Purpose: To systematically review the literature on effectiveness of remote physiotherapeutic e-Health interventions on pain in patients with musculoskeletal disorders. Materials and methods: Using online data sources PubMed, Embase, and Cochrane in adults with musculoskeletal disorders with a pain-related complaint. Remote physiotherapeutic e-Health interventions were analysed. Control interventions were not specified. Outcomes on effect of remote e-Health interventions in terms of pain intensity. Results: From 11,811 studies identified, 27 studies were included. There is limited evidence for the effectiveness for remote e-Health for patients with back pain based on five articles. Twelve articles studied chronic pain and the effectiveness was dependent on the control group and involvement of healthcare providers. In patients with osteoarthritis (five articles), total knee surgery (two articles), and knee pain (three articles) no significant effects were found for remote e-Health compared to control groups. Conclusions: There is limited evidence for the effectiveness of remote physiotherapeutic e-Health interventions to decrease pain intensity in patients with back pain. There is some evidence for effectiveness of remote e-Health in patients with chronic pain. For patients with osteoarthritis, after total knee surgery and knee pain, there appears to be no effect of e-Health when solely looking at reduction of pain. Implications for rehabilitation This review shows that e-Health can be an effective way of reducing pain in some populations. Remote physiotherapeutic e-Health interventions may decrease pain intensity in patients with back pain. Autonomous e-Health is more effective than no treatment in patients with chronic pain. There is no effect of e-Health in reduction of pain for patients with osteoarthritis, after total knee surgery and knee pain.Implications for rehabilitation* This review shows that e-Health can be an effective way of reducing pain in some populations.* Remote physiotherapeutic e-Health interventions may decrease pain intensity in patients with back pain.* Autonomous e-Health is more effective than no treatment in patients with chronic pain.* There is no effect of e-Health in reduction of pain for patients with osteoarthritis, after total knee surgery and knee pain.
A significant proportion of adolescents with chronic musculoskeletal pain (CMP) experience difficulties in physical functioning, mood and social functioning, contributing to diminished quality of life. Generalized joint hypermobility (GJH) is a risk factor for developing CMP with a striking 35-48% of patients with CMP reporting GJH. In case GJH occurs with one or more musculoskeletal manifestations such as chronic pain, trauma, disturbed proprioception and joint instability, it is referred to as generalized hypermobility spectrum disorder (G-HSD). Similar characteristics have been reported in children and adolescents with the hypermobile Ehlers-Danlos Syndrome (hEDS). In the management of CMP, a biopsychosocial approach is recommended as several studies have confirmed the impact of psychosocial factors in the development and maintenance of CMP. The fear-avoidance model (FAM) is a cognitive-behavioural framework that describes the role of pain-related fear as a determinant of CMP-related disability. Pubmed was used to identify existing relevant literature focussing on chronic musculoskeletal pain, generalized joint hypermobility, pain-related fear and disability. Relevant articles were cross-referenced to identify articles possibly missed during the primary screening. In this paper the current state of scientific evidence is presented for each individual component of the FAM in hypermobile adolescents with and without CMP. Based on this overview, the FAM is proposed explaining a possible underlying mechanism in the relations between GJH, pain-related fear and disability. It is assumed that GJH seems to make you more vulnerable for injury and experiencing more frequent musculoskeletal pain. But in addition, a vulnerability for heightened pain-related fear is proposed as an underlying mechanism explaining the relationship between GJH and disability. Further scientific confirmation of this applied FAM is warranted to further unravel the underlying mechanism. In explaining disability in individuals with G-HSD/hEDS, it is important to focus on both the physical components related to joint hypermobility, in tandem with the psychological components such as pain-related fear, catastrophizing thoughts and generalized anxiety.
Low back pain is the leading cause of disability worldwide and a significant contributor to work incapacity. Although effective therapeutic options are scarce, exercises supervised by a physiotherapist have shown to be effective. However, the effects found in research studies tend to be small, likely due to the heterogeneous nature of patients' complaints and movement limitations. Personalized treatment is necessary as a 'one-size-fits-all' approach is not sufficient. High-tech solutions consisting of motions sensors supported by artificial intelligence will facilitate physiotherapists to achieve this goal. To date, physiotherapists use questionnaires and physical examinations, which provide subjective results and therefore limited support for treatment decisions. Objective measurement data obtained by motion sensors can help to determine abnormal movement patterns. This information may be crucial in evaluating the prognosis and designing the physiotherapy treatment plan. The proposed study is a small cohort study (n=30) that involves low back pain patients visiting a physiotherapist and performing simple movement tasks such as walking and repeated forward bending. The movements will be recorded using sensors that estimate orientation from accelerations, angular velocities and magnetometer data. Participants complete questionnaires about their pain and functioning before and after treatment. Artificial analysis techniques will be used to link the sensor and questionnaire data to identify clinically relevant subgroups based on movement patterns, and to determine if there are differences in prognosis between these subgroups that serve as a starting point of personalized treatments. This pilot study aims to investigate the potential benefits of using motion sensors to personalize the treatment of low back pain. It serves as a foundation for future research into the use of motion sensors in the treatment of low back pain and other musculoskeletal or neurological movement disorders.
