Generative AI in orthopedics: an explainable deep few-shot image augmentation pipeline for plain knee radiographs and Kellgren-Lawrence grading.

OBJECTIVES: Recently, deep learning medical image analysis in orthopedics has become highly active. However, progress has been restricted by the absence of large-scale and standardized ground-truth images. To the best of our knowledge, this study is the first to propose an innovative solution, namely a deep few-shot image augmentation pipeline, that addresses this challenge by synthetically generating knee radiographs for training downstream tasks, with a specific focus on knee osteoarthritis Kellgren-Lawrence (KL) grading. MATERIALS AND METHODS: This study leverages a deep few-shot image augmentation pipeline to generate synthetic knee radiographs. Despite the limited availability of training samples, we demonstrate the capability of our proposed computational strategy to produce high-fidelity plain knee radiographs and use them to successfully train a KL grade classifier. RESULTS: Our experimental results showcase the effectiveness of the proposed computational pipeline. The generated synthetic radiographs exhibit remarkable fidelity, evidenced by the achieved average Frechet Inception Distance (FID) score of 26.33 for KL grading and 22.538 for bilateral knee radiographs. For KL grading classification, the classifier achieved a test Cohen's Kappa and accuracy of 0.451 and 0.727, respectively. Our computational strategy also resulted in a publicly and freely available imaging dataset of 86 000 synthetic knee radiographs. CONCLUSIONS: Our approach demonstrates the capability to produce top-notch synthetic knee radiographs and use them for KL grading classification, even when working with a constrained training dataset. The results obtained emphasize the effectiveness of the pipeline in augmenting datasets for knee osteoarthritis research, opening doors for broader applications in orthopedics, medical image analysis, and AI-powered diagnosis.

Development and Validation of a Mobile Phone Application for Measuring Knee Range of Motion.

Knee range of motion (ROM) is an important indicator of knee function. Outside the clinical setting, patients may not be able to accurately assess knee ROM, which may impair recovery following trauma or surgery. This study aims to validate a smartphone mobile application developed to measure knee ROM compared to visual and goniometer ROM measurements. A knee ROM Android mobile application was developed to measure knee ROM. Patients ≥ 18 years old presenting to an orthopaedic clinic with native knee complaints were approached to participate. Knee ROM was measured bilaterally by an arthroplasty-trained surgeon using (1) vision, (2) goniometer, and (3) the mobile application. Measurements were compared in flexion and extension using a one-way analysis of variance with post hoc Tukey test (alpha = 0.05). Eighty-four knee ROM measurements (40 left, 44 right) were obtained in 47 patients. Median Kellgren-Lawrence grade from available radiographs was grade 3. In flexion, mobile application (117.6 ± 14.7 degrees) measurements were not significantly different from visual (116.1 ± 13.6 degrees) or goniometer (116.2 ± 13.6 degrees) measurements. In extension, mobile application (4.8 ± 7.3 degrees) measurements were significantly different from visual (1.9 ± 4.1 degrees) measurements on post hoc analysis (p < 0.01), while no differences were present compared to goniometer (3.1 ± 5.8 degrees) measurements. Our study found that a mobile application for evaluating knee ROM was noninferior to goniometer-based measurements performed by an arthroplasty-trained surgeon. Future studies will investigate this application's utility in (1) remote patient care, (2) accelerating recovery during rehabilitation, (3) detecting early postoperative complications including arthrofibrosis, and (4) adding additional functionalities to the application to provide more detail-oriented descriptive analyses of patient knee function.

Data Collection and Management of mHealth, Wearables, and Internet of Things in Digital Behavioral Health Interventions With the Awesome Data Acquisition Method (ADAM): Development of a Novel Informatics Architecture.

Unlabelled: The integration of health and activity data from various wearable devices into research studies presents technical and operational challenges. The Awesome Data Acquisition Method (ADAM) is a versatile, web-based system that was designed for integrating data from various sources and managing a large-scale multiphase research study. As a data collecting system, ADAM allows real-time data collection from wearable devices through the device's application programmable interface and the mobile app's adaptive real-time questionnaires. As a clinical trial management system, ADAM integrates clinical trial management processes and efficiently supports recruitment, screening, randomization, data tracking, data reporting, and data analysis during the entire research study process. We used a behavioral weight-loss intervention study (SMARTER trial) as a test case to evaluate the ADAM system. SMARTER was a randomized controlled trial that screened 1741 participants and enrolled 502 adults. As a result, the ADAM system was efficiently and successfully deployed to organize and manage the SMARTER trial. Moreover, with its versatile integration capability, the ADAM system made the necessary switch to fully remote assessments and tracking that are performed seamlessly and promptly when the COVID-19 pandemic ceased in-person contact. The remote-native features afforded by the ADAM system minimized the effects of the COVID-19 lockdown on the SMARTER trial. The success of SMARTER proved the comprehensiveness and efficiency of the ADAM system. Moreover, ADAM was designed to be generalizable and scalable to fit other studies with minimal editing, redevelopment, and customization. The ADAM system can benefit various behavioral interventions and different populations.

A Reliable and Accessible Caregiving Language Model (CaLM) to Support Tools for Caregivers: Development and Evaluation Study.

BACKGROUND: In the United States, 1 in 5 adults currently serves as a family caregiver for an individual with a serious illness or disability. Unlike professional caregivers, family caregivers often assume this role without formal preparation or training. Thus, there is an urgent need to enhance the capacity of family caregivers to provide quality care. Leveraging technology as an educational tool or an adjunct to care is a promising approach that has the potential to enhance the learning and caregiving capabilities of family caregivers. Large language models (LLMs) can potentially be used as a foundation technology for supporting caregivers. An LLM can be categorized as a foundation model (FM), which is a large-scale model trained on a broad data set that can be adapted to a range of different domain tasks. Despite their potential, FMs have the critical weakness of "hallucination," where the models generate information that can be misleading or inaccurate. Information reliability is essential when language models are deployed as front-line help tools for caregivers. OBJECTIVE: This study aimed to (1) develop a reliable caregiving language model (CaLM) by using FMs and a caregiving knowledge base, (2) develop an accessible CaLM using a small FM that requires fewer computing resources, and (3) evaluate the model's performance compared with a large FM. METHODS: We developed a CaLM using the retrieval augmented generation (RAG) framework combined with FM fine-tuning for improving the quality of FM answers by grounding the model on a caregiving knowledge base. The key components of the CaLM are the caregiving knowledge base, a fine-tuned FM, and a retriever module. We used 2 small FMs as candidates for the foundation of the CaLM (LLaMA [large language model Meta AI] 2 and Falcon with 7 billion parameters) and adopted a large FM (GPT-3.5 with an estimated 175 billion parameters) as a benchmark. We developed the caregiving knowledge base by gathering various types of documents from the internet. We focused on caregivers of individuals with Alzheimer disease and related dementias. We evaluated the models' performances using the benchmark metrics commonly used in evaluating language models and their reliability for providing accurate references with their answers. RESULTS: The RAG framework improved the performance of all FMs used in this study across all measures. As expected, the large FM performed better than the small FMs across all metrics. Interestingly, the small fine-tuned FMs with RAG performed significantly better than GPT 3.5 across all metrics. The fine-tuned LLaMA 2 with a small FM performed better than GPT 3.5 (even with RAG) in returning references with the answers. CONCLUSIONS: The study shows that a reliable and accessible CaLM can be developed using small FMs with a knowledge base specific to the caregiving domain.

Perceived and calculated diet quality improvements in a randomized mHealth weight loss trial.

The diet quality of US adults is poor and cross-sectional analyses suggest self-perception of healthful dietary intake may be overestimated. This analysis assessed the concordance between calculated and perceived diet quality and changes in diet quality among adults seeking weight loss and enrolled in a 12-month randomized behavioral trial. Healthy Eating Index-2015 diet quality (HEI) was calculated from self-administered 24-hour recalls. Perceived diet quality (PDQ) was measured on a 100-point scale. Higher scores indicate better diet quality. Concordance was assessed using the concordance correlation coefficient and Bland-Altman plots. The one hundred and five participants with complete dietary data were mostly female and white. There was good agreement between HEI and PDQ scores at 12 months for less than a third of participants. Most of the disagreement arose from PDQ scores being higher than HEI scores. Even fewer participants had good agreement between HEI changes and PDQ changes. Participants perceived greater improvement in diet quality than indicated by HEI score changes. Concordance was low at 12 months and for change in diet quality. Despite the diet quality of adults seeking weight loss being suboptimal and not improving, many perceived their diet quality and diet quality improvements as better than calculated. Future studies might explore the effect of misperceptions on weight loss outcomes.

The iMHere 2.0 System for Family Caregivers of Older Adults: A Focus Group.

Background: Family caregivers with continuous caregiving responsibilities are at increased risk for adverse physical and mental health outcomes. In response to the challenges of caregiving, a mobile health system (iMHere 2.0) was developed to support caregivers. The study's objective was to gather feedback from family caregivers of older adults on the current features of iMHere 2.0 and to formulate design criteria for future iterations of the system. Methods: An exploratory qualitative study with thematic analyses of focus group feedback. Findings: A total of 10 caregivers of older adults participated in a focus group. Five themes emerged: (1) Monitoring health data, (2) Setting up customized reminders, (3) Supporting care coordination, (4) Balancing security and multiple user access, and (5) Disseminating iMHere 2.0 into the community, along with some potential barriers to implementation. Conclusions: Design criteria were developed to provide a framework for iterative design and development of the iMHere system to support caregivers of older adults.

The Brazilian version of the telehealth usability questionnaire (telehealth usability questionnaire Brazil): translation, cross-cultural adaptation, and psychometric properties.

OBJECTIVE: The objectives of this study were to translate and cross-culturally adapt the telehealth usability questionnaire into Brazilian Portuguese and to evaluate its psychometric properties. METHODS: This was a methodological validation study carried out in two phases. In phase 1, the telehealth usability questionnaire was cross-culturally adapted with 10 participants comprising the expert committee members, including 5 healthcare professionals with theoretical and practical knowledge of telehealth, 1 methodologist, and 4 translators. This phase was performed at Universidade Federal de Juiz de Fora Physiotherapy Clinic School. In phase 2, the psychometric properties of telehealth usability questionnaire Brazil were analyzed. This phase included in-person assessments at Márcio Cunha Hospital, Minas Gerais. The recruitment period for both phases was from April 2020 to February 2021. Content validity, reliability, internal consistency, and criterion validity were analyzed. The criterion validity was evaluated using correlation with a validated instrument: the system usability scale. RESULTS: The telehealth usability questionnaire was adequately translated and cross-culturally adapted. The telehealth usability questionnaire Brazil presented an excellent content validity index of 0.96 with percentages of understanding higher than 90%. The telehealth usability questionnaire Brazil demonstrated great internal consistency (α=0.94 and ω=0.94), excellent intra-rater reliability (intraclass correlation coefficient=0.85, 95%CI 0.75-0.91), no difference between the test and retest [T (0.425), p>0.673], and no proportional bias (p=0.205). There was a moderate correlation between telehealth usability questionnaire Brazil and the system usability scale (r=0.52, p<0.0001). CONCLUSION: The telehealth usability questionnaire was adequately translated and cross-culturally adapted into Brazilian Portuguese and showed adequate psychometric properties for use in telehealth clinical practice and research in Brazilian-Portuguese-speaking individuals.

ReDWINE: A clinical datamart with text analytical capabilities to facilitate rehabilitation research.

Rehabilitation research focuses on determining the components of a treatment intervention, the mechanism of how these components lead to recovery and rehabilitation, and ultimately the optimal intervention strategies to maximize patients' physical, psychologic, and social functioning. Traditional randomized clinical trials that study and establish new interventions face challenges, such as high cost and time commitment. Observational studies that use existing clinical data to observe the effect of an intervention have shown several advantages over RCTs. Electronic Health Records (EHRs) have become an increasingly important resource for conducting observational studies. To support these studies, we developed a clinical research datamart, called ReDWINE (Rehabilitation Datamart With Informatics iNfrastructure for rEsearch), that transforms the rehabilitation-related EHR data collected from the UPMC health care system to the Observational Health Data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to facilitate rehabilitation research. The standardized EHR data stored in ReDWINE will further reduce the time and effort required by investigators to pool, harmonize, clean, and analyze data from multiple sources, leading to more robust and comprehensive research findings. ReDWINE also includes deployment of data visualization and data analytics tools to facilitate cohort definition and clinical data analysis. These include among others the Open Health Natural Language Processing (OHNLP) toolkit, a high-throughput NLP pipeline, to provide text analytical capabilities at scale in ReDWINE. Using this comprehensive representation of patient data in ReDWINE for rehabilitation research will facilitate real-world evidence for health interventions and outcomes.

Design of Lung Transplant Go (LTGO): A randomized controlled trial evaluating the efficacy of a telerehabilitation behavioral exercise intervention to improve physical activity, physical function, and blood pressure control after lung transplantation.

Background: Lung transplantation is an established treatment option for persons with advanced lung disease. After transplantation, lung function typically returns to near normal levels, however exercise capacity remains low due to chronic deconditioning, limited physical function, and inactive lifestyles which undermine the intended benefits of the highly selective, resource-intensive transplant procedure. Pulmonary rehabilitation is recommended to improve fitness and activity tolerance, however due to multiple barriers, lung transplant recipients either never participate, or fail to complete, pulmonary rehabilitation programs. Purpose: To describe the design of Lung Transplant Go (LTGO), a trial modified for the remote environment based on recommendations to preserve trial integrity during COVID. The aims are to evaluate a behavioral exercise intervention to improve physical function, physical activity, and blood pressure control in lung transplant recipients conducted safely and effectively using a telerehabilitation (telerehab) platform, and to explore the role of potential mediators and moderators of the relationship between LTGO and outcomes. Methods: Single-site, 2-group randomized controlled trial with lung transplant recipients randomized 1:1 to either the LTGO intervention (a 2-phased, supervised, telerehab behavioral exercise program), or to enhanced usual care (activity tracking and monthly newsletters). All study activities, including intervention delivery, recruitment, consenting, assessment, and data collection, will be performed remotely. Conclusion: If efficacious, this fully scalable and replicable telerehab intervention could be efficiently translated to reach large numbers of lung recipients to improve and sustain self-management of exercise habits by overcoming barriers to participation in existing, in-person pulmonary rehabilitation programs.

Formative Development of ClockWork for the Postpartum Period: A Theory-Based Intervention to Harness the Circadian Timing System to Address Cardiometabolic Health-Related Behaviors.

Individuals with body mass index (BMI) ≥ 25 kg/m2 before pregnancy have greater difficulty losing the weight gained during pregnancy, and this postpartum weight retention predicts higher risk for cardiometabolic disease. The postpartum period involves substantial disruptions in circadian rhythms, including rhythms related to eating, physical activity, sleep, and light/dark exposure, each of which are linked to obesity and cardiometabolic disease in non-pregnant adult humans and animals. We posit that a multi-component, circadian timing system-based behavioral intervention that uses digital tools-ClockWork-will be feasible and acceptable to postpartum individuals and help promote weight- and cardiometabolic health-related behaviors. We provide data from stakeholder interviews with postpartum individuals (pre-pregnancy BMI ≥ 25; n = 7), which were conducted to obtain feedback on and improve the relevance and utility of digital self-monitoring tools for health behaviors and weight during the postpartum period. Participants perceived the ClockWork intervention and digital monitoring app to be helpful for management of postpartum weight-related health behaviors. They provided specific recommendations for increasing the feasibility intervention goals and improving app features for monitoring behaviors. Personalized, easily accessible interventions are needed to promote gestational weight loss after delivery; addressing circadian behaviors is an essential component of such interventions. Future studies will evaluate the efficacy of the ClockWork intervention and associated digital tools for improving cardiometabolic health-related behaviors linked to the circadian timing system during the postpartum period.

Pilot RCT examining feasibility and disability outcomes of a mobile health platform for strategy training in inpatient stroke rehabilitation (iADAPT).

BACKGROUND: Strategy training is an intervention that may reduce disability when delivered in inpatient rehabilitation following stroke. However, shorter lengths of stay and challenges with continuity of care following discharge results in difficulties in achieving adequate intervention dosage and carryover of training. OBJECTIVE: We examined whether strategy training using a mobile health platform (iADAPT) is feasible during inpatient stroke rehabilitation and following discharge. METHODS: In this RCT, participants were randomized to receive strategy training using either the iADAPT application (n = 16) or a workbook (n = 15). Participants in both groups received 7 in-person sessions during inpatient rehabilitation and 3 remote sessions following discharge. We calculated descriptive statistics to examine acceptance, attendance, and adherence, and within-group effect sizes on satisfaction and disability. RESULTS: Participants in the iADAPT group attended fewer total intervention sessions (n = 5.5, workbook n = 9.0) but attempted a similar number of goals (n = 7.6, workbook n = 8.2). Both groups reported similar satisfaction with in-person intervention (Treatment Expectancy: iADAPT d = 0.60, workbook d = 0.47; Patient Provider Connection: iADAPT d = 0.18, workbook d = 0.31), but the mobile health group reported greater satisfaction with remote intervention (Treatment Expectancy: iADAPT d = -0.91, workbook d = -0.97; Patient Provider Connection: iADAPT d = 0.85, workbook d = -1.80). . CONCLUSIONS: Considering these promising feasibility metrics and the benefits of mobile health, it is worth continuing to explore the efficacy of strategy training using a mobile health platform.

Effect of an mHealth weight loss intervention on Healthy Eating Index diet quality: the SMARTER randomised controlled trial.

In the few weight loss studies assessing diet quality, improvements have been minimal and recommended calculation methods have not been used. This secondary analysis of a parallel group randomised trial (regsitered: https://clinicaltrials.gov/ct2/show/NCT03367936) assessed whether self-monitoring with feedback (SM + FB) v. self-monitoring alone (SM) improved diet quality. Adults with overweight/obesity (randomised: SM n 251, SM + FB n 251; analysed SM n 170, SM + FB n 186) self-monitored diet, physical activity and weight. Real-time, personalised feedback, delivered via a study-specific app up to three times daily, was based on reported energy, fat and added sugar intake. Healthy Eating Index 2015 (HEI-2015) scores were calculated from 24-hour recalls. Higher scores represent better diet quality. Data were collected August 2018 to March 2021 and analysed spring 2022. The sample was mostly female (78·9 %) and white (85·4 %). At baseline, HEI-2015 total scores and bootstrapped 95 % CI were similar by treatment group (SM + FB: 63·11 (60·41, 65·24); SM: 61·02 (58·72, 62·81)) with similar minimal improvement observed at 6 months (SM + FB: 65·42 (63·30, 67·20); SM: 63·19 (61·22, 64·97)) and 12 months (SM + FB: 63·94 (61·40, 66·29); SM: 63·56 (60·81, 65·42)). Among those who lost ≥ 5 % of baseline weight, HEI-2015 scores improved (baseline: 62·00 (58·94, 64·12); 6 months: 68·02 (65·41, 71·23); 12 months: 65·93 (63·40, 68·61)). There was no effect of the intervention on diet quality change. Clinically meaningful weight loss was related to diet quality improvement. Feedback may need to incorporate more targeted nutritional content.

The Brazilian version of the telehealth usability questionnaire (telehealth usability questionnaire Brazil): translation, cross-cultural adaptation, and psychometric properties

SUMMARY OBJECTIVE: The objectives of this study were to translate and cross-culturally adapt the telehealth usability questionnaire into Brazilian Portuguese and to evaluate its psychometric properties. METHODS: This was a methodological validation study carried out in two phases. In phase 1, the telehealth usability questionnaire was cross-culturally adapted with 10 participants comprising the expert committee members, including 5 healthcare professionals with theoretical and practical knowledge of telehealth, 1 methodologist, and 4 translators. This phase was performed at Universidade Federal de Juiz de Fora Physiotherapy Clinic School. In phase 2, the psychometric properties of telehealth usability questionnaire Brazil were analyzed. This phase included in-person assessments at Márcio Cunha Hospital, Minas Gerais. The recruitment period for both phases was from April 2020 to February 2021. Content validity, reliability, internal consistency, and criterion validity were analyzed. The criterion validity was evaluated using correlation with a validated instrument: the system usability scale. RESULTS: The telehealth usability questionnaire was adequately translated and cross-culturally adapted. The telehealth usability questionnaire Brazil presented an excellent content validity index of 0.96 with percentages of understanding higher than 90%. The telehealth usability questionnaire Brazil demonstrated great internal consistency (α=0.94 and ω=0.94), excellent intra-rater reliability (intraclass correlation coefficient=0.85, 95%CI 0.75-0.91), no difference between the test and retest [T (0.425), p>0.673], and no proportional bias (p=0.205). There was a moderate correlation between telehealth usability questionnaire Brazil and the system usability scale (r=0.52, p<0.0001). CONCLUSION: The telehealth usability questionnaire was adequately translated and cross-culturally adapted into Brazilian Portuguese and showed adequate psychometric properties for use in telehealth clinical practice and research in Brazilian-Portuguese-speaking individuals.

The Effect of Tailored, Daily, Smartphone Feedback to Lifestyle Self-Monitoring on Weight Loss at 12 Months: the SMARTER Randomized Clinical Trial.

BACKGROUND: Self-monitoring (SM) is the centerpiece of behavioral weight loss treatment, but the efficacy of smartphone-delivered SM feedback (FB) has not been tested in large, long-term, randomized trials. OBJECTIVE: The aim of this study was to establish the efficacy of providing remote FB to diet, physical activity (PA), and weight SM on improving weight loss outcomes when comparing the SM plus FB (SM+FB) condition to the SM-only condition in a 12-month randomized controlled trial. The study was a single-site, population-based trial that took place in southwestern Pennsylvania, USA, conducted between 2018 and 2021. Participants were smartphone users age ≥18 years, able to engage in moderate PA, with a mean BMI between 27 and 43 kg/m2. METHODS: All participants received a 90-minute, one-to-one, in-person behavioral weight loss counseling session addressing behavioral strategies, establishing participants' dietary and PA goals, and instructing on use of the PA tracker (Fitbit Charge 2), smart scale, and diet SM app. Only SM+FB participants had access to an investigator-developed smartphone app that read SM data, in which an algorithm selected tailored messages sent to the smartphone up to 3 times daily. The SM-only participants did not receive any tailored FB based on SM data. The primary outcome was percent weight change from baseline to 12 months. Secondary outcomes included engagement with digital tools (eg, monthly percentage of FB messages opened and monthly percentage of days adherent to the calorie goal). RESULTS: Participants (N=502) were on average 45.0 (SD 14.4) years old with a mean BMI of 33.7 (SD 4.0) kg/m2. The sample was 79.5% female (n=399/502) and 82.5% White (n=414/502). At 12 months, retention was 78.5% (n=394/502) and similar by group (SM+FB: 202/251, 80.5%; SM: 192/251, 76.5%; P=.28). There was significant percent weight loss from baseline in both groups (SM+FB: -2.12%, 95% CI -3.04% to -1.21%, P<.001; SM: -2.39%, 95% CI -3.32% to -1.47%; P<.001), but no difference between the groups (-0.27%; 95% CI -1.57% to 1.03%; t =-0.41; P=.68). Similarly, 26.3% (66/251) of the SM+FB group and 29.1% (73/251) of the SM group achieved ≥5% weight loss (chi-square value=0.49; P=.49). A 1% increase in FB messages opened was associated with a 0.10 greater percent weight loss at 12 months (b=-0.10; 95% CI -0.13 to -0.07; t =-5.90; P<.001). A 1% increase in FB messages opened was associated with 0.12 greater percentage of days adherent to the calorie goal per month (b=0.12; 95% CI 0.07-0.17; F=22.19; P<.001). CONCLUSIONS: There were no significant between-group differences in weight loss; however, the findings suggested that the use of commercially available digital SM tools with or without FB resulted in a clinically significant weight loss in over 25% of participants. Future studies need to test additional strategies that will promote greater engagement with digital tools. TRIAL REGISTRATION: Clinicaltrials.gov NCT03367936; https://clinicaltrials.gov/ct2/show/NCT03367936.

Mobile Health to Support Community-Integration of Individuals With Disabilities Using iMHere 2.0: Focus Group Study.

BACKGROUND: Mobile health (mHealth) systems that support self-management can improve medical, functional, and psychosocial outcomes for individuals with disabilities and chronic conditions. The mHealth systems can potentially be expanded to support community integration. OBJECTIVE: The purposes of this study were to (1) partner with a community-based organization that supports community integration of individuals with disabilities; (2) identify software requirements needed to support community participation; and (3) iteratively refine an existing mHealth application to include new requirements. METHODS: Community Living and Support Services (CLASS), a nonprofit organization that serves individuals with disabilities in Pittsburgh, Pennsylvania, was identified as the focus group for this study. Key stakeholders within the Community Partners Program at CLASS proposed design requirements for an existing mHealth application, Interactive Mobile Health and Rehabilitation (iMHere) 2.0, that has been used to support self-management. RESULTS: We gathered qualitative data from a focus group composed of CLASS members to develop and iteratively revise iMHere 2.0 to include new modules and features to support community integration. A caregiver app was also developed. The new system contains features to support finance, transportation, client and caregiver communication, calendar and checklist management, upcoming medical and nonmedical appointments, social engagement, pain management, and access to a personal profile. Modifications were made to the following existing modules: education, mood, personal health record, goals, medications, and nutrition. CONCLUSIONS: A successful partnership with a community-based organization that supports individuals with disabilities resulted in a newly designed mHealth system with features to support community integration.

User-Centered Design to Enhance mHealth Systems for Individuals With Dexterity Impairments: Accessibility and Usability Study.

BACKGROUND: Mobile health systems have been shown to be useful in supporting self-management by promoting adherence to schedules and longitudinal health interventions, especially in people with disabilities. The Interactive Mobile Health and Rehabilitation (iMHere) system was developed to empower people with disabilities and those with chronic conditions with supports needed for self-management and independent living. Since the first iteration of the iMHere 1.0 app, several studies have evaluated the accessibility and usability of the system. Potential opportunities to improve and simplify the user interface were identified, and the iMHere modules were redesigned accordingly. OBJECTIVE: In this study, we aim to evaluate the usability of the redesigned modules within the iMHere 1.0 app. METHODS: We evaluated the original and redesigned iMHere modules-MyMeds and SkinCare. The Purdue Pegboard Test was administered to assess the participants' dexterity levels. Participants were then asked to perform a set of tasks using both the original and redesigned MyMeds and SkinCare modules to assess their efficiency and effectiveness. Usability was measured using the Telehealth Usability Questionnaire to evaluate 10 new accessibility features that were added to the redesigned app. Participants were also asked which version they preferred. RESULTS: In total, 24 participants with disabilities and varying degrees of dexterity impairments completed the entire study protocol. Participants displayed improved efficiency and effectiveness when using the redesigned modules compared with the original modules. The participants also reported improved usability and preferred the redesigned modules. CONCLUSIONS: This study demonstrated that the iMHere system became more efficient, effective, and usable for individuals with dexterity impairments after redesigning it according to user-centered principles.

Effect of tailored, daily feedback with lifestyle self-monitoring on weight loss: The SMARTER randomized clinical trial.

OBJECTIVE: This study aimed to test the efficacy of self-monitoring and feedback (SM+FB) versus SM without FB (SM) in a behavioral weight-loss intervention at 6 months. METHODS: This was a randomized clinical trial. Eligibility criteria included the following: ≥18 years of age, BMI ≥ 27 and ≤43, smartphone user, and ability to engage in moderate physical activity. All participants received a 90-minute 1:1 counseling session, a Fitbit Charge 2, and a digital scale for SM. SM+FB participants were provided access to a customized smartphone application that provided three daily FB messages. The primary outcome was percentage of weight change from 0 to 6 months. RESULTS: The sample (N = 502) was 45 (SD 14.4) years old, BMI was 33.7 (SD 4.00) kg/m2 , 79.5% of participants were female (n = 399), and 84.3% were White (n = 423). At 6 months, there was 85.86% retention and a significant percentage of weight change in both groups (SM+FB: -3.16%, 95% CI: -3.85% to -2.47%, p < 0.0001; SM: -3.20%, 95% CI: -3.86% to -2.54%, p < 0.0001) but no significant between-group mean difference (-0.04%, 95% CI: -0.99% to 0.91%, p = 0.940). A ≥5% weight loss was achieved by 31.9% of the SM+FB group and 28.3% of the SM group. CONCLUSIONS: There was no significant between-group difference in weight loss at 6 months.

Development and Validation of a Comprehensive Well-Being Scale for People in the University Environment (Pitt Wellness Scale) Using a Crowdsourcing Approach: Cross-Sectional Study.

BACKGROUND: Well-being has multiple domains, and these domains are unique to the population being examined. Therefore, to precisely assess the well-being of a population, a scale specifically designed for that population is needed. OBJECTIVE: The goal of this study was to design and validate a comprehensive well-being scale for people in a university environment, including students, faculty, and staff. METHODS: A crowdsourcing approach was used to determine relevant domains for the comprehensive well-being scale in this population and identify specific questions to include in each domain. A web-based questionnaire (Q1) was used to collect opinions from a group of university students, faculty, and staff about the domains and subdomains of the scale. A draft of a new well-being scale (Q2) was created in response to the information collected via Q1, and a second group of study participants was invited to evaluate the relevance and clarity of each statement. A newly created well-being scale (Q3) was then used by a third group of university students, faculty, and staff. A psychometric analysis was performed on the data collected via Q3 to determine the validity and reliability of the well-being scale. RESULTS: In the first step, a group of 518 university community members (students, faculty, and staff) indicated the domains and subdomains that they desired to have in a comprehensive well-being scale. In the second step, a second group of 167 students, faculty, and staff evaluated the relevance and clarity of the proposed statements in each domain. In the third step, a third group of 546 students, faculty, and staff provided their responses to the new well-being scale (Pitt Wellness Scale). The psychometric analysis indicated that the reliability of the well-being scale was high. CONCLUSIONS: Using a crowdsourcing approach, we successfully created a comprehensive and highly reliable well-being scale for people in the university environment. Our new Pitt Wellness Scale may be used to measure the well-being of people in the university environment.

Using a Smartphone App and Clinician Portal to Enhance Brief Cognitive Behavioral Therapy for Childhood Anxiety Disorders.

Cognitive behavioral therapy (CBT) is an efficacious treatment for child anxiety disorders, but 40%-50% of youth do not respond fully to treatment, and time commitments for standard CBT can be prohibitive for some families and lead to long waiting lists for trained CBT therapists in the community. SmartCAT 2.0 is an adjunctive mobile health program designed to improve and shorten CBT treatment for anxiety disorders in youth by providing them with the opportunity to practice CBT skills outside of session using an interactive and gamified interface. It consists of an app and an integrated clinician portal connected to the app for secure 2-way communication with the therapist. The goal of the present study was to evaluate SmartCAT 2.0 in an open trial to establish usability, feasibility, acceptability, and preliminary efficacy of brief (8 sessions) CBT combined with SmartCAT. We also explored changes in CBT skills targeted by the app. Participants were 34 youth (ages 9-14) who met DSM-5 criteria for generalized, separation, and/or social anxiety disorder. Results demonstrated strong feasibility and usability of the app/portal and high satisfaction with the intervention. Youth used the app an average of 12 times between each therapy session (M = 5.8 mins per day). At posttreatment, 67% of youth no longer met diagnostic criteria for an anxiety disorder, with this percentage increasing to 86% at 2-month follow-up. Youth showed reduced symptom severity over time across raters and also improved from pre- to posttreatment in CBT skills targeted by the app, demonstrating better emotion identification and thought challenging and reductions in avoidance. Findings support the feasibility of combining brief CBT with SmartCAT. Although not a controlled trial, when benchmarked against the literature, the current findings suggest that SmartCAT may enhance the utility of brief CBT for childhood anxiety disorders.

The SMARTER Trial: Design of a trial testing tailored mHealth feedback to impact self-monitoring of diet, physical activity, and weight.

BACKGROUND: Self-monitoring food intake and physical activity (PA) is positively related to weight loss and the addition of feedback (FB) messages has been shown to reinforce behavior change. Moreover, the more immediate the delivery of reinforcing FB messages, the more likely they will promote the desired behaviors. PURPOSE: Describe design and rationale of SMARTER, a National Institute of Heart, Lung, and Blood (NHLBI)-sponsored randomized, controlled trial, which compares the differential efficacy of two weight loss treatments among 530 adults, ages 18 and older. METHODS: Single-site, 2-group design trial with subjects randomized 1:1 to either: 1) self-monitoring (SM), where participants self-monitor diet, PA, and weight using a commercial smartphone application (app); or 2) SM + FB, where participants self-monitor and receive real-time, tailored feedback (FB) as pop-up messages up to 3 times/day for 12 months. Daily FB messages address diet and PA behaviors and a weekly FB message addresses self-weighing. We hypothesize that subjects assigned to SM + FB will show greater weight loss at 6 and 12 months and greater sustained engagement in the program than the SM group, measured by adherence to the study's lifestyle and SM protocol. We will explore temporal relationships of the frequency, timing, and type of FB delivered and subsequent lifestyle behaviors through examination of serially collected real-time SM (diet, PA, weight) data over 12 months. CONCLUSIONS: If efficacious, this fully scalable intervention could be efficiently translated and disseminated to reach large numbers of individuals through commercial apps at lower cost than existing in-person weight loss programs.