Physical Therapists' Acceptance of a Wearable, Fabric-Based Sensor System (Motion Tape) for Use in Clinical Practice: Qualitative Focus Group Study.

BACKGROUND: Low back pain (LBP) is a costly global health condition that affects individuals of all ages and genders. Physical therapy (PT) is a commonly used and effective intervention for the management of LBP and incorporates movement assessment and therapeutic exercise. A newly developed wearable, fabric-based sensor system, Motion Tape, uses novel sensing and data modeling to measure lumbar spine movements unobtrusively and thus offers potential benefits when used in conjunction with PT. However, physical therapists' acceptance of Motion Tape remains unexplored. OBJECTIVE: The primary aim of this research study was to evaluate physical therapists' acceptance of Motion Tape to be used for the management of LBP. The secondary aim was to explore physical therapists' recommendations for future device development. METHODS: Licensed physical therapists from the American Physical Therapy Association Academy of Leadership Technology Special Interest Group participated in this study. Overall, 2 focus groups (FGs; N=8) were conducted, in which participants were presented with Motion Tape samples and examples of app data output on a poster. Informed by the Technology Acceptance Model, we conducted semistructured FGs and explored the wearability, usefulness, and ease of use of and suggestions for improvements in Motion Tape for PT management of LBP. FG data were transcribed and analyzed using rapid qualitative analysis. RESULTS: Regarding wearability, participants perceived that Motion Tape would be able to adhere for several days, with some variability owing to external factors. Feedback was positive for the low-profile and universal fit, but discomfort owing to wires and potential friction with clothing was of concern. Other concerns included difficulty with self-application and potential skin sensitivity. Regarding usefulness, participants expressed that Motion Tape would enhance the efficiency and specificity of assessments and treatment. Regarding ease of use, participants stated that the app would be easy, but data management and challenges with interpretation were of concern. Physical therapists provided several recommendations for future design improvements including having a wireless system or removable wires, customizable sizes for the tape, and output including range of motion data and summary graphs and adding app features that consider patient input and context. CONCLUSIONS: Several themes related to Motion Tape's wearability, usefulness, and ease of use were identified. Overall, physical therapists expressed acceptance of Motion Tape's potential for assessing and monitoring low back posture and movement, both within and outside clinical settings. Participants expressed that Motion Tape would be a valuable tool for the personalized treatment of LBP but highlighted several future improvements needed for Motion Tape to be used in practice.

Voice-Enabled Response Analysis Agent (VERAA): Leveraging Large Language Models to Map Voice Responses in SDoH Survey.

Social Determinants of Health (SDoH) have been shown to have profound impacts on health-related outcomes, yet this data suffers from high rates of missingness in electronic health records (EHR). Moreover, limited English proficiency in the United States can be a barrier to communication with health care providers. In this study, we have designed a multilingual conversational agent capable of conducting SDoH surveys for use in healthcare environments. The agent asks questions in the patient's native language, translates responses into English, and subsequently maps these responses via a large language model (LLM) to structured options in a SDoH survey. This tool can be extended to a variety of survey instruments in either hospital or home settings, enabling the extraction of structured insights from free-text answers. The proposed approach heralds a shift towards more inclusive and insightful data collection, marking a significant stride in SDoH data enrichment for optimizing health outcome predictions and interventions.

Preliminary Validity and Acceptability of Motion Tape for Measuring Low Back Movement: Mixed Methods Study.

BACKGROUND: Low back pain (LBP) is a significant public health problem that can result in physical disability and financial burden for the individual and society. Physical therapy is effective for managing LBP and includes evaluation of posture and movement, interventions directed at modifying posture and movement, and prescription of exercises. However, physical therapists have limited tools for objective evaluation of low back posture and movement and monitoring of exercises, and this evaluation is limited to the time frame of a clinical encounter. There is a need for a valid tool that can be used to evaluate low back posture and movement and monitor exercises outside the clinic. To address this need, a fabric-based, wearable sensor, Motion Tape (MT), was developed and adapted for a low back use case. MT is a low-profile, disposable, self-adhesive, skin-strain sensor developed by spray coating piezoresistive graphene nanocomposites directly onto commercial kinesiology tape. OBJECTIVE: The objectives of this study were to (1) validate MT for measuring low back posture and movement and (2) assess the acceptability of MT for users. METHODS: A total of 10 participants without LBP were tested. A 3D optical motion capture system was used as a reference standard to measure low back kinematics. Retroreflective markers and a matrix of MTs were placed on the low back to measure kinematics (motion capture) and strain (MT) simultaneously during low back movements in the sagittal, frontal, and axial planes. Cross-correlation coefficients were calculated to evaluate the concurrent validity of MT strain in reference motion capture kinematics during each movement. The acceptability of MT was assessed using semistructured interviews conducted with each participant after laboratory testing. Interview data were analyzed using rapid qualitative analysis to identify themes and subthemes of user acceptability. RESULTS: Visual inspection of concurrent MT strain and kinematics of the low back indicated that MT can distinguish between different movement directions. Cross-correlation coefficients between MT strain and motion capture kinematics ranged from -0.915 to 0.983, and the strength of the correlations varied across MT placements and low back movement directions. Regarding user acceptability, participants expressed enthusiasm toward MT and believed that it would be helpful for remote interventions for LBP but provided suggestions for improvement. CONCLUSIONS: MT was able to distinguish between different low back movements, and most MTs demonstrated moderate to high correlation with motion capture kinematics. This preliminary laboratory validation of MT provides a basis for future device improvements, which will also involve testing in a free-living environment. Overall, users found MT acceptable for use in physical therapy for managing LBP.

Ascertaining Whether an Intelligent Voice Assistant Can Meet Older Adults' Health-Related Needs in the Context of a Geriatrics 5Ms Framework.

The Geriatrics 5Ms: Medications, Mind, Mobility, what Matters most and Multicomplexity is a framework to address the complex needs of older adults. Intelligent Voice Assistants (IVAs) are increasingly popular and have potential to support health-related needs of older adults. We utilized previously collected qualitative data on older adults' views of how an IVA may address their health-related needs and ascertained their fit into the Geriatrics 5Ms framework. The codes describing health challenges and potential IVA solutions fit the framework: (1) Medications: difficulty remembering medications. SOLUTION: reminders. (2) Mind: isolation, anxiety, memory loss. SOLUTION: companionship, memory aids. (3) Mobility: barriers to exercise. SOLUTION: incentives, exercise ideas. (4) Matters most: eating healthy foods. SOLUTION: suggest and order nutritious foods, (5) Multicomplexity; managing multimorbidity. SOLUTION: symptom tracking and communicating with health care professionals. Incorporating the 5Ms framework into IVA design can aid in addressing health care priorities of older adults.

Voice-Enabled Response Analysis Agent (VERAA): Leveraging Large Language Models to Map Voice Responses in SDoH Survey.

Social Determinants of Health (SDoH) have been shown to have profound impacts on health-related outcomes, yet this data suffers from high rates of missingness in electronic health records (EHR). Moreover, limited English proficiency in the United States can be a barrier to communication with health care providers. In this study, we have designed a multilingual conversational agent capable of conducting SDoH surveys for use in healthcare environments. The agent asks questions in the patient's native language, translates responses into English, and subsequently maps these responses via a large language model (LLM) to structured options in a SDoH survey. This tool can be extended to a variety of survey instruments in either hospital or home settings, enabling the extraction of structured insights from free-text answers. The proposed approach heralds a shift towards more inclusive and insightful data collection, marking a significant stride in SDoH data enrichment for optimizing health outcome predictions and interventions.

How do Older Adults Set Up Voice Assistants? Lessons Learned from a Deployment Experience for Older Adults to Set Up Standalone Voice Assistants.

While standalone Voice Assistants (VAs) are promising to support older adults' daily routine and wellbeing management, onboarding and setting up these devices can be challenging. Although some older adults choose to seek assistance from technicians and adult children, easy set up processes that facilitate independent use are still critical, especially for those who do not have access to external resources. We aim to understand the older adults' experience while setting up commercially available voice-only and voice-first screen-based VAs. Rooted in participants observations and semi-structured interviews, we designed a within-subject study with 10 older adults using Amazon Echo Dot and Echo Show. We identified the values of the built-in touchscreen and the instruction documents, as well as the impact of form factors, and outline important directions to support older adult independence with VAs.

Screen or No Screen? Lessons Learnt from a Real-World Deployment Study of Using Voice Assistants With and Without Touchscreen for Older Adults.

While voice user interfaces offer increased accessibility due to hands-free and eyes-free interactions, older adults often have challenges such as constructing structured requests and perceiving how such devices operate. Voice-first user interfaces have the potential to address these challenges by enabling multimodal interactions. Standalone voice + touchscreen Voice Assistants (VAs), such as Echo Show, are specific types of devices that adopt such interfaces and are gaining popularity. However, the affordances of the additional touchscreen for older adults are unknown. Through a 40-day real-world deployment with older adults living independently, we present a within-subjects study (N = 16; age M = 82.5, SD = 7.77, min. = 70, max. = 97) to understand how a built-in touchscreen might benefit older adults during device setup, conducting self-report diary survey, and general uses. We found that while participants appreciated the visual outputs, they still preferred to respond via speech instead of touch. We identified six design implications that can inform future innovations of senior-friendly VAs for managing healthcare and improving quality of life.

Process evaluation of Dulce Digital-Me: an adaptive mobile health (mHealth) intervention for underserved Hispanics with diabetes.

Type 2 diabetes disproportionately impacts ethnic minorities and individuals from low socioeconomic status. Diabetes self-management education and support has been shown to improve clinical outcomes in these populations, and mobile health (mHealth) interventions can reduce barriers to access. Dulce Digital-Me (DD-Me) was developed to integrate adaptive mHealth technologies to enhance self-management and reduce disparities in the high-risk, underserved Hispanic population. The objective of the present study was to evaluate reach, adoption, and implementation of an mHealth diabetes self-management education and support intervention in this underrepresented population. The present analysis is a multimethod process evaluation using the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. The study was effective in reaching a sample that was representative of the intended population; only modest but significant differences were observed in sex and age. The DD-Me health coach (HC) cited several important facilitators of intervention adoption, including outreach frequency and personalization, and the automated HC report. Implementation fidelity was high, with participants receiving >90% of intended interventions. Participants who received DD-Me with support from a HC were most engaged, suggesting utility and acceptability of integrating HCs with mHealth interventions. Perceptions of implementation among study participants were positive and consistent across study arms. This evaluation revealed the target population was successfully reached and engaged in the digital health interventions, which was implemented with high fidelity. Further studies should evaluate the efficacy and maintenance of the study following the RE-AIM model to determine whether this intervention warrants expansion to additional settings and populations.

Type 2 diabetes disproportionately impacts ethnic minorities, including Hispanic individuals; however, these populations are often underrepresented in clinical research, especially in studies using digital technologies. The Dulce Digital-Me study was developed to provide diabetes self-management education and support using mobile health technologies with the goal of improving clinical outcomes by reducing barriers to accessing support. This analysis revealed that the Dulce Digital-Me study was successful at reaching the target population and engaging them with the intervention, while also delivering the study intervention with high fidelity. This process evaluation provides critical context for understanding the study's clinical outcomes and the potential for further dissemination.

Towards Visualization of Time-Series Ecological Momentary Assessment (EMA) Data on Standalone Voice-First Virtual Assistants.

Population aging is an increasingly important consideration for health care in the 21th century, and continuing to have access and interact with digital health information is a key challenge for aging populations. Voice-based Intelligent Virtual Assistants (IVAs) are promising to improve the Quality of Life (QoL) of older adults, and coupled with Ecological Momentary Assessments (EMA) they can be effective to collect important health information from older adults, especially when it comes to repeated time-based events. However, this same EMA data is hard to access for the older adult: although the newest IVAs are equipped with a display, the effectiveness of visualizing time-series based EMA data on standalone IVAs has not been explored. To investigate the potential opportunities for visualizing time-series based EMA data on standalone IVAs, we designed a prototype system, where older adults are able to query and examine the time-series EMA data on Amazon Echo Show - a widely used commercially available standalone screen-based IVA. We conducted a preliminary semi-structured interview with a geriatrician and an older adult, and identified three findings that should be carefully considered when designing such visualizations.

Dulce Digital-Me: protocol for a randomized controlled trial of an adaptive mHealth intervention for underserved Hispanics with diabetes.

BACKGROUND: By 2034, the number of US individuals with diabetes is predicted to increase from 23.7 to 44.1 million, and annual diabetes-related spending is expected to grow from $113 to $336 billion. Up to 55% of US Hispanics born in the year 2000 are expected to develop diabetes during their lifetime. Poor healthcare access and cultural barriers prevent optimal care, adherence, and clinical benefit, placing Hispanics at disproportionate risk for costly diabetes complications. Mobile technology is increasingly prevalent in all populations and can circumvent such barriers. Our group developed Dulce Digital, an educational text messaging program that improved glycemic control relative to usual care. Dulce Digital-Me (DD-Me) has been tailored to a participant's individual needs with a greater focus on health behavior change. METHODS: This is a three-arm, parallel group, randomized trial with equal allocation ratio enrolling Hispanic adults with low income and poorly managed type 2 diabetes (N = 414) from a San Diego County Federally Qualified Health Center. Participants are randomized to receive Dulce Digital, Dulce Digital-Me-Automated, or Dulce Digital-Me-Telephonic. The DD-Me groups include Dulce Digital components plus personalized goal-setting and feedback delivered via algorithm-driven automated text messaging (DD-Me-Automated) or by the care team health coach (DD-Me-Telephonic) over a 12-month follow-up period. The study will examine the comparative effectiveness of the three groups in improving diabetes clinical control [HbA1c, primary outcome; low-density lipoprotein cholesterol (LDL-C), and systolic blood pressure (SBP)] and patient-provider communication and patient adherence (i.e., medication, self-management tasks) over 12 months and will examine cost-effectiveness of the three interventions. DISCUSSION: Our comparative evaluation of three mHealth approaches will elucidate how technology can be integrated most effectively and efficiently within primary care-based chronic care model approaches to reduce diabetes disparities in Hispanics and will assess two modes of personalized messaging delivery (i.e., automated messaging vs. telephonic by health coach) to inform cost and acceptability. TRIAL REGISTRATION: NCT03130699-All items from the WHO Trial Registration data set are available in https://clinicaltrials.gov/ct2/show/study/NCT03130699 .

Toward a Unified Metadata Schema for Ecological Momentary Assessment with Voice-First Virtual Assistants.

Ecological momentary assessment (EMA) is used to evaluate subjects' behaviors and moods in their natural environments, yet collecting real-time and self-report data with EMA is challenging due to user burden. Integrating voice into EMA data collection platforms through today's intelligent virtual assistants (IVAs) is promising due to hands-free and eye-free nature. However, efficiently managing conversations and EMAs is non-trivial and time consuming due to the ambiguity of the voice input. We approach this problem by rethinking the data modeling of EMA questions and what is needed to deploy them on voice-first user interfaces. We propose a unified metadata schema that models EMA questions and the necessary attributes to effectively and efficiently integrate voice as a new EMA modality. Our schema allows user experience researchers to write simple rules that can be rendered at run-time, instead of having to edit the source code. We showcase an example EMA survey implemented with our schema, which can run on multiple voice-only and voice-first devices. We believe that our work will accelerate the iterative prototyping and design process of real-world voice-based EMA data collection platforms.

Understanding Barriers and Design Opportunities to Improve Healthcare and QOL for Older Adults through Voice Assistants.

Voice-based Intelligent Virtual Assistants (IVAs) promise to improve healthcare management and Quality of Life (QOL) by introducing the paradigm of hands-free and eye-free interactions. However, there has been little understanding regarding the challenges for designing such systems for older adults, especially when it comes to healthcare related tasks. To tackle this, we consider the processes of care delivery and QOL enhancements for older adults as a collaborative task between patients and providers. By interviewing 16 older adults living independently or semi-independently and 5 providers, we identified 12 barriers that older adults might encounter during daily routine and while managing health. We ultimately highlighted key design challenges and opportunities that might be introduced when integrating voice-based IVAs into the life of older adults. Our work will benefit practitioners who study and attempt to create full-fledged IVA-powered smart devices to deliver better care and support an increased QOL for aging populations.

Feasibility of Mobile and Sensor Technology for Remote Monitoring in Cancer Care and Prevention.

Objectives. Remote monitoring (RM) of health-related outcomes may optimize cancer care and prevention outside of clinic settings. CYCORE is a software-based system for collection and analyses of sensor and mobile data. We evaluated CYCORE's feasibility in studies assessing: (1) physical functioning in colorectal cancer (CRC) patients; (2) swallowing exercise adherence in head and neck cancer (HNC) patients during radiation therapy; and (3) tobacco use in cancer survivors post-tobacco treatment (TTP). Methods. Participants completed RM: for CRC, blood pressure, activity, GPS; for HNC, video of swallowing exercises; for TTP, expired carbon monoxide. Patient-reported outcomes were assessed daily. Results. For CRC, HNC and TTP, respectively, 50, 37, and 50 participants achieved 96%, 84%, 96% completion rates. Also, 91-100% rated ease and self-efficacy as highly favorable, 72-100% gave equivalent ratings for overall satisfaction, 72-93% had low/no data privacy concerns. Conclusion. RM was highly feasible and acceptable for patients across diverse use cases.

Identifying early dehydration risk with home-based sensors during radiation treatment: a feasibility study on patients with head and neck cancer.

BACKGROUND: Systems that enable remote monitoring of patients' symptoms and other health-related outcomes may optimize cancer care outside of the clinic setting. CYCORE (CYberinfrastructure for COmparative effectiveness REsearch) is a software-based prototype for a user-friendly cyberinfrastructure supporting the comprehensive collection and analyses of data from multiple domains using a suite of home-based and mobile sensors. This study evaluated the feasibility of using CYCORE to address early at-home identification of dehydration risk in head and neck cancer patients undergoing radiation therapy. METHODS: Head and neck cancer patients used home-based sensors to capture weight, blood pressure, pulse, and patient-reported outcomes for two 5-day periods during radiation therapy. Data were sent to the radiation oncologist of each head and neck cancer patient, who viewed them online via a Web-based interface. Feasibility outcomes included study completion rate, acceptability and perceived usefulness of the intervention, and adherence to the monitoring protocol. We also evaluated whether sensor data could identify dehydration-related events. RESULTS: Fifty patients consented to participate, and 48 (96%) completed the study. More than 90% of patients rated their ease, self-efficacy, and satisfaction regarding use of the sensor suite as extremely favorable, with minimal concerns expressed regarding data privacy issues. Patients highly valued the ability to have immediate access to objective, self-monitoring data related to personal risk for dehydration. Clinician assessments indicated a high degree of satisfaction with the ease of using the CYCORE system and the resulting ability to monitor their patients remotely. CONCLUSION: Implementing CYCORE in a clinical oncology care setting is feasible and highly acceptable to both patients and providers.

CYberinfrastructure for COmparative effectiveness REsearch (CYCORE): improving data from cancer clinical trials.

Improved approaches and methodologies are needed to conduct comparative effectiveness research (CER) in oncology. While cancer therapies continue to emerge at a rapid pace, the review, synthesis, and dissemination of evidence-based interventions across clinical trials lag in comparison. Rigorous and systematic testing of competing therapies has been clouded by age-old problems: poor patient adherence, inability to objectively measure the environmental influences on health, lack of knowledge about patients' lifestyle behaviors that may affect cancer's progression and recurrence, and limited ability to compile and interpret the wide range of variables that must be considered in the cancer treatment. This lack of data integration limits the potential for patients and clinicians to engage in fully informed decision-making regarding cancer prevention, treatment, and survivorship care, and the translation of research results into mainstream medical care. Particularly important, as noted in a 2009 report on CER to the President and Congress, the limited focus on health behavior-change interventions was a major hindrance in this research landscape (DHHS 2009). This paper describes an initiative to improve CER for cancer by addressing several of these limitations. The Cyberinfrastructure for Comparative Effectiveness Research (CYCORE) project, informed by the National Science Foundation's 2007 report "Cyberinfrastructure Vision for 21(st) Century Discovery" has, as its central aim, the creation of a prototype for a user-friendly, open-source cyberinfrastructure (CI) that supports acquisition, storage, visualization, analysis, and sharing of data important for cancer-related CER. Although still under development, the process of gathering requirements for CYCORE has revealed new ways in which CI design can significantly improve the collection and analysis of a wide variety of data types, and has resulted in new and important partnerships among cancer researchers engaged in advancing health-related CI.