Hybrid Ubiquitous Coaching With a Novel Combination of Mobile and Holographic Conversational Agents Targeting Adherence to Home Exercises: Four Design and Evaluation Studies

Human cues in eHealth to promote lifestyle change: An experimental field study to examine adherence to self-help interventions

eHealth lifestyle interventions without human support (self-help interventions) are generally less effective, as they suffer from lower adherence levels. To solve this, we investigated whether (1) using a text-based conversational agent (TCA) and applying human cues contribute to a working alliance with the TCA, and whether (2) adding human cues and establishing a positive working alliance increase intervention adherence. Participants (N = 121) followed a TCA-supported app-based physical activity intervention. We manipulated two types of human cues: visual (ie, message appearance) and relational (ie, message content). We employed a 2 (visual cues: yes, no) x 2 (relational cues: yes, no) between-subjects design, resulting in four experimental groups: (1) visual and relational cues, (2) visual cues only, (3) relational cues only, or (4) no human cues. We measured the working alliance with the Working Alliance Inventory Short Revised form and intervention adherence as the number of days participants responded to the TCA's messages. Contrary to expectations, the working alliance was unaffected by using human cues. Working alliance was positively related to adherence (t(78) = 3.606, p = .001). Furthermore, groups who received visual cues showed lower adherence levels compared to those who received relational cues only or no cues (U = 1140.5, z = -3.520, p < .001). We replicated the finding that establishing a working alliance contributes to intervention adherence, independently of the use of human cues in a TCA. However, we were unable to show that adding human cues impacted the working alliance and increased adherence. The results indicate that adding visual cues to a TCA may even negatively affect adherence, possibly because it may create confusion concerning the true nature of the coach, which may prompt unrealistic expectations.

Virtual Reality-Based Training in Chronic Low Back Pain: Systematic Review and Meta-Analysis of Randomized Controlled Trials

BACKGROUND

Low back pain is one of the most prevalent pain conditions worldwide. Virtual reality-based training has been used for low back pain as a new treatment strategy. Present evidence indicated that the effectiveness of virtual reality-based training for people with chronic low back pain is inconclusive.

OBJECTIVE

This study conducted a meta-analysis to evaluate the immediate- and short-term effects of virtual reality-based training on pain, pain-related fear, and disability in people with chronic low back pain.

METHODS

We searched the PubMed, Embase, Web of Science, PEDro, CENTRAL, and CINAHL databases from inception until January 2024. Only randomized controlled trials assessing the effects of virtual reality-based training on individuals with chronic low back pain were selected. The outcomes were focused on pain, pain-related fear measured by the Tampa Scale of Kinesiophobia, and disability measured by the Oswestry Disability Index. The immediate term was defined as the immediate period after intervention, and the short term was defined as 3 to 6 months after intervention. The Cochrane Risk of Bias tool and the GRADE (Grading of Recommendations, Assessment, Development and Evaluation) approach were used to evaluate the quality of the methodology and evidence, respectively.

RESULTS

In total, 20 randomized controlled trials involving 1059 patients were eligible for analysis. Virtual reality-based training showed significant improvements in pain (mean difference [MD] -1.43; 95% CI -1.86 to -1.00; I2=95%; P<.001), pain-related fear using the Tampa Scale of Kinesiophobia (MD -5.46; 95% CI -9.40 to 1.52; I2=90%; P=.007), and disability using the Oswestry Disability Index (MD -11.50; 95% CI -20.00 to -3.01; I2=95%; P=.008) in individuals with chronic low back pain immediately after interventions. However, there were no significant differences observed in pain (P=.16), pain-related fear (P=.10), and disability (P=.43) in the short term.

CONCLUSIONS

These findings indicated that virtual reality-based training can be used effectively for individuals with chronic low back pain in the immediate term, especially to reduce pain, alleviate pain-related fear, and improve disability. However, the short-term benefits need more high-quality trials to be demonstrated.

TRIAL REGISTRATION

PROSPERO CRD42021292633; http://tinyurl.com/25mydxpz.

The evolution of augmented reality to augment physical therapy: A scoping review

Augmented reality is increasingly used in health care, yet little is known about how AR is being used in physical therapy practice and what clinical outcomes could occur with technology use. In this scoping review, a broad literature review was conducted to gain an understanding of current knowledge of AR use and outcomes in physical therapy practice. A structured literature search of articles published between 2000 to September 2023 that examined the use of AR in a physical therapy context was conducted. Reference lists of articles for full review were searched for additional studies. Data from articles meeting inclusion criteria were extracted and synthesized across studies. 549 articles were identified; 40 articles met criteria for full review. Gait and balance of neurological and older adult populations were most frequently targeted, with more recent studies including orthopedic and other populations. Approximately half were pilot or observational studies and half are experimental. Many studies found within group improvements. Of studies reporting between group differences, AR interventions were more effective in improving function almost half of the time, with 20%, 27% and 28% showing efficacy in disability, balance, and gait outcomes. AR in physical therapy holds promise; however, efficacy outcomes are unclear.

Factors Influencing the Acceptability, Acceptance, and Adoption of Conversational Agents in Health Care: Integrative Review

BACKGROUND

Conversational agents (CAs), also known as chatbots, are digital dialog systems that enable people to have a text-based, speech-based, or nonverbal conversation with a computer or another machine based on natural language via an interface. The use of CAs offers new opportunities and various benefits for health care. However, they are not yet ubiquitous in daily practice. Nevertheless, research regarding the implementation of CAs in health care has grown tremendously in recent years.

OBJECTIVE

This review aims to present a synthesis of the factors that facilitate or hinder the implementation of CAs from the perspectives of patients and health care professionals. Specifically, it focuses on the early implementation outcomes of acceptability, acceptance, and adoption as cornerstones of later implementation success.

METHODS

We performed an integrative review. To identify relevant literature, a broad literature search was conducted in June 2021 with no date limits and using all fields in PubMed, Cochrane Library, Web of Science, LIVIVO, and PsycINFO. To keep the review current, another search was conducted in March 2022. To identify as many eligible primary sources as possible, we used a snowballing approach by searching reference lists and conducted a hand search. Factors influencing the acceptability, acceptance, and adoption of CAs in health care were coded through parallel deductive and inductive approaches, which were informed by current technology acceptance and adoption models. Finally, the factors were synthesized in a thematic map.

RESULTS

Overall, 76 studies were included in this review. We identified influencing factors related to 4 core Unified Theory of Acceptance and Use of Technology (UTAUT) and Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) factors (performance expectancy, effort expectancy, facilitating conditions, and hedonic motivation), with most studies underlining the relevance of performance and effort expectancy. To meet the particularities of the health care context, we redefined the UTAUT2 factors social influence, habit, and price value. We identified 6 other influencing factors: perceived risk, trust, anthropomorphism, health issue, working alliance, and user characteristics. Overall, we identified 10 factors influencing acceptability, acceptance, and adoption among health care professionals (performance expectancy, effort expectancy, facilitating conditions, social influence, price value, perceived risk, trust, anthropomorphism, working alliance, and user characteristics) and 13 factors influencing acceptability, acceptance, and adoption among patients (additionally hedonic motivation, habit, and health issue).

CONCLUSIONS

This review shows manifold factors influencing the acceptability, acceptance, and adoption of CAs in health care. Knowledge of these factors is fundamental for implementation planning. Therefore, the findings of this review can serve as a basis for future studies to develop appropriate implementation strategies. Furthermore, this review provides an empirical test of current technology acceptance and adoption models and identifies areas where additional research is necessary.

TRIAL REGISTRATION

PROSPERO CRD42022343690; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=343690.

Development of "LvL UP 1.0": a smartphone-based, conversational agent-delivered holistic lifestyle intervention for the prevention of non-communicable diseases and common mental disorders

Background

Non-communicable diseases (NCDs) and common mental disorders (CMDs) are the leading causes of death and disability worldwide. Lifestyle interventions via mobile apps and conversational agents present themselves as low-cost, scalable solutions to prevent these conditions. This paper describes the rationale for, and development of, "LvL UP 1.0″, a smartphone-based lifestyle intervention aimed at preventing NCDs and CMDs.

Materials and Methods

A multidisciplinary team led the intervention design process of LvL UP 1.0, involving four phases: (i) preliminary research (stakeholder consultations, systematic market reviews), (ii) selecting intervention components and developing the conceptual model, (iii) whiteboarding and prototype design, and (iv) testing and refinement. The Multiphase Optimization Strategy and the UK Medical Research Council framework for developing and evaluating complex interventions were used to guide the intervention development.

Results

Preliminary research highlighted the importance of targeting holistic wellbeing (i.e., both physical and mental health). Accordingly, the first version of LvL UP features a scalable, smartphone-based, and conversational agent-delivered holistic lifestyle intervention built around three pillars: Move More (physical activity), Eat Well (nutrition and healthy eating), and Stress Less (emotional regulation and wellbeing). Intervention components include health literacy and psychoeducational coaching sessions, daily "Life Hacks" (healthy activity suggestions), breathing exercises, and journaling. In addition to the intervention components, formative research also stressed the need to introduce engagement-specific components to maximise uptake and long-term use. LvL UP includes a motivational interviewing and storytelling approach to deliver the coaching sessions, as well as progress feedback and gamification. Offline materials are also offered to allow users access to essential intervention content without needing a mobile device.

Conclusions

The development process of LvL UP 1.0 led to an evidence-based and user-informed smartphone-based intervention aimed at preventing NCDs and CMDs. LvL UP is designed to be a scalable, engaging, prevention-oriented, holistic intervention for adults at risk of NCDs and CMDs. A feasibility study, and subsequent optimisation and randomised-controlled trials are planned to further refine the intervention and establish effectiveness. The development process described here may prove helpful to other intervention developers.

Mixed Reality Platforms in Telehealth Delivery: Scoping Review

BACKGROUND

The distinctive features of the digital reality platforms, namely augmented reality (AR), virtual reality (VR), and mixed reality (MR) have extended to medical education, training, simulation, and patient care. Furthermore, this digital reality technology seamlessly merges with information and communication technology creating an enriched telehealth ecosystem. This review provides a composite overview of the prospects of telehealth delivered using the MR platform in clinical settings.

OBJECTIVE

This review identifies various clinical applications of high-fidelity digital display technology, namely AR, VR, and MR, delivered using telehealth capabilities. Next, the review focuses on the technical characteristics, hardware, and software technologies used in the composition of AR, VR, and MR in telehealth.

METHODS

We conducted a scoping review using the methodological framework and reporting design using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Full-length articles in English were obtained from the Embase, PubMed, and Web of Science databases. The search protocol was based on the following keywords and Medical Subject Headings to obtain relevant results: "augmented reality," "virtual reality," "mixed-reality," "telemedicine," "telehealth," and "digital health." A predefined inclusion-exclusion criterion was developed in filtering the obtained results and the final selection of the articles, followed by data extraction and construction of the review.

RESULTS

We identified 4407 articles, of which 320 were eligible for full-text screening. A total of 134 full-text articles were included in the review. Telerehabilitation, telementoring, teleconsultation, telemonitoring, telepsychiatry, telesurgery, and telediagnosis were the segments of the telehealth division that explored the use of AR, VR, and MR platforms. Telerehabilitation using VR was the most commonly recurring segment in the included studies. AR and MR has been mainly used for telementoring and teleconsultation. The most important technical features of digital reality technology to emerge with telehealth were virtual environment, exergaming, 3D avatars, telepresence, anchoring annotations, and first-person viewpoint. Different arrangements of technology-3D modeling and viewing tools, communication and streaming platforms, file transfer and sharing platforms, sensors, high-fidelity displays, and controllers-formed the basis of most systems.

CONCLUSIONS

This review constitutes a recent overview of the evolving digital AR and VR in various clinical applications using the telehealth setup. This combination of telehealth with AR, VR, and MR allows for remote facilitation of clinical expertise and further development of home-based treatment. This review explores the rapidly growing suite of technologies available to users within the digital health sector and examines the opportunities and challenges they present.

Factors Influencing Adherence to mHealth Apps for Prevention or Management of Noncommunicable Diseases: Systematic Review

Background

Mobile health (mHealth) apps show vast potential in supporting patients and health care systems with the increasing prevalence and economic costs of noncommunicable diseases (NCDs) worldwide. However, despite the availability of evidence-based mHealth apps, a substantial proportion of users do not adhere to them as intended and may consequently not receive treatment. Therefore, understanding the factors that act as barriers to or facilitators of adherence is a fundamental concern in preventing intervention dropouts and increasing the effectiveness of digital health interventions.

Objective

This review aimed to help stakeholders develop more effective digital health interventions by identifying factors influencing the continued use of mHealth apps targeting NCDs. We further derived quantified adherence scores for various health domains to validate the qualitative findings and explore adherence benchmarks.

Methods

A comprehensive systematic literature search (January 2007 to December 2020) was conducted on MEDLINE, Embase, Web of Science, Scopus, and ACM Digital Library. Data on intended use, actual use, and factors influencing adherence were extracted. Intervention-related and patient-related factors with a positive or negative influence on adherence are presented separately for the health domains of NCD self-management, mental health, substance use, nutrition, physical activity, weight loss, multicomponent lifestyle interventions, mindfulness, and other NCDs. Quantified adherence measures, calculated as the ratio between the estimated intended use and actual use, were derived for each study and compared with the qualitative findings.

Results

The literature search yielded 2862 potentially relevant articles, of which 99 (3.46%) were included as part of the inclusion criteria. A total of 4 intervention-related factors indicated positive effects on adherence across all health domains: personalization or tailoring of the content of mHealth apps to the individual needs of the user, reminders in the form of individualized push notifications, user-friendly and technically stable app design, and personal support complementary to the digital intervention. Social and gamification features were also identified as drivers of app adherence across several health domains. A wide variety of patient-related factors such as user characteristics or recruitment channels further affects adherence. The derived adherence scores of the included mHealth apps averaged 56.0% (SD 24.4%).

Conclusions

This study contributes to the scarce scientific evidence on factors that positively or negatively influence adherence to mHealth apps and is the first to quantitatively compare adherence relative to the intended use of various health domains. As underlying studies mostly have a pilot character with short study durations, research on factors influencing adherence to mHealth apps is still limited. To facilitate future research on mHealth app adherence, researchers should clearly outline and justify the app’s intended use; report objective data on actual use relative to the intended use; and, ideally, provide long-term use and retention data.

The Use of Artificial Intelligence-Based Conversational Agents (Chatbots) for Weight Loss: Scoping Review and Practical Recommendations

Background

Overweight and obesity have now reached a state of a pandemic despite the clinical and commercial programs available. Artificial intelligence (AI) chatbots have a strong potential in optimizing such programs for weight loss.

Objective

This study aimed to review AI chatbot use cases for weight loss and to identify the essential components for prolonging user engagement.

Methods

A scoping review was conducted using the 5-stage framework by Arksey and O’Malley. Articles were searched across nine electronic databases (ACM Digital Library, CINAHL, Cochrane Central, Embase, IEEE Xplore, PsycINFO, PubMed, Scopus, and Web of Science) until July 9, 2021. Gray literature, reference lists, and Google Scholar were also searched.

Results

A total of 23 studies with 2231 participants were included and evaluated in this review. Most studies (8/23, 35%) focused on using AI chatbots to promote both a healthy diet and exercise, 13% (3/23) of the studies used AI chatbots solely for lifestyle data collection and obesity risk assessment whereas only 4% (1/23) of the studies focused on promoting a combination of a healthy diet, exercise, and stress management. In total, 48% (11/23) of the studies used only text-based AI chatbots, 52% (12/23) operationalized AI chatbots through smartphones, and 39% (9/23) integrated data collected through fitness wearables or Internet of Things appliances. The core functions of AI chatbots were to provide personalized recommendations (20/23, 87%), motivational messages (18/23, 78%), gamification (6/23, 26%), and emotional support (6/23, 26%). Study participants who experienced speech- and augmented reality–based chatbot interactions in addition to text-based chatbot interactions reported higher user engagement because of the convenience of hands-free interactions. Enabling conversations through multiple platforms (eg, SMS text messaging, Slack, Telegram, Signal, WhatsApp, or Facebook Messenger) and devices (eg, laptops, Google Home, and Amazon Alexa) was reported to increase user engagement. The human semblance of chatbots through verbal and nonverbal cues improved user engagement through interactivity and empathy. Other techniques used in text-based chatbots included personally and culturally appropriate colloquial tones and content; emojis that emulate human emotional expressions; positively framed words; citations of credible information sources; personification; validation; and the provision of real-time, fast, and reliable recommendations. Prevailing issues included privacy; accountability; user burden; and interoperability with other databases, third-party applications, social media platforms, devices, and appliances.

Conclusions

AI chatbots should be designed to be human-like, personalized, contextualized, immersive, and enjoyable to enhance user experience, engagement, behavior change, and weight loss. These require the integration of health metrics (eg, based on self-reports and wearable trackers), personality and preferences (eg, based on goal achievements), circumstantial behaviors (eg, trigger-based overconsumption), and emotional states (eg, chatbot conversations and wearable stress detectors) to deliver personalized and effective recommendations for weight loss.

A Taxonomy for Augmented and Mixed Reality Applications to Support Physical Exercises in Medical Rehabilitation—A Literature Review

In the past 20 years, a vast amount of research has shown that Augmented and Mixed Reality applications can support physical exercises in medical rehabilitation. In this paper, we contribute a taxonomy, providing an overview of the current state of research in this area. It is based on a comprehensive literature review conducted on the five databases Web of Science, ScienceDirect, PubMed, IEEE Xplore, and ACM up to July 2021. Out of 776 identified references, a final selection was made of 91 papers discussing the usage of visual stimuli delivered by AR/MR or similar technology to enhance the performance of physical exercises in medical rehabilitation. The taxonomy bridges the gap between a medical perspective (Patient Type, Medical Purpose) and the Interaction Design, focusing on Output Technologies and Visual Guidance. Most approaches aim to improve autonomy in the absence of a therapist and increase motivation to improve adherence. Technology is still focused on screen-based approaches, while the deeper analysis of Visual Guidance revealed 13 distinct, reoccurring abstract types of elements. Based on the analysis, implications and research opportunities are presented to guide future work.

The Use of Virtual Reality in Back Pain Rehabilitation: A Systematic Review and Meta-Analysis

This systematic review aimed to synthesize the existing evidence of extended reality (XR) on pain and motor function outcomes in patients with back pain. Following the Cochrane guidelines, relevant articles of any language were selected by 2 independent reviewers from CINAHL, Cochrane, Embase, Medline and Web of Knowledge databases. Of 2,050 unique citations, 24 articles were included in our review. These studies included a total of 900 back pain patients. Despite broader XR search, all interventions were virtual reality (VR) based and involved physical exercises (n = 17, 71%), hippotherapy (n = 4, 17%), motor imagery (n = 1, 4%), distraction (n = 1, 4%), and cognitive-behavior therapy (n = 1, 4%). Sixteen controlled studies were included in a meta-analysis which suggested that VR provides a significant improvement in terms of back pain intensity over control interventions (Mean Difference: -0.67; 95% CI: -1.12 to -0.23; I² = 85%). Almost all included studies presented high risk of bias, highlighting the need to improve methodology in the examination of VR interventions. While the specific set of studies showed high heterogeneity across several methodological factors, a tentative conclusion could be drawn that VR was effective improving back pain intensity and tends to have a positive effect on improving other pain outcomes and motion function. PERSPECTIVE: Extended reality technologies have appeared as interesting nonpharmacological options for the treatment of back pain, with the potential to minimise the need for opioid medications. Our systematic review summarised existing applications of extended reality for back pain and proposed a few recommendations to direct further studies in the field.

Neurorehabilitation From a Distance: Can Intelligent Technology Support Decentralized Access to Quality Therapy?

Current neurorehabilitation models primarily rely on extended hospital stays and regular therapy sessions requiring close physical interactions between rehabilitation professionals and patients. The current COVID-19 pandemic has challenged this model, as strict physical distancing rules and a shift in the allocation of hospital resources resulted in many neurological patients not receiving essential therapy. Accordingly, a recent survey revealed that the majority of European healthcare professionals involved in stroke care are concerned that this lack of care will have a noticeable negative impact on functional outcomes. COVID-19 highlights an urgent need to rethink conventional neurorehabilitation and develop alternative approaches to provide high-quality therapy while minimizing hospital stays and visits. Technology-based solutions, such as, robotics bear high potential to enable such a paradigm shift. While robot-assisted therapy is already established in clinics, the future challenge is to enable physically assisted therapy and assessments in a minimally supervized and decentralized manner, ideally at the patient’s home. Key enablers are new rehabilitation devices that are portable, scalable and equipped with clinical intelligence, remote monitoring and coaching capabilities. In this perspective article, we discuss clinical and technological requirements for the development and deployment of minimally supervized, robot-assisted neurorehabilitation technologies in patient’s homes. We elaborate on key principles to ensure feasibility and acceptance, and on how artificial intelligence can be leveraged for embedding clinical knowledge for safe use and personalized therapy adaptation. Such new models are likely to impact neurorehabilitation beyond COVID-19, by providing broad access to sustained, high-quality and high-dose therapy maximizing long-term functional outcomes.