Sit And be More Fit: An Examination of Sedentary Behavior. Ph.D. Thesis, Middle Tennessee State University, Murfreesboro, TN, USA, 2013. [Google Scholar]
  91. Schuna, J.J.M.; Swift, D.L.; Hendrick, C.A.; Duet, M.T.; Johnson, W.D.; Martin, C.K.; Martin, C.; Church, T.; Tudor-Locke, C. Evaluation of a workplace treadmill desk intervention: A randomized controlled trial. J. Occup. Environ. Med. 2014, 56, 1266–1276. [Google Scholar] [CrossRef] [PubMed]
  92. Tudor-Locke, C.; Hendrick, C.A.; Duet, M.T.; Swift, D.L.; Schuna, J.M., Jr.; Martin, C.K.; Johnson, W.D.; Church, T.S. Implementation and adherence issues in a workplace treadmill desk intervention. Appl. Ergon. 2014, 39, 1104–1111. [Google Scholar] [CrossRef]
  93. Straker, L.; Abbott, R.A.; Heiden, M.; Mathiassen, S.E.; Toomingas, A. Sit–stand desks in call centres: Associations of use and ergonomics awareness with sedentary behavior. Appl. Ergon. 2013, 44, 517–522. [Google Scholar] [CrossRef]
  94. Swartz, A.M.; Rote, A.E.; Welch, W.A.; Maeda, H.; Hart, T.L.; Cho, Y.I.; Strath, S.J. Peer Reviewed: Prompts to Disrupt Sitting Time and Increase Physical Activity at Work, 2011–2012. Prev. Chronic Dis. 2014, 11, E73. [Google Scholar] [CrossRef]
  95. Taylor, W.C.; Paxton, R.J.; Shegog, R.; Coan, S.P.; Dubin, A.; Page, T.F.; Rempel, D.M. Peer Reviewed: Impact of Booster Breaks and Computer Prompts on Physical Activity and Sedentary Behavior Among Desk-Based Workers: A Cluster-Randomized Controlled Trial. Prev. Chronic Dis. 2016, 13, E155. [Google Scholar] [CrossRef]
  96. Tobin, R.; Leavy, J.; Jancey, J. Uprising: An examination of sit-stand workstations; mental health and work ability in sedentary office workers; in Western Australia. Work 2016, 55, 359–371. [Google Scholar] [CrossRef] [PubMed]
  97. Urda, J.L.; Lynn, J.S.; Gorman, A.; Larouere, B. Health. Effects of a minimal workplace intervention to reduce sedentary behaviors and improve perceived wellness in middle-aged women office workers. J. Phys. Act. Health 2016, 13, 838–844. [Google Scholar] [CrossRef] [PubMed]
  98. Van Berkel, J.; Boot, C.R.; Proper, K.I.; Bongers, P.M.; van der Beek, A.J. Effectiveness of a worksite mindfulness-based multi-component intervention on lifestyle behaviors. Int. J. Behav. Nutr. Phys. Act. 2014, 11, 9. [Google Scholar] [CrossRef] [PubMed][Green Version]
  99. Venema, T.A.; Kroese, F.M.; De Ridder, D.T. I’m still standing: A longitudinal study on the effect of a default nudge. Psychol. Health 2018, 33, 669–681. [Google Scholar] [CrossRef] [PubMed]
  100. Verweij, L.M.; Proper, K.I.; Weel, A.N.; Hulshof, C.T.; van Mechelen, W. The application of an occupational health guideline reduces sedentary behaviour and increases fruit intake at work: Results from an RCT. Occup. Environ. Med. 2012, 69, 500–5007. [Google Scholar] [CrossRef] [PubMed]
  101. Biddle, S.J.; Bennie, J. Editorial for Special Issue: Advances in Sedentary Behavior Research and Translation. AIMS Public Health 2017, 4, 33. [Google Scholar] [CrossRef]
  102. Côté, P.; van der Velde, G.; Cassidy, J.D.; Carroll, L.J.; Hogg-Johnson, S.; Holm, L.W.; Carragee, E.J.; Haldemean, S.; Nordic, M.; Hurwitz, E.L.; et al. The burden and determinants of neck pain in workers. Eur. Spine J. 2008, 17, 60–74. [Google Scholar] [CrossRef]
  103. Ranasinghe, P.; Perera, Y.S.; Lamabadusuriya, D.A.; Kulatunga, S.; Jayawardana, N.; Rajapakse, S.; Katulanda, P. Work-related complaints of arm, neck and shoulder among computer office workers in an Asian country: Prevalence and validation of a risk-factor questionnaire. BMC Musculoskelet. Disord. 2011, 12, 68. [Google Scholar] [CrossRef]
  104. Brakenridge, C.; Chong, Y.; Winkler, E.; Hadgraft, N.; Fjeldsoe, B.; Johnston, V. Evaluating Short-Term Musculoskeletal Pain Changes in Desk-Based Workers Receiving a Workplace Sitting-Reduction Intervention. Int. J. Environ. Res. Public Health 2018, 15, 1975. [Google Scholar] [CrossRef]
  105. Zhai, L.; Zhang, Y.; Zhang, D. Sedentary behaviour and the risk of depression: A meta-analysis. Br. J. Sports Med. 2015, 49, 705–709. [Google Scholar] [CrossRef]
  106. Sloan, R.A.; Sawada, S.S.; Girdano, D.; Liu, Y.T.; Biddle, J.; Blair, S.N. Associations of sedentary behavior and physical activity with psychological distress: A cross-sectional study from Singapore. BMC Public Health 2013, 13, 885. [Google Scholar] [CrossRef] [PubMed]
  107. Kilpatrick, M.; Sanderson, K.; Blizzard, L.; Teale, B.; Venn, A. Cross-sectional associations between sitting at work and psychological distress: Reducing sitting time may benefit mental health. Ment. Health Phys. Act. 2013, 6, 103–109. [Google Scholar] [CrossRef]
  108. Simon, G.E.; Revicki, D.; Heiligenstein, J.; Grothaus, L.; VonKorff, M.; Katon, W.J.; Hylan, T.R. Recovery from depression, work productivity, and health care costs among primary care patients. Gen. Hosp. Psychiarty 2000, 22, 153–162. [Google Scholar] [CrossRef]
  109. Boles, M.; Pelletier, B.; Lynch, W.; Medicine, E. The relationship between health risks and work productivity. J. Occup. Environ. Med. 2004, 46, 737–745. [Google Scholar] [CrossRef] [PubMed]
  110. Goetzel, R.Z.; Ozminkowski, R.J.; Bruno, J.A.; Rutter, K.R.; Isaac, F.; Wang, S. The long-term impact of Johnson & Johnson’s Health & Wellness Program on employee health risks. J. Occup. Environ. Med. 2002, 44, 417–424. [Google Scholar] [PubMed]
  111. Goetzel, R.Z.; Henke, R.M.; Tabrizi, M.; Pelletier, K.R.; Loeppke, R.; Ballard, D.W.; Grossmeier, J.; Andreson, D.R.; Yach, D.; Kelly, R.K.; et al. Do workplace health promotion (wellness) programs work? J. Occup. Environ. Med. 2014, 56, 927–934. [Google Scholar] [CrossRef] [PubMed]
  112. Ajunwa, I.; Crawford, K.; Schultz, J. Limitless worker surveillance. Calif. Law Rev. 2017, 105, 735. [Google Scholar]
  113. Stieb, D.M.; Boot, C.R.; Turner, M.C. Promise and pitfalls in the application of big data to occupational and environmental health. BMC Public Health 2017, 17, 372. [Google Scholar] [CrossRef]
  114. Reich, M.R. Public-private partnerships for public health. In Public-Private Partnerships for Public Health; Harvard Center for Population and Development Studies: Cambridge, MA, USA, 2002; pp. 1–18. [Google Scholar]
  115. Glasgow, R.E.; Lichtenstein, E.; Marcus, A.C. Why don’t we see more translation of health promotion research to practice? Rethinking the efficacy-to-effectiveness transition. Am. J. Public Health 2003, 93, 1261–1267. [Google Scholar] [CrossRef]
  116. Adams, E.J.; Chalkley, A.E.; Esliger, D.W.; Sherar, L.B. Evaluation of the implementation of a whole-workplace walking programme using the RE-AIM framework. BMC Public Health 2017, 17, 466. [Google Scholar] [CrossRef]
  117. Gaglio, B.; Phillips, S.M.; Heurtin-Roberts, S.; Sanchez, M.A.; Glasgow, R.E. How pragmatic is it? Lessons learned using PRECIS and RE-AIM for determining pragmatic characteristics of research. Implement. Sci. 2014, 9, 96. [Google Scholar] [CrossRef] [PubMed][Green Version]
  118. Schwingel, A.; Gálvez, P.; Linares, D.; Sebastião, E. Using a mixed-methods RE-AIM framework to evaluate community health programs for older Latinas. J. Ageing Health 2017, 29, 551–593. [Google Scholar] [CrossRef] [PubMed]
  119. Forman, J.; Heisler, M.; Damschroder, L.J.; Kaselitz, E.; Kerr, E.A. Development and application of the RE-AIM QuEST mixed methods framework for program evaluation. Prev. Med. Rep. 2017, 6, 322–328. [Google Scholar] [CrossRef] [PubMed]
  120. Draper, C.E.; Kolbe-Alexander, T.L.; Lambert, E.V. A retrospective evaluation of a community-based physical activity health promotion program. J. Phys. Act. Health 2009, 6, 578–588. [Google Scholar] [CrossRef]
  121. Schwartz, D.; Lellouch, J. Explanatory and pragmatic attitudes in therapeutical trials. J. Clin. Epidemiol. 2009, 62, 499–505. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Flow diagram of studies included in the review. RE-AIM—reach, effectiveness, adoption, implementation, and maintenance.
Figure 1. Flow diagram of studies included in the review. RE-AIM—reach, effectiveness, adoption, implementation, and maintenance.
Ijerph 15 02876 g001
Figure 2. The total proportion of reporting across all indicators within each RE-AIM dimension.
Figure 2. The total proportion of reporting across all indicators within each RE-AIM dimension.
Ijerph 15 02876 g002
Figure 3. Percentage of studies reporting reach indicators.
Figure 3. Percentage of studies reporting reach indicators.
Ijerph 15 02876 g003
Figure 4. Percentage of interventions reporting efficacy/effectiveness indicators.
Figure 4. Percentage of interventions reporting efficacy/effectiveness indicators.
Ijerph 15 02876 g004
Figure 5. Percentage of interventions reporting adoption indicators.
Figure 5. Percentage of interventions reporting adoption indicators.
Ijerph 15 02876 g005
Figure 6. Percentage of interventions reporting implementation indicators.
Figure 6. Percentage of interventions reporting implementation indicators.
Ijerph 15 02876 g006
Figure 7. Percentage of interventions reporting maintenance indicators.
Figure 7. Percentage of interventions reporting maintenance indicators.
Ijerph 15 02876 g007
Table 1. Inclusion and exclusion criteria and search terms based on PICOS (population, intervention type, and comparator, outcomes of interest, and setting).
Table 1. Inclusion and exclusion criteria and search terms based on PICOS (population, intervention type, and comparator, outcomes of interest, and setting).
PICOS TableInclusion CriteriaExclusion CriteriaSearch Terms
Participants/PopulationAdult office workersChildren, non-working adults, workers outside of office setting, older adultsOffice staff, worksite, work *, employ *, staff, adults, white collar
InterventionAll interventions targeting SB in the workplace experimental and quasi-experimental designs, natural experiment and qualitativeSystematic reviews, meta-analysis, commentaries, conference proceedings, methodology studies, validation studies, lab-based studiesPragmatic evaluation, process evaluation, program evaluation, feasibility, pilot, health promotion, health program, program *, trial, program theory, theory of change, logic model, health behaviour change, intervention, sitting desk, sitting workstation, cycle * workstation, treadmill desk, treadmill workstation *, active * workstation *, active * permissive workstation *, sitting workstation *, seated workstation *, height adjusted workstation *, hot desk, sit-stand desk
ComparatorAll comparison or self-comparison (pre-post design, natural experiment)
OutcomeSB measured & RE-AIM checklist elements SB (sedentary, sedentary behave *, sedentary time, active *, sitting, sitting time, sitting behave *, screen time, screen based, chair based, deskbound, physical inactive *, inactive lifestyle, lack of activity) & RE-AIM-(Validity, external validity, internal validity, behaviour change, policy change, community change, participation, quality of life, reach, influence, effect *, success, usefulness, efficacy, adoption, acceptance, maintenance, preservation, acceptability, rate, appraise, analyses, implement, deliver *)
SettingOffice setting
SB—sedentary behaviour; *—truncation symbol; RE-AIM: reach, efficacy/effectivness, adoption, intervention, maintenance.
Table 2. Characteristics of included articles.
Table 2. Characteristics of included articles.
Study Author and YearContinent (Country)Number of ParticipantsOutcome MeasurementMeasurement MethodStudy TypeIntervention Duration
Aittasalo et al. (2012) [29]Europe (Finland)n = 295Primary—SB and PA
Secondary—work ability and employee participation
Objective—accelerometer
Subjective—workforce sitting questionnaire and additional questions on work ability
Pre- and post-longitudinal12 months
Alkhajah et al. (2012) [30]Australia (Australia)n = 32Primary—SB
Secondary—body fat, fasting total cholesterol, HDL cholesterol, triglycerides, and glucose levels
Objective—ActivPAL, bioimpedance, and cholestech LDX analyzerQuasi-experimental design3 months
Arrogi et al. (2017) [31] Europe (Belgium)n = 300Primary—SB and PA
Secondary—change in health-related anthropometric measures and change in psycho-social variables
Objectively—sensewear accelerometerRandomised control trial (RCT)3 months
Barbieri et al. (2017) [12]South America (Brazil)n = 24Primary—SBObjective—monitoring sit–stand table positionsRandomised 2 group design2 months
Ben-Ner et al. (2014) [32]North America (USA)n = 43Primary—SB and PA
Secondary—effects of work performance
Objective—Actical accelerometer
Subjective—Likert scale questionnaire
RCT12 months
Bort-Roig et al. (2014) [33]; connected to [34,35]Europe (Spain)n = 100Primary—Update of strategies and EngagementSubjective—semi-structured interviews and questionnairesMix methods21 weeks
Brakenridge et al. (2016) [36];connected to [37]Australia (Australia)n = 50Primary—SB
Secondary—standing and moving time, reliability and validity of the LUMOback, and predictors of change.
Objective—ActivPALCluster randomised trial3 months
Brakenridge et al. (2017) [37];connected to [36]Australia (Australia)n = 50Primary—participants perceptions of interventionSubjective—interview and focus groupsQualitative study12 months
Carr et al. (2016) [38] North America (USA)n = 54Primary—SB and PA
Secondary—cardio metabolic health outcomes, musculoskeletal discomfort, and work productivity
Objective—GENEActiv accelerometer, sphygmomanometer, Subjective—WHO Health and Work Performance Questionnaire 3, Standardized Nordic Musculoskeletal Symptom QuestionnaireTwo-group RCT4 months
Carr et al. (2013) [39]North America (USA)n = 49Primary—SB and PA
Secondary—heart rate, blood pressure, height, weight, waist circumference, percent body fat, cardiorespiratory fitness, and fasting lipids
Objective—stepwatch, stethoscope, sphygmomanometer, and cholestech LDX analyzerRCT3 months
Carr et al. (2012) [40]North America (USA)n = 18Primary—SB and PASubjective—questionnairePre- and post-descriptive study1-month
Chau, Daley, and Srinivasan et al. (2014) [41]; connected to [42]Australia (Australia)n = 42Primary—evaluate the acceptability, feasibility, and perceptions of using sit–stand workstationsSubjective—focus groupsQualitative1 month
Chau, Daley, and Dunn et al. (2014) [42];connected to [41]Australia (Australia)n = 49Primary—SB and PAObjective—ActiGraph accelerometer
Subjective—occupational sitting and physical activity questionnaire (OSPAQ)
RCT1 month
Chau et al. (2016) [43]Australia (Australia)n = 31Primary—SB and PA
Secondary—productivity outcomes
Subjective—OSPAQQuasi-experimental with control2 weeks
Cifuentes et al. (2015) [44]North America (USA)n = 5Primary—usability, safety, comfort, and productivity using treadmill work stations in a real-world settingSubjective—Interview and focus groupQualitative6 months
Coenen et al. (2017) [45]; connected to [46,47,48,49]Australia (Australia)n = 231Primary—musculoskeletal symptomsSubjective—27-item Nordic Musculoskeletal QuestionnaireCross-sectionalNo intervention
Coffeng et al. (2014) [50]Europe (Netherlands)n = 412Primary—recovery experience
Secondary—work-related stress, small breaks, physical activity (i.e., stair climbing, active commuting, sport activities, light/moderate/vigorous physical activity), and sedentary behaviour.
Subjective—questionnaireRCT12 months
Cooley et al. (2014) [14]; connected to [51]Australia (Australia)n = 47Primary—perceptions of the outcomes associated with a workplace health intervention designed to reduce prolonged occupational sitting timeSubjective—Semi-structured interviewsQualitative13 weeks
Danquah IH, Kloster S, Holtermann A, Aadahl M, Tolstrup J et al. (2017) [52];connected to [53]Europe (Denmark and Greenland)n = 461Primary—SB
Secondary—musculoskeletal pain
Objective—ActiGraph
Subjective—three items on pain in neck-shoulders
Cluster RCT3 months
Danquah Danquah IH, Kloster S, Holtermann A, Aadahl M, Bauman A, Ersbøll AK, et al. (2017); [53] connected to [52]Europe (Denmark and Greenland)n = 461Primary—SB
Secondary—waist circumference and body fat percentage
Objective—ActiGraph and bioimpedanceCluster RCT3 months
Davis et al. (2014) [54]North America (USA)n = 37Primary—SB, productivity discomfortObjective—video analysisQuasi-experimental with cross over1 month
De Cocker et al., (2015) [55]Europe (Belgium)n = 47Primary—SB
Secondary—feasibility and acceptability
Subjective—QuestionnairesDescriptive study2 weeks
De Cocker et al., (2016) [56]; connected to [57]Europe (Belgium)n = 213Primary—SB
Secondary—psycho-social correlates of sitting
Objective—ActivPalRCT3 months
De Cocker et al., (2017) [57]; connected to [56]Europe (Belgium)n = 213Primary—SB
Secondary—psycho-social correlates of sitting
Subjective—Workforce Sitting Questionnaire (WSQ)Cluster RCT1 month
Dewa et al. (2009) [58] North America (Canada)n = 28Primary—SB, PA, and mental health statusSubjective—international physical activity questionnaire (IPAQ)Quasi-experimental with control1 month
Donath et al. (2015) [59]Europe (Switzerland)n = 38Primary—SB
Secondary—concentration, postural sway, and lower limb strength endurance
Objectively—ActiGraphRCT3 months
Ellegast (2012) [60]Europe (Germany)n = 25Primary—SB and PA
Secondary—health outcomes
Subjectively—Activity logsRCT3 months
Engelen et al. (2016) [61]Australia (Australia)n = 34Primary—SB and PA
Secondary—perceptions and productivity
Objective—accelerometer
Subjective—online activity logs, mood state questionnaire, and orthopaedic
medical check-up (G-46)
Natural experiment2 months
Evans et al. (2012) [62]Europe (U.K.)n = 30Primary—SBObjective—ActivPALRCT5 days
Fennel et al. (2016) [63]North America (USA)n = 62Primary—SB, PA, and fitness related variables
Secondary—associated psychometric factors
Subjective—IPAQ questionnaire, international personality item pool, self-efficacy and exercise habits survey, behavioural regulation in exercise questionnaire-3RCT4 months
Ganesan et al. (2016) [64]Australia (Australia)n = 69,219Primary—SB and PA
Secondary—weight change/BMI change and dietary change
Subjective—questionnaireNatural experiment100 days
Gao et al. (2016) [65]Europe (Finland)n = 45Primary—SB
Secondary—musculoskeletal discomfort and work ability
Subjective—questionnaire and Likert scale items RCT6 months
Gilson et al. (2009) [66]Europe (U.K.)n = 179Primary—SB and PASubjective—log bookRCT10 weeks
Gilson et al. (2016) [67]Australia (Australia)n = 57Primary—SBObjective—chair fitted sitting monitorQuasi-experimental5 months
Gorman et al. (2013) [68]North America (Canada)n = 72Primary—SB and PA
Secondary—body composition, fasting cardio-metabolic blood profile, job performance, and job satisfaction
Objective—ActivPALNatural experiment4 months
Graves et al. (2015) [69]Europe (U.K.)n = 47Primary—SB
Secondary—behavioural, cardiometabolic, and musculoskeletal
Subjective—momentary assessment diaryRCT2 months
Green et al. (2016) [70] North America (USA)n = 3Primary—SBObjective—ActivGraphPre- and post-designNR
Hadgraft and Winkler et al. (2017) [46];connected to [45,47,48,49]Australia (Australia)n = 231Primary—perceived behavioural control, self-efficacy, perceived organisational norms, and knowledgeSubjective—questionnaire and Adapted Likert scale single itemsQualitative study12 months
Hadgraft and Willenberg et al. (2017) [47]; connected to [45,46,48,49]Australia (Australia)n = 136Primary—participants’ perspectivesSubjective—semi-structured interviewsQualitative study12 months
Healy et al. (2017) [48];connected to [45,46,47,49]Australia (Australia)n = 231Primary—body composition, blood pressure, glucose metabolism, lipid metabolism, and a composite overall cardiometabolic risk scoreObjectiveCluster RCT12 months
Healy et al. (2013) [71]; connected to [72]Australia (Australia)n = 43Primary—SB
Secondary—standing and stepping
Objective—ActivPALNon-randomised controlled trial1 month
Healy et al. (2016) [49]; connected to [45,46,47,48]Australia (Australia)n = 231Primary—SB
Secondary—standing and stepping
Objectively—ActivPALRCT12 months
Hendriksen et al. (2016) [73]Europe (Netherlands)n = 396Primary—PA, SB, and work-related outcomesSubjective—self-report questionnairePre- and post-design—longitudinal study5 months
Jancey et al. (2016) [74]Australia (Australia)n = 67Primary—SB and PAObjective—ActiGraphNatural experimental4 months
John et al. (2011) [75]North America (USA)n = 12Primary—SB and PA
Secondary—Health outcomes
Objective—ActivPALPre- and post- design—longitudinal study9 months
Jones et al. (2017) [76]North America (USA)n = 47Primary—SBObjective—FitbitPre- and post-prospective cluster intervention6 months
Judice et al. (2015) [77]Europe (Portugal)n = 10Primary—SB
Secondary—Standing and stepping
Objective—ActivPALRCT1 week
Kerr et al. (2016) [78]North America (USA)n = 30Primary—SBObjective—ActivPALRCT2 weeks
Kozey-Keadle et al. (2012) [79]North America (USA)n = 20Primary—SBObjective—ActivPALPre- and post-design—longitudinal study1 week
Kress et al. (2015) [80] North America (USA)n = 33Primary—SB
Secondary—personal factors and perceptions of sit–stand workstations
Subjective—questionnaireNatural experiment3 months
Li et al. (2017) [81] Australia (Australia)n = 33Primary—SB
Secondary—PA
Objective—ActivPALRCT4 weeks
MacEwen et al. (2017) [82]North America (Canada)n = 28Primary—SB and cardio metabolic risk factorsObjective: SB—ActivPAL
Subjective: SB—non-validated questions, Cosmed Quark, Cholestech LDX system, and glycosylated haemoglobin (HbA1c) diazyme SMART analyzer
RCT12 weeks
Mackenzie et al. (2015) [83] Europe (U.K.)n = 24Primary—SB, and participant viewsSubjective—self report sitting log, open ended questionPre- and post-design5 weeks
Mailey et al. (2016) [84]; connected to [85]North America (USA)n = 49Primary—SB and cardio metabolic healthObjective SB—ActiGraph automated blood pressure cuff and Cholestech LDXParallel-group randomized trial8 weeks
Mailey et al. (2017) [85];connected to [85]North America (USA)n = 49Primary—arousal, mood, and fatigueSubjective—activation–deactivation adjective checklist (ADACL), the positive and negative affect schedule (PANAS), and fatigue symptom inventory (FSI)Parallel-group randomized trial8 weeks
Mansoubi et al. (2016) [86] Europe (U.K.)n = 40Primary—SB and PAObjective—ActivPAL and ActiGraph accelerometerPre- and post- design3 months
Neuhaus et al. (2014) [15] Australia (Australia)n = 44Primary—SBObjective—ActivPALRCT3 months
Parry et al. (2013) [87]Australia (Australia)n = 133Primary—SB
Secondary—PA
Objective—ActiGraph accelerometer RCT12 weeks
Pedersen et al. (2014) [51]; connected to [14]Australia (Australia)n = 34Primary—SB and PASubjective—survey built upon the OPAQ and OSPAQRCT13 weeks
Priebe et al. (2015) [88] North America (Canada)n = 142Primary—SB and PASubjective—Not validated SB questionnairePre- and post-designNR
Pronk et al. (2012) [89] North America (USA)n = 34Primary—SB, health related outcomes, and work performanceSubjective—experience sampling methodologyPre- and post-design—two groups7 weeks
Puig-Ribera et al. (2017) [34]; connected to [33,35]Europe (Spain)n = 264Primary—Presenteeism, productivity loss, mental well-being, and productivitySubjective—work limitations questionnaire; Warwick–Edinburgh mental well-being scale;Pre- and post-design—two groups21 weeks
Puig-Ribera et al. (2015) [35]; connected to [33,34]Europe (Spain)n = 264Primary—SB and physical risk factors for chronic diseaseSubjective—self report diary log, blood pressure, weight, and waist measurement Pre- and post- design—two groups21 weeks
Reece et al. (2014) [90] North America (USA)n = 34Primary—SB and PAObjective—Sense Wear armbandRCT17 days
Schuna et al. (2014) [91]; connected to [92]North America (USA)n = 41Primary—SB and PAObjective-Acti-graphRCT3 months
Stephens et al. (2014) [72]; connected to [71]Australia (Australia)n = 43Primary—SBObjective—ActivPALNon-randomised controlled trial4 weeks
Straker et al. (2013) [93]Europe (Sweden)n = 131Primary—SBObjective—inclinometer and portable data loggerNatural experiment—cross sectional1 day analysis
Swartz et al. (2014) [94] North America (USA)n = 78Primary—SB and PAObjective—ActivPALRandomised trial with parallel groups2 weeks
Taylor et al. 2016 [95] North America (USA)n = 185Primary—SB and PASubjective—IPAQ sitting items and self-reported seven-day checklist from the Neighbourhood Quality of Life StudyPA—pedometer and IPAQCluster RCT6 months
Tobin et al. (2016) [96]Australia (Australia)n = 52Primary—SB
Secondary—psychological distress, self-perceived physical and mental health, workability, and perceived benefits
Objective—ActivPAL
Subjective—K10, SF8, and work ability index questionnaire
Pre- and post-design—two groups5 weeks
Tudor-Lock et al. (2014) [92]; connected to [91]North America (USA)n = 41Primary—perceptions of feasibility and acceptabilitySubjective—focus groupsQualitative3 months
Urda et al. (2016) [97] North America (USA)n = 48Primary—SB and perceived wellnessObjective—ActivPAL
Subjective—perceived wellness survey
RCT2 weeks
vanBerkel et al. (2014) [98] Europe (Netherlands)n = 257Primary—SBSubjective—non-validated SB at work questionnaireRCT6 months.
Venema et al. 2017 [99] Europe (Netherlands)n = 606Primary—SBObjective—direct observation and surveyPre- and post-design2 months
Verweij at al. (20d12) [100] Europe (Netherlands)n = 185Primary—SB
Secondary—PA, waist circumference, body weight, and BMI
Subjective—non-validated SB item, IPAQ
Secondary outcomes—PA–(SQUASH) and BMI-calculated
RCT6 months
NR = not reported; BMI—body mass index; HDL—high density lipoproteins; PA—physical activity.
Table 3. Recommendations for improved reporting across reach, effectiveness, adoption, implementation, and maintenance (RE-AIM), and examples of reporting methods used within included interventions.
Table 3. Recommendations for improved reporting across reach, effectiveness, adoption, implementation, and maintenance (RE-AIM), and examples of reporting methods used within included interventions.
RE-AIM DimensionRecommendations for Improved Reporting across the RE-AIM Framework for Interventions Targeting Sedentary Behaviour in Office Workers
Reach
  • Seek or collect basic demographic or health information of all workplace setting employees, which will help to compare participants vs. non-participants. Example method found in De Cocker et al. (2016) and De Cocker et al. (2017) [56,57].
  • Report the number of participants exposed to recruitment activities and illustrate the calculation of participation rate of the study.
  • Employ questionnaire or qualitative methods to understand barriers to reach of study. Example method found in Bort-Riog et al. (2014) [33].
Effectiveness
  • If intention to treat methods are used, report specific method and rationale for appropriateness. Example method found in Arrogi et al. (2017) [31].
  • Seek to use biological outcome measures (e.g., body composition, cardiovascular fitness, glucose metabolism and overall cardiomtabolic risk score). Example methods found in Healy et al. (2017) [48].
  • Use questionnaire and/or qualitative methods to understand impact on quality of life and unintended or unexpected outcomes. Example methods found in Pronk et al. (2012) [89] and Hardgraft et al. (2017) [47].
  • Additional questionnaires utilised for unexpected outcomes including: musculoskeletal (27-item Nordic musculoskeletal questionnaire), presenteeism (work limitations questionnaire (WLQ), percentage of work productivity loss (WLQ index score) and mental well-being (Warwick–Edinburgh mental well-being scale (WEMWBS)). Productivity—the work limitations questionnaire (WLQ) assessed profile of mood states (POMS) questionnaire.
Adoption
  • Record and report on the specific recruitment processes, including: inclusion and exclusion criteria for businesses, the number of companies or sites approached, the number who declined available demographic information to report on representativeness of company demographics compared to local area statistics (e.g., state or province or council demographic statistics.). Example method found in Puig-Ribera et al. (2015) [35].
  • Collect quantitative information from implementation team regarding level of training and expertise and fidelity to implementation strategies. Example method found in Brakenridge et al. 2017 [37] Aittasalo et al. (2012) [29].
  • Report a measure of cost to implement per setting.
Implementation
  • Collect qualitative or questionnaire data from the implementation team regarding the fidelity to implementation strategies and facilitators and barriers to implementation. Example method found in Bort-Riog et al. (2014) [33].
  • Collect qualitative or questionnaire data regarding facilitators and barriers to uptake of behaviour change strategies. Example method found in Bort-Riog et al. (2014) [33].
  • Report on cost (monetary or time commitment) of implementation of individual intervention strategies.
Maintenance
  • Record and report plans for follow-up and any modifications to program.
  • Utilise accessible questionnaire’s from which to collect data at more long-term follow-up time points. Example methods such as self-report logs or sitting items from existing questionnaires found in Coffeng et al. (2014) [50], Gao et al. (2016) [65], and van Berkel et al. (2014) [98].

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Int. J. Environ. Res. Public Health EISSN 1660-4601 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top