Safety at work The objective of the project Safety at Work is to increase safety at the workplace by applying and combining state of the art artefacts from personal protective equipment and ambient intelligence technology. In this state of the art document we focus on the developments with respect to how (persuasive) technology can help to influence behaviour in a natural, automatic way in order to make industrial environments safer. We focus on personal safety, safe environments and safe behaviour. Direct ways to influence safety The most obvious way to influence behaviour is to use direct, physical measures. In particular, this is known from product design. The safe use of a product is related to the characteristics of the product (e.g., sharp edges), the condition of people operating the product (e.g., stressed or tired), the man-machine interface (e.g., intuitive or complex) and the environmental conditions while operating the product (e.g., noisy or crowded). Design guidelines exist to help designers to make safe products. A risk matrix can be made with two axis: product hazards versus personal characteristics. For each combination one might imagine what can go wrong, and what potential solutions are. Except for ‘design for safety’ in the sense of no sharp edges or a redundant architecture, there is a development called ‘safety by design’ as well. Safety by design is a concept that encourages construction or product designers to ‘design out’ health and safety risks during design development. On this topic, we may learn from the area of public safety. Crime Prevention Through Environmental Design (or Designing Out Crime) is a multi-disciplinary approach to deterring criminal behaviour through environmental design. Designing Out Crime uses measures like taking steps to increase (the perception) that people can be seen, limiting the opportunity for crime by taking steps to clearly differentiate between public space and private space, and promoting social control through improved proprietary concern. Senses Neuroscience has shown that we have very little insight into our motivations and, consequently, are poor at predicting our own behaviour. It seems emotions are an important predictor of our behaviour. Input from our senses are important for our emotional state, and therefore influence our behaviour in an ‘ambient’ (invisible) way. The first sense we focus on is sight. Sight encompasses the perception of light intensity (illuminance) and colours (spectral distribution). Several researchers have studied the effects of light and colour in working environments. Results show, e.g., that elderly people can be helped with higher light levels, that cool colours like blue and green have a relaxing effect, while long-wavelength colours such as orange and red are stimulating and give more arousal, and that concentration and motivation of pupils at school can be influenced with light and colour settings. Identically, sound (hearing) has physiological effects (unexpected sounds cause extra cortisol -the fight or flight hormone- and the opposite for soothing sounds), psychological effects (sounds effect our emotions), cognitive effects (sounds effect our concentration) and behavioural effects (the natural behaviour of people is to avoid unpleasant sounds, and embrace pleasurable sounds). Smell affects 75% of daily emotions and plays an important role in memory, itis also important as a warning for danger (gas, burning smell). Research has shown that smell can influence work performance. Haptic feedback is a relative new area of research, and most studies focus on haptic feedback on handheld and automotive devices. Finally, employers have a duty to take every reasonable precaution to protect workers from heat stress disorders. Influence mechanisms: Cialdini To influence behaviour, we may learn from marketing psychology. Robert Cialdini states that if we have to think about every decision
MULTIFILE
Introduction Negative pain-related cognitions are associated with persistence of low-back pain (LBP), but the mechanism underlying this association is not well understood. We propose that negative pain-related cognitions determine how threatening a motor task will be perceived, which in turn will affect how lumbar movements are performed, possibly with negative long-term effects on pain. Objective To assess the effect of postural threat on lumbar movement patterns in people with and without LBP, and to investigate whether this effect is associated with task-specific pain-related cognitions. Methods 30 back-healthy participants and 30 participants with LBP performed consecutive two trials of a seated repetitive reaching movement (45 times). During the first trial participants were threatened with mechanical perturbations, during the second trial participants were informed that the trial would be unperturbed. Movement patterns were characterized by temporal variability (CyclSD), local dynamic stability (LDE) and spatial variability (meanSD) of the relative lumbar Euler angles. Pain-related cognition was assessed with the task-specific ‘Expected Back Strain’-scale (EBS). A three-way mixed Manova was used to assess the effect of Threat, Group (LBP vs control) and EBS (above vs below median) on lumbar movement patterns. Results We found a main effect of threat on lumbar movement patterns. In the threat-condition, participants showed increased variability (MeanSDflexion-extension, p<0.000, η2 = 0.26; CyclSD, p = 0.003, η2 = 0.14) and decreased stability (LDE, p = 0.004, η2 = 0.14), indicating large effects of postural threat. Conclusion Postural threat increased variability and decreased stability of lumbar movements, regardless of group or EBS. These results suggest that perceived postural threat may underlie changes in motor behavior in patients with LBP. Since LBP is likely to impose such a threat, this could be a driver of changes in motor behavior in patients with LBP, as also supported by the higher spatial variability in the group with LBP and higher EBS in the reference condition.
LINK
ObjectiveTo determine the effectiveness of the “Plants for Joints” multidisciplinary lifestyle program in patients with metabolic syndrome-associated osteoarthritis (MSOA).DesignPatients with hip or knee MSOA were randomized to the intervention or control group. The intervention group followed a 16-week program in addition to usual care based on a whole food plant-based diet, physical activity, and stress management. The control group received usual care. The patient-reported Western Ontario and McMasters Universities Osteoarthritis Index (WOMAC) total score (range 0–96) was the primary outcome. Secondary outcomes included other patient-reported, anthropometric, and metabolic measures. An intention-to-treat analysis with a linear-mixed model adjusted for baseline values was used to analyze between-group differences.ResultsOf the 66 people randomized, 64 completed the study. Participants (84% female) had a mean (SD) age of 63 (6) years and body mass index of 33 (5) kg/m2. After 16 weeks, the intervention group (n = 32) had a mean 11-point larger improvement in WOMAC-score (95% CI 6–16; p = 0.0001) compared to the control group. The intervention group also lost more weight (–5 kg), fat mass (–4 kg), and waist circumference (–6 cm) compared to the control group. Patient-Reported Outcomes Measurement Information System (PROMIS) fatigue, pain interference, C-reactive protein, hemoglobin A1c, fasting glucose, and low-density lipoproteins improved in the intervention versus the control group, while other PROMIS measures, blood pressure, high-density lipoproteins, and triglycerides did not differ significantly between the groups.ConclusionThe “Plants for Joints” lifestyle program reduced stiffness, relieved pain, and improved physical function in people with hip or knee MSOA compared to usual care.
MULTIFILE
