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.
Background:Current technology innovations, such as wearables, have caused surprising reactions and feelings of deep connection to devices. Some researchers are calling mobile and wearable technologies cognitive prostheses, which are intrinsically connected to individuals as if they are part of the body, similar to a physical prosthesis. Additionally, while several studies have been performed on the phenomenology of receiving and wearing a physical prosthesis, it is unknown whether similar subjective experiences arise with technology.Objective:In one of the first qualitative studies to track wearables in a longitudinal investigation, we explore whether a wearable can be embodied similar to a physical prosthesis. We hoped to gain insights and compare the phases of embodiment (ie, initial adjustment to the prosthesis) and the psychological responses (ie, accept the prosthesis as part of their body) between wearables and limb prostheses. This approach allowed us to find out whether this pattern was part of a cyclical (ie, period of different usage intensity) or asymptotic (ie, abandonment of the technology) pattern.Methods:We adapted a limb prosthesis methodological framework to be applied to wearables and conducted semistructured interviews over a span of several months to assess if, how, and to what extent individuals come to embody wearables similar to prosthetic devices. Twelve individuals wore fitness trackers for 9 months, during which time interviews were conducted in the following three phases: after 3 months, after 6 months, and at the end of the study after 9 months. A deductive thematic analysis based on Murray’s work was combined with an inductive approach in which new themes were discovered.Results:Overall, the individuals experienced technology embodiment similar to limb embodiment in terms of adjustment, wearability, awareness, and body extension. Furthermore, we discovered two additional themes of engagement/reengagement and comparison to another device or person. Interestingly, many participants experienced a rarely reported phenomenon in longitudinal studies where the feedback from the device was counterintuitive to their own beliefs. This created a blurring of self-perception and a dilemma of “whom” to believe, the machine or one’s self.Conclusions:There are many similarities between the embodiment of a limb prosthesis and a wearable. The large overlap between limb and wearable embodiment would suggest that insights from physical prostheses can be applied to wearables and vice versa. This is especially interesting as we are seeing the traditionally “dumb” body prosthesis becoming smarter and thus a natural merging of technology and body. Future longitudinal studies could focus on the dilemma people might experience of whether to believe the information of the device over their own thoughts and feelings. These studies might take into account constructs, such as technology reliance, autonomy, and levels of self-awareness.
Injuries and lack of motivation are common reasons for discontinuation of running. Real-time feedback from wearables can reduce discontinuation by reducing injury risk and improving performance and motivation. There are however several limitations and challenges with current real-time feedback approaches. We discuss these limitations and challenges and provide a framework to optimise real-time feedback for reducing injury risk and improving performance and motivation. We first discuss the reasons why individuals run and propose that feedback targeted to these reasons can improve motivation and compliance. Secondly, we review the association of running technique and running workload with injuries and performance and we elaborate how real-time feedback on running technique and workload can be applied to reduce injury risk and improve performance and motivation. We also review different feedback modalities and motor learning feedback strategies and their application to real-time feedback. Briefly, the most effective feedback modality and frequency differ between variables and individuals, but a combination of modalities and mixture of real-time and delayed feedback is most effective. Moreover, feedback promoting perceived competence, autonomy and an external focus can improve motivation, learning and performance. Although the focus is on wearables, the challenges and practical applications are also relevant for laboratory-based gait retraining.