Effect of an Artificial Intelligence-Based Self-Management App on Musculoskeletal Health in Patients With Neck and/or Low Back Pain Referred to Specialist Care: A Randomized Clinical Trial

Impact of 25 Years of Mobile Health Tools for Pain Management in Patients With Chronic Musculoskeletal Pain: Systematic Review

BACKGROUND

Mobile technologies are increasingly being used in health care and public health practice for patient communication, monitoring, and education. Mobile health (mHealth) tools have also been used to facilitate adherence to chronic musculoskeletal pain (CMP) management, which is critical to achieving improved pain outcomes, quality of life, and cost-effective health care.

OBJECTIVE

The aim of this systematic review was to evaluate the 25-year trend of the literature on the adherence, usability, feasibility, and acceptability of mHealth interventions in CMP management among patients and health care providers.

METHODS

We searched the PubMed, Cochrane CENTRAL, MEDLINE, EMBASE, and Web of Science databases for studies assessing the role of mHealth in CMP management from January 1999 to December 2023. Outcomes of interest included the effect of mHealth interventions on patient adherence; pain-specific clinical outcomes after the intervention; and the usability, feasibility, and acceptability of mHealth tools and platforms in chronic pain management among target end users.

RESULTS

A total of 89 articles (26,429 participants) were included in the systematic review. Mobile apps were the most commonly used mHealth tools (78/89, 88%) among the included studies, followed by mobile app plus monitor (5/89, 6%), mobile app plus wearable sensor (4/89, 4%), and web-based mobile app plus monitor (1/89, 1%). Usability, feasibility, and acceptability or patient preferences for mHealth interventions were assessed in 26% (23/89) of the studies and observed to be generally high. Overall, 30% (27/89) of the studies used a randomized controlled trial (RCT), cohort, or pilot design to assess the impact of the mHealth intervention on patients' adherence, with significant improvements (all P<.05) observed in 93% (25/27) of these studies. Significant (judged at P<.05) between-group differences were reported in 27 of the 29 (93%) RCTs that measured the effect of mHealth on CMP-specific clinical outcomes.

CONCLUSIONS

There is great potential for mHealth tools to better facilitate adherence to CMP management, and the current evidence supporting their effectiveness is generally high. Further research should focus on the cost-effectiveness of mHealth interventions for better incorporating these tools into health care practices.

TRIAL REGISTRATION

International Prospective Register of Systematic Reviews (PROSPERO) CRD42024524634; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=524634.

Global pattern, trend, and cross-country inequality of early musculoskeletal disorders from 1990 to 2019, with projection from 2020 to 2050

BACKGROUND

This study aims to estimate the burden, trends, forecasts, and disparities of early musculoskeletal (MSK) disorders among individuals ages 15 to 39 years.

METHODS

The global prevalence, years lived with disabilities (YLDs), disability-adjusted life years (DALYs), projection, and inequality were estimated for early MSK diseases, including rheumatoid arthritis (RA), osteoarthritis (OA), low back pain (LBP), neck pain (NP), gout, and other MSK diseases (OMSKDs).

FINDINGS

More adolescents and young adults were expected to develop MSK disorders by 2050. Across five age groups, the rates of prevalence, YLDs, and DALYs for RA, NP, LBP, gout, and OMSKDs sharply increased from ages 15-19 to 35-39; however, these were negligible for OA before age 30 but increased notably at ages 30-34, rising at least 6-fold by 35-39. The disease burden of gout, LBP, and OA attributable to high BMI and gout attributable to kidney dysfunction increased, while the contribution of smoking to LBP and RA and occupational ergonomic factors to LBP decreased. Between 1990 and 2019, the slope index of inequality increased for six MSK disorders, and the relative concentration index increased for gout, NP, OA, and OMSKDs but decreased for LBP and RA.

CONCLUSIONS

Multilevel interventions should be initiated to prevent disease burden related to RA, NP, LBP, gout, and OMSKDs among individuals ages 15-19 and to OA among individuals ages 30-34 to tightly control high BMI and kidney dysfunction.

FUNDING

The Global Burden of Disease study is funded by the Bill and Melinda Gates Foundation. The project is funded by the Scientific Research Fund of Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital (2022QN38).

An Artificial Intelligence-Based App for Self-Management of Low Back and Neck Pain in Specialist Care: Process Evaluation From a Randomized Clinical Trial

BACKGROUND

Self-management is endorsed in clinical practice guidelines for the care of musculoskeletal pain. In a randomized clinical trial, we tested the effectiveness of an artificial intelligence-based self-management app (selfBACK) as an adjunct to usual care for patients with low back and neck pain referred to specialist care.

OBJECTIVE

This study is a process evaluation aiming to explore patients' engagement and experiences with the selfBACK app and specialist health care practitioners' views on adopting digital self-management tools in their clinical practice.

METHODS

App usage analytics in the first 12 weeks were used to explore patients' engagement with the SELFBACK app. Among the 99 patients allocated to the SELFBACK interventions, a purposive sample of 11 patients (aged 27-75 years, 8 female) was selected for semistructured individual interviews based on app usage. Two focus group interviews were conducted with specialist health care practitioners (n=9). Interviews were analyzed using thematic analysis.

RESULTS

Nearly one-third of patients never accessed the app, and one-third were low users. Three themes were identified from interviews with patients and health care practitioners: (1) overall impression of the app, where patients discussed the interface and content of the app, reported on usability issues, and described their app usage; (2) perceived value of the app, where patients and health care practitioners described the primary value of the app and its potential to supplement usual care; and (3) suggestions for future use, where patients and health care practitioners addressed aspects they believed would determine acceptance.

CONCLUSIONS

Although the app's uptake was relatively low, both patients and health care practitioners had a positive opinion about adopting an app-based self-management intervention for low back and neck pain as an add-on to usual care. Both described that the app could reassure patients by providing trustworthy information, thus empowering them to take actions on their own. Factors influencing app acceptance and engagement, such as content relevance, tailoring, trust, and usability properties, were identified.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04463043; https://clinicaltrials.gov/study/NCT04463043.

Identifying the risk of exercises, recommended by an artificial intelligence for patients with musculoskeletal disorders

Musculoskeletal disorders (MSDs) impact people globally, cause occupational illness and reduce productivity. Exercise therapy is the gold standard treatment for MSDs and can be provided by physiotherapists and/or also via mobile apps. Apart from the obvious differences between physiotherapists and mobile apps regarding communication, empathy and physical touch, mobile apps potentially offer less personalized exercises. The use of artificial intelligence (AI) may overcome this issue by processing different pain parameters, comorbidities and patient-specific lifestyle factors and thereby enabling individually adapted exercise therapy. The aim of this study is to investigate the risks of AI-recommended strength, mobility and release exercises for people with MSDs, using physiotherapist risk assessment and retrospective consideration of patient feedback on risk and non-risk exercises. 80 patients with various MSDs received exercise recommendations from the AI-system. Physiotherapists rated exercises as risk or non-risk, based on patient information, e.g. pain intensity (NRS), pain quality, pain location, work type. The analysis of physiotherapists' agreement was based on the frequencies of mentioned risk, the percentage distribution and the Fleiss- or Cohens-Kappa. After completion of the exercises, the patients provided feedback for each exercise on an 11-point Likert scale., e.g. the feedback question for release exercises was "How did the stretch feel to you?" with the answer options ranging from "painful (0 points)" to "not noticeable (10 points)". The statistical analysis was carried out separately for the three types of exercises. For this, an independent t-test was performed. 20 physiotherapists assessed 80 patient examples, receiving a total of 944 exercises. In a three-way agreement of the physiotherapists, 0.08% of the exercises were judged as having a potential risk of increasing patients' pain. The evaluation showed 90.5% agreement, that exercises had no risk. Exercises that were considered by physiotherapists to be potentially risky for patients also received lower feedback ratings from patients. For the 'release' exercise type, risk exercises received lower feedback, indicating that the patient felt more pain (risk: 4.65 (1.88), non-risk: 5.56 (1.88)). The study shows that AI can recommend almost risk-free exercises for patients with MSDs, which is an effective way to create individualized exercise plans without putting patients at risk for higher pain intensity or discomfort. In addition, the study shows significant agreement between physiotherapists in the risk assessment of AI-recommended exercises and highlights the importance of considering individual patient perspectives for treatment planning. The extent to which other aspects of face-to-face physiotherapy, such as communication and education, provide additional benefits beyond the individualization of exercises compared to AI and app-based exercises should be further investigated.Trial registration: 30.12.2021 via OSF Registries, https://doi.org/10.17605/OSF.IO/YCNJQ .

Factors influencing the use of an artificial intelligence-based app (selfBACK) for tailored self-management support among adults with neck and/or low back pain

PURPOSE

Tailored self-management support is recommended as first-line treatment for neck and low back pain, for which mHealth applications could be promising. However, there is limited knowledge about factors influencing the engagement with such apps. The aim of this study was to assess barriers and facilitators for engaging with a self-management mHealth app among adults suffering from neck and/or low back pain.

MATERIALS AND METHODS

We carried out a qualitative descriptive study among adults with neck and/or low back pain. The artificial intelligence-based selfBACK app supports tailored self-management of neck and low back pain and was used for 6 weeks. After these 6 weeks, participants were interviewed by phone.

RESULTS

Thirty-two adults (17 males) with neck and/or low back pain participated (mean age = 54.9 (SD = 15.8)). Our results show that the mode of delivery and the novelty of the selfBACK app were perceived most often as a barrier to use the app. The action plans of the app and health-related factors were perceived most often as facilitating factors.

CONCLUSIONS

This study provides insight into possible strategies to improve an mHealth service. Furthermore, it shows that adults with neck and/or low back pain are willing and ready to receive blended treatment.

Emerging innovation in pain medicines

The treatment of pain remains a critical, unmet public health challenge. According to the CDC, in 2021, an estimated 20.9% of US adults (51.6 million people) endured chronic pain, and 6.9% (17.1 million people) endured high-impact chronic pain. Additionally, the impact of the social determinants of health on pain treatment are beginning to emerge. Treating pain addresses its control and relief, enhancing patient outcomes and quality of life. However, current treatment options have limitations, creating a significant need for innovative solutions. This raises the role of innovation in identifying new pain medicines. Thus, the clinical development of novel pain medicines is an unmet need to address public health worldwide.

Decision Support for Managing Common Musculoskeletal Pain Disorders: Development of a Case-Based Reasoning Application

BACKGROUND

Common interventions for musculoskeletal pain disorders either lack evidence to support their use or have small to modest or short-term effects. Given the heterogeneity of patients with musculoskeletal pain disorders, treatment guidelines and systematic reviews have limited transferability to clinical practice. A problem-solving method in artificial intelligence, case-based reasoning (CBR), where new problems are solved based on experiences from past similar problems, might offer guidance in such situations.

OBJECTIVE

This study aims to use CBR to build a decision support system for patients with musculoskeletal pain disorders seeking physiotherapy care. This study describes the development of the CBR system SupportPrim PT and demonstrates its ability to identify similar patients.

METHODS

Data from physiotherapy patients in primary care in Norway were collected to build a case base for SupportPrim PT. We used the local-global principle in CBR to identify similar patients. The global similarity measures are attributes used to identify similar patients and consisted of prognostic attributes. They were weighted in terms of prognostic importance and choice of treatment, where the weighting represents the relevance of the different attributes. For the local similarity measures, the degree of similarity within each attribute was based on minimal clinically important differences and expert knowledge. The SupportPrim PT's ability to identify similar patients was assessed by comparing the similarity scores of all patients in the case base with the scores on an established screening tool (the short form Örebro Musculoskeletal Pain Screening Questionnaire [ÖMSPQ]) and an outcome measure (the Musculoskeletal Health Questionnaire [MSK-HQ]) used in musculoskeletal pain. We also assessed the same in a more extensive case base.

RESULTS

The original case base contained 105 patients with musculoskeletal pain (mean age 46, SD 15 years; 77/105, 73.3% women). The SupportPrim PT consisted of 29 weighted attributes with local similarities. When comparing the similarity scores for all patients in the case base, one at a time, with the ÖMSPQ and MSK-HQ, the most similar patients had a mean absolute difference from the query patient of 9.3 (95% CI 8.0-10.6) points on the ÖMSPQ and a mean absolute difference of 5.6 (95% CI 4.6-6.6) points on the MSK-HQ. For both ÖMSPQ and MSK-HQ, the absolute score difference increased as the rank of most similar patients decreased. Patients retrieved from a more extensive case base (N=486) had a higher mean similarity score and were slightly more similar to the query patients in ÖMSPQ and MSK-HQ compared with the original smaller case base.

CONCLUSIONS

This study describes the development of a CBR system, SupportPrim PT, for musculoskeletal pain in primary care. The SupportPrim PT identified similar patients according to an established screening tool and an outcome measure for patients with musculoskeletal pain.

Will ChatGPT be Able to Replace a Spine Surgeon in the Clinical Setting?

OBJECTIVE

This study evaluates ChatGPT's performance in diagnosing and managing spinal pathologies.

METHODS

Patients underwent evaluation by two spine surgeons (and the case was discussed and a consensus was reached) and ChatGPT. Patient data, including demographics, symptoms, and available imaging reports, were collected using a standardized form. This information was then processed by ChatGPT for diagnosis and management recommendations. The study assessed ChatGPT's diagnostic and management accuracy through descriptive statistics, comparing its performance to that of experienced spine specialists.

RESULTS

A total of 97 patients with various spinal pathologies participated in the study, with a gender distribution of 40 males and 57 females. ChatGPT achieved a 70% diagnostic accuracy rate and provided suitable management recommendations for 95% of patients. However, it struggled with certain pathologies, misdiagnosing 100% of vertebral trauma and facet joint syndrome, 40% of spondylolisthesis, stenosis, and scoliosis, and 22% of disc-related pathologies. Furthermore, ChatGPT's management recommendations were poor in 53% of cases, often failing to suggest the most appropriate treatment options and occasionally providing incomplete advice.

CONCLUSIONS

While helpful in the medical field, ChatGPT falls short in providing reliable management recommendations, with a 30% misdiagnosis rate and 53% mismanagement rate in our study. Its limitations, including reliance on outdated data and the inability to interactively gather patient information, must be acknowledged. Surgeons should use ChatGPT cautiously as a supplementary tool rather than a substitute for their clinical expertise, as the complexities of healthcare demand human judgment and interaction.

Applications of Artificial Intelligence in Pain Medicine

PURPOSE OF REVIEW

This review explores the current applications of artificial intelligence (AI) in the field of pain medicine with a focus on machine learning.

RECENT FINDINGS

Utilizing a literature search conducted through the PubMed database, several current trends were identified, including the use of AI as a tool for diagnostics, predicting pain progression, predicting treatment response, and performance of therapy and pain management. Results of these studies show promise for the improvement of patient outcomes. Current gaps in the research and subsequent directions for future study involve AI in optimizing and improving nerve stimulation and more thoroughly predicting patients' responses to treatment.

The SelfSTarT intervention for low back pain patients presenting to first contact physiotherapists: A mixed methods service evaluation

INTRODUCTION

Globally, back pain is the leading cause of years of disability. In the United Kingdom, over 20 million people live with musculoskeletal (MSK) pain, with low back pain being one of the most common causes. National strategies promote self-management and the use of digital technologies to empower populations.

AIMS

To evaluate the uptake and impact of providing the SelfSTart approach (STarT Back and SelfBACK App) when delivered by a First Contact Physiotherapist (FCP) to people presenting with low back pain in primary care.

METHODS

Patients presenting with a new episode of low back pain underwent routine assessment and completion of a STarT Back questionnaire. Patients with low/medium scores were offered the SelfBACK App. A control population was provided by the MIDAS-GP study. Patient Experience, outcome measures, healthcare utilisation and retention were captured through the app and clinical systems (EMIS). Interviews with five FCPs explored the experiences of using the SelfSTart approach.

RESULTS

SelfSTarT was taken up by almost half (48%) of those to whom it was offered. Compared to MIDAS-GP, users were more likely to be younger, male, in work, and with higher health literacy. SelfSTarT users reported significant improved experiences relating to receiving an agreed care plan and receiving sufficient information. There were no significant differences in treatments offered. FCPs were positive about the app and felt it had value but wanted feedback on patient progress. They recognised that a digital solution would not be suitable for all.

CONCLUSION

This approach offers an opportunity to empower and support self-management, using robustly evaluated digital technology.

Artificial Intelligence and Decision-Making in Healthcare: A Thematic Analysis of a Systematic Review of Reviews

Introduction

The use of artificial intelligence (AI), which can emulate human intelligence and enhance clinical results, has grown in healthcare decision-making due to the digitalization effects and the COVID-19 pandemic. The purpose of this study was to determine the scope of applications of AI tools in the decision-making process in healthcare service delivery networks.

Materials and methods

This study used a qualitative method to conduct a systematic review of the existing reviews. Review articles published between 2000 and 2024 in English-language were searched in PubMed, Scopus, ProQuest, and Cochrane databases. The CASP (Critical Appraisal Skills Programme) Checklist for Systematic Reviews was used to evaluate the quality of the articles. Based on the eligibility criteria, the final articles were selected and the data extraction was done independently by 2 authors. Finally, the thematic analysis approach was used to analyze the data extracted from the selected articles.

Results

Of the 14 219 identified records, 18 review articles were eligible and included in the analysis, which covered the findings of 669 other articles. The quality assessment score of all reviewed articles was high. And, the thematic analysis of the data identified 3 main themes including clinical decision-making, organizational decision-making, and shared decision-making; which originated from 8 subthemes.

Conclusions

This study revealed that AI tools have been applied in various aspects of healthcare decision-making. The use of AI can improve the quality, efficiency, and effectiveness of healthcare services by providing accurate, timely, and personalized information to support decision-making. Further research is needed to explore the best practices and standards for implementing AI in healthcare decision-making.

Digitally Assisted Mindfulness in Training Self-Regulation Skills for Sustainable Mental Health: A Systematic Review

The onset of the COVID-19 pandemic has led to an increased demand for mental health interventions, with a special focus on digitally assisted ones. Self-regulation describes a set of meta-skills that enable one to take control over his/her mental health and it is recognized as a vital indicator of well-being. Mindfulness training is a promising training strategy for promoting self-regulation, behavioral change, and mental well-being. A growing body of research outlines that smart technologies are ready to revolutionize the way mental health training programs take place. Artificial intelligence (AI); extended reality (XR) including virtual reality (VR), augmented reality (AR), and mixed reality (MR); as well as the advancements in brain computer interfaces (BCIs) are ready to transform these mental health training programs. Mindfulness-based interventions assisted by smart technologies for mental, emotional, and behavioral regulation seem to be a crucial yet under-investigated issue. The current systematic review paper aims to explore whether and how smart technologies can assist mindfulness training for the development of self-regulation skills among people at risk of mental health issues as well as populations with various clinical characteristics. The PRISMA 2020 methodology was utilized to respond to the objectives and research questions using a total of sixty-six experimental studies that met the inclusion criteria. The results showed that digitally assisted mindfulness interventions supported by smart technologies, including AI-based applications, chatbots, virtual coaches, immersive technologies, and brain-sensing headbands, can effectively assist trainees in developing a wide range of cognitive, emotional, and behavioral self-regulation skills, leading to a greater satisfaction of their psychological needs, and thus mental wellness. These results may provide positive feedback for developing smarter and more inclusive training environments, with a special focus on people with special training needs or disabilities.