Placebo and nocebo effects are embodied psycho-neurobiological responses capable of modulating pain and producing changes at different neurobiological, body at perceptual and cognitive levels. These modifications are triggered by different contextual factors (CFs) presented in the therapeutic encounter between patient and healthcare providers, such as healing rituals and signs. The CFs directly impact on the quality of the therapeutic outcome: a positive context, that is a context characterized by the presence of positive CFs, can reduce pain by producing placebo effects, while a negative context, characterized by the presence of negative CFs, can aggravate pain by creating nocebo effects. Despite the increasing interest about this topic; the detailed study of CFs as triggers of placebo and nocebo effects is still lacked in the management of musculoskeletal pain. Increasing evidence suggest a relevant role of CFs in musculoskeletal pain management. CFs are a complex sets of internal, external or relational elements encompassing: patient’s expectation, history, baseline characteristics; clinician’s behavior, belief, verbal suggestions and therapeutic touch; positive therapeutic encounter, patient-centered approach and social learning; overt therapy, posology of intervention, modality of treatment administration; marketing features of treatment and health care setting. Different explanatory models such as classical conditioning and expectancy can explain how CFs trigger placebo and nocebo effects. CFs act through specific neural networks and neurotransmitters that were described as mediators of placebo and nocebo effects. Available findings suggest a relevant clinical role and impact of CFs. They should be integrated in the clinical reasoning to increase the number of treatment solutions, boosts their efficacy and improve the quality of the decision-making. From a clinical perspective, the mindful manipulation of CFs represents a useful opportunity to enrich a well-established therapy in therapeutic setting within the ethical border. From a translational perspective, there is a strong need of research studies on CFs close to routine and real-world clinical practice in order to underline the uncertainty of therapy action and help clinicians to implement knowledge in daily practice.

Background: Digitalization is not fully implemented in clinical practice, and several factors have been identified as possible barriers, including the competencies of health care professionals. However, no summary of the available evidence has been provided to date to depict digital health competencies that have been investigated among health care professionals, the tools used in assessing such competencies, and the effective interventions to improve them. Objective: This review aims to summarize digital health competencies investigated to date and the tools used to assess them among health care professionals. Methods: A systematic review based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist was performed. The MEDLINE, Cumulative Index to Nursing and Allied Health Literature, PsycINFO, and Scopus databases were accessed up to September 4, 2021. Studies assessing digital health competencies with quantitative designs, targeting health care professionals, and written in English were included. The methodological quality of included studies was evaluated using the Joanna Briggs Institute tools. Results: A total of 26 studies, published from 1999 to 2021, met the inclusion criteria, and the majority were cross sectional in design, while only 2 were experimental study designs. Most studies were assessed with moderate to low methodological quality; 4 categories and 9 subcategories of investigated digital health competencies have been identified. The most investigated category was “Self-rated competencies,” followed by “Psychological and emotional aspects toward digital technologies,” “Use of digital technologies,” and “Knowledge about digital technologies.” In 35% (9/26) of the studies, a previously validated tool was used to measure the competencies assessed, while others developed ad hoc questionnaires. Conclusions: Mainly descriptive studies with issues regarding methodology quality have been produced to date investigating 4 main categories of digital health competencies mostly with nonvalidated tools. Competencies investigated might be considered while designing curricula for undergraduate, postgraduate, and continuing education processes, whereas the methodological lacks detected might be addressed with future research. There is a need to expand research on psychological and emotional elements and the ability to use digital technology to self-learn and teach others. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42021282775; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=282775

Background: Placebo and nocebo effects embody psychoneurobiological phenomena where behavioural, neurophysiological, perceptive and cognitive changes occur during the therapeutic encounter in the healthcare context. Placebo effects are produced by a positive healthcare context; while nocebo effects are consequences of negative healthcare context. Historically, placebo, nocebo and context-related effects were considered as confounding elements for clinicians and researchers. In the last two decades this attitude started to change, and the understanding of the value of these effects has increased. Despite the growing interest, the knowledge and the awareness of using the healthcare context to trigger placebo and nocebo effects is currently limited and heterogeneous among physiotherapists, reducing their translational value in the physiotherapy field.Objectives: To introduce the placebo, nocebo and context-related effects by: (1) presenting their psychological models; (2) describing their neurophysiological mechanisms; (3) underlining their impact for the physiotherapy profession; and (4) tracing lines for future researches.Conclusion: Several psychological mechanisms are involved in placebo, nocebo and context-related effects; including expectation, learning processes (classical conditioning and observational learning), reinforced expectations, mindset and personality traits. The neurophysiological mechanisms mainly include the endogenous opioid, the endocannabinoid and the dopaminergic systems. Neuroimaging studies have identified different brain regions involved such as the dorsolateral prefrontal cortex, the rostral anterior cingulate cortex, the periaqueductal gray and the dorsal horn of spine. From a clinical perspective, the manipulation of the healthcare context with the best evidence-based therapy represents an opportunity to trigger placebo effects and to avoid nocebo effects respecting the ethical code of conduct. From a managerial perspective, stakeholders, organizations and governments should encourage the assessment of the healthcare context aimed to improve the quality of physiotherapy services. From an educational perspective, placebo and nocebo effects are professional topics that should be integrated in the university program of health and medical professions. From a research perspective, the control of placebo, nocebo and context-related effects offers to the scientific community the chance to better measure the impact of physiotherapy on different outcomes and in different conditions through primary studies.

Background: Artificial intelligence (AI) chatbots are emerging educational tools for students in healthcare science. However, assessing their accuracy is essential prior to adoption in educational settings. This study aimed to assess the accuracy of predicting the correct answers from three AI chatbots (ChatGPT-4, Microsoft Copilot and Google Gemini) in the Italian entrance standardized examination test of healthcare science degrees (CINECA test). Secondarily, we assessed the narrative coherence of the AI chatbots’ responses (i.e., text output) based on three qualitative metrics: the logical rationale behind the chosen answer, the presence of information internal to the question, and presence of information external to the question. Methods: An observational cross-sectional design was performed in September of 2023. Accuracy of the three chatbots was evaluated for the CINECA test, where questions were formatted using a multiple-choice structure with a single best answer. The outcome is binary (correct or incorrect). Chi-squared test and a post hoc analysis with Bonferroni correction assessed differences among chatbots performance in accuracy. A p-value of < 0.05 was considered statistically significant. A sensitivity analysis was performed, excluding answers that were not applicable (e.g., images). Narrative coherence was analyzed by absolute and relative frequencies of correct answers and errors. Results: Overall, of the 820 CINECA multiple-choice questions inputted into all chatbots, 20 questions were not imported in ChatGPT-4 (n = 808) and Google Gemini (n = 808) due to technical limitations. We found statistically significant differences in the ChatGPT-4 vs Google Gemini and Microsoft Copilot vs Google Gemini comparisons (p-value < 0.001). The narrative coherence of AI chatbots revealed “Logical reasoning” as the prevalent correct answer (n = 622, 81.5%) and “Logical error” as the prevalent incorrect answer (n = 40, 88.9%). Conclusions: Our main findings reveal that: (A) AI chatbots performed well; (B) ChatGPT-4 and Microsoft Copilot performed better than Google Gemini; and (C) their narrative coherence is primarily logical. Although AI chatbots showed promising accuracy in predicting the correct answer in the Italian entrance university standardized examination test, we encourage candidates to cautiously incorporate this new technology to supplement their learning rather than a primary resource. Trial registration: Not required.