Effects of an Artificial Intelligence-Assisted Health Program on Workers With Neck/Shoulder Pain/Stiffness and Low Back Pain: Randomized Controlled Trial

Roles, Users, Benefits, and Limitations of Chatbots in Health Care: Rapid Review

BACKGROUND

Chatbots, or conversational agents, have emerged as significant tools in health care, driven by advancements in artificial intelligence and digital technology. These programs are designed to simulate human conversations, addressing various health care needs. However, no comprehensive synthesis of health care chatbots' roles, users, benefits, and limitations is available to inform future research and application in the field.

OBJECTIVE

This review aims to describe health care chatbots' characteristics, focusing on their diverse roles in the health care pathway, user groups, benefits, and limitations.

METHODS

A rapid review of published literature from 2017 to 2023 was performed with a search strategy developed in collaboration with a health sciences librarian and implemented in the MEDLINE and Embase databases. Primary research studies reporting on chatbot roles or benefits in health care were included. Two reviewers dual-screened the search results. Extracted data on chatbot roles, users, benefits, and limitations were subjected to content analysis.

RESULTS

The review categorized chatbot roles into 2 themes: delivery of remote health services, including patient support, care management, education, skills building, and health behavior promotion, and provision of administrative assistance to health care providers. User groups spanned across patients with chronic conditions as well as patients with cancer; individuals focused on lifestyle improvements; and various demographic groups such as women, families, and older adults. Professionals and students in health care also emerged as significant users, alongside groups seeking mental health support, behavioral change, and educational enhancement. The benefits of health care chatbots were also classified into 2 themes: improvement of health care quality and efficiency and cost-effectiveness in health care delivery. The identified limitations encompassed ethical challenges, medicolegal and safety concerns, technical difficulties, user experience issues, and societal and economic impacts.

CONCLUSIONS

Health care chatbots offer a wide spectrum of applications, potentially impacting various aspects of health care. While they are promising tools for improving health care efficiency and quality, their integration into the health care system must be approached with consideration of their limitations to ensure optimal, safe, and equitable use.

Evaluating the Potential and Pitfalls of AI-Powered Conversational Agents as Humanlike Virtual Health Carers in the Remote Management of Noncommunicable Diseases: Scoping Review

BACKGROUND

The rising prevalence of noncommunicable diseases (NCDs) worldwide and the high recent mortality rates (74.4%) associated with them, especially in low- and middle-income countries, is causing a substantial global burden of disease, necessitating innovative and sustainable long-term care solutions.

OBJECTIVE

This scoping review aims to investigate the impact of artificial intelligence (AI)-based conversational agents (CAs)-including chatbots, voicebots, and anthropomorphic digital avatars-as human-like health caregivers in the remote management of NCDs as well as identify critical areas for future research and provide insights into how these technologies might be used effectively in health care to personalize NCD management strategies.

METHODS

A broad literature search was conducted in July 2023 in 6 electronic databases-Ovid MEDLINE, Embase, PsycINFO, PubMed, CINAHL, and Web of Science-using the search terms "conversational agents," "artificial intelligence," and "noncommunicable diseases," including their associated synonyms. We also manually searched gray literature using sources such as ProQuest Central, ResearchGate, ACM Digital Library, and Google Scholar. We included empirical studies published in English from January 2010 to July 2023 focusing solely on health care-oriented applications of CAs used for remote management of NCDs. The narrative synthesis approach was used to collate and summarize the relevant information extracted from the included studies.

RESULTS

The literature search yielded a total of 43 studies that matched the inclusion criteria. Our review unveiled four significant findings: (1) higher user acceptance and compliance with anthropomorphic and avatar-based CAs for remote care; (2) an existing gap in the development of personalized, empathetic, and contextually aware CAs for effective emotional and social interaction with users, along with limited consideration of ethical concerns such as data privacy and patient safety; (3) inadequate evidence of the efficacy of CAs in NCD self-management despite a moderate to high level of optimism among health care professionals regarding CAs' potential in remote health care; and (4) CAs primarily being used for supporting nonpharmacological interventions such as behavioral or lifestyle modifications and patient education for the self-management of NCDs.

CONCLUSIONS

This review makes a unique contribution to the field by not only providing a quantifiable impact analysis but also identifying the areas requiring imminent scholarly attention for the ethical, empathetic, and efficacious implementation of AI in NCD care. This serves as an academic cornerstone for future research in AI-assisted health care for NCD management.

TRIAL REGISTRATION

Open Science Framework; https://doi.org/10.17605/OSF.IO/GU5PX.

Approaches for the use of Artificial Intelligence in workplace health promotion and prevention: A systematic scoping review

BACKGROUND

Artificial intelligence (AI) is an umbrella term for various algorithms and rapidly emerging technologies with huge potential for workplace health promotion and prevention (WHPP). WHPP interventions aim to improve people's health and well-being through behavioral and organizational measures or by minimizing the burden of workplace-related diseases and associated risk factors. While AI has been the focus of research in other health-related fields, such as Public Health or biomedicine, the transition of AI into WHPP research has yet to be systematically investigated.

OBJECTIVE

The systematic scoping review aims to comprehensively assess an overview of the current use of AI in WHPP. The results will be then used to point to future research directions. The following research questions were derived: (1) what are the study characteristics of studies on AI algorithms and technologies in the context of WHPP, (2) what specific WHPP fields (prevention, behavioral, and organizational approaches) were addressed by the AI algorithms and technologies, and (3) what kind of interventions lead to which outcomes?

METHODS

A systematic scoping literature review (PRISMA-ScR) was conducted in the three academic databases PubMed, IEEE, and ACM in July 2023, searching for articles published between January 2000 and December 2023. Studies needed to be 1) peer-reviewed, 2) written in English, and 3) focused on any AI-based algorithm or technology that (4) were conducted in the context of WHPP or (5) an associated field. Information on study design, AI algorithms and technologies, WHPP fields, and the PICO framework were extracted blindly with Rayyan and summarized.

RESULTS

A total of ten studies were included. Risk prevention and modeling were the most identified WHPP fields (n=6), followed by behavioral health promotion (n=4) and organizational health promotion (n=1). Four studies focused on mental health. Most AI algorithms were machine learning-based, and three studies used combined deep learning algorithms. AI algorithms and technologies were primarily implemented in smartphone applications (eg, in the form of a Chatbot) or used the smartphone as a data source (eg, GPS). Behavioral approaches ranged from 8 to 12 weeks and were compared to control groups. Three studies evaluated the robustness and accuracy of an AI model or framework.

CONCLUSIONS

Although AI has caught increasing attention in health-related research, the review reveals that AI in WHPP is marginally investigated. Our results indicate that AI is promising for individualization and risk prediction in WHPP, but current research does not cover the scope of WHPP. Beyond that, future research will profit from an extended range of research in all fields of WHPP, longitudinal data, and reporting guidelines.

CLINICALTRIAL

Registered on 5th July 2023 at Open Science Framework [1].

The effectiveness of digital interventions for self-management of chronic pain in employment settings: a systematic review

INTRODUCTION

Chronic pain affects over a quarter of the workforce with high economic burden for individuals, employers and healthcare services. Access to work-related advice for people with chronic pain is variable. This systematic review aims to explore the effectiveness of workplace-delivered digital interventions for the self-management of chronic pain.

SOURCE OF DATA

MEDLINE, EMBASE, CINAHL, PsycINFO, the Cochrane Library, JBI, Open Science Framework, Epistemonikos and Google Scholar. Articles published between January 2001 and December 2023 were included. Searches were conducted between October 2023 and December 2023.

AREAS OF AGREEMENT

Workplace-delivered digital interventions to support self-management of chronic pain at work may improve pain and health-related quality of life in vocationally active adults. Delivering interventions outside of clinical services, through the workplace setting, may help to reduce inequity in access to work-related advice for people with chronic pain, and ultimately reduce the burden on individuals, employers and healthcare services. Interventions include mobile apps and web-based programmes.

AREAS OF CONTROVERSY

Studies were moderate-to-low quality. Most studies focused on exercise, few considered other aspects of pain self-management. Given the limited evidence in the current literature, consensus on best intervention format and delivery is lacking.

GROWING POINTS

More high-quality studies are needed given the heterogeneity in study design, interventions and outcome measures.

AREAS TIMELY FOR DEVELOPING RESEARCH

No interventions included advice on work-related adjustments or support. Few studies included work-related outcomes, despite the known impact of pain on work and work on health.

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 .

Mobile Health App and Web Platform (eDOL) for Medical Follow-Up of Patients With Chronic Pain: Cohort Study Involving the French eDOL National Cohort After 1 Year

BACKGROUND

Chronic pain affects approximately 30% of the general population, severely degrades quality of life and professional life, and leads to additional health care costs. Moreover, the medical follow-up of patients with chronic pain remains complex and provides only fragmentary data on painful daily experiences. This situation makes the management of patients with chronic pain less than optimal and may partly explain the lack of effectiveness of current therapies. Real-life monitoring of subjective and objective markers of chronic pain using mobile health (mHealth) programs could better characterize patients, chronic pain, pain medications, and daily impact to help medical management.

OBJECTIVE

This cohort study aimed to assess the ability of our mHealth tool (eDOL) to collect extensive real-life medical data from chronic pain patients after 1 year of use. The data collected in this way would provide new epidemiological and pathophysiological data on chronic pain.

METHODS

A French national cohort of patients with chronic pain treated at 18 pain clinics has been established and followed up using mHealth tools. This cohort makes it possible to collect the determinants and repercussions of chronic pain and their evolutions in a real-life context, taking into account all environmental events likely to influence chronic pain. The patients were asked to complete several questionnaires, body schemes, and weekly meters, and were able to interact with a chatbot and use educational modules on chronic pain. Physicians could monitor their patients' progress in real time via an online platform.

RESULTS

The cohort study included 1427 patients and analyzed 1178 patients. The eDOL tool was able to collect various sociodemographic data; specific data for characterizing pain disorders, including body scheme; data on comorbidities related to chronic pain and its psychological and overall impact on patients' quality of life; data on drug and nondrug therapeutics and their benefit-to-risk ratio; and medical or treatment history. Among the patients completing weekly meters, 49.4% (497/1007) continued to complete them after 3 months of follow-up, and the proportion stabilized at 39.3% (108/275) after 12 months of follow-up. Overall, despite a fairly high attrition rate over the follow-up period, the eDOL tool collected extensive data. This amount of data will increase over time and provide a significant volume of health data of interest for future research involving the epidemiology, care pathways, trajectories, medical management, sociodemographic characteristics, and other aspects of patients with chronic pain.

CONCLUSIONS

This work demonstrates that the mHealth tool eDOL is able to generate a considerable volume of data concerning the determinants and repercussions of chronic pain and their evolutions in a real-life context. The eDOL tool can incorporate numerous parameters to ensure the detailed characterization of patients with chronic pain for future research and pain management.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04880096; https://clinicaltrials.gov/ct2/show/NCT04880096.

A systematic review and bayesian meta-analysis of medical devices used in chronic pain management

Whilst. pharmacological therapies remain the cornerstone of pain management in chronic pain, factors including the current opioid epidemic have led to non-pharmacological techniques becoming a more attractive proposition. We explored the prevalence of medical device use and their treatment efficacy in non-cancer pain management. A systematic methodology was developed, peer reviewed and published in PROSPERO (CRD42021235384). Key words of medical device, pain management devices, chronic pain, lower back pain, back pain, leg pain and chronic pelvic pain using Science direct, PubMed, Web of Science, PROSPERO, MEDLINE, EMBASE, PorQuest and ClinicalTrials.gov. All clinical trials, epidemiology and mixed methods studies that reported the use of medical devices for non-cancer chronic pain management published between the 1st of January 1990 and the 30th of April 2022 were included. 13 studies were included in systematic review, of these 6 were used in the meta-analysis. Our meta-analysis for pain reduction showed that transcutaneous electrical nerve stimulation combined with instrument-assisted soft tissue mobilization treatment and pulsed electromagnetic therapy produced significant treatment on chronic lower back pain patients. Pooled evidence revealed the use of medical device related interventions resulted in 0.7 degree of pain reduction under a 0-10 scale. Significant improvement in disability scores, with a 7.44 degree reduction in disability level compared to a placebo using a 50 score range was also seen. Our analysis has shown that the optimal use of medical devices in a sustainable manner requires further research, needing larger cohort studies, greater gender parity, in a more diverse range of geographical locations.

A scoping review of methodologies for applying artificial intelligence to physical activity interventions

PURPOSE

This scoping review aimed to offer researchers and practitioners an understanding of artificial intelligence (AI) applications in physical activity (PA) interventions; introduce them to prevalent machine learning (ML), deep learning (DL), and reinforcement learning (RL) algorithms; and encourage the adoption of AI methodologies.

METHODS

A scoping review was performed in PubMed, Web of Science, Cochrane Library, and EBSCO focusing on AI applications for promoting PA or predicting related behavioral or health outcomes. AI methodologies were summarized and categorized to identify synergies, patterns, and trends informing future research. Additionally, a concise primer on predominant AI methodologies within the realm of PA was provided to bolster understanding and broader application.

RESULTS

The review included 24 studies that met the predetermined eligibility criteria. AI models were found effective in detecting significant patterns of PA behavior and associations between specific factors and intervention outcomes. Most studies comparing AI models to traditional statistical approaches reported higher prediction accuracy for AI models on test data. Comparisons of different AI models yielded mixed results, likely due to model performance being highly dependent on the dataset and task. An increasing trend of adopting state-of-the-art DL and RL models over standard ML was observed, addressing complex human-machine communication, behavior modification, and decision-making tasks. Six key areas for future AI adoption in PA interventions emerged: personalized PA interventions, real-time monitoring and adaptation, integration of multimodal data sources, evaluation of intervention effectiveness, expanding access to PA interventions, and predicting and preventing injuries.

CONCLUSION

The scoping review highlights the potential of AI methodologies for advancing PA interventions. As the field progresses, staying informed and exploring emerging AI-driven strategies is essential for achieving significant improvements in PA interventions and fostering overall well-being.

Artificial Intelligence and Occupational Health and Safety, Benefits and Drawbacks

This paper discusses the impact of artificial intelligence (AI) on occupational health and safety. Although the integration of AI into the field of occupational health and safety is still in its early stages, it has numerous applications in the workplace. Some of these applications offer numerous benefits for the health and safety of workers, such as continuous monitoring of workers' health and safety and the workplace environment through wearable devices and sensors. However, AI might have negative impacts in the workplace, such as ethical worries and data privacy concerns. To maximize the benefits and minimize the drawbacks of AI in the workplace, certain measures should be applied, such as training for both employers and employees and setting policies and guidelines regulating the integration of AI in the workplace.

Patient Engagement with Conversational Agents in Health Applications 2016-2022: A Systematic Review and Meta-Analysis

Clinicians and patients seeking electronic health applications face challenges in selecting effective solutions due to a high market failure rate. Conversational agent applications ("chatbots") show promise in increasing healthcare user engagement by creating bonds between the applications and users. It is unclear if chatbots improve patient adherence or if past trends to include chatbots in electronic health applications were due to technology hype dynamics and competitive pressure to innovate. We conducted a systematic literature review using Preferred Reporting Items for Systematic reviews and Meta-Analyses methodology on health chatbot randomized control trials. The goal of this review was to identify if user engagement indicators are published in eHealth chatbot studies. A meta-analysis examined patient clinical trial retention of chatbot apps. The results showed no chatbot arm patient retention effect. The small number of studies suggests a need for ongoing eHealth chatbot research, especially given the claims regarding their effectiveness made outside the scientific literatures.

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.

Reviewing the Potential Role of Artificial Intelligence in Delivering Personalized and Interactive Pain Medicine Education for Chronic Pain Patients

The integration of artificial intelligence (AI) in patient pain medicine education has the potential to revolutionize pain management. By harnessing the power of AI, patient education becomes more personalized, interactive, and supportive, empowering patients to understand their pain, make informed decisions, and actively participate in their pain management journey. AI tailors the educational content to individual patients' needs, providing personalized recommendations. It introduces interactive elements through chatbots and virtual assistants, enhancing engagement and motivation. AI-powered platforms improve accessibility by providing easy access to educational resources and adapting content to diverse patient populations. Future AI applications in pain management include explaining pain mechanisms, treatment options, predicting outcomes based on individualized patient-specific factors, and supporting monitoring and adherence. Though the literature on AI in pain medicine and its applications are scarce yet growing, we propose avenues where AI may be applied and review the potential applications of AI in pain management education. Additionally, we address ethical considerations, patient empowerment, and accessibility barriers.

The immediate effect of cervical rotation-traction manipulation on cervical paravertebral soft tissue: a study using soft tissue tension cloud chart technology

BACKGROUND

To evaluate the reliability of the Soft Tissue Tension Cloud Chart (STTCC) technology, an original method combining multi-point Cervical Paravertebral Soft Tissue Test (CPSTT) with MATLAB software, we conducted a preliminary analysis on the immediate effects of Orthopaedic Manual Therapy (OMT) on cervical paravertebral soft tissue.

METHODS

30 patients with Cervical Spondylotic Radiculopathy (CSR) were included in this study. We analyzed the differences in CPSTT before and after treatment with Cervical Rotation-Traction Manipulation (CRTM), a representative OMT technique in Traditional Chinese Medicine, using the STTCC technology.

RESULTS

The STTCC results demonstrated that post-treatment CPSTT levels in CSR patients were significantly lower than pre-treatment levels after application of CRTM, with a statistically significant difference (P < 0.001). Additionally, pre-treatment CPSTT levels on the symptomatic side (with radicular pain or numbness) were higher across the C5 to C7 vertebrae compared to the asymptomatic side (without symptoms) (P < 0.001). However, this difference disappeared after CRTM treatment (P = 0.231).

CONCLUSIONS

The STTCC technology represents a reliable method for analyzing the immediate effects of OMT. CSR patients display uneven distribution of CPSTT characterized by higher tension on the symptomatic side. CRTM not only reduces overall cervical soft tissue tension in CSR patients, but can also balance the asymmetrical tension between the symptomatic and asymptomatic sides.

TRIAL REGISTRATION

This study was approved by the Chinese Clinical Trials Registry (Website: . https://www.chictr.org.cn .) on 20/04/2021 and the Registration Number is ChiCTR2100045648.

Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines

The Consolidated Standards of Reporting Trials extension for Artificial Intelligence interventions (CONSORT-AI) was published in September 2020. Since its publication, several randomised controlled trials (RCTs) of AI interventions have been published but their completeness and transparency of reporting is unknown. This systematic review assesses the completeness of reporting of AI RCTs following publication of CONSORT-AI and provides a comprehensive summary of RCTs published in recent years. 65 RCTs were identified, mostly conducted in China (37%) and USA (18%). Median concordance with CONSORT-AI reporting was 90% (IQR 77-94%), although only 10 RCTs explicitly reported its use. Several items were consistently under-reported, including algorithm version, accessibility of the AI intervention or code, and references to a study protocol. Only 3 of 52 included journals explicitly endorsed or mandated CONSORT-AI. Despite a generally high concordance amongst recent AI RCTs, some AI-specific considerations remain systematically poorly reported. Further encouragement of CONSORT-AI adoption by journals and funders may enable more complete adoption of the full CONSORT-AI guidelines.

The Impact of Intervention Design on User Engagement in Digital Therapeutics Research: Factorial Experiment With a Mixed Methods Study

BACKGROUND

User engagement is crucial for digital therapeutics (DTx) effectiveness; due to variations in the conceptualization of engagement and intervention design, assessment and retention of engagement remain challenging.

OBJECTIVE

We investigated the influence of the perceived acceptability of experimental intervention components and satisfaction with core intervention components in DTx on user engagement, while also identifying potential barriers and facilitators to user engagement.

METHODS

We conducted a mixed methods study with a 2 × 2 factorial design, involving 12 outpatients with atopic dermatitis. Participants were randomized into 4 experimental groups based on push notification ("basic" or "advanced") and human coach ("on" or "off") experimental intervention components. All participants engaged in self-monitoring and learning courses as core intervention components within an app-based intervention over 8 weeks. Data were collected through in-app behavioral data, physician- and self-reported questionnaires, and semistructured interviews assessed at baseline, 4 weeks, and 8 weeks. Descriptive statistics and thematic analysis were used to evaluate user engagement, perceived acceptability of experimental intervention components (ie, push notification and human coach), satisfaction with core intervention components (ie, self-monitoring and learning courses), and intervention effectiveness through clinical outcomes.

RESULTS

The primary outcome indicated that group 4, provided with "advanced-level push notifications" and a "human coach," showed higher completion rates for self-monitoring forms and learning courses compared to the predetermined threshold of clinical significance. Qualitative data analysis revealed three key themes: (1) perceived acceptability of the experimental intervention components, (2) satisfaction with the core intervention components, and (3) suggestions for improvement in the overall intervention program. Regarding clinical outcomes, the Perceived Stress Scale and Dermatology Life Quality Index scores presented the highest improvement in group 4.

CONCLUSIONS

These findings will help refine the intervention and inform the design of a subsequent randomized trial to test its effectiveness. Furthermore, this design may serve as a model for broadly examining and optimizing overall engagement in DTx and for future investigation into the complex relationship between engagement and clinical outcomes.

TRIAL REGISTRATION

Clinical Research Information Service KCT0007675; http://tinyurl.com/2m8rjrmv.

Effects of Digital Physical Health Exercises on Musculoskeletal Diseases: Systematic Review With Best-Evidence Synthesis

BACKGROUND

Musculoskeletal diseases affect 1.71 billion people worldwide, impose a high biopsychosocial burden on patients, and are associated with high economic costs. The use of digital health interventions is a promising cost-saving approach for the treatment of musculoskeletal diseases. As physical exercise is the best clinical practice in the treatment of musculoskeletal diseases, digital health interventions that provide physical exercises could have a highly positive impact on musculoskeletal diseases, but evidence is lacking.

OBJECTIVE

This systematic review aims to evaluate the impact of digital physical health exercises on patients with musculoskeletal diseases concerning the localization of the musculoskeletal disease, patient-reported outcomes, and medical treatment types.

METHODS

We performed systematic literature research using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The search was conducted using the PubMed, BISp, Cochrane Library, and Web of Science databases. The Scottish Intercollegiate Guidelines Network checklist was used to assess the quality of the included original studies. To determine the evidence and direction of the impact of digital physical health exercises, a best-evidence synthesis was conducted, whereby only studies with at least acceptable methodological quality were included for validity purposes.

RESULTS

A total of 8988 studies were screened, of which 30 (0.33%) randomized controlled trials met the inclusion criteria. Of these, 16 studies (53%) were of acceptable or high quality; they included 1840 patients (1008/1643, 61.35% female; 3 studies including 197 patients did not report gender distribution) with various musculoskeletal diseases. A total of 3 different intervention types (app-based interventions, internet-based exercises, and telerehabilitation) were used to deliver digital physical health exercises. Strong evidence was found for the positive impact of digital physical health exercises on musculoskeletal diseases located in the back. Moderate evidence was found for diseases located in the shoulder and hip, whereas evidence for the entire body was limited. Conflicting evidence was found for diseases located in the knee and hand. For patient-reported outcomes, strong evidence was found for impairment and quality of life. Conflicting evidence was found for pain and function. Regarding the medical treatment type, conflicting evidence was found for operative and conservative therapies.

CONCLUSIONS

Strong to moderate evidence was found for a positive impact on musculoskeletal diseases located in the back, shoulder, and hip and on the patient-reported outcomes of impairment and quality of life. Thus, digital physical health exercises could have a positive effect on a variety of symptoms of musculoskeletal diseases.

Neuroimaging and artificial intelligence for assessment of chronic painful temporomandibular disorders-a comprehensive review

Chronic Painful Temporomandibular Disorders (TMD) are challenging to diagnose and manage due to their complexity and lack of understanding of brain mechanism. In the past few decades' neural mechanisms of pain regulation and perception have been clarified by neuroimaging research. Advances in the neuroimaging have bridged the gap between brain activity and the subjective experience of pain. Neuroimaging has also made strides toward separating the neural mechanisms underlying the chronic painful TMD. Recently, Artificial Intelligence (AI) is transforming various sectors by automating tasks that previously required humans' intelligence to complete. AI has started to contribute to the recognition, assessment, and understanding of painful TMD. The application of AI and neuroimaging in understanding the pathophysiology and diagnosis of chronic painful TMD are still in its early stages. The objective of the present review is to identify the contemporary neuroimaging approaches such as structural, functional, and molecular techniques that have been used to investigate the brain of chronic painful TMD individuals. Furthermore, this review guides practitioners on relevant aspects of AI and how AI and neuroimaging methods can revolutionize our understanding on the mechanisms of painful TMD and aid in both diagnosis and management to enhance patient outcomes.

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.

Artificial intelligence in physical rehabilitation: A systematic review

BACKGROUND

Physical disabilities become more common with advancing age. Rehabilitation restores function, maintaining independence for longer. However, the poor availability and accessibility of rehabilitation limits its clinical impact. Artificial Intelligence (AI) guided interventions have improved many domains of healthcare, but whether rehabilitation can benefit from AI remains unclear.

METHODS

We conducted a systematic review of AI-supported physical rehabilitation technology tested in the clinical setting to understand: 1) availability of AI-supported physical rehabilitation technology; 2) its clinical effect; 3) and the barriers and facilitators to implementation. We searched in MEDLINE, EMBASE, CINAHL, Science Citation Index (Web of Science), CIRRIE (now NARIC), and OpenGrey.

RESULTS

We identified 9054 articles and included 28 projects. AI solutions spanned five categories: App-based systems, robotic devices that replace function, robotic devices that restore function, gaming systems and wearables. We identified five randomised controlled trials (RCTs), which evaluated outcomes relating to physical function, activity, pain, and health-related quality of life. The clinical effects were inconsistent. Implementation barriers included technology literacy, reliability, and user fatigue. Enablers included greater access to rehabilitation programmes, remote monitoring of progress, reduction in manpower requirements and lower cost.

CONCLUSION

Application of AI in physical rehabilitation is a growing field, but clinical effects have yet to be studied rigorously. Developers must strive to conduct robust clinical evaluations in the real-world setting and appraise post implementation experiences.

Assessing Non-Specific Neck Pain through Pose Estimation from Images Based on Ensemble Learning

BACKGROUND

Mobile phones, laptops, and computers have become an indispensable part of our lives in recent years. Workers may have an incorrect posture when using a computer for a prolonged period of time. Using these products with an incorrect posture can lead to neck pain. However, there are limited data on postures in real-life situations.

METHODS

In this study, we used a common camera to record images of subjects carrying out three different tasks (a typing task, a gaming task, and a video-watching task) on a computer. Different artificial intelligence (AI)-based pose estimation approaches were applied to analyze the head's yaw, pitch, and roll and coordinate information of the eyes, nose, neck, and shoulders in the images. We used machine learning models such as random forest, XGBoost, logistic regression, and ensemble learning to build a model to predict whether a subject had neck pain by analyzing their posture when using the computer.

RESULTS

After feature selection and adjustment of the predictive models, nested cross-validation was applied to evaluate the models and fine-tune the hyperparameters. Finally, the ensemble learning approach was utilized to construct a model via bagging, which achieved a performance with 87% accuracy, 92% precision, 80.3% recall, 95.5% specificity, and an AUROC of 0.878.

CONCLUSIONS

We developed a predictive model for the identification of non-specific neck pain using 2D video images without the need for costly devices, advanced environment settings, or extra sensors. This method could provide an effective way for clinically evaluating poor posture during real-world computer usage scenarios.

Cluster Analysis of Subjective Shoulder Stiffness and Muscle Hardness: Associations with Central Sensitization-Related Symptoms

Background and Objectives: Understanding the relationships between subjective shoulder stiffness, muscle hardness, and various factors is crucial. Our cross-sectional study identified subgroups of shoulder stiffness based on symptoms and muscle hardness and investigated associated factors. Materials and Methods: measures included subjective stiffness, pain, muscle hardness, and factors like physical and psychological conditions, pressure pain threshold, postural alignment, heart rate variability, and electroencephalography in 40 healthy young individuals. Results: Three clusters were identified: Cluster 1 with high stiffness, pain, and muscle hardness; Cluster 2 with low stiffness and pain but high muscle hardness; and Cluster 3 with low levels of all factors. Cluster 1 had significantly higher central sensitization-related symptoms (CSS) scores than Cluster 2. Subjective stiffness is positively correlated with psychological factors. Conclusions: our results suggest that CSS impacts subjective symptom severity among individuals with similar shoulder muscle hardness.

An artificial intelligence-powered, patient-centric digital tool for self-management of chronic pain: a prospective, multicenter clinical trial

OBJECTIVE

To investigate how a behavioral health, artificial intelligence (AI)-powered, digital self-management tool affects the daily functions in adults with chronic back and neck pain.

DESIGN

Eligible subjects were enrolled in a 12-week prospective, multicenter, single-arm, open-label study and instructed to use the digital coach daily. Primary outcome was a change in Patient-Reported Outcomes Measurement Information Systems (PROMIS) scores for pain interference. Secondary outcomes were changes in PROMIS physical function, anxiety, depression, pain intensity scores and pain catastrophizing scale (PCS) scores.

METHODS

Subjects logged daily activities, using PainDrainerTM, and data analyzed by the AI engine. Questionnaire and web-based data were collected at 6 and 12 weeks and compared to subjects' baseline.

RESULTS

Subjects completed the 6- (n = 41) and 12-week (n = 34) questionnaires. A statistically significant Minimal Important Difference (MID) for pain interference was demonstrated in 57.5% of the subjects. Similarly, MID for physical function was demonstrated in 72.5% of the subjects. A pre- to post-intervention improvement in depression score was also statistically significant, observed in 100% of subjects, as was the improvement in anxiety scores, evident in 81.3% of the subjects. PCS mean scores was also significantly decreased at 12 weeks.

CONCLUSION

Chronic pain self-management, using an AI-powered, digital coach anchored in behavioral health principles significantly improved subjects' pain interference, physical function, depression, anxiety, and pain catastrophizing over the 12-week study period.

Intervertebral Canals and Intracanal Ligaments as New Terms in Terminologia anatomica

This study addresses the cervical part of the vertebral column. Clinical pictures of dystrophic diseases of the cervical part of the vertebral column do not always correspond only to the morphological changes-they may be represented by connective tissue formation and nerve and vessel compression. To find out the possible reason, this morphometric study of the cervical part of the vertebral column in 40 cadavers was performed. CT scans were performed on 17 cadaveric material specimens. A total of 12 histological samples of connective tissue structures located in intervertebral canals (IC) were studied. One such formation, an intracanal ligament (IL) located in the IC, was found. Today, there is no term "intervertebral canal", nor is there a detailed description of the intervertebral canal in the cervical part of the vertebral column. Cervical intervertebral canals make up five pairs in segments C2-C7. On cadavers, the IC lateral and medial apertures were 0.9-1.5 cm and 0.5-0.9 cm, correspondingly. According to our histological study, the connective tissue structures in the IC are ligaments-IL. According to the presence of these ligaments, ICs were classified into three types. Complete regional anatomy characterization of the IC of the cervical part of the vertebral column with a description of its constituent anatomical elements was provided. The findings demonstrate the need to include the terms "intervertebral canal" and "intervertebral ligament" in the Terminologia anatomica.

The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review

Background

Artificial intelligence (AI) is a broad outlet of computer science aimed at constructing machines capable of simulating and performing tasks usually done by human beings. The aim of this scoping review is to map existing evidence on the use of AI in the delivery of medical care.

Methods

We searched PubMed and Scopus in March 2022, screened identified records for eligibility, assessed full texts of potentially eligible publications, and extracted data from included studies in duplicate, resolving differences through discussion, arbitration, and consensus. We then conducted a narrative synthesis of extracted data.

Results

Several AI methods have been used to detect, diagnose, classify, manage, treat, and monitor the prognosis of various health issues. These AI models have been used in various health conditions, including communicable diseases, non-communicable diseases, and mental health.

Conclusions

Presently available evidence shows that AI models, predominantly deep learning, and machine learning, can significantly advance medical care delivery regarding the detection, diagnosis, management, and monitoring the prognosis of different illnesses.

Impact assessment of e-trainings in occupational safety and health: a literature review

BACKGROUND

Implementing workplace preventive interventions reduces occupational accidents and injuries, as well as the negative consequences of those accidents and injuries. Online occupational safety and health training is one of the most effective preventive interventions. This study aims to present current knowledge on e-training interventions, make recommendations on the flexibility, accessibility, and cost-effectiveness of online training, and identify research gaps and obstacles.

METHOD

All studies that addressed occupational safety and health e-training interventions designed to address worker injuries, accidents, and diseases were chosen from PubMed and Scopus until 2021. Two independent reviewers conducted the screening process for titles, abstracts, and full texts, and disagreements on the inclusion or exclusion of an article were resolved by consensus and, if necessary, by a third reviewer. The included articles were analyzed and synthesized using the constant comparative analysis method.

RESULT

The search identified 7,497 articles and 7,325 unique records. Following the title, abstract, and full-text screening, 25 studies met the review criteria. Of the 25 studies, 23 were conducted in developed and two in developing countries. The interventions were carried out on either the mobile platform, the website platform, or both. The study designs and the number of outcomes of the interventions varied significantly (multi-outcomes vs. single-outcome). Obesity, hypertension, neck/shoulder pain, office ergonomics issues, sedentary behaviors, heart disease, physical inactivity, dairy farm injuries, nutrition, respiratory problems, and diabetes were all addressed in the articles.

CONCLUSION

According to the findings of this literature study, e-trainings can significantly improve occupational safety and health. E-training is adaptable, affordable, and can increase workers' knowledge and abilities, resulting in fewer workplace injuries and accidents. Furthermore, e-training platforms can assist businesses in tracking employee development and ensuring that training needs are completed. Overall, this analysis reveals that e-training has enormous promise in the field of occupational safety and health for both businesses and employees.

Exploring mHealth applications for self-management of chronic low back pain: A survey of features and benefits

The adoption of Mobile Health (mHealth) for self-management is growing. mHealth solutions are commonly used in public healthcare and health services, where they are appreciated for their ease of use, broad reach, and wide acceptance. Chronic Low Back Pain (CLBP) is one of the most common health problems and a leading cause of disability. As such, it imposes a tremendous burden on patients and society. Studies have proposed that mHealth self-management solutions, such as mobile applications, can supplement traditional care methods and benefit patients, particularly in self-managing CLBP easier. To this end, the number of available mobile applications for CLBP has increased. This paper i) provides an overview of scientific studies on mobile applications for CLBP management from three different viewpoints: researchers, health professionals, and patients, ii) uncovers the application features that were seen as beneficial in the studies, and iii) contrasts the currently available applications for CLBP in Google Play Store and Apple App Store against the discovered features. The findings show that "Personalization and customization" is the most significant feature as it is beneficial from stakeholders' viewpoint and is represented by most applications. In contrast, "Gamification" and "Artificial intelligence" are the least significant features, indicating a lack of attention from application creators and researchers in this area.

A Systematic Review and Meta-Analysis of the Effects of Rehabilitation Using Digital Healthcare on Musculoskeletal Pain and Quality of Life

Rehabilitation using digital healthcare (DHC) has the potential to enhance the effectiveness of treatment for musculoskeletal disorders (MSDs) and associated pain by improving patient outcomes, while being cost-effective, safe, and measurable. This systematic review and meta-analysis aimed to evaluate the effectiveness of musculoskeletal rehabilitation using DHC. We searched PubMed, Ovid-Embase, Cochrane Library, and PEDro Physiotherapy Evidence Database from inception to October 28, 2022 for controlled clinical trials comparing DHC to conventional rehabilitation. We used a random-effects model for the meta-analysis, pooling the effects of DHC on pain and quality of life (QoL) by calculating standardized mean differences (SMDs) with 95% confidence intervals (CIs) between DHC rehabilitation and conventional rehabilitation (control). Fifty-four studies with 6240 participants met the inclusion criteria. The sample size ranged from 26 to 461, and the average age of the participants ranged from 21.9 to 71.8 years. The majority of the included studies focused on knee or hip joint MSD (n = 23), and the most frequently utilized DHC interventions were mobile applications (n = 26) and virtual or augmented reality (n = 16). Our meta-analysis of pain (n = 45) revealed that pain reduction was greater in DHC rehabilitation than in conventional rehabilitation (SMD: -0.55, 95% CI: -0.74, -0.36), indicating that rehabilitation using DHC has the potential to ameliorate MSD pain. Furthermore, DHC significantly improved health-related QoL and disease-specific QoL (SMD: 0.66, 95% CI: 0.29, 1.03; SMD: -0.44, 95% CI: -0.87, -0.01) compared to conventional rehabilitation. Our findings suggest that DHC offers a practical and flexible rehabilitation alternative for both patients with MSD and healthcare professionals. Nevertheless, further researches are needed to elucidate the underlying mechanisms by which DHC affects patient-reported outcomes, which may vary depending on the type and design of the DHC intervention.

Role of Internet of Things (IoT), Artificial Intelligence and Machine Learning in Musculoskeletal Pain: A Scoping Review

Artificial intelligence (AI), Internet of Things (IoT), and machine learning (ML) have considerably increased in numerous critical medical sectors and significantly impacted our daily lives. Digital health interventions support cost-effective, accessible, and preferred interventions that meet time and resource constraints for large patient populations. Musculoskeletal conditions significantly impact society, the economy, and people's life. Adults with chronic neck and back pain are frequently the victims, rendering them physically unable to move. They often experience discomfort, necessitating them to take over-the-counter medications or painkilling gels. Technologies driven by AI have been suggested as an alternative approach to improve adherence to exercise therapy, which in turn helps patients undertake exercises every day to relieve pain associated with the musculoskeletal system. Even though there are many computer-aided evaluations available for physiotherapy rehabilitation, current approaches to computer-aided performance and monitoring lack flexibility and robustness. A thorough literature search was conducted using key databases like PubMed and Google Scholar, as well as Medical Subject Headings (MeSH) terms and related keywords. This research aimed to determine if AI-operated digital health therapies that use cutting-edge IoT, brain imaging, and ML technologies are beneficial in lowering pain and enhancing functional impairment in patients with musculoskeletal diseases. The secondary goal was to ascertain whether solutions driven by machine learning or artificial intelligence can improve exercise compliance and be viewed as a lifestyle choice.

Using artificial intelligence to improve pain assessment and pain management: a scoping review

CONTEXT

Over 20% of US adults report they experience pain on most days or every day. Uncontrolled pain has led to increased healthcare utilization, hospitalization, emergency visits, and financial burden. Recognizing, assessing, understanding, and treating pain using artificial intelligence (AI) approaches may improve patient outcomes and healthcare resource utilization. A comprehensive synthesis of the current use and outcomes of AI-based interventions focused on pain assessment and management will guide the development of future research.

OBJECTIVES

This review aims to investigate the state of the research on AI-based interventions designed to improve pain assessment and management for adult patients. We also ascertain the actual outcomes of Al-based interventions for adult patients.

METHODS

The electronic databases searched include Web of Science, CINAHL, PsycINFO, Cochrane CENTRAL, Scopus, IEEE Xplore, and ACM Digital Library. The search initially identified 6946 studies. After screening, 30 studies met the inclusion criteria. The Critical Appraisals Skills Programme was used to assess study quality.

RESULTS

This review provides evidence that machine learning, data mining, and natural language processing were used to improve efficient pain recognition and pain assessment, analyze self-reported pain data, predict pain, and help clinicians and patients to manage chronic pain more effectively.

CONCLUSIONS

Findings from this review suggest that using AI-based interventions has a positive effect on pain recognition, pain prediction, and pain self-management; however, most reports are only pilot studies. More pilot studies with physiological pain measures are required before these approaches are ready for large clinical trial.

Validity of "OfficeCheck": A self-musculoskeletal assessment tool for screening work-related musculoskeletal disorders in office workers

BACKGROUND

Early self-assessment for work-related musculoskeletal disorders (WMSDs) is crucial in preventing severe symptoms and long-term consequences. Accessible tools are necessary for proactive management.

OBJECTIVE

To validate the OfficeCheck web application as a screening tool to classify office workers as capable of self-management for specific symptoms of WMSDs or requiring professional consultation.

METHODS

This study was conducted to determine the criterion-related validity of OfficeCheck using physical therapy assessment as the reference standard. In total, 223 office workers who work with a computer more than two hours a day with or without symptoms of WMSDs participated in this study. All of them were classified by self-assessment on the OfficeCheck process flow (Kappa = 0.841) and physical therapy assessment, respectively. For statistical analysis, classification numbers were calculated for sensitivity, specificity, false positive rate (FPR), false negative rate (FNR), positive predictive value (PPV), and negative predictive value (NPV).

RESULTS

A total of 223 workers with a mean age of 38.9±9.0 years and mean body mass index (BMI) of 24.3±5.2 kg/m2 were illustrated. The most common areas of complaint were neck/upper back and lower back/hip. The results indicated that OfficeCheck had high sensitivity (95.1%), low specificity (42.0%), low PPV (38.0%), and high NPV (95.8%). The FPR was 58.0% and the FNR was 4.9%.

CONCLUSION

OfficeCheck was found to have high sensitivity to classify office workers as capable of self-management for specific symptoms of WMSDs or requiring professional consultation. The use of OfficeCheck is thus recommended for self-detection and management to stop the consequences of WMSDs.

Mobile app use to support therapeutic exercise for musculoskeletal pain conditions may help improve pain intensity and self-reported physical function: a systematic review

QUESTION

What is the effect of therapeutic exercise or tailored physical activity programs supported by a mobile app (compared with exercise or physical activity programs delivered using other modes) for people with musculoskeletal pain conditions?

DESIGN

Systematic review of published randomised controlled trials with meta-analysis.

PARTICIPANTS

People of all ages with musculoskeletal pain conditions.

INTERVENTION

Therapeutic exercise or tailored physical activity programs supported by a mobile app.

OUTCOME MEASURES

Pain intensity, pain interference, self-reported physical function, physical performance, adherence, psychosocial outcomes, health-related quality of life, work participation, physical activity, goal attainment and satisfaction.

RESULTS

Eleven studies were eligible for inclusion, with a total of 845 participants. There was low certainty evidence that using mobile apps to deliver exercise programs helps to reduce pain intensity to a worthwhile extent (SMD -0.60, 95% CI -0.93 to -0.27). There was low certainty evidence that using mobile apps to deliver exercise programs helps to improve self-reported physical function to a worthwhile extent (SMD -0.92, 95% CI -1.57 to -0.27). Although the effect of using mobile apps to deliver exercise programs on pain interference was also estimated to be a worthwhile benefit (SMD -0.66), this estimate came with marked uncertainty (95% CI -1.52 to 0.19) so the effect remains unclear. The remainder of the outcomes were unclear due to sparse evidence. The most common behaviour change intervention functions in the mobile app interventions were: training, enablement and environmental restructuring.

CONCLUSION

Mobile apps supporting therapeutic exercise or tailored physical activity programs for musculoskeletal pain conditions may help in reducing pain intensity and improving physical function. The mobile apps utilised a limited range of behaviour change intervention functions.

REGISTRATION

CRD42021248046.

Relationship between musculoskeletal discomfort and cell phone use among young adults: A cross-sectional survey

BACKGROUND

Cell phone usage is highly prevalent among young adults. They are used for multiple purposes including communication, studies, social networking and entertainment. However, its excessive usage has been associated with adverse health outcomes.

OBJECTIVE

The objective was to find the association of cell phone usage with musculoskeletal discomfort (MSD) and its associated factors.

METHODS

A cross-sectional study was conducted on young adult students from a low-middle income country over a period of 3 months from December 2018 to February 2019. A structured questionnaire based on the Disabilities of the Arm, Shoulder and Hand (QuickDASH scoring) was used to record the musculoskeletal discomfort.

RESULTS

Out of 803 questionnaires, data of 754 (94%) were entered and the remaining questionnaires (n = 49) were discarded due to incomplete answers. The mean age was 20.83 (1.62) years. In our study, there were 194 (25.7%) males and 560 (74.3%) female participants. Neck and shoulder were the most frequently affected regions. A significant difference in QuickDASH score was observed between genders (p-value p ≤ 0.001). Left-handed individuals scored significantly higher on QuickDASH score compared to right-handed individuals (p < 0.05). Increased musculoskeletal discomfort was reported by individuals with daily cell phone use of more than four hours (p < 0.05). A positive significant correlation was found between continuous one hour cell-phone use and higher mean QuickDASH scores (correlation coefficient 0.124, p value 0.001). Shorter eye-to-screen distance was significantly associated with MSD (p < 0.05).

CONCLUSION

Musculoskeletal discomfort is associated with the female gender, duration of cell phone use and a small eye-to-screen distance.

Measurement of Adherence to mHealth Physical Activity Interventions and Exploration of the Factors That Affect the Adherence: Scoping Review and Proposed Framework

BACKGROUND

Mobile health (mHealth) is widely used as an innovative approach to delivering physical activity (PA) programs. Users' adherence to mHealth programs is important to ensure the effectiveness of mHealth-based programs.

OBJECTIVE

Our primary aim was to review the literature on the methods used to assess adherence, factors that could affect users' adherence, and the investigation of the association between adherence and health outcomes. Our secondary aim was to develop a framework to understand the role of adherence in influencing the effectiveness of mHealth PA programs.

METHODS

MEDLINE, PsycINFO, EMBASE, and CINAHL databases were searched to identify studies that evaluated the use of mHealth to promote PA in adults aged ≥18 years. We used critical interpretive synthesis methods to summarize the data collected.

RESULTS

In total, 54 papers were included in this review. We identified 31 specific adherence measurement methods, which were summarized into 8 indicators; these indicators were mapped to 4 dimensions: length, breadth, depth, and interaction. Users' characteristics (5 factors), technology-related factors (12 factors), and contextual factors (1 factor) were reported to have impacts on adherence. The included studies reveal that adherence is significantly associated with intervention outcomes, including health behaviors, psychological indicators, and clinical indicators. A framework was developed based on these review findings.

CONCLUSIONS

This study developed an adherence framework linking together the adherence predictors, comprehensive adherence assessment, and clinical effectiveness. This framework could provide evidence for measuring adherence comprehensively and guide further studies on adherence to mHealth-based PA interventions. Future research should validate the utility of this proposed framework.

Evaluation of the Effect of Patient Education and Strengthening Exercise Therapy Using a Mobile Messaging App on Work Productivity in Japanese Patients With Chronic Low Back Pain: Open-Label, Randomized, Parallel-Group Trial

Background

Artificial intelligence–assisted interactive health promotion systems are useful tools for the management of musculoskeletal conditions.

Objective

This study aimed to explore the effects of web-based video patient education and strengthening exercise therapy, using a mobile messaging app, on work productivity and pain in patients with chronic low back pain (CLBP) receiving pharmacological treatment.

Methods

Patients with CLBP were randomly allocated to either the exercise group, who received education and exercise therapy using a mobile messaging app, or the conventional group. For patient education, a web-based video program was used to provide evidence-based thinking regarding the importance of a cognitive behavioral approach for CLBP. The exercise therapy was developed in accordance with the recommendations for alignment, core muscles, and endogenous activation, including improvement of posture and mobility for proper alignment, stimulation and/or strengthening of deep muscles for spinal stability, and operation of intrinsic pain for the activation of endogenous substances by aerobic exercise. Both groups continued to receive the usual medical care with pharmacological treatment. The end points were changes in work productivity, pain intensity, quality of life, fear of movement, and depression. The observation period for this study was 12 weeks. An analysis adjusted for baseline values, age at the time of consent acquisition, sex, and willingness to strengthen the exercise therapy was performed.

Results

The exercise and conventional groups included 48 and 51 patients, with a mean age of 47.9 years (SD 10.2 years; n=27, 56.3% male patients) and 46.9 years (SD 12.3 years; n=28, 54.9% male patients) in the full analysis set, respectively. No significant impact of these interventions on work productivity was observed in the exercise group compared with the conventional group (primary end point: Quantity and Quality method; 0.062 vs 0.114; difference between groups −0.053, 95% CI −0.184 to 0.079; P=.43). However, the exercise group showed consistently better trends for the other end points than did the conventional group. Compared with the conventional group, the exercise group showed a significant improvement in the symptoms of low back pain (3.2 vs 3.8; difference between groups −0.5, 95% CI −1.1 to 0.0; P=.04), quality of life (EuroQoL 5 Dimensions 5 Level: 0.068 vs 0.006; difference between groups 0.061, 95% CI 0.008 to 0.114; P=.03), and fear of movement at week 12 (−2.3 vs 0.5; difference between groups −2.8, 95% CI −5.5 to −0.1; P=.04).

Conclusions

This study suggests that patient education and strengthening exercise therapy using a mobile messaging app may be useful for treating CLBP. This study does not reveal the effect of therapeutic interventions on CLBP on work productivity. Thus, further research is required to assess work productivity with therapeutic interventions.

Trial Registration

University Hospital Medical Information Network Clinical Trials Registry UMIN000041037; https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000046866

A vision for the future of wearable sensors in spine care and its challenges: narrative review

OBJECTIVE

This review aimed to: (I) provide a brief overview of some topical areas of current literature regarding applications of wearable sensors in the management of low back pain (LBP); (II) present a vision for a future comprehensive system that integrates wearable sensors to measure multiple parameters in the real world that contributes data to guide treatment selection (aided by artificial intelligence), uses wearables to aid treatment support, adherence and outcome monitoring, and interrogates the response of the individual patient to the prescribed treatment to guide future decision support for other individuals who present with LBP; and (III) consider the challenges that will need to be overcome to make such a system a reality.

BACKGROUND

Advances in wearable sensor technologies are opening new opportunities for the assessment and management of spinal conditions. Although evidence of improvements in outcomes for individuals with LBP from the use of sensors is limited, there is enormous future potential.

METHODS

Narrative review and literature synthesis.

CONCLUSIONS

Substantial research is underway by groups internationally to develop and test elements of this system, to design innovative new sensors that enable recording of new data in new ways, and to fuse data from multiple sources to provide rich information about an individual's experience of LBP. Together this system, incorporating data from wearable sensors has potential to personalise care in ways that were hitherto thought impossible. The potential is high but will require concerted effort to develop and ultimately will need to be feasible and more effective than existing management.