About

Dr. Eric Hekler is a Professor in the Herbert Wertheim School of Public Health and Human Longevity Science and the Design Lab at the University of California, San Diego (UCSD). His work is characterized by a transdisciplinary approach that intersects health psychology, design, systems science, and public health. His mission is to develop methods and processes that serve diverse populations and promote a more vital, just, and resonantly diverse society and planet. Dr. Hekler has numerous publications across multiple disciplines, active federal and foundation funding including as an NIH R01-funded principal investigator, and has contributed to creating new educational opportunities at UCSD. He is recognized internationally as an expert in applied health science methods and digital health, actively engaging in public health practice within the San Diego region.

Research topics

• Computer Science
• Artificial Intelligence
• Medicine
• Psychology
• Data science
• Social psychology
• Clinical psychology
• Applied psychology
• Management science
• World Wide Web
• Mathematics
• Engineering
• Nursing
• Psychiatry
• Psychotherapist
• Human–computer interaction

Selected publications

• Using Formal and Computational Modelling to Develop an Initial Within-Person System Dynamics Model of Relapse in Smoking Cessation: A Participatory, Iterative, Multi-Method Approach

  OSF Preprints (OSF Preprints) · 2026-03-27

  preprintOpen accessSenior author

  Popular relapse prevention theories are represented using natural language descriptions and lack temporal information about how phenomena of interest (i.e., ‘relapse’, ‘prolapse’, ‘abstinence’) are dynamically caused over time and within individuals. We drew on the Theory Construction Methodology to develop a formal and computational model of relapse in smoking cessation. We used a participatory, iterative, multi-method approach involving an informal theory and computational model review, stakeholder interviews with researchers, people with lived experience, stop smoking practitioners, and policymakers (N = 15) and in silico simulations. We propose an initial within-person system dynamics model of relapse (‘COMPLAPSE’) in which biopsychosocial factors (e.g., stressors, cigarette cues, cravings, self-efficacy) are represented as time-varying inputs and state variables. These factors jointly determine the momentary preference for each behavioural option (i.e., smoke a cigarette, use a regulatory strategy, do nothing), with the probability of selecting each option (i.e., the output) generated by a softmax function. The simulations highlight the model’s ability to generate representational patterns of relapse, prolapse and abstinence, thus providing an early sense-check of its explanatory adequacy. In addition, local sensitivity analyses demonstrate that systematic variation of selected model parameters leads to expected qualitative shifts from, for example, prolapse to relapse. We discuss the implications of our work for relapse prevention theories and real-world applications, including the development and optimisation of technology-mediated just-in-time adaptive interventions for relapse prevention in smoking cessation.

  Publisher

• Data-Driven Control of Nonlinear Process Systems Using a Three-Degree-of-Freedom Model-on-Demand Model Predictive Control Framework

  Industrial & Engineering Chemistry Research · 2025-04-21 · 3 citations

  articleCorresponding

  This paper presents a Model-on-Demand (MoD) approach to system identification and its integration with a three-degree-of-freedom Kalman filter-based Model Predictive Control (3DoF-KF MPC) framework. MoD estimation represents a hybrid of local and global modeling techniques, judiciously formulated to take advantage of both while not being computationally demanding. The 3DoF-KF MPC algorithm enables responses to set point changes and measured and unmeasured disturbances to be tuned intuitively and independently, thereby providing superior performance and ease of use over tuning with move suppression and error weights as done with conventional MPC algorithms. The algorithm proposed in this paper involves estimating MoD-based predictive models that are seamlessly integrated into 3DoF-KF MPC to generate control actions that vary with operating conditions. This results in notable performance enhancements in the context of both SISO and MIMO control compared to conventional ARX models. Performance and robustness of the 3DoF-KF MoD MPC framework are demonstrated in this paper through two case studies involving (i) epidemic control of a variant of the widely used SISO Susceptible-Infected-Removed (SIR) model and (ii) a nonlinear, highly interactive MIMO Continuous Stirred Tank Reactor (CSTR) model. The second case study further provides guidelines for designing informative databases for effective MoD-based MIMO identification and implementing 3DoF-KF MPC-based control for a demanding class of systems. Overall, this paper demonstrates technological and practical improvements in system identification and control of nonlinear SISO and MIMO systems through the synergistic integration of MoD estimation and 3DoF-KF MPC, providing an effective approach for operating complex nonlinear process systems.

  PublisherDOI

• A Scoping Review of Sensor-Based Capture of Eating and Drinking Occasions That Could Be Used for Enhancing Personalized Nutrition Interventions in Real Time

  Advances in Nutrition · 2025-12-20 · 1 citations

  articleOpen access

  BACKGROUND: Traditional dietary assessment methods used in nutrition research and practice are self-reported, burdensome, and prone to error, limiting utility. In recent years, sensor-based devices and machine learning approaches have emerged as promising tools for automating eating behavior detection and initiating different approaches to assessing intake. These technologies have potential to enhance dietary assessment and its accuracy, support personalized dietary interventions through real time, context-aware feedback, and reduce burden on respondents and practitioners. A prior 2021 review by the authors concluded that existing devices are not yet feasible for dietetic practice. OBJECTIVES: This study aims to conduct a scoping review of sensor-based devices capable of detecting eating and drinking and to evaluate whether recent advancements have improved their feasibility for use in real-world nutrition applications. METHODS: A scoping review was conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses for Scoping Reviews framework. Studies published between January 2022 and September 2025 that evaluated the performance of sensor-based devices in identifying food and/or beverage intake were included. Devices were evaluated against 6 feasibility criteria to assess real-world applicability: ≥80% accuracy, freedom in food and beverage selection; social acceptability and comfort; long battery life; real-time detection; and ability to detect both eating and drinking. RESULTS: Fifty studies (52 devices) were included: 19 wrist-worn, 8 neck-worn, 7 ear-worn, 7 glasses-type, 6 in the "other" category, and 5 multiposition devices. None met all 6 feasibility criteria. The most common unmet criterion was adequate battery life (n = 43), followed by real-time processing (n = 37), variety of foods or behaviors in testing (n = 31), detection of both eating and drinking (n = 31), social acceptability and comfort (n = 15), and accuracy (n = 10). CONCLUSIONS: Although no sensor-based devices met all criteria for real-world feasibility, recent advancements suggest meaningful progress in areas of social acceptability and computational efficiency. These improvements signal a shift toward more practical, user-friendly designs that may soon be capable of supporting automated dietary assessment and individualized nutrition care.

  PublisherDOI

• Bridging the Divide in Digital Therapeutics (DTx): Partnership Strategies for Broader Representation Across DTx Development and Deployment

  2025-06-19

  preprintOpen accessSenior author

  While Digital Therapeutics (DTx) are widely considered a key strategy to reach certain populations with unmet healthcare needs, a range of differences in the impact and adoption of DTx still exists. These differences are not just rooted in access, but also in gaps in knowledge about how to produce community-relevant DTx, primarily stemming from the implicit or explicit exclusion of those with both relevant trained expertise (gained through formal education or professional experience) and lived expertise (gained through personal and direct experience). This paper expands the traditional conceptualization of the digital divide beyond access to encompass four interconnected domains: the Digital Knowledge Divide, Digital Evidence Generation Divide, Digital Production Divide, and Digital Adoption Divide. Drawing on Ridgeway's cultural schema theory of status, we demonstrate how conventional team hierarchies in DTx development systematically allocate status and decision-making authority through automatic cultural defaults, credentials, professional roles, demographic characteristics, rather than through contextual assessment of who possesses the most relevant expertise for specific decisions. To address this challenge, we propose a theoretical framework for dynamic expertise integration that deliberately disrupts rapid-stabilizing hierarchies by creating explicit relational spaces where teams can recognize and value both lived and trained expertise contextually. We operationalize this framework through the DTx Team Building Worksheet, a practical tool that integrates team science approaches with Community-Led Transformation principles and Culturally and Community Responsive Design. The Worksheet provides structured processes for assessing diverse forms of expertise, defining roles dynamically, and identifying decision-making priorities that shift appropriately across the DTx lifecycle. This integrated approach including problem analysis, theoretical framework, and practical tool, offers a pathway toward more equitable DTx development by enabling teams to make status dynamics explicit, expand what counts as expertise, and establish new consensual norms about contextually-appropriate status allocation. We invite stakeholders across sectors to test and refine these tools in diverse contexts, recognizing that creating equitable DTx requires sustained commitment to partnerships that genuinely honor multiple forms of expertise and willingness to disrupt comfortable hierarchies in service of producing interventions truly designed for and with the communities they aim to serve.

  PublisherOA PDFDOI

• The California adverse childhood experiences screening roll-out: a survey study of ACEs screening implementation in primary care

  Frontiers in Public Health · 2025-04-02 · 2 citations

  articleOpen accessSenior author

  Background: California adopted universal screening of adverse childhood experiences (ACEs) in January 2020 and dedicated significant financial and human resources to "ACES Aware," a statewide campaign to scale ACEs screening throughout the state. Provider perspectives after the roll-out of ACEs Aware have been understudied. The aim of this study was to understand provider perspectives on universal ACEs screening in primary care. We explored indicators of acceptability, utility, and barriers and facilitators of screening for ACEs. We also investigated treatments offered for disclosed ACEs. Methods: A cross-sectional survey with quantitative and qualitative components was distributed via Facebook, Twitter, and electronic listservs between March and April 2022, 2 years after the launch of ACEs Aware. The survey included the validated and reliable "Acceptability of Implementation Measure" and "Feasibility of Implementation Measure" as well as multiple choice, ranking, and free-text items to understand determinants of screening and treatment approaches. Results: Eighty two primary care providers in California, working primarily in pediatrics (84%), completed the survey. The majority (78%) received training on assessing ACEs and 60% reported using the Pediatric ACEs and Related Life-events Screener (PEARLS). About 22% "strongly agree" that PEARLS is acceptable and 32% "strongly agree" that PEARLS is feasible. Only 17% "strongly agree" that they like PEARLS. The top barriers were: (1) insufficient time; (2) unclear treatment pathway for detected ACEs; and (3) inadequate staffing to perform screening. The top facilitators for screening were: (1) financial incentives for providers to screen; (2) financial incentives for organizational leadership to implement screening; and (3) leadership support of screeners. The top approaches for addressing ACEs were: (1) behavioral therapy; (2) case navigation; and (3) trauma-informed care. Conclusion: This study provided a first look at provider perspectives on ACEs screening and treatment in a sample of California providers. Most responding providers report currently screening for ACEs and using PEARLS. Perceptions of feasibility were slightly higher than for acceptability. Facilitators were largely top-down and organizational in nature, such as financial incentives and leadership support. Future directions could include an exploration into why some providers may find ACEs unappealing and research to identify effective and accessible treatment approaches for ACEs.

  PublisherOA PDFDOI

• Using Formal and Computational Modelling to Develop an Initial Within-Person System Dynamics Model of Relapse in Smoking Cessation: A Participatory, Iterative, Multi-Method Approach

  2025-12-12

  articleOpen accessSenior author

  Popular relapse prevention theories are represented using natural language descriptions and lack temporal information about how phenomena of interest (i.e., ‘relapse’, ‘prolapse’, ‘abstinence’) are dynamically caused over time and within individuals. We drew on the Theory Construction Methodology to develop a formal and computational model of relapse in smoking cessation. We used a participatory, iterative, multi-method approach involving an informal theory and computational model review, stakeholder interviews with researchers, people with lived experience, stop smoking practitioners, and policymakers (N = 15) and in silico simulations. We propose an initial within-person system dynamics model of relapse (‘COMPLAPSE’) in which biopsychosocial factors (e.g., stressors, cigarette cues, cravings, self-efficacy) are represented as time-varying inputs and state variables. These factors jointly determine the momentary preference for each behavioural option (i.e., smoke a cigarette, use a regulatory strategy, do nothing), with the probability of selecting each option (i.e., the output) generated by a softmax function. The simulations highlight the model’s ability to generate representational patterns of relapse, prolapse and abstinence, thus providing an early sense-check of its explanatory adequacy. In addition, local sensitivity analyses demonstrate that systematic variation of selected model parameters leads to expected qualitative shifts from, for example, prolapse to relapse. We discuss the implications of our work for relapse prevention theories and real-world applications, including the development and optimisation of technology-mediated just-in-time adaptive interventions for relapse prevention in smoking cessation.

  PublisherDOI

• Optimizing and Testing an Individualized and Adaptive Physical Activity Digital Health Intervention: Protocol for a Control Optimization Trial Embedded Within a Randomized Controlled Trial

  JMIR Research Protocols · 2025-06-11 · 1 citations

  articleOpen accessSenior author

  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.

  PublisherDOI

• Learning the Capacity to Feel Better: Toward a Theory of Tuning Through Guided-Experimentation

  2025-06-12

  preprintOpen access

  Experiment in a Box (XB), is a framework that supports sustainable health practices through guided, time-limited experiments. XB invites individuals to test and adapt simple health heuristics, such as movement or eating strategies, within their everyday lives. While our previous studies demonstrated XB’s positive impact on health behaviors, questions remained about how individuals establish heuristics in XB and how the experience fosters the development of interoceptive awareness, which supports the ability to judge whether actions are “healthy” or not by sensing and identifying bodily signals (e.g., finding fullness when eating). Using Theory Construction Methodology (TCM), we explored qualitative data from participant’s experiences during the Build Burn Better study series 2023, resulting in a prototheory of tuning. We offer tuning as a process through which individuals cultivate interoceptive awareness and use it, rather than external metrics, to guide experimentation and determine which actions support healthier physical, mental, and social states. If successful, this line of research will yield a formal, empirically validated model of tuning to inform the design of future digital health technologies. While still emerging, we propose tuning as a dynamic, person-centered, and potentially scalable process for guiding health behavior through felt experience at both individual and collective levels.

  PublisherDOI

• A dynamic Bayesian network approach to modeling engagement and walking behavior: insights from a yearlong micro-randomized trial ( <i>Heartsteps II</i> )

  Health Psychology and Behavioral Medicine · 2025-09-18

  articleOpen access

  INTRODUCTION: Mobile health (mHealth) technologies such as wearable activity trackers (e.g. Fitbit) and digital applications (apps), can support behavior change in real-world contexts. Since effectiveness is dependent, in part, on participants' engagement with the digital technology (e.g. app page views) and the intervention components (e.g. anti-sedentary messages), there is a need for modeling approaches that support the investigation of engagement in digital interventions and the refinement of dynamic theories of behavior change. METHODS: Dynamic Bayesian Networks (DBN) were used to model the idiographic (individual) dynamic relationships between a participant's daily app engagement (page views), walking behavior, and intervention messages, accounting for context (e.g. temperature), and psychological variables (e.g. perceived restedness and perceived busyness). Additionally, we explored differences in the resulting DBN models between participants of Hispanic/Latino and non-Hispanic/Latino White backgrounds. RESULTS: Data from 10 participants in the HeartSteps II study (n = 5 Hispanic/Latinos and n = 5 non-Hispanic/Latino Whites) was used. Across participants (100%, n = 10), there was a strong positive effect of the number of messages/prompts received on their daily app page views with a predicted increase range of 12.84 (12.19-13.57) to 25.84 (24.28-27.59) app page views per day per message received. Among the majority of Hispanic/Latino participants (n = 4/5, 80%), there was a strong positive relationship between daily app page views and walking behavior with predictions ranging from a mean of 6.70 (6.37-7.05) to 10.93 (10.14-11.78) steps per minute of Fitbit wear time per app page view. Both groups showed idiographic differences in the effects of temperature and perceived busyness on walking behavior. CONCLUSION: The results demonstrate the benefits of DBNs to model the daily-level idiographic behavioral dynamics of engagement in digital intervention studies. This approach can be leveraged to support the refinement of dynamic theories of behavior change and improving personalized mHealth intervention strategies.

  PublisherDOI

• Parkour and Intrinsic Motivation: An Exploratory Multimethod Analysis of Self-Determination Theory in an Emerging Sport

  International Journal of Environmental Research and Public Health · 2025-10-27

  articleOpen access

  Self Determination Theory posits that individuals may be more likely to initiate and maintain behaviors tied to intrinsic (vs. extrinsic) motivations and may provide a useful framework for understanding youth participation in novel sports. Using the Intrinsic Motivation Inventory (IMI) and Patient-Centered Assessment and Counseling for Exercise Plus Nutrition (PACE+) surveys, motivation and physical activity habits were explored in 27 children/adolescents (ages 7-16) enrolled in Parkour, an individual, non-competitive youth sport. Fifteen Parkour participants were also interviewed to gain an understanding of their motivations for participating. Study participants had high median IMI subscale scores related to interest/enjoyment (6.71/7) and perceived choice (6.40/7) compared to the whole scale. Similarly median sub-scale Pros and Self-Efficacy scores for physical activity from the PACE+ were high (4.25/5 and 3.91/5, respectively). The themes of autonomy and enjoyment were consistently reported in the qualitative interviews, expanding on the quantitative results. Other themes included appreciation for camaraderie, creativity, and a drive for improvement. These results provide early evidence that Parkour, and similar lifestyle sports, may be tied more to intrinsic than extrinsic motivations and could have potential for adoption and maintenance by youth with low motivation to engage in physical activity to promote healthy behaviors.

  PublisherDOI

Recent grants

• SCH: Control Systems Engineering for Counteracting Notification Fatigue: An Examination of Health Behavior Change

  NIH · $995k · 2019–2024

• Optimizing Individualized and Adaptive mHealth Interventions via Control Systems Engineering Methods

  NIH · $3.8M · 2020–2027

• EAGER: Defining a Dynamical Behavioral Model to Support a Just in Time Adaptive Intervention

  NSF · $288k · 2014–2016

• Advanced data analytics training for behavioral and social sciences research

  NIH · $1.3M · 2020–2025

Frequent coauthors

• Matthew P. Buman

  Arizona State University

  152 shared

• Guillaume Chevance

  University of California, San Diego

  95 shared

• Job Godino

  Family Health Centers of San Diego

  88 shared

• Natalie M. Golaszewski

  Human Longevity (United States)

  69 shared

• Daniel E. Rivera

  69 shared

• Kevin Patrick

  66 shared

• Predrag Klasnja

  University of Michigan–Ann Arbor

  54 shared

• Jennifer J. Otten

  University of Washington

  45 shared

Labs

• Agile Science Design LabPI

Education

• Ph.D., Public Health

  University of California, San Diego

  2009

• M.S., Public Health

  University of California, San Diego

  2005

• B.A., Environmental Science

  University of California, San Diego

  2001