Kevin Patrick

Background: Only approximately half of adults with hypertension, a leading cardiovascular risk factor, achieve blood pressure (BP) control, with persistent disparities among low-income and minority populations. Wireless home BP monitoring (HBPM) with remote patient monitoring can improve BP control, but evidence is limited in medically underserved, predominantly Hispanic populations served by federally qualified health centers (FQHCs). Objective: The primary objective was to evaluate the feasibility and acceptability of wireless HBPM among predominantly Hispanic adults with hypertension receiving FQHC care. Comparing BP outcomes between wireless and conventional HBPM was a secondary exploratory objective. Methods: We conducted a 6-month, 2-arm randomized controlled pilot feasibility and acceptability trial nested within the San Diego: Heart Attack and Stroke Free Zone program. Adults aged ≥18 years with uncontrolled or newly diagnosed hypertension (BP ≥140/90 mm Hg) were enrolled across 3 FQHC networks and randomized 1:1 by unrestricted simple randomization to wireless HBPM (Bluetooth cuff and cellular hub; weekly transmitted summaries reviewed by health coaches) or standard HBPM (home monitor with paper log). Both arms received standardized health coaching. The primary outcomes were feasibility (proportion of 210 expected BP readings transmitted) and acceptability (postintervention satisfaction survey in the wireless arm). Exploratory analyses compared BP outcomes between arms among participants with >2 BP measurements. Results: Of 316 recruited participants, 290 (92%) were eligible, and 200 (63%) were analyzed (wireless: n=73, 23%; and standard: n=127, 40%); arm-level allocation counts were not retained. The mean age was 61 years (SD 9.1), 57% (115/200) were women, and 51% (n=102) self-identified as Hispanic, with no significant baseline differences between arms. Among analyzed wireless-arm participants, 86% (63/73) transmitted multiple readings (mean 160.6, SD 129.1) over 6 months, corresponding to 76.2% (160/210) of expected readings among transmitting participants and 66% across all 73; only 5% (7/127) of standard-arm participants returned paper logs. Of 60 respondents (82.2% response rate), 95% (n=57) agreed or strongly agreed that the device was easy to set up and use. Both arms showed significant within-arm BP reductions at 6 months ( P <.001). At 6 months, 63% (46/73) of the wireless arm and 74% (94/127) of the standard arm achieved systolic BP P =.08; adjusted odds ratio, standard vs wireless, 1.6, 95% CI 0.78‐3.4; P =.19). These exploratory comparisons were not powered and do not constitute evidence of equivalence. Conclusions: Among analyzed participants, wireless HBPM was feasible, and survey respondents reported high acceptability in a medically underserved, predominantly Hispanic FQHC population. Feasibility among all randomized participants could not be estimated because arm-level allocation counts were not retained. The absence of a between-arm difference in BP control is not evidence of equivalence because this pilot was not powered to compare clinical effectiveness; the BP findings are hypothesis generating.

Objective assessment of low back pain (LBP) is challenging due to subtle, task-dependent movement impairments that are poorly captured by existing sensing technologies. Motion Tape (MT), which is a self-adhesive elastic fabric skin strain sensor, enables skin-conforming measurement of localized biomechanical strain during functional movement, but its discriminative utility for LBP remains unclear. We examine this question in a multi-sensor, multi-movement setting and analyze whether MT signals encode discriminative structure that distinguishes individuals with LBP from healthy controls. Using data from 20 participants performing 19 functional movements with six sensors, we evaluate movement-specific classification under a leave-pair-out protocol and examine which movements, sensor placements, and features are most informative. Our analysis reveals that group separation is highly selective: only a small subset of movements, most notably forward flexion, consistently supports accurate classification, while many movements remain at near-chance level. We find that temporal dynamics features help in resolving difficult cases that global strain statistics fail to separate, and that informative signals are spatially localized to the lower lumbar spine. In contrast, pretrained time-series foundation models show negligible sensitivity to participant-level structure in MT signals. Overall, the findings from this exploratory study establish when and how MT sensing can effectively differentiate individuals with LBP from healthy controls, providing a principled foundation for larger-scale validation.

Low back pain (LBP) is a major global health problem and can result in a variety of movement impairments. Advances in smart technology have enabled the collection of novel streams of movement data, and machine learning (ML) methods have been increasingly used for data analysis. However, many existing technologies remain expensive and unsuitable for widespread clinical use, and ML approaches have largely focused on distinguishing people with LBP from healthy controls rather than identifying meaningful subgroups within the LBP population. Motion Tape (MT) is a recently developed wearable strain sensor that translates skin deformation from underlying movement and muscle engagement into electrical signals. In this exploratory study involving 10 participants with LBP, we demonstrate that MT data from six sensors applied on the lower back capture rich movement information capable of characterizing movement patterns among participants with LBP. We propose a feature engineering approach based on biomechanical features as well as time-series causal discovery applied to multivariate sensor time-series data to extract directed inter-segment coordination patterns. We further develop an exploratory subgroup discovery pipeline by aggregating clustering coassociation information across diverse movement tasks. Our causal coordination features show promising discriminative information across several movement types, capturing aspects of motor control not reflected in amplitude-based or embedding-based features alone, such as asymmetries and movement restrictions. Preliminary ensemble clustering analysis indicates three potential LBP subgroups distinguished by biomechanical and inter-segment coordination patterns, which may reflect varied strategies under different movement demands. We investigate the differences in clinical characteristics among these LBP subgroups. We show that time-series foundation models are not well suited for LBP subgrouping due to their uninterpretability, which is improved in our feature engineering pipeline. This framework could reveal additional subgroups with larger cohorts and may generalize to other sensor modalities.

BACKGROUND Hypertension is a leading cardiovascular risk factor, yet only about half of affected individuals achieve blood pressure (BP) control, with persistent disparities among low-income and racial/ethnic minority populations. Wireless home blood pressure monitoring (HBPM) integrated with remote patient monitoring shows promise for improving BP management, but evidence is limited in medically underserved, predominantly Hispanic populations served by Federally Qualified Health Centers (FQHCs). OBJECTIVE This pilot study aimed to evaluate the feasibility and acceptability of wireless HBPM among predominantly Hispanic adults with hypertension receiving care at FQHCs and to explore its effect on BP control compared with conventional HBPM using paper logs. METHODS We conducted a 6-month prospective, two-arm randomized controlled pilot trial nested within the San Diego: Heart Attack and Stroke Free Zone cardiovascular risk-reduction program. Adults aged ≥18 years with uncontrolled or newly diagnosed hypertension (BP ≥140/90 mmHg) were enrolled across three FQHC networks and randomized to a wireless HBPM arm (Qualcomm Life 2NET hub with an A&D UA-651 BLE monitor; weekly transmitted summaries reviewed by health coaches) or a standard arm (calibrated home BP monitor with paper log). Both arms received standardized health coaching. Feasibility was defined as the proportion of expected BP readings successfully transmitted; acceptability was assessed via a post-intervention satisfaction survey in the wireless arm. Exploratory analyses compared BP outcomes between arms using t-tests, chi-square/Fisher exact tests, and multivariable logistic regression. RESULTS Of 200 participants (73 wireless; 127 standard), mean age was 61 years, 57% were women, and 51% self-identified as Hispanic, with no significant baseline differences between arms. In the wireless arm, 86% (63/73) successfully transmitted multiple readings, with a median of 159 readings (mean 160.6, SD 129.1) over 6 months, corresponding to 76.2% adherence to the prescribed schedule; only 5% of standard-arm participants returned their paper logs. On the satisfaction survey (response rate 82.2%), 95% agreed or strongly agreed that the device was easy to set up and use, and 75% reported they would more consistently use the wireless than the conventional cuff. Both arms showed significant reductions in systolic and diastolic BP at 6 months (P<.001). At 6 months, 63% of the wireless arm and 74% of the standard arm achieved systolic BP <140 mmHg (P=.079); adjusted analyses showed no significant between-group difference in BP control (adjusted odds ratio 1.6, 95% CI 0.78-3.4; P=.19). CONCLUSIONS Wireless HBPM was feasible and highly acceptable in a medically underserved, predominantly Hispanic FQHC population, with strong data-transmission adherence and high user satisfaction. Although this pilot was not powered for definitive efficacy comparisons, the findings support wireless HBPM as a viable platform for hypertension management in underserved settings and offer foundational evidence to inform contemporary digital health and remote patient monitoring program design.

Background: While effective physical activity (PA) interventions exist, interventions often work only for some individuals or only for a limited time. Thus, there is a need for digital health interventions that account for dynamic, idiosyncratic PA determinants to support each person’s PA. We hypothesize that supporting individuals with their personal PA goals requires a personalized intervention that both supports each person in forming daily habits of walking more and develops personalized knowledge, skills, and practices regarding engaging in exercise routines. We operationalized these adaptive features via a digital health intervention called YourMove that uses a control systems approach to support personalized habit formation and a self-experimentation approach to develop personalized knowledge, skills, and practices. Objective: The primary aim is to evaluate differences in minutes of moderate to vigorous PA (MVPA) per week at 12 months comparing our personalized intervention, called YourMove, with an active control that is similar but without personalization of the intervention components and mimics best-in-class digital health worksite wellness programs. Methods: The YourMove study is a 12-month randomized controlled trial that involves 386 inactive adults aged 25 to 80 years. All participants receive (1) a Fitbit Versa smartwatch and corresponding smartphone app; (2) weekly PA goal suggestions and feedback, behavior change strategies, and reminders via SMS text messaging; and (3) up to US $50 in incentives for reaching daily step goals. Participants randomized to the active control group, modeled after worksite wellness programs, receive all the elements described in addition to a static daily step goal and static point rewards. Participants randomized to the intervention group receive (1) a habit formation element with daily personalized step goals and personalized point rewards generated through a control optimization trial approach and (2) a knowledge, skill, and practice development element featuring a self-guided self-experimentation tool that helps individuals find strategies to improve MVPA. The primary outcome is objectively assessed weekly minutes of MVPA via an ActiGraph monitor. Results: Recruitment began in October 2022 and concluded in August 2024. Data collection will conclude in August 2025, with results expected by early 2026. Conclusions: We hypothesize that the intervention group will show greater improvement in MVPA than the active control group at 12 months. If the hypothesis is supported, this will provide compelling evidence to suggest that personalized and perpetually adaptive support can enhance PA more effectively than intervention elements commonly used in digital health worksite wellness programs. If the trial is successful, the results will provide justification to explore both the control optimization trial approach and self-experimentation approach for other complex, idiosyncratic, and dynamic behaviors such as weight management, smoking, or substance abuse. Trial Registration: ClinicalTrials.gov NCT05598996; https://clinicaltrials.gov/study/NCT05598996 International Registered Report Identifier (IRRID): DERR1-10.2196/70599

Background: The rising prevalence of mental health concerns among students is prompting universities to explore innovative solutions to support student well-being. This paper describes the protocol for the development, implementation, and evaluation of a mobile app designed to address the mental health and wellness needs of students. This project employs a student-centered approach, partnering with students from the initial needs analysis through to the final design and implementation stages. Objective: The app aims to increase the use of campus resources that address student mental health and wellness by improving the awareness of these resources through user-designated preferences that are established on the initial use of the app and then iteratively refined as it is used. The app is linked to the campus student’s electronic health record so that health and wellness services can be coordinated and enhanced and the student journey to and through care become more seamless. The long-term objective is to leverage data from both the app and electronic health record to improve individual and population health for the entire campus. Methods: At the beginning of the project, a comprehensive logic model was created to outline the core inputs, activities, outputs, outcomes, and long-term impacts that were desired for the app. The model emphasized the integration of the app within existing campus mental health and wellness services and its potential to foster a culture of well-being across the university community. An evaluation plan was developed that incorporates both quantitative and qualitative methods through biannual assessments to track trends and app impact across campus in addition to feasibility, acceptability, and usability as well as its reach, effectiveness, and sustainability. Validated measures such as the Patient Health Questionnaire and Generalized Anxiety Disorder scale were selected to track changes in mental health and wellness, while custom surveys and analytics will gauge user engagement and satisfaction. New students, including freshmen, transfers, and first-year medical students, are invited to participate after giving informed consent. They receive compensation for their involvement in both quantitative and qualitative assessments. Results: As of March 2025, we have collected over 600 survey responses from freshmen, transfer, and medical students. A second survey round and additional focus groups are planned for April to May 2025. No analyses have been conducted yet. The findings from this project have the potential to inform similar efforts at other institutions and contribute to the broader field of digital mental health innovation and the development of well-being interventions tailored for young people. Conclusions: By leveraging digital technology and actively engaging students in supporting their well-being, this initiative represents an innovative user-centered approach to improve mental health and wellness support on university campuses. International Registered Report Identifier (IRRID): DERR1-10.2196/68368

Monitoring posture and movement accurately and efficiently is essential for both physical therapy and athletic training evaluation and interventions. Motion Tape (MT), a self-adhesive wearable skin-strain sensor made of piezoresistive graphene nanosheets (GNS), has demonstrated promise in capturing low back posture and movements. However, to address some of its limitations, this work explores alternative materials by replacing GNS with multi-walled carbon nanotubes (MWCNT). This study aimed to characterize the electromechanical properties of MWCNT-based MT. Cyclic load tests for different peak tensile strains ranging from 1% to 10% were performed on MWCNT-MT made with an aqueous ink of 2% MWCNT. Additional tests to examine load rate sensitivity and fatigue were also conducted. After characterizing the properties of MWCNT-MT, a human subject study with 10 participants was designed to test its ability to capture different postures and movements. Sets of six sensors were made from each material (GNS and MWCNT) and applied in pairs at three levels along each side of the lumbar spine. To record movement of the lower back, all participants performed forward flexion, left and right bending, and left and right rotation movements. The results showed that MWCNT-MT exceeded GNS-MT with respect to consistency of signal stability even when strain limits were surpassed. In addition, both types of MT could assess lower back movements.

BACKGROUND While effective physical activity (PA) interventions exist, interventions often work only for some individuals or only for a limited time. Thus, there is a need for digital health interventions that account for dynamic, idiosyncratic PA determinants to support each person’s PA. We hypothesize that supporting individuals with their personal PA goals requires a personalized intervention that both supports each person in forming daily habits of walking more and develops personalized knowledge, skills, and practices regarding engaging in exercise routines. We operationalized these adaptive features via a digital health intervention called YourMove that uses a control systems approach to support personalized habit formation and a self-experimentation approach to develop personalized knowledge, skills, and practices. OBJECTIVE The primary aim is to evaluate differences in minutes of moderate to vigorous PA (MVPA) per week at 12 months comparing our personalized intervention, called YourMove, with an active control that is similar but without personalization of the intervention components and mimics best-in-class digital health worksite wellness programs. METHODS The YourMove study is a 12-month randomized controlled trial that involves 386 inactive adults aged 25 to 80 years. All participants receive (1) a Fitbit Versa smartwatch and corresponding smartphone app; (2) weekly PA goal suggestions and feedback, behavior change strategies, and reminders via SMS text messaging; and (3) up to US $50 in incentives for reaching daily step goals. Participants randomized to the active control group, modeled after worksite wellness programs, receive all the elements described in addition to a static daily step goal and static point rewards. Participants randomized to the intervention group receive (1) a habit formation element with daily personalized step goals and personalized point rewards generated through a control optimization trial approach and (2) a knowledge, skill, and practice development element featuring a self-guided self-experimentation tool that helps individuals find strategies to improve MVPA. The primary outcome is objectively assessed weekly minutes of MVPA via an ActiGraph monitor. RESULTS Recruitment began in October 2022 and concluded in August 2024. Data collection will conclude in August 2025, with results expected by early 2026. CONCLUSIONS We hypothesize that the intervention group will show greater improvement in MVPA than the active control group at 12 months. If the hypothesis is supported, this will provide compelling evidence to suggest that personalized and perpetually adaptive support can enhance PA more effectively than intervention elements commonly used in digital health worksite wellness programs. If the trial is successful, the results will provide justification to explore both the control optimization trial approach and self-experimentation approach for other complex, idiosyncratic, and dynamic behaviors such as weight management, smoking, or substance abuse. CLINICALTRIAL ClinicalTrials.gov NCT05598996; https://clinicaltrials.gov/study/NCT05598996 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/70599

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: The rising prevalence of mental health concerns among students is prompting universities to explore innovative solutions to support student well-being. This paper describes the protocol for the development, implementation, and evaluation of a mobile app designed to address the mental health and wellness needs of students. This project employs a student-centered approach, partnering with students from the initial needs analysis through to the final design and implementation stages. Objective: The app aims to increase the use of campus resources that address student mental health and wellness by improving the awareness of these resources through user-designated preferences that are established on the initial use of the app and then iteratively refined as it is used. The app is linked to the campus student’s electronic health record so that health and wellness services can be coordinated and enhanced and the student journey to and through care become more seamless. The long-term objective is to leverage data from both the app and electronic health record to improve individual and population health for the entire campus. Methods: At the beginning of the project, a comprehensive logic model was created to outline the core inputs, activities, outputs, outcomes, and long-term impacts that were desired for the app. The model emphasized the integration of the app within existing campus mental health and wellness services and its potential to foster a culture of well-being across the university community. An evaluation plan was developed that incorporates both quantitative and qualitative methods through biannual assessments to track trends and app impact across campus in addition to feasibility, acceptability, and usability as well as its reach, effectiveness, and sustainability. Validated measures such as the Patient Health Questionnaire and Generalized Anxiety Disorder scale were selected to track changes in mental health and wellness, while custom surveys and analytics will gauge user engagement and satisfaction. New students, including freshmen, transfers, and first-year medical students, are invited to participate after giving informed consent. They receive compensation for their involvement in both quantitative and qualitative assessments. Results: As of March 2025, we have collected over 600 survey responses from freshmen, transfer, and medical students. A second survey round and additional focus groups are planned for April to May 2025. No analyses have been conducted yet. The findings from this project have the potential to inform similar efforts at other institutions and contribute to the broader field of digital mental health innovation and the development of well-being interventions tailored for young people. Conclusions: By leveraging digital technology and actively engaging students in supporting their well-being, this initiative represents an innovative user-centered approach to improve mental health and wellness support on university campuses.