Feasibility and Acceptability of Mobile Phone-Based Auto-Personalized Physical Activity Recommendations for Chronic Pain Self-Management: Pilot Study on Adults

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.

Health information-seeking experience in people with head and neck neoplasms undergoing treatment: a qualitative study

PURPOSE

To describe the health information-seeking experience and its influencing factors of people with head and neck neoplasms undergoing treatment.

METHODS

This was a descriptive phenomenology study. Participants were recruited by purposive sampling. The semistructured interviews and all observation results were recorded. The data were analysed using Colaizzi's method.

RESULTS

Fourteen participants were selected. We identified four themes that illustrate factors that influence the health information-seeking behaviour of participants: patients' awareness of health information needs, patients' competence, doctor-patient communication, and online advertising interference. We also determined the value of different types of information and patients' information needs and sources.

CONCLUSION

These findings can help professionals understand patients' behaviours and think about how to deliver practical information support in a network environment to guide patients in continuous information seeking while taking specific factors into account.

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.

Mapping the Design Space of Technology-Based Solutions for Better Chronic Pain Care: Introducing the Pain Tech Landscape

OBJECTIVES

Technology has substantial potential to transform and extend care for persons with chronic pain, a burdensome and costly condition. To catalyze the development of impactful applications of technology in this space, we developed the Pain Tech Landscape (PTL) model, which integrates pain care needs with characteristics of technological solutions.

METHODS

Our interdisciplinary group representing experts in pain and human factors research developed PTL through iterative discussions. To demonstrate one potential use of the model, we apply data generated from a narrative review of selected pain and technology journals (2000-2020) in the form of heat map overlays, to reveal where pain tech research attention has focused to date.

RESULTS

The PTL comprises three two-dimensional planes, with pain care needs on each x axis (measurement to management) and technology applications on the y axes according to a) user agency (user- to system-driven), b) usage time frame (temporary to lifelong), and c) collaboration (single-user to collaborative). Heat maps show that existing applications reside primarily in the "user-driven/management" quadrant (e.g., self-care apps). Examples of less developed areas include artificial intelligence and Internet of Things (i.e., Internet-linked household objects), and collaborative/social tools for pain management.

CONCLUSIONS

Collaborative development between the pain and tech fields in early developmental stages using the PTL as a common language could yield impactful solutions for chronic pain management. The PTL could also be used to track developments in the field over time. We encourage periodic reassessment and refinement of the PTL model, which can also be adapted to other chronic conditions.

The Advances and Utility of Artificial Intelligence and Robotics in Regional Anesthesia: An Overview of Recent Developments

The integration of artificial intelligence (AI) and robotics in regional anesthesia has brought about transformative changes in acute pain management for surgical procedures. This review explores the evolving landscape of AI and robotics applications in regional anesthesia, outlining their potential benefits, challenges, and ethical considerations. AI-driven pain assessment, real-time guidance for needle placement during nerve blocks, and predictive modeling solutions for nerve blocks have the potential to enhance procedural precision and improve patient outcomes. Robotic technology aids in accurate needle insertion, reducing complications and improving pain relief. This review also highlights the ethical and safety considerations surrounding AI implementation, emphasizing data security and professional training. While challenges such as costs and regulatory hurdles exist, ongoing research and clinical trials demonstrate the practical utility of these technologies. In conclusion, AI and robotics have the potential to reshape regional anesthesia practice, ultimately improving patient care and procedural accuracy in pain management.

Understanding the role and adoption of artificial intelligence techniques in rheumatology research: An in-depth review of the literature

The major and upward trend in the number of published research related to rheumatic and musculoskeletal diseases, in which artificial intelligence plays a key role, has exhibited the interest of rheumatology researchers in using these techniques to answer their research questions. In this review, we analyse the original research articles that combine both worlds in a five- year period (2017-2021). In contrast to other published papers on the same topic, we first studied the review and recommendation articles that were published during that period, including up to October 2022, as well as the publication trends. Secondly, we review the published research articles and classify them into one of the following categories: disease identification and prediction, disease classification, patient stratification and disease subtype identification, disease progression and activity, treatment response, and predictors of outcomes. Thirdly, we provide a table with illustrative studies in which artificial intelligence techniques have played a central role in more than twenty rheumatic and musculoskeletal diseases. Finally, the findings of the research articles, in terms of disease and/or data science techniques employed, are highlighted in a discussion. Therefore, the present review aims to characterise how researchers are applying data science techniques in the rheumatology medical field. The most immediate conclusions that can be drawn from this work are: multiple and novel data science techniques have been used in a wide range of rheumatic and musculoskeletal diseases including rare diseases; the sample size and the data type used are heterogeneous, and new technical approaches are expected to arrive in the short-middle term.

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.

Associations between physical exercise patterns and pain symptoms in individuals with endometriosis: a cross-sectional mHealth-based investigation

OBJECTIVES

This study investigates the association of daily physical exercise with pain symptoms in endometriosis. We also examined whether an individual's typical weekly (ie, habitual) exercise frequency influences (ie, moderates) the relationship between their pain symptoms on a given day (day t) and previous-day (day t-1) exercise.

PARTICIPANTS

The sample included 90 382 days of data from 1009 participants (~85% non-Hispanic white) living with endometriosis across 38 countries.

STUDY DESIGN

This was an observational, retrospective study conducted using data from a research mobile app (Phendo) designed for collecting self-reported data on symptoms and self-management of endometriosis.

PRIMARY OUTCOME MEASURES

The two primary outcomes were the composite day-level pain score that includes pain intensity and location, and the change in this score from previous day (Δ-score). We applied generalised linear mixed-level models to examine the effect of previous-day exercise and habitual exercise frequency on these outcomes. We included an interaction term between the two predictors to assess the moderation effect, and adjusted for previous-day pain, menstrual status, education level and body mass index.

RESULTS

The association of previous-day (day t-1) exercise with pain symptoms on day t was moderated by habitual exercise frequency, independent of covariates (rate ratio=0.96, 95% CI=0.95 to 0.98, p=0.0007 for day-level pain score, B=-0.14, 95% CI=-0.26 to -0.016, p=0.026 for Δ-score). Those who regularly engaged in exercise at least three times per week were more likely to experience favourable pain outcomes after having a bout of exercise on the previous day.

CONCLUSIONS

Regular exercise might influence the day-level (ie, short-term) association of pain symptoms with exercise. These findings can inform exercise recommendations for endometriosis pain management, especially for those who are at greater risk of lack of regular exercise due to acute exacerbation in their pain after exercise.

Data-driven Interpretable Policy Construction for Personalized Mobile Health

To promote healthy behaviors, many mobile health applications provide message-based interventions, such as tips, motivational messages, or suggestions for healthy activities. Ideally, the intervention policies should be carefully designed so that users obtain the benefits without being overwhelmed by overly frequent messages. As part of the HeartSteps physical-activity intervention, users receive messages intended to disrupt sedentary behavior. HeartSteps uses an algorithm to uniformly spread out the daily message budget over time, but does not attempt to maximize treatment effects. This limitation motivates constructing a policy to optimize the message delivery decisions for more effective treatments. Moreover, the learned policy needs to be interpretable to enable behavioral scientists to examine it and to inform future theorizing. We address this problem by learning an effective and interpretable policy that reduces sedentary behavior. We propose Optimal Policy Trees + (OPT+), an innovative batch off-policy learning method, that combines a personalized threshold learning and an extension of Optimal Policy Trees under a budget-constrained setting. We implement and test the method using data collected in HeartSteps V2/V3. Computational results demonstrate a significant reduction in sedentary behavior with a lower delivery budget. OPT+ produces a highly interpretable and stable output decision tree thus enabling theoretical insights to guide future research.

Digital Therapeutic Care and Decision Support Interventions for People With Low Back Pain: Systematic Review

Background

Low back pain (LBP) is the leading cause of worldwide years lost because of disability, with a tremendous economic burden for health care systems. Digital therapeutic care (DTC) programs provide a scalable, universally accessible, and low-cost approach to the multidisciplinary treatment of LBP. Moreover, novel decision support interventions such as personalized feedback messages, push notifications, and data-driven activity recommendations amplify DTC by guiding the user through the program while aiming to increase overall engagement and sustainable behavior change.

Objective

This systematic review aims to synthesize recent scientific literature on the impact of DTC apps for people with LBP and outline the implementation of add-on decision support interventions, including their effect on user retention and attrition rates.

Methods

We searched bibliographic databases, including MEDLINE, Cochrane Library, Web of Science, and the Physiotherapy Evidence Database, from March 1, 2016, to October 15, 2020, in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and conducted this review based on related previously published systematic reviews. Besides randomized controlled trials (RCTs), we also included study designs with the evidence level of at least a retrospective comparative study. This enables the consideration of real-world user-generated data and provides information regarding the adoption and effectiveness of DTC apps in a real-life setting. For the appraisal of the risk of bias, we used the Risk of Bias 2 Tool and the Risk of Bias in Non-Randomized Studies of Interventions Tool for the RCTs and nonrandomized trials, respectively. The included studies were narratively synthesized regarding primary and secondary outcome measures, DTC components, applied decision support interventions, user retention, and attrition rates.

Results

We retrieved 1388 citations, of which 12 studies are included in this review. Of the 12 studies, 6 (50%) were RCTs and 6 (50%) were nonrandomized trials. In all included studies, lower pain levels and increased functionality compared with baseline values were observed in the DTC intervention group. A between-group comparison revealed significant improvements in pain and functionality levels in 67% (4/6) of the RCTs. The study population was mostly homogeneous, with predominantly female, young to middle-aged participants of normal to moderate weight. The methodological quality assessment revealed moderate to high risks of biases, especially in the nonrandomized trials.

Conclusions

This systematic review demonstrates the benefits of DTC for people with LBP. There is also evidence that decision support interventions benefit overall engagement with the app and increase participants’ ability to self-manage their recovery process. Finally, including retrospective evaluation studies of real-world user-generated data in future systematic reviews of digital health intervention trials can reveal new insights into the benefits, challenges, and real-life adoption of DTC programs.

Predicting Participant Compliance With Fitness Tracker Wearing and Ecological Momentary Assessment Protocols in Information Workers: Observational Study

Background

Studies that use ecological momentary assessments (EMAs) or wearable sensors to track numerous attributes, such as physical activity, sleep, and heart rate, can benefit from reductions in missing data. Maximizing compliance is one method of reducing missing data to increase the return on the heavy investment of time and money into large-scale studies.

Objective

This paper aims to identify the extent to which compliance can be prospectively predicted from individual attributes and initial compliance.

Methods

We instrumented 757 information workers with fitness trackers for 1 year and conducted EMAs in the first 56 days of study participation as part of an observational study. Their compliance with the EMA and fitness tracker wearing protocols was analyzed. Overall, 31 individual characteristics (eg, demographics and personalities) and behavioral variables (eg, early compliance and study portal use) were considered, and 14 variables were selected to create beta regression models for predicting compliance with EMAs 56 days out and wearable compliance 1 year out. We surveyed study participation and correlated the results with compliance.

Results

Our modeling indicates that 16% and 25% of the variance in EMA compliance and wearable compliance, respectively, could be explained through a survey of demographics and personality in a held-out sample. The likelihood of higher EMA and wearable compliance was associated with being older (EMA: odds ratio [OR] 1.02, 95% CI 1.00-1.03; wearable: OR 1.02, 95% CI 1.01-1.04), speaking English as a first language (EMA: OR 1.38, 95% CI 1.05-1.80; wearable: OR 1.39, 95% CI 1.05-1.85), having had a wearable before joining the study (EMA: OR 1.25, 95% CI 1.04-1.51; wearable: OR 1.50, 95% CI 1.23-1.83), and exhibiting conscientiousness (EMA: OR 1.25, 95% CI 1.04-1.51; wearable: OR 1.34, 95% CI 1.14-1.58). Compliance was negatively associated with exhibiting extraversion (EMA: OR 0.74, 95% CI 0.64-0.85; wearable: OR 0.67, 95% CI 0.57-0.78) and having a supervisory role (EMA: OR 0.65, 95% CI 0.54-0.79; wearable: OR 0.66, 95% CI 0.54-0.81). Furthermore, higher wearable compliance was negatively associated with agreeableness (OR 0.68, 95% CI 0.56-0.83) and neuroticism (OR 0.85, 95% CI 0.73-0.98). Compliance in the second week of the study could help explain more variance; 62% and 66% of the variance in EMA compliance and wearable compliance, respectively, was explained. Finally, compliance correlated with participants’ self-reflection on the ease of participation, usefulness of our compliance portal, timely resolution of issues, and compensation adequacy, suggesting that these are avenues for improving compliance.

Conclusions

We recommend conducting an initial 2-week pilot to measure trait-like compliance and identify participants at risk of long-term noncompliance, performing oversampling based on participants’ individual characteristics to avoid introducing bias in the sample when excluding data based on noncompliance, using an issue tracking portal, and providing special care in troubleshooting to help participants maintain compliance.

Smartphone-Based Interventions to Reduce Sedentary Behavior and Promote Physical Activity Using Integrated Dynamic Models: Systematic Review

BACKGROUND

Traditional psychological theories are inadequate to fully leverage the potential of smartphones and improve the effectiveness of physical activity (PA) and sedentary behavior (SB) change interventions. Future interventions need to consider dynamic models taken from other disciplines, such as engineering (eg, control systems). The extent to which such dynamic models have been incorporated in the development of interventions for PA and SB remains unclear.

OBJECTIVE

This review aims to quantify the number of studies that have used dynamic models to develop smartphone-based interventions to promote PA and reduce SB, describe their features, and evaluate their effectiveness where possible.

METHODS

Databases including PubMed, PsycINFO, IEEE Xplore, Cochrane, and Scopus were searched from inception to May 15, 2019, using terms related to mobile health, dynamic models, SB, and PA. The included studies involved the following: PA or SB interventions involving human adults; either developed or evaluated integrated psychological theory with dynamic theories; used smartphones for the intervention delivery; the interventions were adaptive or just-in-time adaptive; included randomized controlled trials (RCTs), pilot RCTs, quasi-experimental, and pre-post study designs; and were published from 2000 onward. Outcomes included general characteristics, dynamic models, theory or construct integration, and measured SB and PA behaviors. Data were synthesized narratively. There was limited scope for meta-analysis because of the variability in the study results.

RESULTS

A total of 1087 publications were screened, with 11 publications describing 8 studies included in the review. All studies targeted PA; 4 also included SB. Social cognitive theory was the major psychological theory upon which the studies were based. Behavioral intervention technology, control systems, computational agent model, exploit-explore strategy, behavioral analytic algorithm, and dynamic decision network were the dynamic models used in the included studies. The effectiveness of quasi-experimental studies involved reduced SB (1 study; P=.08), increased light PA (1 study; P=.002), walking steps (2 studies; P=.06 and P<.001), walking time (1 study; P=.02), moderate-to-vigorous PA (2 studies; P=.08 and P=.81), and nonwalking exercise time (1 study; P=.31). RCT studies showed increased walking steps (1 study; P=.003) and walking time (1 study; P=.06). To measure activity, 5 studies used built-in smartphone sensors (ie, accelerometers), 3 of which used the phone's GPS, and 3 studies used wearable activity trackers.

CONCLUSIONS

To our knowledge, this is the first systematic review to report on smartphone-based studies to reduce SB and promote PA with a focus on integrated dynamic models. These findings highlight the scarcity of dynamic model-based smartphone studies to reduce SB or promote PA. The limited number of studies that incorporate these models shows promising findings. Future research is required to assess the effectiveness of dynamic models in promoting PA and reducing SB.

TRIAL REGISTRATION

International Prospective Register of Systematic Reviews (PROSPERO) CRD42020139350; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=139350.

Tailored physical activity behavior change interventions: challenges and opportunities

A physically active lifestyle provides innumerable benefits; yet, few individuals are physically active enough to reap those benefits. Tailored physical activity interventions may address low rates of physical activity by offering individualized strategies that consider a person's characteristics, needs, preferences, and/or context, rather than the traditional one-size-fits-all approach. However, the tailoring methodology is in its nascency, and an understanding of how best to develop such interventions is needed. In this commentary, we identify future directions to enhance the impact of tailored interventions designed to increase physical activity participation. A multi-country collaborative was established to review the literature and discuss an agenda for future research. Two overarching research opportunities are suggested for improving the development of tailored, behavioral physical activity interventions: (a) optimize the engagement of diverse knowledge users in intervention co-design and (b) examine ethical considerations that may impact the use of technology to support tailored physical activity delivery. Specifically, there is a need for better reporting and evaluation of knowledge user involvement alongside targeting diversity in the inclusion of knowledge users. Furthermore, while technology boasts many opportunities to increase the scale and precision of interventions, examinations of how it impacts recipients' experiences of and participation in tailored interventions are needed to ensure the benefits of technology use outweigh the risks. A better understanding of these research areas will help ensure that the diverse needs of individuals are met, technology is appropriately used to support tailoring, and ultimately it improves the effectiveness of tailored physical activity interventions.

Smartphone-Based Interventions for Physical Activity Promotion: Scoping Review of the Evidence Over the Last 10 Years

Background

Several reviews of mobile health (mHealth) physical activity (PA) interventions suggest their beneficial effects on behavior change in adolescents and adults. Owing to the ubiquitous presence of smartphones, their use in mHealth PA interventions seems obvious; nevertheless, there are gaps in the literature on the evaluation reporting processes and best practices of such interventions.

Objective

The primary objective of this review is to analyze the development and evaluation trajectory of smartphone-based mHealth PA interventions and to review systematic theory- and evidence-based practices and methods that are implemented along this trajectory. The secondary objective is to identify the range of evidence (both quantitative and qualitative) available on smartphone-based mHealth PA interventions to provide a comprehensive tabular and narrative review of the available literature in terms of its nature, features, and volume.

Methods

We conducted a scoping review of qualitative and quantitative studies examining smartphone-based PA interventions published between 2008 and 2018. In line with scoping review guidelines, studies were not rejected based on their research design or quality. This review, therefore, includes experimental and descriptive studies, as well as reviews addressing smartphone-based mHealth interventions aimed at promoting PA in all age groups (with a subanalysis conducted for adolescents). Two groups of studies were additionally included: reviews or content analyses of PA trackers and meta-analyses exploring behavior change techniques and their efficacy.

Results

Included articles (N=148) were categorized into 10 groups: commercial smartphone app content analyses, smartphone-based intervention review studies, activity tracker content analyses, activity tracker review studies, meta-analyses of PA intervention studies, smartphone-based intervention studies, qualitative formative studies, app development descriptive studies, qualitative follow-up studies, and other related articles. Only 24 articles targeted children or adolescents (age range: 5-19 years). There is no agreed evaluation framework or taxonomy to code or report smartphone-based PA interventions. Researchers did not state the coding method, used various evaluation frameworks, or used different versions of behavior change technique taxonomies. In addition, there is no consensus on the best behavior change theory or model that should be used in smartphone-based interventions for PA promotion. Commonly reported systematic practices and methods have been successfully identified. They include PA recommendations, trial designs (randomized controlled trials, experimental trials, and rapid design trials), mixed methods data collection (surveys, questionnaires, interviews, and focus group discussions), scales to assess app quality, and industry-recognized reporting guidelines.

Conclusions

Smartphone-based mHealth interventions aimed at promoting PA showed promising results for behavior change. Although there is a plethora of published studies on the adult target group, the number of studies and consequently the evidence base for adolescents is limited. Overall, the efficacy of smartphone-based mHealth PA interventions can be considerably improved through a more systematic approach of developing, reporting, and coding of the interventions.

Personalized mobile technologies for lifestyle behavior change: A systematic review, meta-analysis, and meta-regression

Given that the one-size-fits-all approach to mobile health interventions have limited effects, a personalized approach might be necessary to promote healthy behaviors and prevent chronic conditions. Our systematic review aims to evaluate the effectiveness of personalized mobile interventions on lifestyle behaviors (i.e., physical activity, diet, smoking and alcohol consumption), and identify the effective key features of such interventions. We included any experimental trials that tested a personalized mobile app or fitness tracker and reported any lifestyle behavior measures. We conducted a narrative synthesis for all studies, and a meta-analysis of randomized controlled trials. Thirty-nine articles describing 31 interventions were included (n = 77,243, 64% women). All interventions personalized content and rarely personalized other features. Source of data included system-captured (12 interventions), user-reported (11 interventions) or both (8 interventions). The meta-analysis showed a moderate positive effect on lifestyle behavior outcomes (standardized difference in means [SDM] 0.663, 95% CI 0.228 to 1.10). A meta-regression model including source of data found that interventions that used system-captured data for personalization were associated with higher effectiveness than those that used user-reported data (SDM 1.48, 95% CI 0.76 to 2.19). In summary, the field is in its infancy, with preliminary evidence of the potential efficacy of personalization in improving lifestyle behaviors. Source of data for personalization might be important in determining intervention effectiveness. To fully exploit the potential of personalization, future high-quality studies should investigate the integration of multiple data from different sources and include personalized features other than content.

Machine Learning in Chronic Pain Research: A Scoping Review

Given the high prevalence and associated cost of chronic pain, it has a significant impact on individuals and society. Improvements in the treatment and management of chronic pain may increase patients’ quality of life and reduce societal costs. In this paper, we evaluate state-of-the-art machine learning approaches in chronic pain research. A literature search was conducted using the PubMed, IEEE Xplore, and the Association of Computing Machinery (ACM) Digital Library databases. Relevant studies were identified by screening titles and abstracts for keywords related to chronic pain and machine learning, followed by analysing full texts. Two hundred and eighty-seven publications were identified in the literature search. In total, fifty-three papers on chronic pain research and machine learning were reviewed. The review showed that while many studies have emphasised machine learning-based classification for the diagnosis of chronic pain, far less attention has been paid to the treatment and management of chronic pain. More research is needed on machine learning approaches to the treatment, rehabilitation, and self-management of chronic pain. As with other chronic conditions, patient involvement and self-management are crucial. In order to achieve this, patients with chronic pain need digital tools that can help them make decisions about their own treatment and care.

Pain and Function in Chronic Musculoskeletal Pain-Treating the Whole Person

Chronic pain is often associated with functional limitations that have a huge impact on patients' lives. However, despite being relatively common, chronic musculoskeletal pain is still viewed by some as a symptom of another disease rather than its own condition, and is therefore poorly addressed. This is compounded by other challenges in the field, including education gaps for both healthcare professionals and patients, a lack of universal and comprehensive assessment tools, poor societal perceptions of chronic pain, and the current stigma around the use of opioids. Here, we review the current chronic musculoskeletal pain management landscape in the United States and offer professional insight into emerging methods that can be used to improve patient outcomes, in particular, the achievement of meaningful functional goals. This perspective incorporates our combined multidisciplinary (psychiatry, psychology, nursing, physical therapy, and general medicine) experience and insights. We believe that chronic pain is a multifactorial experience and treatment requires an integrated, multidisciplinary approach from a range of healthcare providers. For the best patient outcomes, this team should work together to assess and treat the patient as a whole, addressing their pain and also providing education, empowerment, and support to enable patients to set and achieve meaningful functional goals that will provide real improvement in their quality of life. We believe that the healthcare community should elevate the conversation around chronic musculoskeletal pain management beyond that of just pain, to encompass the meaningful benefits that improvement in functional outcomes brings to patients.

Barriers and facilitators to patient uptake and utilisation of digital interventions for the self-management of low back pain: a systematic review of qualitative studies

OBJECTIVES

Low back pain (LBP) is a leading contributor to disability globally. Self-management is a core component of LBP management. We aimed to synthesise published qualitative literature concerning digital health interventions (DHIs) to support LBP self-management to: (1) determine engagement strategies, (2) identify barriers and facilitators affecting patient uptake/utilisation and (3) develop a preliminary conceptual model of barriers and facilitators to uptake/utilisation.

DESIGN

Systematic review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.

DATA SOURCES

MEDLINE, Embase, CINAHL, PsycINFO, Cochrane Library, DoPHER, TRoPHI, Web of Science and OT Seeker, from January 2000 to December 2018, using the concepts: LBP, DHI and self-management.

ELIGIBILITY CRITERIA

Peer-reviewed qualitative study (or component) examining engagement with, or barriers and/or facilitators to the uptake/utilisation of an interactive DHI for self-management of LBP in adults (community, primary or secondary care settings).

DATA EXTRACTION AND SYNTHESIS

Standardised data extraction form was completed. COREQ (Consolidated criteria for Reporting Qualitative research) checklist was used to assess methodology. Data was synthesised narratively for engagement strategies, thematically for barriers/facilitators to uptake/utilisation and normalisation process theory was applied to produce a conceptual model.

RESULTS

We identified 14 191 citations, of which 105 full-text articles were screened, and five full-text articles from four studies included. These were from community and primary care contexts in Europe and the USA, and involved 56 adults with LBP and 19 healthcare professionals. There was a lack of consideration on how to sustain engagement with DHIs. Examination of barriers and facilitators for uptake/utilisation identified four major themes: IT (information technology) usability-accessibility; quality-quantity of content; tailoring-personalisation; and motivation-support. These themes informed the development of a preliminary conceptual model for uptake/utilisation of a DHI for LBP self-management.

CONCLUSIONS

We highlight key barriers and facilitators that should be considered when designing DHIs for LBP self-management. Our findings are in keeping with reviews of DHIs for other long-term conditions, implying these findings may not be condition specific.

SYSTEMATIC REVIEW REGISTRATION

A protocol for this systematic review was registered with https://www.crd.york.ac.uk/PROSPERO/ (CRD42016051182) on 10 November 2016. https://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42016051182.

Exploring the Feasibility of Relapse Prevention Strategies in Interdisciplinary Multimodal Pain Therapy Programs: Qualitative Study

BACKGROUND

Although interdisciplinary multimodal pain treatment (IMPT) programs are widely regarded as treatment of choice for patients with chronic pain, there are signs that many patients are unable to maintain their treatment gains in the long term. To facilitate the maintenance of positive treatment outcomes over time, we developed two relapse prevention strategies.

OBJECTIVE

The main objective of this study was to explore the feasibility of these strategies within the context of IMPT programs.

METHODS

We performed a feasibility study using 3 workbook prototypes containing either one or both strategies. For a period of 6 months, the workbooks were made available in two IMPT facilities. Qualitative data were collected through a focus group and semistructured interviews. We performed a thematic analysis using a deductive approach with (1) applicability to the treatment program, (2) acceptability of the workbook content, and (3) form, as predefined themes.

RESULTS

The final dataset consisted of transcripts from a focus group with health care providers and 11 telephone interviews and 2 additional in-depth interviews with patients. In general, the intervention was perceived as useful, easy to use, and in line with the treatment program. The data also include suggestions to further improve the use of both strategies, including more specific implementation guidelines, revised goal-setting procedure, and development of a mobile health version. However, several factors, including a high dropout rate and small sample size, impact the external validity of our findings.

CONCLUSIONS

This study should be regarded as a first step in the process of transforming the prototype workbook into an effective intervention for clinical practice. Although these initial results indicate a favorable evaluation of both behavior regulation strategies within the workbook, this study encountered multiple barriers regarding implementation and data collection that limit the generalizability of these results. Future research efforts should specifically address the fidelity of HCPs and patients and should include clear procedures regarding recruitment and use of both relapse prevention strategies during treatment.

Sensor Technologies to Manage the Physiological Traits of Chronic Pain: A Review

Non-oncologic chronic pain is a common high-morbidity impairment worldwide and acknowledged as a condition with significant incidence on quality of life. Pain intensity is largely perceived as a subjective experience, what makes challenging its objective measurement. However, the physiological traces of pain make possible its correlation with vital signs, such as heart rate variability, skin conductance, electromyogram, etc., or health performance metrics derived from daily activity monitoring or facial expressions, which can be acquired with diverse sensor technologies and multisensory approaches. As the assessment and management of pain are essential issues for a wide range of clinical disorders and treatments, this paper reviews different sensor-based approaches applied to the objective evaluation of non-oncological chronic pain. The space of available technologies and resources aimed at pain assessment represent a diversified set of alternatives that can be exploited to address the multidimensional nature of pain.

ReVibe: A Context-assisted Evening Recall Approach to Improve Self-report Adherence

Besides passive sensing, ecological momentary assessments (EMAs) are one of the primary methods to collect in-the-moment data in ubiquitous computing and mobile health. While EMAs have the advantage of low recall bias, a disadvantage is that they frequently interrupt the user and thus long-term adherence is generally poor. In this paper, we propose a less-disruptive self-reporting method, "assisted recall," in which in the evening individuals are asked to answer questions concerning a moment from earlier in the day assisted by contextual information such as location, physical activity, and ambient sounds collected around the moment to be recalled. Such contextual information is automatically collected from phone sensor data, so that self-reporting does not require devices other than a smartphone. We hypothesized that providing assistance based on such automatically collected contextual information would increase recall accuracy (i.e., if recall responses for a moment match the EMA responses at the same moment) as compared to no assistance, and we hypothesized that the overall completion rate of evening recalls (assisted or not) would be higher than for in-the-moment EMAs. We conducted a two-week study (N=54) where participants completed recalls and EMAs each day. We found that providing assistance via contextual information increased recall accuracy by 5.6% (p = 0.032) and the overall recall completion rate was on average 27.8% (p < 0.001) higher than that of EMAs.

Development of an Evaluative, Educational, and Communication-Facilitating App for Older Adults with Chronic Low Back Pain: Patient Perceptions of Usability and Utility

OBJECTIVE

The purpose of this study is to examine the usability and utility of an office-based iPad app that we developed for older adults with chronic low back pain (CLBP). The app screens for conditions that contribute to back pain and pain interference and provides personalized education based on patient responses. It also facilitates patient-provider communication regarding treatment targets and expectations.

METHODS

Forty-six older adults (age ≥60 years) with CLBP were recruited from the Veterans Affairs and from the Pittsburgh community. Testing was split into two phases. Alpha testing (N = 15) was used to drive design changes to the app. Beta testing (N = 30, after one participant withdrew) used a structured questionnaire to evaluate the app's usability and utility.

RESULTS

The application was rated highly for usability and utility (9.6 and 8.9 out of 10, respectively). The majority of participants (82.1%) agreed that the app would help them communicate with their doctor and that it gave them useful information about potentially harmful or unnecessary interventions such as opioids and imaging (79.2% and 75.0%). Participants (age ≥60 years, mean age = 75.5 years) were able to successfully use the application without assistance and would be willing to do so in their primary care office.

CONCLUSIONS

We present the development of a CLBP app that screens for pain contributors and provides personalized education based on patient responses. Such an app could be employed in a variety of clinical settings to help educate patients about their CLBP and to curtail unnecessary interventions. Patient outcomes are being tested in an ongoing clinical trial.

Adapting Evidence-Based Treatments for Digital Technologies: a Critical Review of Functions, Tools, and the Use of Branded Solutions

PURPOSE OF REVIEW

We provide a critical review of digital technologies in evidence-based treatments (EBTs) for mental health with a focus on the functions technologies are intended to serve. The review highlights issues related to clarity of purpose, usability, and assumptions related to EBT technology integration, branding, and packaging.

RECENT FINDINGS

Developers continue to use technology in creative ways, often combining multiple functions to convey existing EBTs or to create new technology-enabled EBTs. Developers have a strong preference for creating and investigating whole-source, branded solutions related to specific EBTs, in comparison to developing or investigating technology tools related to specific components of behavior change, or developing specific clinical protocols that can be delivered via existing technologies. Default assumptions that new applications are required for each individual EBT, that EBTs are best served by the use of only one technology solution rather than multiple tools, and that an EBT-specific technology product should include or convey all portions of an EBT slow scientific progress and increase risk of usability issues that negatively impact uptake. We contend that a purposeful, functions-based approach should guide the selection, development, and application of technology in support of EBT delivery.

Music-Enhanced Analgesia and Antiseizure Activities in Animal Models of Pain and Epilepsy: Toward Preclinical Studies Supporting Development of Digital Therapeutics and Their Combinations With Pharmaceutical Drugs

Digital therapeutics (software as a medical device) and mobile health (mHealth) technologies offer a means to deliver behavioral, psychosocial, disease self-management and music-based interventions to improve therapy outcomes for chronic diseases, including pain and epilepsy. To explore new translational opportunities in developing digital therapeutics for neurological disorders, and their integration with pharmacotherapies, we examined analgesic and antiseizure effects of specific musical compositions in mouse models of pain and epilepsy. The music playlist was created based on the modular progression of Mozart compositions for which reduction of seizures and epileptiform discharges were previously reported in people with epilepsy. Our results indicated that music-treated mice exhibited significant analgesia and reduction of paw edema in the carrageenan model of inflammatory pain. Among analgesic drugs tested (ibuprofen, cannabidiol (CBD), levetiracetam, and the galanin analog NAX 5055), music intervention significantly decreased paw withdrawal latency difference in ibuprofen-treated mice and reduced paw edema in combination with CBD or NAX 5055. To the best of our knowledge, this is the first animal study on music-enhanced antinociceptive activity of analgesic drugs. In the plantar incision model of surgical pain, music-pretreated mice had significant reduction of mechanical allodynia. In the corneal kindling model of epilepsy, the cumulative seizure burden following kindling acquisition was lower in animals exposed to music. The music-treated group also exhibited significantly improved survival, warranting further research on music interventions for preventing Sudden Unexpected Death in Epilepsy (SUDEP). We propose a working model of how musical elements such as rhythm, sequences, phrases and punctuation found in K.448 and K.545 may exert responses via parasympathetic nervous system and the hypothalamic-pituitary-adrenal (HPA) axis. Based on our findings, we discuss: (1) how enriched environment (EE) can serve as a preclinical surrogate for testing combinations of non-pharmacological modalities and drugs for the treatment of pain and other chronic diseases, and (2) a new paradigm for preclinical and clinical development of therapies leading to drug-device combination products for neurological disorders, depression and cancer. In summary, our present results encourage translational research on integrating non-pharmacological and pharmacological interventions for pain and epilepsy using digital therapeutics.