publications

2026

1. Personalized modeling of stress and blood pressure reactivity using mobile health data

   Ali Kargarandehkordi, Aditi Jaiswal, Agnik Banerjee, and 17 more authors

   npj Mental Health Research May 2026

   Abs https://doi.org/10.1038/s44184-026-00210-9

   Psychological stress is a key driver of short-term blood pressure (BP) elevations and cardiovascular risk, yet its moment-to-moment impact in daily life remains difficult to predict. In this longitudinal observational study, we collected multimodal data from 20 adults with self-reported hypertension, including continuous wearable-derived heart rate and activity, ecological momentary assessment (EMA) stress ratings, and ambulatory BP measurements in free-living conditions. The dataset comprised 3694 EMA responses and 3812 BP measurements collected over approximately four weeks per participant (mean 24.1 ± 8.5 days). We evaluated whether participant-specific (“personalized”) models outperform a single pooled population model. Two prediction tasks were examined: (i) prediction of near-term BP elevations from wearable signals and stress EMA responses and (ii) prediction of self-reported stress from wearable signals and BP. Across both tasks, personalized models consistently improved predictive performance. For BP prediction, personalized models achieved a mean AUROC of 0.803, exceeding the population model by 0.235, while for stress prediction they achieved a mean AUROC of 0.849, exceeding the population model by 0.208. These findings suggest that personalized wearable-based models can capture individual patterns of stress and BP dynamics, with direct implications for precision mental health assessment and just-in-time adaptive intervention design in future work.

2025

1. Current Challenges and Opportunities for Active and Passive Data Collection for Mobile Health Sensing: A Scoping Review

   Christopher Slade, Yinan Sun, Wei Cheng Chao, and 3 more authors

   JAMIA Open Jul 2025

   Abs https://doi.org/10.1093/jamiaopen/ooaf025

   Objective: Mobile and ubiquitous devices enable health data collection “in a free-living environment” to support applications such as remote patient monitoring and adaptive digital interventions using machine learning (ML). Despite their potential, significant data collection challenges persist, including issues related to user compliance with reporting data, passive data consistency, and authorization. This scoping review identifies and analyzes these challenges, focusing on barriers to effective data collection.
   Methods: We searched IEEE, ACM, and Web of Science for papers involving training ML models using both active and passive mobile sensing. We used the following search terms: ’mobile OR ubiquitous’, ’EMA’, ’health’, ’passive’, and ’deep learning OR machine learning.’ We only included papers that collected both passive and active data and excluded papers that used a pre-existing data set.
   Results: A total of 77 studies met the inclusion criteria. These studies utilized smartphones, smartwatches, wearable devices, and environmental sensors for data collection. Several studies reported challenges with participant compliance in active data collection, while passive data collection faced data consistency and authorization issues. Efforts to address these challenges were documented in some but not all studies. Using this information, we outline current challenges and corresponding opportunities for data collection in mobile sensing studies.
   Discussion: ML techniques can reduce participant burden in active data collection by timing prompts, auto-filling responses, and minimizing prompt frequency. Simplified interfaces such as smartwatch prompts can further improve compliance. For passive data collection, techniques such as optimization of recording times to preserve battery life and motivational techniques to encourage proper device use can increase data consistency.
   Conclusion: Mobile sensing offers opportunities for developing intelligent mobile health applications but faces data collection challenges with respect to factors such as compliance, consistency, and authorization. Innovations in ML and user interface design show promise for addressing these barriers.

2. Associations Between Social Determinants of Health and Adherence in Mobile-Based Ecological Momentary Assessment: Scoping Review

   Yinan Sun, Aditi Jaiswal, Christopher Slade, and 3 more authors

   J Med Internet Res Sep 2025

   Abs https://doi.org/10.2196/69831

   Background: Ecological momentary assessment (EMA) involves repeated prompts to capture real-time self-reported health outcomes and behaviors via mobile devices. With the rise of mobile health (mHealth) technologies, EMA has been applied across diverse populations and health domains. However, the extent to which EMA engagement and data quality vary across social determinants of health (SDoH) remains underexplored. Emerging evidence suggests that EMA adherence and data completeness may be sometimes associated with participant characteristics such as socioeconomic status, race/ethnicity, and education level. These associations may sometimes influence who engages with EMA protocols and the types of contextual data captured. Despite growing interest in these patterns, no review to date has synthesized evidence on how SDoH relate to EMA compliance and engagement. Objective: We conducted a scoping review to study two research questions: (R1) how EMA compliance rates in health studies can differ across SDoH and (R2) what types of SDoH have been identified through EMA health studies. Methods: Following PRISMA-ScR guidelines, we searched PubMed, Web of Science, and EBSCOhost using two sets of queries targeting EMA and its relationship to SDoH. Eligible studies were peer reviewed, were published in English between 2013 and 2024, and used mobile-based EMA methods. Studies were included if they (1) reported on differences in EMA compliance by SDoH or (2) reported at least one SDoH observed or uncovered during an EMA study. We used the social ecological model (SEM) as a guiding framework to categorize and interpret SDoH across individual, interpersonal, community, and societal levels. A qualitative thematic synthesis was conducted to iteratively and collaboratively extract, categorize, and review determinants. Results: We analyzed 48 eligible studies, of which 35 addressed R1 by examining compliance patterns across various SDoH. Using the SEM, we identified 13 determinants categorized across 4 levels: individual (eg, daily routine, biological sex, age, socioeconomic status, language, education, and race or ethnicity), interpersonal (eg, social support), community and organizational (eg, social context, social acceptance, stigmatization, and youth culture), and policy or societal (eg, systemic and structural barriers). These studies described differences in EMA response rates, compliance, and dropout associated with these determinants, often among vulnerable populations. The remaining 13 studies addressed R2, demonstrating examples of the types of SDoH that EMA research can uncover, including family culture, social support, social contexts, stigmatization, gender norms, heroic narratives, LGBTQ+ culture, racial discrimination, and systematic and structural barriers. Conclusions: This scoping review illustrates how EMA compliance rates can differ across SDoH and highlights the potential of EMA to uncover social and cultural factors linked to health behaviors and outcomes. Our findings underscore the importance of integrating SDoH considerations into EMA study designs to capture context-specific sociocultural dynamics.

2024

1. Personalized Deep Learning for Substance Use in Hawaii: Protocol for a Passive Sensing and Ecological Momentary Assessment Study

   Yinan Sun, Ali Kargarandehkordi, Christopher Slade, and 5 more authors

   JMIR Research Protocols Feb 2024

   https://doi.org/10.2196/46493
2. Design Guidelines for Improving Mobile Sensing Data Collection: Prospective Mixed Methods Study

   Christopher Slade, Roberto M Benzo, and Peter Washington

   J Med Internet Res Nov 2024

   Abs https://doi.org/10.2196/55694

   Background: Machine learning models often use passively recorded sensor data streams as inputs to train machine learning models that predict outcomes captured through ecological momentary assessments (EMA). Despite the growth of mobile data collection, challenges in obtaining proper authorization to send notifications, receive background events, and perform background tasks persist. Objective: We investigated challenges faced by mobile sensing apps in real-world settings in order to develop design guidelines. For active data, we compared 2 prompting strategies: setup prompting, where the app requests authorization during its initial run, and contextual prompting, where authorization is requested when an event or notification occurs. Additionally, we evaluated 2 passive data collection paradigms: collection during scheduled background tasks and persistent reminders that trigger passive data collection. We investigated the following research questions (RQs): (RQ1) how do setup prompting and contextual prompting affect scheduled notification delivery and the response rate of notification-initiated EMA? (RQ2) Which authorization paradigm, setup or contextual prompting, is more successful in leading users to grant authorization to receive background events? and (RQ3) Which polling-based method, persistent reminders or scheduled background tasks, completes more background sessions? Methods: We developed mobile sensing apps for iOS and Android devices and tested them through a 30-day user study asking college students (n=145) about their stress levels. Participants responded to a daily EMA question to test active data collection. The sensing apps collected background location events, polled for passive data with persistent reminders, and scheduled background tasks to test passive data collection. Results: For RQ1, setup and contextual prompting yielded no significant difference (ANOVA F1,144=0.0227; P=.88) in EMA compliance, with an average of 23.4 (SD 7.36) out of 30 assessments completed. However, qualitative analysis revealed that contextual prompting on iOS devices resulted in inconsistent notification deliveries. For RQ2, contextual prompting for background events was 55.5% (\chi21=4.4; P=.04) more effective in gaining authorization. For RQ3, users demonstrated resistance to installing the persistent reminder, but when installed, the persistent reminder performed 226.5% more background sessions than traditional background tasks. Conclusions: We developed design guidelines for improving mobile sensing on consumer mobile devices based on our qualitative and quantitative results. Our qualitative results demonstrated that contextual prompts on iOS devices resulted in inconsistent notification deliveries, unlike setup prompting on Android devices. We therefore recommend using setup prompting for EMA when possible. We found that contextual prompting is more efficient for authorizing background events. We therefore recommend using contextual prompting for passive sensing. Finally, we conclude that developing a persistent reminder and requiring participants to install it provides an additional way to poll for sensor and user data and could improve data collection to support adaptive interventions powered by machine learning.

3. Monitoring Substance Use with Fitbit Biosignals: A Case Study on Training Deep Learning Models Using Ecological Momentary Assessments and Passive Sensing

   Shizhe Li, Chunzhi Fan, Ali Kargarandehkordi, and 6 more authors

   AI Nov 2024

   Abs https://doi.org/10.3390/ai5040131

   Substance use disorders affect 17.3% of Americans. Digital health solutions that use machine learning to detect substance use from wearable biosignal data can eventually pave the way for real-time digital interventions. However, difficulties in addressing severe between-subject data heterogeneity have hampered the adaptation of machine learning approaches for substance use detection, necessitating more robust technological solutions. We tested the utility of personalized machine learning using participant-specific convolutional neural networks (CNNs) enhanced with self-supervised learning (SSL) to detect drug use. In a pilot feasibility study, we collected data from 9 participants using Fitbit Charge 5 devices, supplemented by ecological momentary assessments to collect real-time labels of substance use. We implemented a baseline 1D-CNN model with traditional supervised learning and an experimental SSL-enhanced model to improve individualized feature extraction under limited label conditions. Results: Among the 9 participants, we achieved an average area under the receiver operating characteristic curve score across participants of 0.695 for the supervised CNNs and 0.729 for the SSL models. Strategic selection of an optimal threshold enabled us to optimize either sensitivity or specificity while maintaining reasonable performance for the other metric. Conclusion: These findings suggest that Fitbit data have the potential to enhance substance use monitoring systems. However, the small sample size in this study limits its generalizability to diverse populations, so we call for future research that explores SSL-powered personalization at a larger scale.

4. Personalized AI-Driven Real-Time Models to Predict Stress-Induced Blood Pressure Spikes Using Wearable Devices: Proposal for a Prospective Cohort Study

   Ali Kargarandehkordi, Christopher Slade, and Peter Washington

   JMIR Research Protocols Mar 2024

   https://doi.org/10.2196/55615

2020

1. Liberating Legacy System Data with Rails, Intelligent Use of Conflict Data with Automated Class Scheduling Tools.

   Stuart L Wolthuis, and Christopher Slade

   Information Systems Education Journal Mar 2020
2. Wilt Thou Not Place a Stumbling Block in My Way

   Christopher R Slade

   University Wide Devotional given at Brigham Young University - Hawaii. Given March 31 Mar 2020

2011

1. Using Interactive Graphical Tools to Overcome the" Collage Effect" in Collaborative Learning Environments

   Christopher R Slade, and Daniel D Suthers

   In 2011 44th Hawaii International Conference on System Sciences Mar 2011

   https://doi.org/10.1109/hicss.2011.477

2005

1. On-Disk Sequence Cache (ODSC): Using Excess Disk Capacity to Increase Performance

   Christopher R Slade

   Mar 2005