Is it time to reframe how we care for people with non-traumatic musculoskeletal pain?

The current approach to musculoskeletal pain is failing The majority of persistent non-traumatic musculoskeletal pain disorders do not have a pathoanatomical diagnosis that adequately explains the individual’s pain experience and disability. We contend this has resulted in two concerning developments in the management of people with such disorders. First, structural changes observed on imaging that are highly prevalent in pain free populations, such as rotator cuff tears, intervertebral disc degeneration, labral tears and cartilage changes, are ascribed to individuals as a diagnosis for their condition. In this context, this information may result in the individual believing that their body is damaged, fragile and in need of protection, resulting in a cascade of movement and activity avoidance behaviours and seeking interventions to correct the structural deficits. This trend has led to exponential increases in elective surgery rates and associated costs, while the efficacy of repairing (eg, rotator cuff and medical meniscal tears), reshaping (eg, subacromial decompression) or replacing (eg, lumbar intervertebral discs) the structures considered to be at fault has been substantially challenged. Second, it is arguable that musculoskeletal clinicians have invented treatments for conditions that may not exist or be readily detected (such as trigger points, sacral torsions), and they have developed and perpetuated treatment paradigms (such as ‘correcting’ upper body posture and muscle imbalances) that do not conform to current research evidence. These two trends have created an expectation that interventions (frequently ‘passive’) will provide a ’cure’, and typically quickly, with minimal self-contribution. This expectation may have been derived from a conversation with a friend or family member, from the Internet or from an advertising campaign, but almost certainly originated from health professionals.Peer reviewe

LOW BACK PAIN IN ADOLESCENT FEMALE ROWERS AND THE ASSOCIATED FACTORS

There were two aims of the current study; firstly, to determine the incidence of low back pain in adolescent female rowers and secondly, to determine the differences between LBP and no-LBP groups for a range of physical tests and psycho-social variables. The point prevalence of LBP was 47.5% for rowers and 15.5% for controls indicating that LBP is common in this group of rowers. When no-LBP and LBP groups were compared for the data collected in this study, LBP subjects showed significantly decreased lower limb endurance and back muscle endurance and sitting with more erect postures. Although this study cannot determine causation, it has the potential to direct interventions to decrease the incidence of LBP in this group

GENDER DIFFERENCES IN MOTOR CONTROL OF THE TRUNK DURING PROLONGED ERGOMETER ROWING

The aim of the study was to compare the temporal kinematics of a stroke, spino-pelvic kinematics, trunk and quadriceps muscle activation in prolonged ergometer rowing between males and female rowers. Twelve adolescent rowers performed a 20 minute rowing ergometer trial at a high self perceived rate of exertion. Spino-pelvic kinematics, muscle activity and temporal kinematics data were compared in the 1st, 10th and 20th minute. The results from this study indicate there is a difference in temporal kinematics of a rowing stroke between adolescent males and females. Furthermore, males row with a more flexed thoracic spine and a posteriorly rotated sacrum compared to females at the catch and the finish positions

Case study: the implementation of a data-driven industrial analytics methodology and platform for smart manufacturing

Integrated, real-time and open approaches relating to the development of industrial analytics capabilities are needed to support smart manufacturing. However, adopting industrial analytics can be challenging due to its multidisciplinary and cross-departmental (e.g. Operation and Information Technology) nature. These challenges stem from the significant effort needed to coordinate and manage teams and technologies in a connected enterprise. To address these challenges, this research presents a formal industrial analytics methodology that may be used to inform the development of industrial analytics capabilities. The methodology classifies operational teams that comprise the industrial analytics ecosystem, and presents a technology agnostic reference architecture to facilitate the industrial analytics lifecycle. Finally, the proposed methodology is demonstrated in a case study, where an industrial analytics platform is used to identify an operational issue in a largescale Air Handling Unit (AHU)

IAMM: A maturity model for measuring industrial analytics capabilities in large-scale manufacturing facilities

Industrial big data analytics is an emerging multidisciplinary field, which incorporates aspects of engineering, statistics and computing, to produce data-driven insights that can enhance operational efficiencies, and produce knowledgebased competitive advantages. Developing industrial big data analytics capabilities is an ongoing process, whereby facilities continuously refine collaborations, workflows and processes to improve operational insights. Such activities should be guided by formal measurement methods, to strategically identify areas for improvement, demonstrate the impact of analytics initiatives, as well as deriving benchmarks across facilities and departments. This research presents a formal multi-dimensional maturity model for approximating industrial analytics capabilities, and demonstrates the model’s ability to assess the impact of an initiative undertaken in a real-world facility