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). 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. 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. 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). 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.

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.

BackgroundThe 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. ObjectiveThe 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. MethodsAt 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. ResultsAs 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. ConclusionsBy 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

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

The past decade has seen an unprecedented uptake of mobile phones and related technologies and their application to health-related issues—frequently called mHealth. MHealth applications are being developed and evaluated in adults for a variety of conditions, including asthma, smoking cessation, diabetes, obesity, stress, and depression. Although evidence of their overall effectiveness is limited, mobile access to health-related information and services is growing in importance, and an increasing proportion of health care services for people of all ages will be supported via mobile technologies. Recent national surveys indicate that 20% of Americans are using some form of technology to track an aspect of their health. Moreover, blending of the Web and mobile experience—the mobile Web—is happening at a rapid pace, and, importantly, is happening across essentially the entire socioeconomic spectrum, with many in mid-to-low–income communities using mobile devices more than computers for Web access. Thus, past research that demonstrates success with either Web- or mobile-based health interventions suggests promise for this integrated experience in the future.

BackgroundLow 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. ObjectiveThe 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. MethodsLicensed 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. ResultsRegarding 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. ConclusionsSeveral 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.

Strong evidence indicates physical activity (PA) reduces risk of various cancers, yet only a third of adults in the US meet guidelines for PA. While effective 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 (DHIs) 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 coupled with the development of personalized knowledge, skills, and practices in 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 via a self-experimentation approach to develop personalized knowledge, skills, and practices. The primary aim is to evaluate differences in minutes of moderate to vigorous physical activity (MVPA) per week at 12-month, 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. The YourMove Study is a 12-month randomized controlled trial (RCT) that includes 386 inactive adults aged 25-80 years. All participants receive, 1) a Fitbit Versa smartwatch and corresponding smartphone application, 2) weekly PA goal suggestions and feedback, behavioral change strategies, and reminders via text messaging, and 3) up to $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 by “Control Optimization Trial” (COT) approach, and 2) a “knowledge, skills, and practices 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, assessed via Actigraph. Recruitment began in October 2022 and concluded in August 2024. Data collection will conclude in August 2025 with results expected by the early 2026. 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, it 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 successful, results will provide justification to explore both the COT approach and self-experimentation approach for other complex, idiosyncratic, and dynamic behaviors such as weight management, smoking, or substance abuse. ClinicalTrials.gov NCT05598996

Digital therapeutics (DTx) are a promising way to provide safe, effective, accessible, sustainable, scalable, and equitable approaches to advance individual and population health. However, developing and deploying DTx is inherently complex in that DTx includes multiple interacting components, such as tools to support activities like medication adherence, health behavior goal-setting or self-monitoring, and algorithms that adapt the provision of these according to individual needs that may change over time. While myriad frameworks exist for different phases of DTx development, no single framework exists to guide evidence production for DTx across its full life cycle, from initial DTx development to long-term use. To fill this gap, we propose the DTx real-world evidence (RWE) framework as a pragmatic, iterative, milestone-driven approach for developing DTx. The DTx RWE framework is derived from the 4-phase development model used for behavioral interventions, but it includes key adaptations that are specific to the unique characteristics of DTx. To ensure the highest level of fidelity to the needs of users, the framework also incorporates real-world data (RWD) across the entire life cycle of DTx development and use. The DTx RWE framework is intended for any group interested in developing and deploying DTx in real-world contexts, including those in industry, health care, public health, and academia. Moreover, entities that fund research that supports the development of DTx and agencies that regulate DTx might find the DTx RWE framework useful as they endeavor to improve how DTxcan advance individual and population health.

BackgroundLow 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. ObjectiveThe 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. MethodsA 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. ResultsVisual 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. ConclusionsMT 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.

Background: Motion Tape (MT) is a low-profile, disposable, self-adhesive wearable sensor that measures skin strain. Preliminary studies have validated MT for measuring lower back movement. However, further analysis is needed to determine if MT can be used to measure lower back muscle engagement. The purpose of this study was to measure differences in MT strain between conditions in which the lower back muscles were relaxed versus maximally activated. Methods: Ten participants without low back pain were tested. A matrix of six MTs was placed on the lower back, and strain data were captured under a series of conditions. The first condition was a baseline trial, in which participants lay prone and the muscles of the lower back were relaxed. The subsequent trials were maximum voluntary isometric contractions (MVICs), in which participants did not move, but resisted the examiner force in extension or rotational directions to maximally engage their lower back muscles. The mean MT strain was calculated for each condition. A repeated measures ANOVA was conducted to analyze the effects of conditions (baseline, extension, right rotation, and left rotation) and MT position (1–6) on the MT strain. Post hoc analyses were conducted for significant effects from the overall analysis. Results: The results of the ANOVA revealed a significant main effect of condition (p < 0.001) and a significant interaction effect of sensor and condition (p = 0.01). There were significant differences in MT strain between the baseline condition and the extension and rotation MVIC conditions, respectively, for sensors 4, 5, and 6 (p = 0.01–0.04). The largest differences in MT strain were observed between baseline and rotation conditions for sensors 4, 5, and 6. Conclusions: MT can capture maximal lower back muscle engagement while the trunk remains in a stationary position. Lower sensors are better able to capture muscle engagement than upper sensors. Furthermore, MT captured muscle engagement during rotation conditions better than during extension.

Mobile sensing and interventions have been a growing resource towards tracking and supporting mental health conditions. Most participants in research studies are willing to share and receive various forms of information with the app/researchers owing to external incentives. As we explore translating such work as a university or organization-level design for mental health apps, it is imperative to understand user preferences and openness to share different active/passive sensors and responses to different notifications. At a personal level mobile health features could provide valuable insights to an interested user. Additionally, quantifying the prevalence of such users at an organizational level can drive decisions on inclusive app design by the organization for its stakeholders. Through a survey-driven approach we explore user preferences and characterize personas of different users to promote the design of a mental health app for students in a large-scale university in the US. We find that while most users are generally open to share certain data, their preferences significantly vary by each sensor and that those who share one modality are very likely to share others.

Background Extensive research suggests that physical activity (PA) is important for brain and cognitive health and may help to delay or prevent Alzheimer's disease and related dementias. Most PA interventions designed to improve brain health in older adults have been conducted in laboratory, gym, or group settings that require extensive resources and travel to the study site or group sessions. Research is needed to develop novel interventions that leverage mobile health (mHealth) technologies to help older adults increase their engagement in PA in free-living environments, reducing participant burden and increasing generalizability of research findings. Moreover, promoting engagement in moderate-to-vigorous PA (MVPA) may be most beneficial to brain health; thus, using mHealth to help older adults increase time spent in MVPA in free-living environments may help to offset the burden of Alzheimer's disease and related dementias and improve quality of life in older age. Objective We developed a novel PA intervention that leverages mHealth to help older adults achieve more minutes of MVPA independently. This pilot study was a 12-week randomized controlled trial to investigate the feasibility of providing just-in-time (JIT) feedback about PA intensity during free-living exercise sessions to help older adults meet current PA recommendations (150 minutes per week of MVPA). Methods Participants were eligible if they were cognitively healthy English speakers aged between 65 and 80 years without major cardiovascular, neurologic, or mental health conditions; could ambulate independently; and undergo magnetic resonance imaging. Enrollment occurred from October 2017 to March 2020. Participants randomized to the PA condition received an individualized exercise prescription and an mHealth device that provided heart rate–based JIT feedback on PA intensity, allowing them to adjust their behavior in real time to maintain MVPA during exercise sessions. Participants assigned to the healthy aging education condition received a reading prescription consisting of healthy aging topics and completed weekly quizzes based on the materials. Results In total, 44 participants were randomized to the intervention. A follow-up manuscript will describe the results of the intervention as well as discuss screening, recruitment, adverse events, and participants’ opinions regarding their participation in the intervention. Conclusions The long-term goal of this intervention is to better understand how MVPA affects brain and cognitive health in the real world and extend laboratory findings to everyday life. This pilot randomized controlled trial was conducted to determine the feasibility of using JIT heart rate zone feedback to help older adults independently increase time spent in MVPA while collecting data on the plausible mechanisms of change (frontal and medial temporal cerebral blood flow and cardiorespiratory fitness) that may affect cognition (memory and executive function) to help refine a planned stage 2 behavioral trial. Trial Registration ClinicalTrials.gov NCT03058146; https://clinicaltrials.gov/ct2/show/NCT03058146 International Registered Report Identifier (IRRID) DERR1-10.2196/42980

Background Although it is widely recognized that physical activity is an important determinant of health, assessing this complex behavior is a considerable challenge. Objective The purpose of this systematic review and meta-analysis is to examine, quantify, and report the current state of evidence for the validity of energy expenditure, heart rate, and steps measured by recent combined-sensing Fitbits. Methods We conducted a systematic review and Bland-Altman meta-analysis of validation studies of combined-sensing Fitbits against reference measures of energy expenditure, heart rate, and steps. Results A total of 52 studies were included in the systematic review. Among the 52 studies, 41 (79%) were included in the meta-analysis, representing 203 individual comparisons between Fitbit devices and a criterion measure (ie, n=117, 57.6% for heart rate; n=49, 24.1% for energy expenditure; and n=37, 18.2% for steps). Overall, most authors of the included studies concluded that recent Fitbit models underestimate heart rate, energy expenditure, and steps compared with criterion measures. These independent conclusions aligned with the results of the pooled meta-analyses showing an average underestimation of −2.99 beats per minute (k comparison=74), −2.77 kcal per minute (k comparison=29), and −3.11 steps per minute (k comparison=19), respectively, of the Fitbit compared with the criterion measure (results obtained after removing the high risk of bias studies; population limit of agreements for heart rate, energy expenditure, and steps: −23.99 to 18.01, −12.75 to 7.41, and −13.07 to 6.86, respectively). Conclusions Fitbit devices are likely to underestimate heart rate, energy expenditure, and steps. The estimation of these measurements varied by the quality of the study, age of the participants, type of activities, and the model of Fitbit. The qualitative conclusions of most studies aligned with the results of the meta-analysis. Although the expected level of accuracy might vary from one context to another, this underestimation can be acceptable, on average, for steps and heart rate. However, the measurement of energy expenditure may be inaccurate for some research purposes.

6076 Background: Remote patient monitoring (RPM) may improve the early detection and mitigation of cancer treatment-related complications, health-related outcomes and quality of life. RPM’s success may depend, in part, on patients’ adherence to remote monitoring protocols. However, factors that influence adherence to RPM are largely unknown. Daily blood pressure/pulse (BP/P), weight, and electronic patient-reported outcomes (ePROs) were monitored remotely in head and neck cancer (HNC) patients undergoing radiation treatment (RT) to identify dehydration risk. We evaluated potential factors associated with RPM adherence. Methods: During RT (average 6 to 7 weeks), participants were asked to take daily (Monday-Friday) measures of BP/P and weight using Bluetooth-enabled devices and to complete daily ePROs using a mobile tablet application (app). Data were provided to their physicians for daily review. The MD Anderson Symptom Inventory-Head and Neck (MDASI-HN) was completed at baseline and end of RT, and 6-8 weeks post-RT completion. The Patient Activation Measure (PAM) was completed at baseline and 6-8 weeks post-RT completion. A device usability survey measuring perceived usefulness of RPM was completed at the end of RT. Adherence to daily monitoring was recorded objectively. Longitudinal analyses compared the relationship between demographic, clinical, and PRO data and monitoring adherence. Results: Participants (n = 169) were 80% male, 87% White, and 91% married. Overall adherence to monitoring BP/P, weight, and ePROs was 83%, 82% and 74%, respectively. Greater HN-specific symptom severity and interference was associated with decreased adherence to daily monitoring of BP/P, weight, and ePROs (P< 0.021). Higher PAM scores were associated with higher adherence to daily monitoring of BP/P only (p = 0.006). Participants reported modest levels of perceived usefulness of RPM across four categories: symptom management, early problem detection, illness monitoring by healthcare provider, and feeling of security during RT. Only a single item indicating perceived feeling of security was associated with greater adherence to daily monitoring of blood pressure/pulse (p = 0.032) and weight (p = 0.007). Conclusions: A benefit of frequent RPM may be early detection and mitigation of symptoms during RT for HNC, however, increasing symptom burden experienced during treatment may interfere with adherence to daily monitoring. Better adherence may be attributed to patients perceiving a sense of security from daily monitoring and may suggest a potentially important value that patients gain from RPM. Understanding factors that impact patient adherence to RPM may help improve acceptability and clinical utility of RPM in oncology. Clinical trial information: NCT02253238.

Background: Self-reported physical activity is often inaccurate. Wearable devices utilizing multiple sensors are now widespread. The aim of this study was to determine acceptability of Fitbit Charge HR for children and their families, and to determine best practices for processing its objective data. Methods: Data were collected via Fitbit Charge HR continuously over the course of 3 weeks. Questionnaires were given to each child and their parent/guardian to determine the perceived usability of the device. Patterns of data were evaluated and best practice inclusion criteria recommended. Results: Best practices were established to extract, filter, and process data to evaluate device wear, r and establish minimum wear time to evaluate behavioral patterns. This resulted in usable data available from 137 (89%) of the sample. Conclusions: Activity trackers are highly acceptable in the target population and can provide objective data over longer periods of wear. Best practice inclusion protocols that reflect physical activity in youth are provided.

Background Excess weight gain in young adulthood is associated with future weight gain and increased risk of chronic disease. Although multimodal, technology-based weight-loss interventions have the potential to promote weight loss among young adults, many interventions have limited personalization, and few have been deployed and evaluated for longer than a year. We aim to assess the effects of a highly personalized, 2-year intervention that uses popular mobile and social technologies to promote weight loss among young adults. Methods The Social Mobile Approaches to Reducing Weight (SMART) 2.0 Study is a 24-month parallel-group randomized controlled trial that will include 642 overweight or obese participants, aged 18–35 years, from universities and community colleges in San Diego, CA. All participants receive a wearable activity tracker, connected scale, and corresponding app. Participants randomized to one intervention group receive evidence-based information about weight loss and behavior change techniques via personalized daily text messaging (i.e., SMS/MMS), posts on social media platforms, and online groups. Participants in a second intervention group receive the aforementioned elements in addition to brief, technology-mediated health coaching. Participants in the control group receive a wearable activity tracker, connected scale, and corresponding app alone. The primary outcome is objectively measured weight in kilograms over 24 months. Secondary outcomes include anthropometric measurements; physiological measures; physical activity, diet, sleep, and psychosocial measures; and engagement with intervention modalities. Outcomes are assessed at baseline and 6, 12, 18, and 24 months. Differences between the randomized groups will be analyzed using a mixed model of repeated measures and will be based on the intent-to-treat principle. Discussion We hypothesize that both SMART 2.0 intervention groups will significantly improve weight loss compared to the control group, and the group receiving health coaching will experience the greatest improvement. We further hypothesize that differences in secondary outcomes will favor the intervention groups. There is a critical need to advance understanding of the effectiveness of multimodal, technology-based weight-loss interventions that have the potential for long-term effects and widespread dissemination among young adults. Our findings should inform the implementation of low-cost and scalable interventions for weight loss and risk-reducing health behaviors. Trial registration ClinicalTrials.gov NCT03907462 . Registered on April 9, 2019

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.

Urbanicity is a growing environmental challenge for mental health. Here, we investigate correlations of urbanicity with brain structure and function, neuropsychology and mental illness symptoms in young people from China and Europe (total n = 3,867). We developed a remote-sensing satellite measure (UrbanSat) to quantify population density at any point on Earth. UrbanSat estimates of urbanicity were correlated with brain volume, cortical surface area and brain network connectivity in the medial prefrontal cortex and cerebellum. UrbanSat was also associated with perspective-taking and depression symptoms, and this was mediated by neural variables. Urbanicity effects were greatest when urban exposure occurred in childhood for the cerebellum, and from childhood to adolescence for the prefrontal cortex. As UrbanSat can be generalized to different geographies, it may enable assessments of correlations of urbanicity with mental illness and resilience globally.