Manual material handling training: the effect of self-observation, hetero-observational and intrinsic feedback on workers’ knowledge and behaviour

International Journal of Environmental Research and Public Health Article Manual Material Handling Training: The Effect of Self-Observation, Hetero-Observational and Intrinsic Feedback on Workers’ Knowledge and Behaviour Anna M. Sene-Mir 1, Mariona Portell 2,* , M. Teresa Anguera 3 and Salvador Chacón-Moscoso 4,5 1Physical Activity and Sports Studies Centre, University of Vic-Central University of Catalonia, 08500 Vic, Barcelona, Spain; [email protected] 2Department of Psychobiology and Methodology in Health Sciences, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, 08193 Barcelona, Spain 3Faculty of Psychology, Institute of Neurosciences, University of Barcelona, 08035 Barcelona, Spain; [email protected] 4Departamento de Psicología Experimental, Universidad de Sevilla, 41018 Seville, Spain; [email protected] 5Departamento de Psicología, Universidad Autónoma de Chile, Santiago 7500138, Chile *Correspondence: [email protected]; Tel.: +34-935-812-813 Received: 15 October 2020; Accepted: 30 October 2020; Published: 3 November 2020   Abstract: This study aimed to assess the effect of systematic self-observation, hetero-observational feedback, and feedforward and intrinsic feedback (SsObserWork components) on workers’ knowledge and behaviour of a manual material handling (MMH) technique in the industrial sector. Blue-collar workers recruited from a food processing company in Catalonia (Spain) were randomized into SsObserWork (N=31) and control (N=30) groups. SsObserWork group members participated individually in two sessions and a three-week follow-up between sessions where they received the SsObserWork components. The control group participated individually in two sessions where they received a standard MMH training. An ad hoc instrumentcalled the MMH-SsObserWork instrument was used to assess the MMH behaviour, and an adaption of the instrument was done to assess the workers’ knowledge. Significant differences were found between groups for the identification of recommended back positions in the first session and also on comparing both sessions. However, no differences were found for the rest of the criteria. There also were significant differences between groups in the score changes of the back, knee joints, elbow joints, and interaction criterion, indicating that the SsObserWork group improved the MMH performance in these criteria (behaviour). SsObserWork intervention showed a positive effect on improving the knowledge and behaviour of the MMH technique, specifically on back posture. Keywords: observation; self-observation; hetero-observation; feedback; feedforward; manual material handling; knowledge; behaviour; workplace intervention; SsObserWork 1. Introduction Manual handling has been associated with an increased risk of back disorders [ 1 ], mainly lower back pain [ 2 ]. In the industrial sector, most tasks have been automated, although manual handling is still being carried out when it cannot be avoided. Furthermore, people are not only exposed to manual handling at work, but there also are many situations in daily life where it is required and can increase the risk of back disorders. Hence, European Union directives, such as Council Directive 90/269/EEC and national legislations of member states have been outlining appropriate MMH training [ 3 ]. Considering the requirement for MMH training in workplaces, numerous studies have Int. J. Environ. Res. Public Health 2020,17, 8095; doi:10.3390/ijerph17218095 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2020,17, 8095 2 of 19 been focused on developing, implementing, and evaluating the efficacy of manual handling training on reduction of lower back pain or back injury prevention [ 4 ]. It has been observed that fewer studies have been conducted on the effectiveness of manual handling training in industries outside of the healthcare sector [5]. Different systematic reviews have concluded that the majority of manual handling training procedures are not effective to reduce lower back pain or back injury [ 5 ]. This lack of effectiveness is associated with one-dimensional interventions, lack of transferability of training, and a lack of training based on the theory of changing health behaviour. Moreover, studies tend to evaluate the effectiveness of training on long-term results, such as reduction in musculoskeletal disorders [ 5 , 6 ], and have omitted intermediate variables. Hogan et al. [ 7 ] pointed out that there is the need to evaluate the effect of manual handling training on intermediate variables, such as knowledge, behaviour change, and training transferability. To date, few studies have evaluated the effect of MMH training on knowledge and behaviour change [5,6]. In the last decades, studies focused on MMH training have evaluated different training methods. They tended to combine lifting training with back school, lumbar support, verbal feedback, practice, biofeedback, warm-up exercise, and ergonomic redesign of equipment. In addition, physical exercise has been highlighted as a component of MMH training [ 5 ] because it helps to improve leg strength and consequently avoid back loading [8]. However, one of the current challenges is how to actively involve employees in the training, taking into account that the higher the involvement in the training, the more effective it will be [ 9 ]. In this sense, a training method that could emphasize the active role of the employee in the training is that of self-observation. Self-observation is a relevant method to promote behaviour change, because it influences self-confidence, self-awareness, and self-efficacy, and has an emotional impact [ 10 , 11 ]. Self-observation has been evaluated in interventions focused on behaviour change, such as in the treatment of attention deficit hyperactivity disorder, autism, emotional disturbances, speaking ability, and so on [ 12 ], and for skill acquisition in sport [ 13 , 14 ]. Moreover, self-observation has been applied in different training programs to improve the interaction skills of professionals [ 15 ], and to increase one’s own teaching [ 16 ]. Nonetheless, there is little evidence of self-observation as a formative method in training for risk prevention and health promotion at work, and specifically in MMH training. As far as we know, self-observation (specifically self-modelling) has been applied as a training method for reducing musculoskeletal risk among office workers using computers, showing positive effects [ 17 ]. It has also been implemented to improve movement awareness among nursing students, who highlighted its usefulness and its cognitive impact with regard to changing body postures during patient transfer [18,19]. In all these cases, self-observation was done through video. In the learning process of motor activities (e.g., MMH technique), video self-observation can provide two types of external feedback: knowledge of results and knowledge of performance. Usually, video self-observation tends to be used to give knowledge of performance [ 20 ]. However, video self-observation can provide a lot of information that could have a negative effect on employee attention. This is why video self-observation should be complemented with external feedback from the technician, who should give transitional information that reports what is well done [ 21 ], what should be improved, and how it should be done—also called feedforward [ 10 ]. We call this type of external feedback hetero-observational feedback and feedforward. Even so, it is important to consider that the employee will not always receive these external feedbacks because the training takes a particular period of time. Therefore, the training should also promote the use of intrinsic feedback (information provided from intrinsic sources, such as vision and proprioception) with the proposal of making the employees aware of their movements and positions adopted. Schmidt and Lee [ 20 ] suggested that it is this intrinsic feedback that one must learn to interpret, because it will always be available to the learner. Thus, by promoting intrinsic feedback, employees will be capable of regulating themselves once the training has finished. Int. J. Environ. Res. Public Health 2020,17, 8095 3 of 19 Additionally, another way to conduct self-observation in a natural environment is through ambulatory assessment (e.g., using a self-report questionnaire) [ 22 , 23 ]. Studies have shown that ambulatory assessment as an intervention component promotes self-awareness and behavioural change [ 19 , 24 ]. However, it is important to take into account that these behaviour changes can also be caused by reactivity [22,25]. The positive effects of self-observation, hetero-observational and intrinsic feedbacks have been widely studied in sport skill acquisition [ 20 , 25 , 26 ], even though there is a lack of studies on risk prevention and health promotion. Therefore, the aim of this study was to assess the effect of the previous components on worker’s knowledge and behaviour of the MMH technique by employing a randomised controlled trial. We use the name SsObserWork to identify the intervention. Specifically, we tested and evaluated the effect of the main components of SsObserWork, which are systematic self-observation (SSO), hetero-observational feedback and feedforward (HFF), and intrinsic feedback. It was hypothesized that these three main components improved employee knowledge and behaviour of the MMH technique. 2. Materials and Methods 2.1. Study Design A parallel randomised trial of two groups was conducted among blue-collar workers recruited from a food processing company in Catalonia, Spain. Participants were randomly allocated to the SsObserWork or control groups. To evaluate the intervention effect, we adopted a methodological complementarity perspective [ 27 ], combining experimental design with observational methodology. Observational methodology is essential in this study in order to evaluate behaviour, to implement the self-observation component, and to plan the data collection [ 28 – 31 ]. In this sense, according to observational methodology, the study adopted a follow-up, nomothetic, and multidimensional design [28]. The Ethics Committee of the Autonomous University of Barcelona approved the study protocol (PRO1742). Prior to commencement of the intervention, the participants were made aware of their right to withdraw from the study at any time and steps to safeguard information. In accordance with the principles of the Declaration of Helsinki, the participants were informed that they were being recorded. They were shown the location of the video cameras, which were positioned directly to minimize reactivity bias. Informed consent was also obtained. 2.2. Participants and Procedure We used a nonprobability sampling. A convenience sample was initially composed of 103 blue-collar workers at a leading company in the Spanish meat sector, certified by the food safety regulations of the International Food Standards (IFS) and the British Retail Consortium (BRC). Completing the questionnaire and participating in the study was voluntary. The questionnaires were collected during September and October 2015. From the first sample, purposive sampling was applied, using the information provided in the questionnaire. The inclusion criteria were (i) to be older than 18 years old, (ii) to be employed as a blue-collar worker, (iii) not suffer any chronic bone, muscle, or joint disease in the trunk, and/or chronic or acute pain diagnosed by a specialist, and (iv) not suffer any chronic or acute knee joint disease diagnosed by a specialist. Of the 103 employees who completed the questionnaire, 65 were eligible and willing to participate. The 65 participants were randomly assigned to the SsObserWork or control group (Figure 1). Int. J. Environ. Res. Public Health 2020,17, 8095 4 of 19 Int. J. Environ. Res. Public Health 2020, 17, x 4 of 20 Figure 1. Consort flow diagram. 2.3. Intervention The SsObserWork intervention was designed taking into consideration the Health Belief Model [32] and trans-theoretical model of behaviour change [33]. The aim of the intervention was to raise awareness and promote the adoption of proper postural back habits during MMH tasks, considered as complex tasks and a risk factor to develop lower back pain [2]. We considered that by adopting a proper bask posture during MMH, it could be transferred to other working and daily life tasks. The SsObserWork intervention was made up of components, formative activities, and didactic materials, which are described in Appendix A. The intervention consisted of two sessions and a threeweek follow-up between sessions. The intervention was implemented by a trained physical activity specialist. Figure 2 shows the intervention structure and how the components, formative activities, didactic materials, and data collection were distributed orderly. As shown, SSO (including self-report questionnaire), HFF, and intrinsic feedback are the components that evaluated their effect. Derivatively, MMH practice (which promotes intrinsic feedback) and messages to remind workers to answer the self-report questionnaire were only implemented in the SsObserWork group. Instead of the SsObserWork components, the control group received a standard MMH training based on theoretical information. The theoretical information consisted of explaining how to perform a MMH task and without any type of practice. Each SsObserWork and control session was carried out individually with each participant during November and December 2015. Figure 1. Consort flow diagram. 2.3. Intervention The SsObserWork intervention was designed taking into consideration the Health Belief Model [ 32 ] and trans-theoretical model of behaviour change [ 33 ]. The aim of the intervention was to raise awareness and promote the adoption of proper postural back habits during MMH tasks, considered as complex tasks and a risk factor to develop lower back pain [ 2 ]. We considered that by adopting a proper bask posture during MMH, it could be transferred to other working and daily life tasks. The SsObserWork intervention was made up of components, formative activities, and didactic materials, which are described in Appendix A. The intervention consisted of two sessions and a three-week follow-up between sessions. The intervention was implemented by a trained physical activity specialist. Figure 2shows the intervention structure and how the components, formative activities, didactic materials, and data collection were distributed orderly. As shown, SSO (including self-report questionnaire), HFF, and intrinsic feedback are the components that evaluated their effect. Derivatively, MMH practice (which promotes intrinsic feedback) and messages to remind workers to answer the self-report questionnaire were only implemented in the SsObserWork group. Instead of the SsObserWork components, the control group received a standard MMH training based on theoretical information. The theoretical information consisted of explaining how to perform a MMH task and without any type of practice. Each SsObserWork and control session was carried out individually with each participant during November and December 2015. Int. J. Environ. Res. Public Health 2020,17, 8095 5 of 19 Int. J. Environ. Res. Public Health 2020, 17, x 5 of 20 Figure 2. Intervention structure with data collection according to SsObserWork and control groups. 2.4. Measures Baseline characteristics of participants. An ad hoc questionnaire was made to select the sample. It consisted of demographic, social, and health questions, and it included the Standardised Nordic Questionnaire [34], SF-12v2 Health Survey [35], and the Utrecht Work Engagement Scale [36] and stages-of-change Items [37]. MMH behaviour: MMH behaviour was measured with an ad hoc instrument called the MMH- SsObserWork instrument. This observational instrument allows to identify and describe the body positions adopted continually during MMH tasks by using a set of criteria and categories. The criteria are: feet, knee joint, back, elbow joint, load position, and interaction between back tilt and move around. At the beginning of each session, workers had to lift, carry, and lower five boxes (8 kg each) while they were recorded from the sagittal plane. The video camera was positioned at the height of the worker’s hip. The MMH-SsObserWork instrument had to be used using a software application for the record and computation of observational data (e.g., Lince) in which the video recording can be displayed. The MMH-SsObserWork instrument has a very good inter-observer reliability [38]. According to Bakeman et al. [39] for time-event data, the global kappa index based on time units Figure 2. Intervention structure with data collection according to SsObserWork and control groups. 2.4. Measures Baseline characteristics of participants. An ad hoc questionnaire was made to select the sample. It consisted of demographic, social, and health questions, and it included the Standardised Nordic Questionnaire [ 34 ], SF-12v2 Health Survey [ 35 ], and the Utrecht Work Engagement Scale [ 36 ] and stages-of-change Items [37]. MMH behaviour: MMH behaviour was measured with an ad hoc instrument called the MMH-SsObserWork instrument. This observational instrument allows to identify and describe the body positions adopted continually during MMH tasks by using a set of criteria and categories. The criteria are: feet, knee joint, back, elbow joint, load position, and interaction between back tilt and move around. At the beginning of each session, workers had to lift, carry, and lower five boxes (8 kg each) while they were recorded from the sagittal plane. The video camera was positioned at the height of the worker’s hip. The MMH-SsObserWork instrument had to be used using a software application for the record and computation of observational data (e.g., Lince) in which the video recording can Int. J. Environ. Res. Public Health 2020,17, 8095 6 of 19 be displayed. The MMH-SsObserWork instrument has a very good inter-observer reliability [ 38 ]. According to Bakeman et al. [ 39 ] for time-event data, the global kappa index based on time units ranged from 0.90 to 0.97 for all criteria, and the global kappa index based on events ranged from 0.72 to 0.87 for all criteria. MMH Knowledge: MMH knowledge was measured with a instrument developed and based on the MMH-SsObserWork instrument [ 38 ]. This instrument consists of five criteria related to the main parts of the body involved in the MMH task (feet, knee joint, back, elbow joint), and the load position. Each criterion has its categories that describe the different positions that can be adopted. There are 16 categories in total. Categories are represented with pictures and a brief description. Workers had to indicate, for each criterion, which position was the most recommended to adopt during the MMH task by choosing a category. They had to indicate the most recommended for each MMH phase (lifting, carrying, and lowering). Hence, there were three identifications for each participant. 2.5. Data Management To analyse MMH knowledge, we calculated the number of recommended positions identified for each criterion, and then the score change was computed in each criterion comparing the different moments (pre and post the first session; pre-first session and post-second session). In this sense, it was implied that a positive value meant an increase in the frequency of recommended positions identified. To analyse MMH behaviour, the Lince software [ 40 ] was used to generate the observational record for each worker and session. The observational record provided information of time duration (frames and milliseconds) of each category for each criterion. This observational record was exported to MS-Excel. We did a recording of categories for each criterion, determining if they were placed in the recommended or non-recommended position, taking into account the literature review of MMH technique. As has been justified in previous works [ 38 ], the recommended positions established were: feet placed asymmetrically, one beside the load and the other behind it; knees in a semi-squat position (moderate flexion); a neutral back position at any front inclination of back; load placed closed to the body; arms extended or slightly flexed, and do not start or finish carrying phase while lifting and lowering phases are being carried out. The relative duration in which each criterion was found in the recommended position was calculated (time unit of recommended category as regards total duration). The change scores for the different dependent variables (MMH knowledge and behaviour) were computed in order to control the hypothetical baseline effect. 2.6. Statistical Analysis A basic statistical description had been made for each dependent variable, by group and time moment. The aim was to characterize the empirical distribution obtained. We assessed the assumption of normal distribution of variables using the Shapiro-Wilk test. During the three-week follow-up, only 25 out of 31 participants from the SsObserWork group that initiated the follow-up answered the self-report questionnaire. Thirteen participants answered it between 11–15 days, 7 participants answered it between 6–10 days, and 5 participants answered it between 1–5 days. Due to the fact that there was irregular participation in answering the self-report questionnaire, we tested whether there were statistical differences in the dependent variables among those participants from the SsObserWork group who answered the self-report depending the number of days. For non-normal distributed variables, the differences were assessed using the Kruskal-Wallis test. For normally distributed variables, the differences were assessed using one-way analysis of variance. The components effect on outcomes (MMH knowledge and behaviour) was assessed using independent samples t-test or Mann-Whitney U test on change scores, depending on the assumption of normality. The Mann-Whitney U test has been used when normality cannot be assumed. We specify the statistical test applied (t-test or Mann-Whitney) in Tables 2 and 3. Regarding the change score, a positive value indicates a change in the direction defined by the hypothesis. Int. J. Environ. Res. Public Health 2020,17, 8095 7 of 19 Cliff’s δ statistics [ 41 ] were applied to assess component effect size for data that did not pass the normality test. The amount of effect sizes was interpreted as trivial (<0.147), small (between 0.147 and 0.33), medium (between 0.33 and 0.474), or strong (>0.474) [ 42 ]. For data that passed the normality test, effect sizes were examined using Cohen’s dstatistics. Effect sizes were assumed as trivial (<0.20), small (between 0.20 and 0.49), medium (between 0.50 and 0.79), or large (>0.80) [ 43 ]. Analyses were performed using SPSS version 23 (SPSS Institute, Cary, NC, USA). However, R programme version 3.3.3 (R Core Team, R Foundation for Statistical Computing, Vienna, Austria) was used to analyse effect sizes. Statistical significance was set at p≤0.05. We conducted a supplementary analysis focused on delving into the temporal structure of employee behaviour, specifically with participants of the SsObserWork group who experienced an improvement in back position. The type of observational instrument and observational design that we used and adopted allowed to conduct a T-pattern analysis. This type of analysis allows to detect hidden or non-obvious temporal patterns in behaviour that are not always visible. Our aim was to detect T-patterns that involved a recommended back position, and we compared them between sessions of the SsObserWork group. From the observational record of each SsObserWork participant and session, we could detect the criteria co-occurrences during the MMH period (we called it a forward event). To detect temporal regularities in the order of event occurrences, we used the detection algorithm developed by Magnusson [ 44 , 45 ], and implemented in THEME TM software (Patternvision Ltd., Reykjavík, Iceland). This detection algorithm first identifies significant (non-random) recurrences of any two events within a similar temporal configuration (critical interval) in real-time behavioural data and then proceeds to identify a hierarchical relationship with any other antecedent or subsequent events. This statistical method of detecting temporal patterns (T-patterns) of related behavioural events provides behavioural structures that may not be identifiable by traditional sequential methods [ 46 , 47 ]. Data was analysed using Theme 6.0. Default temporal patterns search parameters were used, an acceptable level of significance was set at 0.005, minimum occurrences at 3, and minimum percentage of samples (workers on this data) in which a pattern must occur, was set at 51%. The results have been validated by simulation, through randomization of data on five occasions, with acceptance only of patterns for which the probability of the randomized data coinciding with the real data is zero. The T-pattern differences between sessions were assessed using Pearson’s chi-squared test. 3. Results 3.1. Baseline Characteristics of Participants Table 1shows characteristics of the participants as regards demographic variables, presence of musculoskeletal disorders in the last 12 months, and the stage-of-change in physical exercise. In both groups, half of participants indicated that they had suffered lower back pain or discomfort in some time in the last 12 months. Additionally, Table 1shows the mean and standard deviation of the self-perceived health status of participants and their work engagement. The two groups were very similar and with no statistically significant differences between them, suggesting that the randomization was successful. Int. J. Environ. Res. Public Health 2020,17, 8095 8 of 19 Table 1. Baseline characteristics of participants in both study groups. SsObserWork Group Control Group Sex: N (%) Men 12 (38.7) 14 (46.7) Women 19 (61.3) 16 (53.3) Age: N (%) 18 to 28 years - 2 (6.7) 29 to 39 years 12 (38.7) 10 (33.3) 40 to 50 years 11 (35.5) 7 (23.3) +50 years 8 (25.8) 11 (36.7) Years working in the company: N (%) Less than one year 1 (3.2) 2 (6.7) 1 to 5 years 7 (22.6) 7 (23.3) 5 to 10 years 7 (22.6) 7 (23.3) More than 10 years 16 (51.6) 14 (46.7) Musculoskeletal disorders in the last 12 months: N (%) Suffered pain or discomfort at any body part 30 (96.8) 21 (70) Suffered lower back pain or discomfort a16 (53.3) 15 (71.4) Stage of change in physical exercise: N (%) Precontemplation 3 (9.7) 3 (10) Contemplation 2 (6.5) 6 (20) Preparation 14 (45.2) 12 (40) Action - - Maintenance 12 (38.7) 9 (30) Self-perceived health status: M (SD) Physical Component Summary 50.69 (7.01) 49.47 (7.78) Mental Component Summary 50.18 (5.89) 50.20 (6.07) Work engagement: M (SD) Vigor 4.51 (1.11) 4.69 (0.95) Dedication 3.90 (1.67) 3.97 (1.49) Absorption 4.09 (1.36) 4.17 (1.44) Total score 4.19 (1.26) 4.29 (1.16) Abbreviations: M—Mean; SD—Standard Deviation. a Percentages obtained with respect to the participants who indicated having suffered pain or discomfort at any body part. 3.2. MMH Knowledge Table 2presents the change scores (mean and standard deviation) of the number of recommended positions identified for each criterion that was carried out before and after the self-observation (in the SsObserWork group) and standard training (in the control group) of the first session. There were no significant differences between the groups in knowledge at baseline (first identification before starting the training). Significant differences between groups were found for the identification of recommended back position (U = − 2.113, p=0.035) before and after the first session, but no differences were found for the rest of criteria. The SsObserWork group reported better knowledge of back position that should be adopted in MMH compared to the control group ( δ =0.24) at the end of the first session. Moreover, Table 2shows the scores changes (mean and standard deviation) of the number of recommended positions identified for each criterion that was carried out at the beginning of the first session and at the beginning of the second session. Significant differences between groups were also found for the identification of recommended back position (U = − 2.032, p=0.042), but there were no differences for the rest of criteria. The SsObserWork group still reported better knowledge of back position compared to the control group ( δ =0.29) after the follow-up period. As a result of irregular participation answering the self-report questionnaire, we also assessed the effect of answering it depending on the number of days on MMH knowledge. There were no statistical differences among participants of the SsObserWork group (see results in Appendix B, Table A2). Int. J. Environ. Res. Public Health 2020,17, 8095 9 of 19 Table 2. Mean and standard deviation of score change of the frequency of recommended positions for criteria identified by the SsObserWork and control group, and differences between them. Before and after the First Session aAt the Beginning of the First and the Second Session b Outcome (Criteria Position) SsObserWork Group Control Group Differences between Groups Effect Size SsObserWork Group Control Group Differences between Groups Effect Size Mean SD Mean SD U cp-Value δdMean SD Mean SD U p-Value δ Feet 1.33 1.06 1.53 0.78 −0.514 0.607 −0.06 0.68 1.31 0.44 1.31 −0.635 0.525 0.09 Knees joint 0.37 0.85 0.73 1.08 −1.547 0.122 −0.20 0.25 0.97 0.63 1.36 −1.406 0.160 −0.20 Back 0.63 1.03 0.10 0.61 −2.113 0.035 * 0.24 0.25 1.21 −0.48 1.40 −2.032 0.042 * 0.29 Elbows joint 1.47 1.31 1.03 1.22 −1.482 0.138 0.22 1.11 1.26 0.52 1.42 −1.579 0.114 0.24 Load position 0.47 0.97 0.80 1.19 −0.988 0.323 −0.12 0.36 1.03 0.30 0.91 −0.010 0.992 0.00 a 30 participants from the SsObserWork group and 30 participants from the control group were included in the analysis. b 28 participants from the SsObserWork group and 27 participants from the control group were included in the analysis. cMann-Whitney U test. dCliff’s δstatistics. * p<0.05. Int. J. Environ. Res. Public Health 2020,17, 8095 16 of 19 Appendix B Table A2. Differences in the score change of the frequency of recommended positions for criteria identified by the SsObserWork group according to the number of days the self-report questionnaire was completed. Before and after the First Session At the Beginning of the First and the Second Session Outcome (Criteria Position) Groups Depending on Number of Days Differences between Groups Differences between Groups Mean SD χ2P-value Mean SD χ2p-Value Feet 1 to 5 days 1.75 0.5 0.757 0.685 1.00 1.15 0.86 0.649 6 to 10 days 1.28 0.95 1.00 1.29 11 to 15 days 1.33 0.98 0.42 1.44 Knees joint 1 to 5 days − 0.25 0.50 2.942 0.230 - - 0.48 0.787 6 to 10 days 0.42 0.79 0.14 1.07 11 to 15 days 0.58 1.08 0.33 1.15 Back 1 to 5 days 0.50 0.58 0.297 0.862 − 0.55 0.50 1.32 0.517 6 to 10 days 1.14 1.46 1.00 1.29 11 to 15 days 0.58 0.99 0.25 1.21 Elbows joint 1 to 5 days 2.25 0.96 0.709 0.701 0–75 1.71 0.886 0.642 6 to 10 days 1.14 1.34 0.86 1.21 11 to 15 days 1.17 1.53 1.25 1.22 Load position 1 to 5 days 0.25 0.50 2.267 0.322 0.25 0.50 2.064 0.356 6 to 10 days 0.58 1.16 − 0.14 0.38 11 to 15 days - - 0.50 1.24 p<0.05; Kruskal-Wallis Test, χ2—Pearson’s chi-squared test. Table A3. Differences in the score change (pre-post) of the relative duration in which each criterion was in the recommended position by the SsObserWork group according to the number of days the self-report questionnaire was completed. Outcome (Criteria Position) Groups Depending on Number of Days Differences between Groups Mean SD χ2p-Value F p-Value Feet 0 days 37.8 26.2 4.40 0.221 1 to 5 days 19.5 22.9 6 to 10 days 5.6 53.2 11 to 15 days 11.6 27.8 Knees joint 0 days 6.4 22.7 0.178 0.911 1 to 5 days 16.8 33.6 6 to 10 days 16.8 20.8 11 to 15 days 11.4 25.9 Back 0 days 11.7 19.6 1.23 0.745 1 to 5 days 4.4 15.8 6 to 10 days 2.7 15.9 11 to 15 days 8.8 17.2 Elbows joint 0 days 17.7 34.7 1.71 0.635 1 to 5 days 52.6 34.7 6 to 10 days 32.4 49.9 11 to 15 days 33.1 36.6 Load position 0 days 18.1 17.9 0.88 0.831 1 to 5 days 12.3 15.5 6 to 10 days 16.4 31.9 11 to 15 days 7.7 16.7 Interaction between back tilt and move around 0 days 7.1 8.2 1.93 0.587 1 to 5 days 3.0 6.4 6 to 10 days 2.3 5.0 11 to 15 days 6.4 8.8 p<0.05; Kruskal-Wallis test, χ2—Pearson’s chi-squared test; ANOVA one way, F—Fisher statistic. Int. J. Environ. Res. Public Health 2020,17, 8095 17 of 19 References 1. 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