1
|
Suh J, Howe E, Lewis R, Hernandez J, Saha K, Althoff T, Czerwinski M. Toward Tailoring Just-in-Time Adaptive Intervention Systems for Workplace Stress Reduction: Exploratory Analysis of Intervention Implementation. JMIR Ment Health 2024; 11:e48974. [PMID: 39264703 DOI: 10.2196/48974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 06/05/2024] [Accepted: 07/17/2024] [Indexed: 09/13/2024] Open
Abstract
BACKGROUND Integrating stress-reduction interventions into the workplace may improve the health and well-being of employees, and there is an opportunity to leverage ubiquitous everyday work technologies to understand dynamic work contexts and facilitate stress reduction wherever work happens. Sensing-powered just-in-time adaptive intervention (JITAI) systems have the potential to adapt and deliver tailored interventions, but such adaptation requires a comprehensive analysis of contextual and individual-level variables that may influence intervention outcomes and be leveraged to drive the system's decision-making. OBJECTIVE This study aims to identify key tailoring variables that influence momentary engagement in digital stress reduction microinterventions to inform the design of similar JITAI systems. METHODS To inform the design of such dynamic adaptation, we analyzed data from the implementation and deployment of a system that incorporates passively sensed data across everyday work devices to send just-in-time stress reduction microinterventions in the workplace to 43 participants during a 4-week deployment. We evaluated 27 trait-based factors (ie, individual characteristics), state-based factors (ie, workplace contextual and behavioral signals and momentary stress), and intervention-related factors (ie, location and function) across 1585 system-initiated interventions. We built logistical regression models to identify the factors contributing to momentary engagement, the choice of interventions, the engagement given an intervention choice, the user rating of interventions engaged, and the stress reduction from the engagement. RESULTS We found that women (odds ratio [OR] 0.41, 95% CI 0.21-0.77; P=.03), those with higher neuroticism (OR 0.57, 95% CI 0.39-0.81; P=.01), those with higher cognitive reappraisal skills (OR 0.69, 95% CI 0.52-0.91; P=.04), and those that chose calm interventions (OR 0.43, 95% CI 0.23-0.78; P=.03) were significantly less likely to experience stress reduction, while those with higher agreeableness (OR 1.73, 95% CI 1.10-2.76; P=.06) and those that chose prompt-based (OR 6.65, 95% CI 1.53-36.45; P=.06) or video-based (OR 5.62, 95% CI 1.12-34.10; P=.12) interventions were substantially more likely to experience stress reduction. We also found that work-related contextual signals such as higher meeting counts (OR 0.62, 95% CI 0.49-0.78; P<.001) and higher engagement skewness (OR 0.64, 95% CI 0.51-0.79; P<.001) were associated with a lower likelihood of engagement, indicating that state-based contextual factors such as being in a meeting or the time of the day may matter more for engagement than efficacy. In addition, a just-in-time intervention that was explicitly rescheduled to a later time was more likely to be engaged with (OR 1.77, 95% CI 1.32-2.38; P<.001). CONCLUSIONS JITAI systems have the potential to integrate timely support into the workplace. On the basis of our findings, we recommend that individual, contextual, and content-based factors be incorporated into the system for tailoring as well as for monitoring ineffective engagements across subgroups and contexts.
Collapse
Affiliation(s)
- Jina Suh
- Microsoft Research, Redmond, WA, United States
| | - Esther Howe
- Idiographic Dynamics Lab, Department of Psychology, University of California, Berkeley, CA, United States
| | - Robert Lewis
- MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA, United States
| | | | - Koustuv Saha
- Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign, Urbana, IL, United States
| | - Tim Althoff
- Paul G Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, United States
| | - Mary Czerwinski
- Human-Centered Design and Engineering, University of Washington, Seattle, WA, United States
| |
Collapse
|
2
|
Bao Y, Bell L, Williamson E, Garnett C, Qian T. Estimating causal effects for binary outcomes using per-decision inverse probability weighting. Biostatistics 2024:kxae025. [PMID: 39078115 DOI: 10.1093/biostatistics/kxae025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 06/29/2024] [Accepted: 07/02/2024] [Indexed: 07/31/2024] Open
Abstract
Micro-randomized trials are commonly conducted for optimizing mobile health interventions such as push notifications for behavior change. In analyzing such trials, causal excursion effects are often of primary interest, and their estimation typically involves inverse probability weighting (IPW). However, in a micro-randomized trial, additional treatments can often occur during the time window over which an outcome is defined, and this can greatly inflate the variance of the causal effect estimator because IPW would involve a product of numerous weights. To reduce variance and improve estimation efficiency, we propose two new estimators using a modified version of IPW, which we call "per-decision IPW." The second estimator further improves efficiency using the projection idea from the semiparametric efficiency theory. These estimators are applicable when the outcome is binary and can be expressed as the maximum of a series of sub-outcomes defined over sub-intervals of time. We establish the estimators' consistency and asymptotic normality. Through simulation studies and real data applications, we demonstrate substantial efficiency improvement of the proposed estimator over existing estimators. The new estimators can be used to improve the precision of primary and secondary analyses for micro-randomized trials with binary outcomes.
Collapse
Affiliation(s)
- Yihan Bao
- Department of Statistics and Data Science, Yale University, 266 Whitney Avenue, New Haven, CT 06511, United States
| | - Lauren Bell
- Leeds Institute of Clinical Trials Research, University of Leeds, Level 10 Worsley Building Clarendon Way, Leeds, LS2 9NL, United Kingdom
| | - Elizabeth Williamson
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, United Kingdom
| | - Claire Garnett
- Department of Behavioural Science and Health, University College, Gower Street, London, WC1E 6BT, United Kingdom
| | - Tianchen Qian
- Department of Statistics, Donald Bren School of Information and Computer Sciences, University of California Irvine, Bren Hall 2019 Irvine, CA 92697, United States
| |
Collapse
|
3
|
Collins LM, Nahum-Shani I, Guastaferro K, Strayhorn JC, Vanness DJ, Murphy SA. Intervention Optimization: A Paradigm Shift and Its Potential Implications for Clinical Psychology. Annu Rev Clin Psychol 2024; 20:21-47. [PMID: 38316143 PMCID: PMC11245367 DOI: 10.1146/annurev-clinpsy-080822-051119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
To build a coherent knowledge base about what psychological intervention strategies work, develop interventions that have positive societal impact, and maintain and increase this impact over time, it is necessary to replace the classical treatment package research paradigm. The multiphase optimization strategy (MOST) is an alternative paradigm that integrates ideas from behavioral science, engineering, implementation science, economics, and decision science. MOST enables optimization of interventions to strategically balance effectiveness, affordability, scalability, and efficiency. In this review we provide an overview of MOST, discuss several experimental designs that can be used in intervention optimization, consider how the investigator can use experimental results to select components for inclusion in the optimized intervention, discuss the application of MOST in implementation science, and list future issues in this rapidly evolving field. We highlight the feasibility of adopting this new research paradigm as well as its potential to hasten the progress of psychological intervention science.
Collapse
Affiliation(s)
- Linda M Collins
- Department of Social and Behavioral Sciences, New York University, New York, NY, USA;
- Department of Biostatistics, New York University, New York, NY, USA
| | - Inbal Nahum-Shani
- Institute for Social Research, University of Michigan, Ann Arbor, Michigan, USA
| | - Kate Guastaferro
- Department of Social and Behavioral Sciences, New York University, New York, NY, USA;
| | - Jillian C Strayhorn
- Department of Social and Behavioral Sciences, New York University, New York, NY, USA;
| | - David J Vanness
- Department of Health Policy and Administration, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Susan A Murphy
- Departments of Statistics and Computer Science, Harvard University, Cambridge, Massachusetts, USA
| |
Collapse
|
4
|
Carey RL, Le H, Coffman DL, Nahum-Shani I, Thirumalai M, Hagen C, Baehr LA, Schmidt-Read M, Lamboy MSR, Kolakowsky-Hayner SA, Marino RJ, Intille SS, Hiremath SV. mHealth-Based Just-in-Time Adaptive Intervention to Improve the Physical Activity Levels of Individuals With Spinal Cord Injury: Protocol for a Randomized Controlled Trial. JMIR Res Protoc 2024; 13:e57699. [PMID: 38941145 PMCID: PMC11245659 DOI: 10.2196/57699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Revised: 04/27/2024] [Accepted: 05/03/2024] [Indexed: 06/29/2024] Open
Abstract
BACKGROUND The lack of regular physical activity (PA) in individuals with spinal cord injury (SCI) in the United States is an ongoing health crisis. Regular PA and exercise-based interventions have been linked with improved outcomes and healthier lifestyles among those with SCI. Providing people with an accurate estimate of their everyday PA level can promote PA. Furthermore, PA tracking can be combined with mobile health technology such as smartphones and smartwatches to provide a just-in-time adaptive intervention (JITAI) for individuals with SCI as they go about everyday life. A JITAI can prompt an individual to set a PA goal or provide feedback about their PA levels. OBJECTIVE The primary aim of this study is to investigate whether minutes of moderate-intensity PA among individuals with SCI can be increased by integrating a JITAI with a web-based PA intervention (WI) program. The WI program is a 14-week web-based PA program widely recommended for individuals with disabilities. A secondary aim is to investigate the benefit of a JITAI on proximal PA, defined as minutes of moderate-intensity PA within 120 minutes of a PA feedback prompt. METHODS Individuals with SCI (N=196) will be randomized to a WI arm or a WI+JITAI arm. Within the WI+JITAI arm, a microrandomized trial will be used to randomize participants several times a day to different tailored feedback and PA recommendations. Participants will take part in the 24-week study from their home environment in the community. The study has three phases: (1) baseline, (2) WI program with or without JITAI, and (3) PA sustainability. Participants will provide survey-based information at the initial meeting and at the end of weeks 2, 8, 16, and 24. Participants will be asked to wear a smartwatch every day for ≥12 hours for the duration of the study. RESULTS Recruitment and enrollment began in May 2023. Data analysis is expected to be completed within 6 months of finishing participant data collection. CONCLUSIONS The JITAI has the potential to achieve long-term PA performance by delivering tailored, just-in-time feedback based on the person's actual PA behavior rather than a generic PA recommendation. New insights from this study may guide intervention designers to develop engaging PA interventions for individuals with disability. TRIAL REGISTRATION ClinicalTrials.gov NCT05317832; https://clinicaltrials.gov/study/NCT05317832. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/57699.
Collapse
Affiliation(s)
- Rachel L Carey
- Department of Health and Rehabilitation Sciences, Temple University, Philadelphia, PA, United States
| | - Ha Le
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, United States
| | - Donna L Coffman
- Department of Psychology, University of South Carolina, Columbia, SC, United States
| | - Inbal Nahum-Shani
- Institute for Social Research, University of Michigan, Ann Arbor, MI, United States
| | - Mohanraj Thirumalai
- Division of Preventive Medicine, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, United States
| | - Cole Hagen
- Department of Health and Rehabilitation Sciences, Temple University, Philadelphia, PA, United States
| | - Laura A Baehr
- Department of Health and Rehabilitation Sciences, Temple University, Philadelphia, PA, United States
| | - Mary Schmidt-Read
- Magee Rehabilitation Hospital, Jefferson Health, Philadelphia, PA, United States
| | - Marlyn S R Lamboy
- MossRehab Hospital, Jefferson Health, Philadelphia, PA, United States
| | | | - Ralph J Marino
- Department of Rehabilitation Medicine, Thomas Jefferson University, Philadelphia, PA, United States
| | - Stephen S Intille
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, United States
| | - Shivayogi V Hiremath
- Department of Health and Rehabilitation Sciences, Temple University, Philadelphia, PA, United States
| |
Collapse
|
5
|
Nahum-Shani I, Greer ZM, Trella AL, Zhang KW, Carpenter SM, Rünger D, Elashoff D, Murphy SA, Shetty V. Optimizing an adaptive digital oral health intervention for promoting oral self-care behaviors: Micro-randomized trial protocol. Contemp Clin Trials 2024; 139:107464. [PMID: 38307224 PMCID: PMC11007589 DOI: 10.1016/j.cct.2024.107464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 12/19/2023] [Accepted: 01/25/2024] [Indexed: 02/04/2024]
Abstract
Dental disease continues to be one of the most prevalent chronic diseases in the United States. Although oral self-care behaviors (OSCB), involving systematic twice-a-day tooth brushing, can prevent dental disease, this basic behavior is not sufficiently practiced. Recent advances in digital technology offer tremendous potential for promoting OSCB by delivering Just-In-Time Adaptive Interventions (JITAIs)- interventions that leverage dynamic information about the person's state and context to effectively prompt them to engage in a desired behavior in real-time, real-world settings. However, limited research attention has been given to systematically investigating how to best prompt individuals to engage in OSCB in daily life, and under what conditions prompting would be most beneficial. This paper describes the protocol for a Micro-Randomized Trial (MRT) to inform the development of a JITAI for promoting ideal OSCB, namely, brushing twice daily, for two minutes each time, in all four dental quadrants (i.e., 2x2x4). Sensors within an electric toothbrush (eBrush) will be used to track OSCB and a matching mobile app (Oralytics) will deliver on-demand feedback and educational information. The MRT will micro-randomize participants twice daily (morning and evening) to either (a) a prompt (push notification) containing one of several theoretically grounded engagement strategies or (b) no prompt. The goal is to investigate whether, what type of, and under what conditions prompting increases engagement in ideal OSCB. The results will build the empirical foundation necessary to develop an optimized JITAI that will be evaluated relative to a suitable control in a future randomized controlled trial.
Collapse
Affiliation(s)
- Inbal Nahum-Shani
- Institute for Social Research, University of Michigan, United States of America.
| | - Zara M Greer
- School of Dentistry, University of California, Los Angeles, United States of America
| | - Anna L Trella
- School of Engineering and Applied Sciences, Harvard University, United States of America
| | - Kelly W Zhang
- School of Engineering and Applied Sciences, Harvard University, United States of America
| | | | - Dennis Rünger
- Division of General Internal Medicine and Health Services Research, University of California, Los Angeles, United States of America
| | - David Elashoff
- Division of General Internal Medicine and Health Services Research, Department of Biostatistics, and Department of Computational Medicine, University of California, Los Angeles, United States of America
| | - Susan A Murphy
- School of Engineering and Applied Sciences, Harvard University, United States of America
| | - Vivek Shetty
- School of Dentistry, University of California, Los Angeles, United States of America
| |
Collapse
|
6
|
Liu X, Qian T, Bell L, Chakraborty B. Incorporating nonparametric methods for estimating causal excursion effects in mobile health with zero-inflated count outcomes. Biometrics 2024; 80:ujae054. [PMID: 38837902 DOI: 10.1093/biomtc/ujae054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 03/12/2024] [Accepted: 05/20/2024] [Indexed: 06/07/2024]
Abstract
In mobile health, tailoring interventions for real-time delivery is of paramount importance. Micro-randomized trials have emerged as the "gold-standard" methodology for developing such interventions. Analyzing data from these trials provides insights into the efficacy of interventions and the potential moderation by specific covariates. The "causal excursion effect," a novel class of causal estimand, addresses these inquiries. Yet, existing research mainly focuses on continuous or binary data, leaving count data largely unexplored. The current work is motivated by the Drink Less micro-randomized trial from the UK, which focuses on a zero-inflated proximal outcome, i.e., the number of screen views in the subsequent hour following the intervention decision point. To be specific, we revisit the concept of causal excursion effect, specifically for zero-inflated count outcomes, and introduce novel estimation approaches that incorporate nonparametric techniques. Bidirectional asymptotics are established for the proposed estimators. Simulation studies are conducted to evaluate the performance of the proposed methods. As an illustration, we also implement these methods to the Drink Less trial data.
Collapse
Affiliation(s)
- Xueqing Liu
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, 169857, Singapore
| | - Tianchen Qian
- Department of Statistics, University of California, Irvine, Irvine, CA 92697, United States
| | - Lauren Bell
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, CB2 0SR, United Kingdom
- Department of Medical Statistics, The London School of Hygiene and Tropical Medicine, London, WC1E 7HT, United Kingdom
| | - Bibhas Chakraborty
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, 169857, Singapore
- Department of Statistics and Data Science, National University of Singapore, Singapore, 117546, Singapore
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, United States
| |
Collapse
|
7
|
Nahum-Shani I, Dziak JJ, Venera H, Pfammatter AF, Spring B, Dempsey W. Design of experiments with sequential randomizations on multiple timescales: the hybrid experimental design. Behav Res Methods 2024; 56:1770-1792. [PMID: 37156958 PMCID: PMC10961682 DOI: 10.3758/s13428-023-02119-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/28/2023] [Indexed: 05/10/2023]
Abstract
Psychological interventions, especially those leveraging mobile and wireless technologies, often include multiple components that are delivered and adapted on multiple timescales (e.g., coaching sessions adapted monthly based on clinical progress, combined with motivational messages from a mobile device adapted daily based on the person's daily emotional state). The hybrid experimental design (HED) is a new experimental approach that enables researchers to answer scientific questions about the construction of psychological interventions in which components are delivered and adapted on different timescales. These designs involve sequential randomizations of study participants to intervention components, each at an appropriate timescale (e.g., monthly randomization to different intensities of coaching sessions and daily randomization to different forms of motivational messages). The goal of the current manuscript is twofold. The first is to highlight the flexibility of the HED by conceptualizing this experimental approach as a special form of a factorial design in which different factors are introduced at multiple timescales. We also discuss how the structure of the HED can vary depending on the scientific question(s) motivating the study. The second goal is to explain how data from various types of HEDs can be analyzed to answer a variety of scientific questions about the development of multicomponent psychological interventions. For illustration, we use a completed HED to inform the development of a technology-based weight loss intervention that integrates components that are delivered and adapted on multiple timescales.
Collapse
Affiliation(s)
- Inbal Nahum-Shani
- Institute for Social Research, University of Michigan, Ann Arbor, MI, USA.
| | - John J Dziak
- Institute for Health Research and Policy, University of Illinois Chicago, Chicago, IL, USA
| | - Hanna Venera
- School of Public Health and Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Angela F Pfammatter
- College of Education, Health, and Human Sciences, The University of Tennessee Knoxville, Knoxville, TN, USA
- Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Bonnie Spring
- Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Walter Dempsey
- School of Public Health and Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| |
Collapse
|
8
|
Carpenter SM, Greer ZM, Newman R, Murphy SA, Shetty V, Nahum-Shani I. Developing Message Strategies to Engage Racial and Ethnic Minority Groups in Digital Oral Self-Care Interventions: Participatory Co-Design Approach. JMIR Form Res 2023; 7:e49179. [PMID: 38079204 PMCID: PMC10750234 DOI: 10.2196/49179] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 08/01/2023] [Accepted: 08/25/2023] [Indexed: 12/28/2023] Open
Abstract
BACKGROUND The prevention of oral health diseases is a key public health issue and a major challenge for racial and ethnic minority groups, who often face barriers in accessing dental care. Daily toothbrushing is an important self-care behavior necessary for sustaining good oral health, yet engagement in regular brushing remains a challenge. Identifying strategies to promote engagement in regular oral self-care behaviors among populations at risk of poor oral health is critical. OBJECTIVE The formative research described here focused on creating messages for a digital oral self-care intervention targeting a racially and ethnically diverse population. Theoretically grounded strategies (reciprocity, reciprocity-by-proxy, and curiosity) were used to promote engagement in 3 aspects: oral self-care behaviors, an oral care smartphone app, and digital messages. A web-based participatory co-design approach was used to develop messages that are resource efficient, appealing, and novel; this approach involved dental experts, individuals from the general population, and individuals from the target population-dental patients from predominantly low-income racial and ethnic minority groups. Given that many individuals from racially and ethnically diverse populations face anonymity and confidentiality concerns when participating in research, we used an approach to message development that aimed to mitigate these concerns. METHODS Messages were initially developed with feedback from dental experts and Amazon Mechanical Turk workers. Dental patients were then recruited for 2 facilitator-mediated group webinar sessions held over Zoom (Zoom Video Communications; session 1: n=13; session 2: n=7), in which they provided both quantitative ratings and qualitative feedback on the messages. Participants interacted with the facilitator through Zoom polls and a chat window that was anonymous to other participants. Participants did not directly interact with each other, and the facilitator mediated sessions by verbally asking for message feedback and sharing key suggestions with the group for additional feedback. This approach plausibly enhanced participant anonymity and confidentiality during the sessions. RESULTS Participants rated messages highly in terms of liking (overall rating: mean 2.63, SD 0.58; reciprocity: mean 2.65, SD 0.52; reciprocity-by-proxy: mean 2.58, SD 0.53; curiosity involving interactive oral health questions and answers: mean 2.45, SD 0.69; curiosity involving tailored brushing feedback: mean 2.77, SD 0.48) on a scale ranging from 1 (do not like it) to 3 (like it). Qualitative feedback indicated that the participants preferred messages that were straightforward, enthusiastic, conversational, relatable, and authentic. CONCLUSIONS This formative research has the potential to guide the design of messages for future digital health behavioral interventions targeting individuals from diverse racial and ethnic populations. Insights emphasize the importance of identifying key stimuli and tasks that require engagement, gathering multiple perspectives during message development, and using new approaches for collecting both quantitative and qualitative data while mitigating anonymity and confidentiality concerns.
Collapse
Affiliation(s)
- Stephanie M Carpenter
- College of Health Solutions, Arizona State University, Phoenix, AZ, United States
- Department of Psychology, University of Wisconsin-Madison, Madison, WI, United States
| | - Zara M Greer
- Oral and Maxillofacial Surgery, School of Dentistry, University of California, Los Angeles, Los Angeles, CA, United States
| | - Rebecca Newman
- Oral and Maxillofacial Surgery, School of Dentistry, University of California, Los Angeles, Los Angeles, CA, United States
| | - Susan A Murphy
- Department of Statistics, Harvard University, Cambridge, MA, United States
- Department of Computer Science, Harvard University, Cambridge, MA, United States
| | - Vivek Shetty
- Oral and Maxillofacial Surgery, School of Dentistry, University of California, Los Angeles, Los Angeles, CA, United States
| | - Inbal Nahum-Shani
- Institute for Social Research, University of Michigan, Ann Arbor, MI, United States
| |
Collapse
|
9
|
Nahum-Shani I, Naar S. Digital Adaptive Behavioral Interventions to Improve HIV Prevention and Care: Innovations in Intervention Approach and Experimental Design. Curr HIV/AIDS Rep 2023; 20:502-512. [PMID: 37924458 PMCID: PMC10988586 DOI: 10.1007/s11904-023-00671-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/06/2023] [Indexed: 11/06/2023]
Abstract
PURPOSE OF REVIEW Recent advances in digital technologies can be leveraged to adapt HIV prevention and treatment services to the rapidly changing needs of individuals in everyday life. However, to fully take advantage of these technologies, it is critical to effectively integrate them with human-delivered components. Here, we introduce a new experimental approach for optimizing the integration and adaptation of digital and human-delivered behavioral intervention components for HIV prevention and treatment. RECENT FINDINGS Typically, human-delivered components can be adapted on a relatively slow timescale (e.g., every few months or weeks), while digital components can be adapted much faster (e.g., every few days or hours). Thus, the systematic integration of these components requires an experimental approach that involves sequential randomizations on multiple timescales. Selecting an experimental approach should be motivated by the type of adaptive intervention investigators would like to develop, and the scientific questions they have about its construction.
Collapse
Affiliation(s)
- Inbal Nahum-Shani
- Institute for Social Research, University of Michigan, Ann Arbor, MI, USA.
| | - Sylvie Naar
- Center for Translational Behavioral Science, Florida State University, Tallahassee, FL, USA
| |
Collapse
|
10
|
Laure T, Engels RCME, Remmerswaal D, Spruijt-Metz D, Konigorski S, Boffo M. Optimization of a Transdiagnostic Mobile Emotion Regulation Intervention for University Students: Protocol for a Microrandomized Trial. JMIR Res Protoc 2023; 12:e46603. [PMID: 37889525 PMCID: PMC10638637 DOI: 10.2196/46603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Revised: 07/20/2023] [Accepted: 08/22/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Many university students experience mental health problems such as anxiety and depression. To support their mental health, a transdiagnostic mobile app intervention has been developed. The intervention provides short exercises rooted in various approaches (eg, positive psychology, mindfulness, self-compassion, and acceptance and commitment therapy) that aim to facilitate adaptive emotion regulation (ER) to help students cope with the various stressors they encounter during their time at university. OBJECTIVE The goals of this study are to investigate whether the intervention and its components function as intended and how participants engage with them. In addition, this study aims to monitor changes in distress symptoms and ER skills and identify relevant contextual factors that may moderate the intervention's impact. METHODS A sequential explanatory mixed methods design combining a microrandomized trial and semistructured interviews will be used. During the microrandomized trial, students (N=200) will be prompted via the mobile app twice a day for 3 weeks to evaluate their emotional states and complete a randomly assigned intervention (ie, an exercise supporting ER) or a control intervention (ie, a health information snippet). A subsample of participants (21/200, 10.5%) will participate in interviews exploring their user experience with the app and the completed exercises. The primary outcomes will be changes in emotional states and engagement with the intervention (ie, objective and subjective engagement). Objective engagement will be evaluated through log data (eg, exercise completion time). Subjective engagement will be evaluated through exercise likability and helpfulness ratings as well as user experience interviews. The secondary outcomes will include the distal outcomes of the intervention (ie, ER skills and distress symptoms). Finally, the contextual moderators of intervention effectiveness will be explored (eg, the time of day and momentary emotional states). RESULTS The study commenced on February 9, 2023, and the data collection was concluded on June 13, 2023. Of the 172 eligible participants, 161 (93.6%) decided to participate. Of these 161 participants, 137 (85.1%) completed the first phase of the study. A subsample of participants (18/172, 10.5%) participated in the user experience interviews. Currently, the data processing and analyses are being conducted. CONCLUSIONS This study will provide insight into the functioning of the intervention and identify areas for improvement. Furthermore, the findings will shed light on potential changes in the distal outcomes of the intervention (ie, ER skills and distress symptoms), which will be considered when designing a follow-up randomized controlled trial evaluating the full-scale effectiveness of this intervention. Finally, the results and data gathered will be used to design and train a recommendation algorithm that will be integrated into the app linking students to relevant content. TRIAL REGISTRATION ClinicalTrials.gov NCT05576883; https://www.clinicaltrials.gov/study/NCT05576883. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/46603.
Collapse
Affiliation(s)
- Tajda Laure
- Department of Psychology, Education, and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University of Rotterdam, Rotterdam, Netherlands
| | - Rutger C M E Engels
- Department of Psychology, Education, and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University of Rotterdam, Rotterdam, Netherlands
| | - Danielle Remmerswaal
- Department of Psychology, Education, and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University of Rotterdam, Rotterdam, Netherlands
| | - Donna Spruijt-Metz
- Dornsife Center for Economic & Social Research, University of Southern California, Los Angeles, CA, United States
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- Department of Psychology, University of Southern California, Los Angeles, CA, United States
| | - Stefan Konigorski
- Department of Statistics, Harvard University, Boston, MA, United States
- Digital Health Center, Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam, Germany
- Icahn School of Medicine at Mount Sinai, Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, United States
| | - Marilisa Boffo
- Department of Psychology, Education, and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University of Rotterdam, Rotterdam, Netherlands
| |
Collapse
|
11
|
Mitra S, Kroeger CM, Xu J, Avery L, Masedunskas A, Cassidy S, Wang T, Hunyor I, Wilcox I, Huang R, Chakraborty B, Fontana L. Testing the Effects of App-Based Motivational Messages on Physical Activity and Resting Heart Rate Through Smartphone App Compliance in Patients With Vulnerable Coronary Artery Plaques: Protocol for a Microrandomized Trial. JMIR Res Protoc 2023; 12:e46082. [PMID: 37782531 PMCID: PMC10580140 DOI: 10.2196/46082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 06/29/2023] [Accepted: 07/24/2023] [Indexed: 10/03/2023] Open
Abstract
BACKGROUND Achieving the weekly physical activity recommendations of at least 150-300 minutes of moderate-intensity or 75-150 minutes of vigorous-intensity aerobic exercise is important for reducing cardiometabolic risk, but evidence shows that most people struggle to meet these goals, particularly in the mid to long term. OBJECTIVE The Messages Improving Resting Heart Health (MIRTH) study aims to determine if (1) sending daily motivational messages through a research app is effective in improving motivation and in promoting adherence to physical activity recommendations in men and women with coronary heart disease randomized to a 12-month intensive lifestyle intervention, and (2) the time of the day when the message is delivered impacts compliance with exercise training. METHODS We will conduct a single-center, microrandomized trial. Participants will be randomized daily to either receive or not receive motivational messages over two 90-day periods at the beginning (phase 1: months 4-6) and at the end (phase 2: months 10-12) of the Lifestyle Vulnerable Plaque Study. Wrist-worn devices (Fitbit Inspire 2) and Bluetooth pairing with smartphones will be used to passively collect data for proximal (ie, physical activity duration, steps walked, and heart rate within 180 minutes of receiving messages) and distal (ie, change values for resting heart rate and total steps walked within and across both phases 1 and 2 of the trial) outcomes. Participants will be recruited from a large academic cardiology office practice (Central Sydney Cardiology) and the Royal Prince Alfred Hospital Departments of Cardiology and Radiology. All clinical investigations will be undertaken at the Charles Perkins Centre Royal Prince Alfred clinic. Individuals aged 18-80 years (n=58) with stable coronary heart disease who have low attenuation plaques based on a coronary computed tomography angiography within the past 3 months and have been randomized to an intensive lifestyle intervention program will be included in MIRTH. RESULTS The Lifestyle Vulnerable Plaque Study was funded in 2020 and started enrolling participants in February 2022. Recruitment for MIRTH commenced in November 2022. As of September 2023, 2 participants were enrolled in the MIRTH study and provided baseline data. CONCLUSIONS This MIRTH microrandomized trial will represent the single most detailed and integrated analysis of the effects of a comprehensive lifestyle intervention delivered through a customized mobile health app on smart devices on time-based motivational messaging for patients with coronary heart disease. This study will also help inform future studies optimizing for just-in-time adaptive interventions. TRIAL REGISTRATION Australian New Zealand Clinical Trials Registry ACTRN12622000731796; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=382861. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/46082.
Collapse
Affiliation(s)
- Sayan Mitra
- Central Clinical School, Faculty of Medicine and Health, The University of Sydney, Camperdown, Australia
- Charles Perkins Centre, The University of Sydney, Camperdown, Australia
| | - Cynthia M Kroeger
- Central Clinical School, Faculty of Medicine and Health, The University of Sydney, Camperdown, Australia
- Charles Perkins Centre, The University of Sydney, Camperdown, Australia
| | - Jing Xu
- Office of Education, Duke-National University of Singapore Medical School, Singapore, Singapore
- Program in Health Services and Systems Research, Duke-National University of Singapore Medical School, Singapore, Singapore
- Centre for Quantitative Medicine, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Leah Avery
- School of Health & Life Sciences, Teesside University, Tees Valley, England, United Kingdom
| | - Andrius Masedunskas
- Central Clinical School, Faculty of Medicine and Health, The University of Sydney, Camperdown, Australia
- Charles Perkins Centre, The University of Sydney, Camperdown, Australia
| | - Sophie Cassidy
- Central Clinical School, Faculty of Medicine and Health, The University of Sydney, Camperdown, Australia
- Charles Perkins Centre, The University of Sydney, Camperdown, Australia
| | - Tian Wang
- Central Clinical School, Faculty of Medicine and Health, The University of Sydney, Camperdown, Australia
- Charles Perkins Centre, The University of Sydney, Camperdown, Australia
| | - Imre Hunyor
- Central Clinical School, Faculty of Medicine and Health, The University of Sydney, Camperdown, Australia
- Department of Cardiology, Royal Prince Alfred Hospital, Sydney, Australia
- Central Sydney Cardiology, Royal Prince Alfred Medical Centre, Sydney, Australia
| | - Ian Wilcox
- Central Clinical School, Faculty of Medicine and Health, The University of Sydney, Camperdown, Australia
- Department of Cardiology, Royal Prince Alfred Hospital, Sydney, Australia
- Central Sydney Cardiology, Royal Prince Alfred Medical Centre, Sydney, Australia
| | - Robin Huang
- School of Computer Science, The University of Sydney, Darlington, Australia
| | - Bibhas Chakraborty
- Program in Health Services and Systems Research, Duke-National University of Singapore Medical School, Singapore, Singapore
- Centre for Quantitative Medicine, Duke-National University of Singapore Medical School, Singapore, Singapore
- Department of Statistics and Data Science, National University of Singapore, Singapore, Singapore
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, United States
| | - Luigi Fontana
- Central Clinical School, Faculty of Medicine and Health, The University of Sydney, Camperdown, Australia
- Charles Perkins Centre, The University of Sydney, Camperdown, Australia
- Department of Endocrinology, Royal Prince Alfred Hospital, Sydney, Australia
- Department of Clinical and Experimental Sciences, Brescia University, Brescia, Italy
| |
Collapse
|
12
|
Ader L, Schick A, Löffler M, Löffler A, Beiner E, Eich W, Vock S, Sirazitdinov A, Malone C, Hesser J, Hopp M, Ruckes C, Flor H, Tesarz J, Reininghaus U. Refocusing of Attention on Positive Events Using Monitoring-Based Feedback and Microinterventions for Patients With Chronic Musculoskeletal Pain in the PerPAIN Randomized Controlled Trial: Protocol for a Microrandomized Trial. JMIR Res Protoc 2023; 12:e43376. [PMID: 37728983 PMCID: PMC10551789 DOI: 10.2196/43376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 07/13/2023] [Accepted: 07/17/2023] [Indexed: 09/22/2023] Open
Abstract
BACKGROUND Chronic musculoskeletal pain (CMSP) affects between 13% and 47% of the population, with a global growth rate of 20.3% within the last 15 years, suggesting that there is a high need for effective treatments. Pain diaries have long been a common tool in nonpharmacological pain treatment for monitoring and providing feedback on patients' symptoms in daily life. More recently, positive refocusing techniques have come to be used, promoting pain-free episodes and positive outcomes rather than focusing on managing the pain. OBJECTIVE This study aims to evaluate the feasibility (ie, acceptability, intervention adherence, and fidelity) and initial signals of efficacy of the PerPAIN app, an ecological momentary intervention for patients with CMSP. The app comprises digitalized monitoring using the experience sampling method (ESM) and feedback. In addition, the patients receive 3 microinterventions targeted at refocusing of attention on positive events. METHODS In a microrandomized trial, we will recruit 35 patients with CMSP who will be offered the app for 12 weeks. Participants will be prompted to fill out 4 ESM monitoring questionnaires a day assessing information on their current context and the proximal outcome variables: absence of pain, positive mood, and subjective activity. Participants will be randomized daily and weekly to receive no feedback, verbal feedback, or visual feedback on proximal outcomes assessed by the ESM. In addition, the app will encourage participants to complete 3 microinterventions based on positive psychology and cognitive behavioral therapy techniques. These microinterventions are prompts to report joyful moments and everyday successes or to plan pleasant activities. After familiarizing themselves with each microintervention individually, participants will be randomized daily to receive 1 of the 3 exercises or none. We will assess whether the 2 feedback types and the 3 microinterventions increase proximal outcomes at the following time point. The microrandomized trial is part of the PerPAIN randomized controlled trial (German Clinical Trials Register DRKS00022792) investigating a personalized treatment approach to enhance treatment outcomes in CMSP. RESULTS Approval was granted by the Ethics Committee II of the University of Heidelberg on August 4, 2020. Recruitment for the microrandomized trial began in May 2021 and is ongoing at the time of submission. By October 10, 2022, a total of 24 participants had been enrolled in the microrandomized trial. CONCLUSIONS This trial will provide evidence on the feasibility of the PerPAIN app and the initial signals of efficacy of the different intervention components. In the next step, the intervention would need to be further refined and investigated in a definitive trial. This ecological momentary intervention presents a potential method for offering low-level accessible treatment to a wide range of people, which could have substantial implications for public health by reducing disease burden of chronic pain in the population. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/43376.
Collapse
Affiliation(s)
- Leonie Ader
- Department of Public Mental Health, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Anita Schick
- Department of Public Mental Health, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Martin Löffler
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Annette Löffler
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Eva Beiner
- Department of General Internal Medicine and Psychosomatics, Heidelberg University, Heidelberg, Germany
| | - Wolfgang Eich
- Department of General Internal Medicine and Psychosomatics, Heidelberg University, Heidelberg, Germany
| | - Stephanie Vock
- Department of General Internal Medicine and Psychosomatics, Heidelberg University, Heidelberg, Germany
| | - Andrei Sirazitdinov
- Data Analysis and Modeling, Mannheim Institute for Intelligent Systems in Medicine, Medical School Mannheim, Heidelberg University, Mannheim, Germany
| | - Christopher Malone
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Jürgen Hesser
- Data Analysis and Modeling, Mannheim Institute for Intelligent Systems in Medicine, Medical School Mannheim, Heidelberg University, Mannheim, Germany
- Central Institute for Scientific Computing, Heidelberg University, Heidelberg, Germany
- Central Institute for Computer Engineering, Heidelberg University, Heidelberg, Germany
- CZS Heidelberg Center for Model-Based AI, Heidelberg University, Heidelberg, Germany
| | - Michael Hopp
- Interdisciplinary Center for Clinical Trials, Johannes Gutenberg University Medical Center, Mainz, Germany
| | - Christian Ruckes
- Interdisciplinary Center for Clinical Trials, Johannes Gutenberg University Medical Center, Mainz, Germany
| | - Herta Flor
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Jonas Tesarz
- Department of General Internal Medicine and Psychosomatics, Heidelberg University, Heidelberg, Germany
| | - Ulrich Reininghaus
- Department of Public Mental Health, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Centre for Epidemiology and Public Health, Health Service and Population Research Department, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
- ESRC Centre for Society and Mental Health, King´s College London, London, United Kingdom
| |
Collapse
|
13
|
Xu J, Yan X, Figueroa C, Williams JJ, Chakraborty B. A flexible micro-randomized trial design and sample size considerations. Stat Methods Med Res 2023; 32:1766-1783. [PMID: 37491804 DOI: 10.1177/09622802231188513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
Technological advancements have made it possible to deliver mobile health interventions to individuals. A novel framework that has emerged from such advancements is the just-in-time adaptive intervention, which aims to suggest the right support to the individuals when their needs arise. The micro-randomized trial design has been proposed recently to test the proximal effects of the components of these just-in-time adaptive interventions. However, the extant micro-randomized trial framework only considers components with a fixed number of categories added at the beginning of the study. We propose a more flexible micro-randomized trial design which allows addition of more categories to the components during the study. Note that the number and timing of the categories added during the study need to be fixed initially. The proposed design is motivated by collaboration on the Diabetes and Mental Health Adaptive Notification Tracking and Evaluation study, which learns to deliver effective text messages to encourage physical activity among patients with diabetes and depression. We developed a new test statistic and the corresponding sample size calculator for the flexible micro-randomized trial using an approach similar to the generalized estimating equation for longitudinal data. Simulation studies were conducted to evaluate the sample size calculators and an R shiny application for the calculators was developed.
Collapse
Affiliation(s)
- Jing Xu
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
| | - Xiaoxi Yan
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore
| | - Caroline Figueroa
- Faculty of Technology, Policy and Management, Delft University of Technology, The Netherlands
- School of Social Welfare, University of California, Berkeley, USA
| | - Joseph Jay Williams
- Department of Computer Science, University of Toronto, ON, Canada
- Department of Statistical Sciences, University of Toronto, ON, Canada
- Department of Psychology, University of Toronto, ON, Canada
- Vector Institute for Artificial Intelligence Faculty Affiliate, University of Toronto, ON, Canada
- Department of Mechanical and Industrial Engineering, University of Toronto, ON, Canada
- Department of Economics, University of Toronto, ON, Canada
| | - Bibhas Chakraborty
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
- Department of Statistics and Data Science, National University of Singapore, Singapore
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| |
Collapse
|
14
|
Mishra SR, Dempsey W, Klasnja P. A Text Messaging Intervention for Priming the Affective Rewards of Exercise in Adults: Protocol for a Microrandomized Trial. JMIR Res Protoc 2023; 12:e46560. [PMID: 37656493 PMCID: PMC10504629 DOI: 10.2196/46560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 05/19/2023] [Accepted: 06/05/2023] [Indexed: 09/02/2023] Open
Abstract
BACKGROUND Physical activity is a critical target for health interventions, but effective interventions remain elusive. A growing body of work suggests that interventions targeting affective attitudes toward physical activity may be more effective for sustaining activity long term than those that rely on cognitive constructs alone, such as goal setting and self-monitoring. Anticipated affective response in particular is a promising target for intervention. OBJECTIVE We will evaluate the efficacy of an SMS text messaging intervention that manipulates anticipated affective response to exercise to promote physical activity. We hypothesize that reminding users of a positive postexercise affective state before their planned exercise sessions will increase their calories burned during this exercise session. We will deploy 2 forms of affective SMS text messages to explore the design space: low-reflection messages written by participants for themselves and high-reflection prompts that require users to reflect and respond. We will also explore the effect of the intervention on affective attitudes toward exercise. METHODS A total of 120 individuals will be enrolled in a 9-week microrandomized trial testing affective messages that remind users about feeling good after exercise (40% probability), control reminders (30% probability), or no message (30% probability). Two types of affective SMS text messages will be deployed: one requiring a response and the other in a read-only format. Participants will write the read-only messages themselves to ensure that the messages accurately reflect the participants' anticipated postexercise affective state. Affective attitudes toward exercise and intrinsic motivation for exercise will be measured at the beginning and end of the study. The weighted and centered least squares method will be used to analyze the effect of delivering the intervention versus not on calories burned over 4 hours around the time of the planned activity, measured by the Apple Watch. Secondary analyses will include the effect of the intervention on step count and active minutes, as well as an investigation of the effects of the intervention on affective attitudes toward exercise and intrinsic motivation for exercise. Participants will be interviewed to gain qualitative insights into intervention impact and acceptability. RESULTS Enrollment began in May 2023, with 57 participants enrolled at the end of July 2023. We anticipate enrolling 120 participants. CONCLUSIONS This study will provide early evidence about the effect of a repeated manipulation of anticipated affective response to exercise. The use of 2 different types of messages will yield insight into optimal design strategies for improving affective attitudes toward exercise. TRIAL REGISTRATION ClinicalTrials.gov NCT05582369; https://classic.clinicaltrials.gov/ct2/show/NCT05582369. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/46560.
Collapse
Affiliation(s)
- Sonali R Mishra
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Walter Dempsey
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States
| | - Predrag Klasnja
- School of Information, University of Michigan, Ann Arbor, MI, United States
| |
Collapse
|
15
|
Cohn ER, Qian T, Murphy SA. Sample size considerations for micro-randomized trials with binary proximal outcomes. Stat Med 2023; 42:2777-2796. [PMID: 37094566 PMCID: PMC10314739 DOI: 10.1002/sim.9748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 04/04/2023] [Accepted: 04/11/2023] [Indexed: 04/26/2023]
Abstract
Micro-randomized trials (MRTs) are a novel experimental design for developing mobile health interventions. Participants are repeatedly randomized in an MRT, resulting in longitudinal data with time-varying treatments. Causal excursion effects are the main quantities of interest in MRT primary and secondary analyses. We consider MRTs where the proximal outcome is binary and the randomization probability is constant or time-varying but not data-dependent. We develop a sample size formula for detecting a nonzero marginal excursion effect. We prove that the formula guarantees power under a set of working assumptions. We demonstrate via simulation that violations of certain working assumptions do not affect the power, and for those that do, we point out the direction in which the power changes. We then propose practical guidelines for using the sample size formula. As an illustration, the formula is used to size an MRT on interventions for excessive drinking. The sample size calculator is implemented in R package MRTSampleSizeBinary and an interactive R Shiny app. This work can be used in trial planning for a wide range of MRTs with binary proximal outcomes.
Collapse
Affiliation(s)
| | - Tianchen Qian
- Department of Statistics, University of California, Irvine
| | | |
Collapse
|
16
|
Carpenter SM, Yap JRT, Patrick ME, Morrell N, Dziak JJ, Almirall D, Yoon C, Nahum-Shani I. Self-relevant appeals to engage in self-monitoring of alcohol use: A microrandomized trial. PSYCHOLOGY OF ADDICTIVE BEHAVIORS 2023; 37:434-446. [PMID: 35834200 PMCID: PMC9843482 DOI: 10.1037/adb0000855] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
OBJECTIVE While self-monitoring can help mitigate alcohol misuse in young adults, engagement with digital self-monitoring is suboptimal. The present study investigates the utility of two types of digital prompts (reminders) to encourage young adults to self-monitor their alcohol use. These prompts leverage information that is self-relevant (i.e., represents and is valuable) to the person. METHOD Five hundred ninety-one college students (Mage = 18; 61% = female, 76% = White) were enrolled in an 8-week intervention study involving biweekly digital self-monitoring of their alcohol use. At baseline, participants selected an item they would like to purchase for themselves and their preferred charitable organization. Then, biweekly, participants were microrandomized to a prompt highlighting the opportunity to either (a) win their preferred item (self-interest prompt); or (b) donate to their preferred charity (prosocial prompt). Following self-monitoring completion, participants allocated reward points toward lottery drawings for their preferred item or charity. RESULTS The self-interest (vs. prosocial) prompt was significantly more effective in promoting proximal self-monitoring at the beginning of the study, Est = exp(.14) = 1.15; 95% confidence interval (CI) [1.01, 1.29], whereas the prosocial (vs. self-interest) prompt was significantly more effective at the end, Est = exp(-.17) = 0.84; 95% CI [0.70, 0.98]. Further, the prosocial (vs. self-interest) prompt was significantly more effective among participants who previously allocated all their reward points to drawings for their preferred item, Est = exp(-.15) = 0.86; 95% CI [.75, .97]. CONCLUSIONS These results suggest that the advantage of prompts that appeal to a person's self-interest (vs. prosocial) motives varies over time and based on what reward options participants prioritized in previous decisions. Theoretical and practical implications for intervention design are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Collapse
Affiliation(s)
| | | | | | - Nicole Morrell
- Institute for Translational Research, University of
Minnesota
| | - John J. Dziak
- Edna Bennett Pierce Prevention Research Center, The
Pennsylvania State University
| | | | - Carolyn Yoon
- Stephen M. Ross School of Business, University of
Michigan
| | | |
Collapse
|
17
|
Liu X, Deliu N, Chakraborty B. Microrandomized Trials: Developing Just-in-Time Adaptive Interventions for Better Public Health. Am J Public Health 2023; 113:60-69. [PMID: 36413704 PMCID: PMC9755932 DOI: 10.2105/ajph.2022.307150] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Just-in-time adaptive interventions (JITAIs) represent an intervention design that adapts the provision and type of support over time to an individual's changing status and contexts, intending to deliver the right support on the right occasion. As a novel strategy for delivering mobile health interventions, JITAIs have the potential to improve access to quality care in underserved communities and, thus, alleviate health disparities, a significant public health concern. Valid experimental designs and analysis methods are required to inform the development of JITAIs. Here, we briefly review the cutting-edge design of microrandomized trials (MRTs), covering both the classical MRT design and its outcome-adaptive counterpart. Associated statistical challenges related to the design and analysis of MRTs are also discussed. Two case studies are provided to illustrate the aforementioned concepts and designs throughout the article. We hope our work leads to better design and application of JITAIs, advancing public health research and practice. (Am J Public Health. 2023;113(1):60-69. https://doi.org/10.2105/AJPH.2022.307150).
Collapse
Affiliation(s)
- Xueqing Liu
- Xueqing Liu is with the Centre for Quantitative Medicine, Duke-National University of Singapore (NUS) Medical School, Singapore. Nina Deliu is with the Medical Research Council Biostatistics Unit, University of Cambridge, UK, and the Department of Methods and Models for Economics, Territory and Finance, Sapienza University of Rome, Italy. Bibhas Chakraborty is with the Centre for Quantitative Medicine and Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore; the Department of Statistics and Data Science, NUS, Singapore; and the Department of Biostatistics and Bioinformatics, Duke University, Durham, NC
| | - Nina Deliu
- Xueqing Liu is with the Centre for Quantitative Medicine, Duke-National University of Singapore (NUS) Medical School, Singapore. Nina Deliu is with the Medical Research Council Biostatistics Unit, University of Cambridge, UK, and the Department of Methods and Models for Economics, Territory and Finance, Sapienza University of Rome, Italy. Bibhas Chakraborty is with the Centre for Quantitative Medicine and Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore; the Department of Statistics and Data Science, NUS, Singapore; and the Department of Biostatistics and Bioinformatics, Duke University, Durham, NC
| | - Bibhas Chakraborty
- Xueqing Liu is with the Centre for Quantitative Medicine, Duke-National University of Singapore (NUS) Medical School, Singapore. Nina Deliu is with the Medical Research Council Biostatistics Unit, University of Cambridge, UK, and the Department of Methods and Models for Economics, Territory and Finance, Sapienza University of Rome, Italy. Bibhas Chakraborty is with the Centre for Quantitative Medicine and Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore; the Department of Statistics and Data Science, NUS, Singapore; and the Department of Biostatistics and Bioinformatics, Duke University, Durham, NC
| |
Collapse
|
18
|
Liao P, Qi Z, Wan R, Klasnja P, Murphy SA. Batch policy learning in average reward Markov decision processes. Ann Stat 2022; 50:3364-3387. [PMID: 37022318 PMCID: PMC10072865 DOI: 10.1214/22-aos2231] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
We consider the batch (off-line) policy learning problem in the infinite horizon Markov Decision Process. Motivated by mobile health applications, we focus on learning a policy that maximizes the long-term average reward. We propose a doubly robust estimator for the average reward and show that it achieves semiparametric efficiency. Further we develop an optimization algorithm to compute the optimal policy in a parameterized stochastic policy class. The performance of the estimated policy is measured by the difference between the optimal average reward in the policy class and the average reward of the estimated policy and we establish a finite-sample regret guarantee. The performance of the method is illustrated by simulation studies and an analysis of a mobile health study promoting physical activity.
Collapse
Affiliation(s)
- Peng Liao
- Department of Statistics, Harvard University
| | - Zhengling Qi
- Department of Decision Sciences, George Washington University
| | | | | | | |
Collapse
|
19
|
Kwasnicka D, Keller J, Perski O, Potthoff S, Ten Hoor GA, Ainsworth B, Crutzen R, Dohle S, van Dongen A, Heino M, Henrich JF, Knox L, König LM, Maltinsky W, McCallum C, Nalukwago J, Neter E, Nurmi J, Spitschan M, Van Beurden SB, Van der Laan LN, Wunsch K, Levink JJJ, Sanderman R. White Paper: Open Digital Health - accelerating transparent and scalable health promotion and treatment. Health Psychol Rev 2022; 16:475-491. [PMID: 35240931 DOI: 10.1080/17437199.2022.2046482] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
In this White Paper, we outline recommendations from the perspective of health psychology and behavioural science, addressing three research gaps: (1) What methods in the health psychology research toolkit can be best used for developing and evaluating digital health tools? (2) What are the most feasible strategies to reuse digital health tools across populations and settings? (3) What are the main advantages and challenges of sharing (openly publishing) data, code, intervention content and design features of digital health tools? We provide actionable suggestions for researchers joining the continuously growing Open Digital Health movement, poised to revolutionise health psychology research and practice in the coming years. This White Paper is positioned in the current context of the COVID-19 pandemic, exploring how digital health tools have rapidly gained popularity in 2020-2022, when world-wide health promotion and treatment efforts rapidly shifted from face-to-face to remote delivery. This statement is written by the Directors of the not-for-profit Open Digital Health initiative (n = 6), Experts attending the European Health Psychology Society Synergy Expert Meeting (n = 17), and the initiative consultant, following a two-day meeting (19-20th August 2021).
Collapse
Affiliation(s)
- Dominika Kwasnicka
- NHMRC CRE in Digital Technology to Transform Chronic Disease Outcomes, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.,Faculty of Psychology, SWPS University of Social Sciences and Humanities, Wrocław, Poland
| | - Jan Keller
- Department of Education and Psychology; Freie Universität Berlin, Berlin, Germany
| | - Olga Perski
- Department of Behavioural Science and Health, University College London, London, UK
| | - Sebastian Potthoff
- Department of Social Work, Education and Community Wellbeing, Northumbria University, Newcastle upon Tyne, UK
| | - Gill A Ten Hoor
- Department of Work & Social Psychology, Maastricht University, Maastricht, The Netherlands
| | - Ben Ainsworth
- Department of Psychology, University of Bath, Bath, UK
| | - Rik Crutzen
- Department of Health Promotion, Maastricht University/CAPHRI, Maastricht, the Netherlands
| | - Simone Dohle
- Department of Psychology, University of Cologne, Cologne, Germany and Institute of General Practice and Family Medicine, University Hospital Bonn, University of Bonn, Bonn, Germany
| | - Anne van Dongen
- Department of Psychology, Health, and Technology, University of Twente, Enschede, the Netherlands
| | - Matti Heino
- Faculty of Social Sciences, University of Helsinki, Helsinki, Finland
| | - Julia F Henrich
- Faculty of Social and Behavioural Sciences, Leiden University, Institute of Psychology, Unit of Health-, Medical- and Neuropsychology, Leiden, The Netherlands
| | - Liam Knox
- Sheffield Institute for Translational Neuroscience, The University of Sheffield, Sheffield, UK
| | - Laura M König
- Faculty of Life Sciences, University of Bayreuth, Bayreuth, Germany
| | - Wendy Maltinsky
- Faculty of Natural Sciences, Division of Psychology, University of Stirling, Stirling, UK
| | - Claire McCallum
- Centre for Digital Health and Care, Faculty of Engineering, University of Bristol, Bristol, UK
| | - Judith Nalukwago
- Center for Communication Programs, USAID-Social and Behavior Change Activity, Johns Hopkins University Bloomberg School of Public Health, Kampala, Uganda
| | - Efrat Neter
- Department of Behavioral Sciences, Ruppin Academic Center, Emeq Hefer, Israel
| | - Johanna Nurmi
- Faculty of Social Sciences, University of Helsinki, Helsinki, Finland.,University of Cambridge, Behavioural Science Group, Primary Care Unit, Institute of Public Health, Forvie Site, Cambridge, UK
| | - Manuel Spitschan
- TUM Department of Sport and Health Sciences (TUM SG), Technical University of Munich, Munich, Germany and Translational Sensory and Circadian Neuroscience, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | | | - L Nynke Van der Laan
- Department of Communication and Cognition, Tilburg University, Tilburg, The Netherlands
| | - Kathrin Wunsch
- Karlsruhe Institute of Technology, Institute of Sports and Sports Science, Karlsruhe, Germany
| | - Jasper J J Levink
- Levink Life Sciences BV & Stichting Feniks Ontwikkelingsbegeleiding, Utrecht, The Netherlands
| | - Robbert Sanderman
- Department of Psychology, Health, and Technology, University of Twente, Enschede, the Netherlands.,Department of Health Psychology, University Medical Center Groningen University of Groningen, Groningen, The Netherlands
| |
Collapse
|
20
|
Nahum-Shani I, Dziak JJ, Wetter DW. MCMTC: A Pragmatic Framework for Selecting an Experimental Design to Inform the Development of Digital Interventions. Front Digit Health 2022; 4:798025. [PMID: 35355685 PMCID: PMC8959436 DOI: 10.3389/fdgth.2022.798025] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 01/24/2022] [Indexed: 11/16/2022] Open
Abstract
Advances in digital technologies have created unprecedented opportunities to deliver effective and scalable behavior change interventions. Many digital interventions include multiple components, namely several aspects of the intervention that can be differentiated for systematic investigation. Various types of experimental approaches have been developed in recent years to enable researchers to obtain the empirical evidence necessary for the development of effective multiple-component interventions. These include factorial designs, Sequential Multiple Assignment Randomized Trials (SMARTs), and Micro-Randomized Trials (MRTs). An important challenge facing researchers concerns selecting the right type of design to match their scientific questions. Here, we propose MCMTC – a pragmatic framework that can be used to guide investigators interested in developing digital interventions in deciding which experimental approach to select. This framework includes five questions that investigators are encouraged to answer in the process of selecting the most suitable design: (1) Multiple-component intervention: Is the goal to develop an intervention that includes multiple components; (2) Component selection: Are there open scientific questions about the selection of specific components for inclusion in the intervention; (3) More than a single component: Are there open scientific questions about the inclusion of more than a single component in the intervention; (4) Timing: Are there open scientific questions about the timing of component delivery, that is when to deliver specific components; and (5) Change: Are the components in question designed to address conditions that change relatively slowly (e.g., over months or weeks) or rapidly (e.g., every day, hours, minutes). Throughout we use examples of tobacco cessation digital interventions to illustrate the process of selecting a design by answering these questions. For simplicity we focus exclusively on four experimental approaches—standard two- or multi-arm randomized trials, classic factorial designs, SMARTs, and MRTs—acknowledging that the array of possible experimental approaches for developing digital interventions is not limited to these designs.
Collapse
Affiliation(s)
- Inbal Nahum-Shani
- Insitute for Social Research, University of Michigan, Ann Arbor, MI, United States
- *Correspondence: Inbal Nahum-Shani
| | - John J. Dziak
- Edna Bennett Pierce Prevention Research Center, The Pennsylvania State University, State College, PA, United States
| | - David W. Wetter
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, United States
| |
Collapse
|
21
|
Mariakakis A, Karkar R, Patel SN, Kientz JA, Fogarty J, Munson SA. Using Health Concept Surveying to Elicit Usable Evidence: Case Studies of a Novel Evaluation Methodology. JMIR Hum Factors 2022; 9:e30474. [PMID: 34982038 PMCID: PMC8764610 DOI: 10.2196/30474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 09/15/2021] [Accepted: 10/09/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Developers, designers, and researchers use rapid prototyping methods to project the adoption and acceptability of their health intervention technology (HIT) before the technology becomes mature enough to be deployed. Although these methods are useful for gathering feedback that advances the development of HITs, they rarely provide usable evidence that can contribute to our broader understanding of HITs. OBJECTIVE In this research, we aim to develop and demonstrate a variation of vignette testing that supports developers and designers in evaluating early-stage HIT designs while generating usable evidence for the broader research community. METHODS We proposed a method called health concept surveying for untangling the causal relationships that people develop around conceptual HITs. In health concept surveying, investigators gather reactions to design concepts through a scenario-based survey instrument. As the investigator manipulates characteristics related to their HIT, the survey instrument also measures proximal cognitive factors according to a health behavior change model to project how HIT design decisions may affect the adoption and acceptability of an HIT. Responses to the survey instrument were analyzed using path analysis to untangle the causal effects of these factors on the outcome variables. RESULTS We demonstrated health concept surveying in 3 case studies of sensor-based health-screening apps. Our first study (N=54) showed that a wait time incentive could influence more people to go see a dermatologist after a positive test for skin cancer. Our second study (N=54), evaluating a similar application design, showed that although visual explanations of algorithmic decisions could increase participant trust in negative test results, the trust would not have been enough to affect people's decision-making. Our third study (N=263) showed that people might prioritize test specificity or sensitivity depending on the nature of the medical condition. CONCLUSIONS Beyond the findings from our 3 case studies, our research uses the framing of the Health Belief Model to elicit and understand the intrinsic and extrinsic factors that may affect the adoption and acceptability of an HIT without having to build a working prototype. We have made our survey instrument publicly available so that others can leverage it for their own investigations.
Collapse
Affiliation(s)
- Alex Mariakakis
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Ravi Karkar
- School of Computer Science & Engineering, University of Washington, Seattle, WA, United States
| | - Shwetak N Patel
- School of Computer Science & Engineering, University of Washington, Seattle, WA, United States
| | - Julie A Kientz
- Department of Human Centered Design & Engineering, University of Washington, Seattle, WA, United States
| | - James Fogarty
- School of Computer Science & Engineering, University of Washington, Seattle, WA, United States
| | - Sean A Munson
- Department of Human Centered Design & Engineering, University of Washington, Seattle, WA, United States
| |
Collapse
|
22
|
Goldstein SP, Zhang F, Klasnja P, Hoover A, Wing RR, Thomas JG. Optimizing a Just-in-Time Adaptive Intervention to Improve Dietary Adherence in Behavioral Obesity Treatment: Protocol for a Microrandomized Trial. JMIR Res Protoc 2021; 10:e33568. [PMID: 34874892 PMCID: PMC8691411 DOI: 10.2196/33568] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 09/28/2021] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Behavioral obesity treatment (BOT) is a gold standard approach to weight loss and reduces the risk of cardiovascular disease. However, frequent lapses from the recommended diet stymie weight loss and prevent individuals from actualizing the health benefits of BOT. There is a need for innovative treatment solutions to improve adherence to the prescribed diet in BOT. OBJECTIVE The aim of this study is to optimize a smartphone-based just-in-time adaptive intervention (JITAI) that uses daily surveys to assess triggers for dietary lapses and deliver interventions when the risk of lapse is high. A microrandomized trial design will evaluate the efficacy of any interventions (ie, theory-driven or a generic alert to risk) on the proximal outcome of lapses during BOT, compare the effects of theory-driven interventions with generic risk alerts on the proximal outcome of lapse, and examine contextual moderators of interventions. METHODS Adults with overweight or obesity and cardiovascular disease risk (n=159) will participate in a 6-month web-based BOT while using the JITAI to prevent dietary lapses. Each time the JITAI detects elevated lapse risk, the participant will be randomized to no intervention, a generic risk alert, or 1 of 4 theory-driven interventions (ie, enhanced education, building self-efficacy, fostering motivation, and improving self-regulation). The primary outcome will be the occurrence of lapse in the 2.5 hours following randomization. Contextual moderators of intervention efficacy will also be explored (eg, location and time of day). The data will inform an optimized JITAI that selects the theory-driven approach most likely to prevent lapses in a given moment. RESULTS The recruitment for the microrandomized trial began on April 19, 2021, and is ongoing. CONCLUSIONS This study will optimize a JITAI for dietary lapses so that it empirically tailors the provision of evidence-based intervention to the individual and context. The finalized JITAI will be evaluated for efficacy in a future randomized controlled trial of distal health outcomes (eg, weight loss). TRIAL REGISTRATION ClinicalTrials.gov NCT04784585; http://clinicaltrials.gov/ct2/show/NCT04784585. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/33568.
Collapse
Affiliation(s)
- Stephanie P Goldstein
- Weight Control and Diabetes Research Center, The Miriam Hospital, Providence, RI, United States
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, United States
| | - Fengqing Zhang
- Department of Psychology, Drexel University, Philadelphia, PA, United States
| | - Predrag Klasnja
- School of Information, University of Michigan, Ann Arbor, MI, United States
| | - Adam Hoover
- Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, United States
| | - Rena R Wing
- Weight Control and Diabetes Research Center, The Miriam Hospital, Providence, RI, United States
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, United States
| | - John Graham Thomas
- Weight Control and Diabetes Research Center, The Miriam Hospital, Providence, RI, United States
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, United States
| |
Collapse
|
23
|
Nahum-Shani I, Rabbi M, Yap J, Philyaw-Kotov ML, Klasnja P, Bonar EE, Cunningham RM, Murphy SA, Walton MA. Translating strategies for promoting engagement in mobile health: A proof-of-concept microrandomized trial. Health Psychol 2021; 40:974-987. [PMID: 34735165 PMCID: PMC8738098 DOI: 10.1037/hea0001101] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE Mobile technologies allow for accessible and cost-effective health monitoring and intervention delivery. Despite these advantages, mobile health (mHealth) engagement is often insufficient. While monetary incentives may increase engagement, they can backfire, dampening intrinsic motivations and undermining intervention scalability. Theories from psychology and behavioral economics suggest useful nonmonetary strategies for promoting engagement; however, examinations of the applicability of these strategies to mHealth engagement are lacking. This proof-of-concept study evaluates the translation of theoretically-grounded engagement strategies into mHealth, by testing their potential utility in promoting daily self-reporting. METHOD A microrandomized trial (MRT) was conducted with adolescents and emerging adults with past-month substance use. Participants were randomized multiple times daily to receive theoretically-grounded strategies, namely reciprocity (the delivery of inspirational quote prior to self-reporting window) and nonmonetary reinforcers (e.g., the delivery of meme/gif following self-reporting completion) to improve proximal engagement in daily mHealth self-reporting. RESULTS Daily self-reporting rates (62.3%; n = 68) were slightly lower than prior literature, albeit with much lower financial incentives. The utility of specific strategies was found to depend on contextual factors pertaining to the individual's receptivity and risk for disengagement. For example, the effect of reciprocity significantly varied depending on whether this strategy was employed (vs. not employed) during the weekend. The nonmonetary reinforcement strategy resulted in different outcomes when operationalized in various ways. CONCLUSIONS While the results support the translation of the reciprocity strategy into this mHealth setting, the translation of nonmonetary reinforcement requires further consideration prior to inclusion in a full scale MRT. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Collapse
|
24
|
Nahum-Shani I, Potter LN, Lam CY, Yap J, Moreno A, Stoffel R, Wu Z, Wan N, Dempsey W, Kumar S, Ertin E, Murphy SA, Rehg JM, Wetter DW. The mobile assistance for regulating smoking (MARS) micro-randomized trial design protocol. Contemp Clin Trials 2021; 110:106513. [PMID: 34314855 PMCID: PMC8824313 DOI: 10.1016/j.cct.2021.106513] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 07/13/2021] [Accepted: 07/16/2021] [Indexed: 11/30/2022]
Abstract
Smoking is the leading preventable cause of death and disability in the U.S. Empirical evidence suggests that engaging in evidence-based self-regulatory strategies (e.g., behavioral substitution, mindful attention) can improve smokers' ability to resist craving and build self-regulatory skills. However, poor engagement represents a major barrier to maximizing the impact of self-regulatory strategies. This paper describes the protocol for Mobile Assistance for Regulating Smoking (MARS) - a research study designed to inform the development of a mobile health (mHealth) intervention for promoting real-time, real-world engagement in evidence-based self-regulatory strategies. The study will employ a 10-day Micro-Randomized Trial (MRT) enrolling 112 smokers attempting to quit. Utilizing a mobile smoking cessation app, the MRT will randomize each individual multiple times per day to either: (a) no intervention prompt; (b) a prompt recommending brief (low effort) cognitive and/or behavioral self-regulatory strategies; or (c) a prompt recommending more effortful cognitive or mindfulness-based strategies. Prompts will be delivered via push notifications from the MARS mobile app. The goal is to investigate whether, what type of, and under what conditions prompting the individual to engage in self-regulatory strategies increases engagement. The results will build the empirical foundation necessary to develop a mHealth intervention that effectively utilizes intensive longitudinal self-report and sensor-based assessments of emotions, context and other factors to engage an individual in the type of self-regulatory activity that would be most beneficial given their real-time, real-world circumstances. This type of mHealth intervention holds enormous potential to expand the reach and impact of smoking cessation treatments.
Collapse
Affiliation(s)
- Inbal Nahum-Shani
- Institute for Social Research, University of Michigan, Ann Arbor, MI, United States of America.
| | - Lindsey N Potter
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, United States of America
| | - Cho Y Lam
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, United States of America
| | - Jamie Yap
- Institute for Social Research, University of Michigan, Ann Arbor, MI, United States of America
| | - Alexander Moreno
- College of Computing, Georgia Institute of Technology, Atlanta, GA, United States of America
| | - Rebecca Stoffel
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, United States of America
| | - Zhenke Wu
- School of Public Health, University of Michigan, Ann Arbor, MI, United States of America
| | - Neng Wan
- Department of Geography, University of Utah, Salt Lake City, UT, United States of America
| | - Walter Dempsey
- School of Public Health, University of Michigan, Ann Arbor, MI, United States of America
| | - Santosh Kumar
- Department of Computer Science, University of Memphis, Memphis, TN, United States of America
| | - Emre Ertin
- Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH, United States of America
| | - Susan A Murphy
- Departments of Statistics & Computer Science, Harvard University, Cambridge, MA, United States of America
| | - James M Rehg
- College of Computing, Georgia Institute of Technology, Atlanta, GA, United States of America
| | - David W Wetter
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, United States of America
| |
Collapse
|
25
|
Qian T, Yoo H, Klasnja P, Almirall D, Murphy SA. Estimating time-varying causal excursion effect in mobile health with binary outcomes. Biometrika 2021; 108:507-527. [PMID: 34629476 DOI: 10.1093/biomet/asaa070] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Advances in wearables and digital technology now make it possible to deliver behavioral mobile health interventions to individuals in their everyday life. The micro-randomized trial is increasingly used to provide data to inform the construction of these interventions. In a micro-randomized trial, each individual is repeatedly randomized among multiple intervention options, often hundreds or even thousands of times, over the course of the trial. This work is motivated by multiple micro-randomized trials that have been conducted or are currently in the field, in which the primary outcome is a longitudinal binary outcome. The primary aim of such micro-randomized trials is to examine whether a particular time-varying intervention has an effect on the longitudinal binary outcome, often marginally over all but a small subset of the individual's data. We propose the definition of causal excursion effect that can be used in such primary aim analysis for micro-randomized trials with binary outcomes. Under rather restrictive assumptions one can, based on existing literature, derive a semiparametric, locally efficient estimator of the causal effect. Starting from this estimator, we develop an estimator that can be used as the basis of a primary aim analysis under more plausible assumptions. Simulation studies are conducted to compare the estimators. We illustrate the developed methods using data from the micro-randomized trial, BariFit. In BariFit, the goal is to support weight maintenance for individuals who received bariatric surgery.
Collapse
Affiliation(s)
- Tianchen Qian
- Department of Statistics, University of California, Irvine, Donald Bren Hall, Irvine, California 92697, U.S.A
| | - Hyesun Yoo
- Department of Statistics, University of Michigan, 323 West Hall, 1085 South University, Ann Arbor, Michigan 48109, U.S.A
| | - Predrag Klasnja
- School of Information, University of Michigan, 4364 North Quad, 105 South State Street, Ann Arbor, Michigan 48109, U.S.A
| | - Daniel Almirall
- Department of Statistics, University of Michigan, 323 West Hall, 1085 South University, Ann Arbor, Michigan 48109, U.S.A
| | - Susan A Murphy
- Department of Statistics, Harvard University, 1 Oxford Street, Cambridge, Massachusetts 02138, U.S.A
| |
Collapse
|
26
|
Tomkins S, Liao P, Klasnja P, Murphy S. IntelligentPooling: Practical Thompson Sampling for mHealth. Mach Learn 2021; 110:2685-2727. [PMID: 34621105 PMCID: PMC8494236 DOI: 10.1007/s10994-021-05995-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 12/10/2020] [Accepted: 05/11/2021] [Indexed: 11/25/2022]
Abstract
In mobile health (mHealth) smart devices deliver behavioral treatments repeatedly over time to a user with the goal of helping the user adopt and maintain healthy behaviors. Reinforcement learning appears ideal for learning how to optimally make these sequential treatment decisions. However, significant challenges must be overcome before reinforcement learning can be effectively deployed in a mobile healthcare setting. In this work we are concerned with the following challenges: 1) individuals who are in the same context can exhibit differential response to treatments 2) only a limited amount of data is available for learning on any one individual, and 3) non-stationary responses to treatment. To address these challenges we generalize Thompson-Sampling bandit algorithms to develop IntelligentPooling. IntelligentPooling learns personalized treatment policies thus addressing challenge one. To address the second challenge, IntelligentPooling updates each user's degree of personalization while making use of available data on other users to speed up learning. Lastly, IntelligentPooling allows responsivity to vary as a function of a user's time since beginning treatment, thus addressing challenge three.
Collapse
|
27
|
Klasnja P, Rosenberg DE, Zhou J, Anau J, Gupta A, Arterburn DE. A quality-improvement optimization pilot of BariFit, a mobile health intervention to promote physical activity after bariatric surgery. Transl Behav Med 2021; 11:530-539. [PMID: 32421187 DOI: 10.1093/tbm/ibaa040] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Bariatric surgery is the most effective treatment for severe obesity (body mass index >40), helping individuals lose, on average, 25%-29% of their body weight over the first year. However, many patients begin to plateau and regain weight within 12-24 months, and 20% of patients begin to regain weight within 6 months postsurgery. As physical activity (PA) is an important predictor of weight loss and maintenance postsurgery, there is a need for scalable, effective lifestyle interventions to help bariatric patients increase PA in order to maximize their weight loss and maintenance. To assess feasibility of using mobile health (mHealth) tools to support PA postsurgery, we conducted a quality-improvement optimization pilot of BariFit, an mHealth intervention that combines commercial devices and custom text messages. Fifty-one bariatric patients enrolled in a 16-week optimization pilot of BariFit. To assess feasibility, pre-post changes in PA were assessed using activPAL. In addition, the pilot randomized, using a 2 × 2 factorial design, two adaptive approaches to daily step goals (variable and 60th percentile goals) and provision of rest days (yes/no), and microrandomized provision of SMS-delivered activity suggestions five times a day for each participant. Adherence to using study equipment was over 95% at 16 weeks. Participants increased PA by 1,866 steps from baseline to end-of-study (p < .007). Participants who received variable step goals averaged 1,141 more steps per day (p = .096) than those who received 60th percentile goals. Activity suggestions had no effect. mHealth interventions are feasible for supporting PA postbariatric surgery.
Collapse
Affiliation(s)
- Predrag Klasnja
- Kaiser Permanente Washington Health Research Institute, Seattle WA, USA.,School of Information, University of Michigan, Ann Arbor, MI, USA
| | - Dori E Rosenberg
- Kaiser Permanente Washington Health Research Institute, Seattle WA, USA
| | - Jing Zhou
- Kaiser Permanente Washington Health Research Institute, Seattle WA, USA
| | - Jane Anau
- Kaiser Permanente Washington Health Research Institute, Seattle WA, USA
| | - Anirban Gupta
- Bariatric Surgery Program, Kaiser Permanente Bellevue, Seattle, WA, USA
| | - David E Arterburn
- Kaiser Permanente Washington Health Research Institute, Seattle WA, USA
| |
Collapse
|
28
|
Yao J, Brunskill E, Pan W, Murphy S, Doshi-Velez F. Power Constrained Bandits. PROCEEDINGS OF MACHINE LEARNING RESEARCH 2021; 149:209-259. [PMID: 34927078 PMCID: PMC8675738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Contextual bandits often provide simple and effective personalization in decision making problems, making them popular tools to deliver personalized interventions in mobile health as well as other health applications. However, when bandits are deployed in the context of a scientific study-e.g. a clinical trial to test if a mobile health intervention is effective-the aim is not only to personalize for an individual, but also to determine, with sufficient statistical power, whether or not the system's intervention is effective. It is essential to assess the effectiveness of the intervention before broader deployment for better resource allocation. The two objectives are often deployed under different model assumptions, making it hard to determine how achieving the personalization and statistical power affect each other. In this work, we develop general meta-algorithms to modify existing algorithms such that sufficient power is guaranteed while still improving each user's well-being. We also demonstrate that our meta-algorithms are robust to various model mis-specifications possibly appearing in statistical studies, thus providing a valuable tool to study designers.
Collapse
Affiliation(s)
- Jiayu Yao
- SEAS, Harvard University, Cambridge, MA, USA
| | | | - Weiwei Pan
- SEAS, Harvard University, Cambridge, MA, USA
| | | | | |
Collapse
|
29
|
Lewis CC, Hannon PA, Klasnja P, Baldwin LM, Hawkes R, Blackmer J, Johnson A. Optimizing Implementation in Cancer Control (OPTICC): protocol for an implementation science center. Implement Sci Commun 2021; 2:44. [PMID: 33892822 PMCID: PMC8062945 DOI: 10.1186/s43058-021-00117-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 01/28/2021] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND Evidence-based interventions (EBIs) could reduce cervical cancer deaths by 90%, colorectal cancer deaths by 70%, and lung cancer deaths by 95% if widely and effectively implemented in the USA. Yet, EBI implementation, when it occurs, is often suboptimal. This manuscript outlines the protocol for Optimizing Implementation in Cancer Control (OPTICC), a new implementation science center funded as part of the National Cancer Institute Implementation Science Consortium. OPTICC is designed to address three aims. Aim 1 is to develop a research program that supports developing, testing, and refining of innovative, efficient methods for optimizing EBI implementation in cancer control. Aim 2 is to support a diverse implementation laboratory of clinical and community partners to conduct rapid, implementation studies anywhere along the cancer care continuum for a wide range of cancers. Aim 3 is to build implementation science capacity in cancer control by training new investigators, engaging established investigators in cancer-focused implementation science, and contributing to the Implementation Science Consortium in Cancer. METHODS Three cores serve as OPTICC's foundation. The Administrative Core plans coordinates and evaluates the Center's activities and leads its capacity-building efforts. The Implementation Laboratory Core (I-Lab) coordinates a network of diverse clinical and community sites, wherein studies are conducted to optimize EBI implementation, implement cancer control EBIs, and shape the Center's agenda. The Research Program Core conducts innovative implementation studies, measurement and methods studies, and pilot studies that advance the Center's theme. A three-stage approach to optimizing EBI implementation is taken-(I) identify and prioritize determinants, (II) match strategies, and (III) optimize strategies-that is informed by a transdisciplinary team of experts leveraging multiphase optimization strategies and criteria, user-centered design, and agile science. DISCUSSION OPTICC will develop, test, and refine efficient and economical methods for optimizing EBI implementation by building implementation science capacity in cancer researchers through applications with our I-Lab partners. Once refined, OPTICC will disseminate its methods as toolkits accompanied by massive open online courses, and an interactive website, the latter of which seeks to simultaneously accumulate knowledge across OPTICC studies.
Collapse
Affiliation(s)
- Cara C Lewis
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Avenue, Suite 1600, Seattle, WA, 98101, USA.
| | - Peggy A Hannon
- Department of Health Services, University of Washington, Seattle, WA, USA
| | - Predrag Klasnja
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Avenue, Suite 1600, Seattle, WA, 98101, USA
- School of Information, University of Michigan, Ann Arbor, Michigan, USA
| | - Laura-Mae Baldwin
- Department of Family Medicine, University of Washington, Seattle, WA, USA
| | - Rene Hawkes
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Avenue, Suite 1600, Seattle, WA, 98101, USA
| | - Janell Blackmer
- Department of Health Services, University of Washington, Seattle, WA, USA
| | - Ashley Johnson
- Department of Family Medicine, University of Washington, Seattle, WA, USA
| |
Collapse
|
30
|
Kim M, Choi HJ. Digital Therapeutics for Obesity and Eating-Related Problems. Endocrinol Metab (Seoul) 2021; 36:220-228. [PMID: 33761233 PMCID: PMC8090472 DOI: 10.3803/enm.2021.107] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 02/17/2021] [Indexed: 12/21/2022] Open
Abstract
In recent years, digital technologies have rapidly advanced and are being applied to remedy medical problems. These technologies allow us to monitor and manage our physical and mental health in our daily lives. Since lifestyle modification is the cornerstone of the management of obesity and eating behavior problems, digital therapeutics (DTx) represent a powerful and easily accessible treatment modality. This review discusses the critical issues to consider for enhancing the efficacy of DTx in future development initiatives. To competently adapt and expand public access to DTx, it is important for various stakeholders, including health professionals, patients, and guardians, to collaborate with other industry partners and policy-makers in the ecosystem.
Collapse
Affiliation(s)
- Meelim Kim
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul,
Korea
- BK21 Plus Biomedical Science Project Team, Seoul National University College of Medicine, Seoul,
Korea
| | - Hyung Jin Choi
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul,
Korea
- BK21 Plus Biomedical Science Project Team, Seoul National University College of Medicine, Seoul,
Korea
| |
Collapse
|
31
|
Golbus JR, Dempsey W, Jackson EA, Nallamothu BK, Klasnja P. Microrandomized Trial Design for Evaluating Just-in-Time Adaptive Interventions Through Mobile Health Technologies for Cardiovascular Disease. Circ Cardiovasc Qual Outcomes 2021; 14:e006760. [PMID: 33430608 DOI: 10.1161/circoutcomes.120.006760] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Smartphone and wearable device use is rising broadly and can be leveraged for chronic disease management. Just-in-time adaptive interventions promise to deliver personalized, dynamic interventions directly to patients through use of push notifications from mobile devices. Although just-in-time adaptive interventions are a powerful tool for shaping health behavior, their application to cardiovascular disease management has been limited as they can be challenging to design. Herein, we provide a general overview and conceptual framework for microrandomized trials, a novel experimental study design that can be used to optimize just-in-time adaptive interventions. Microrandomized trials leverage mobile devices to sequentially randomize participants to types or levels of an intervention to determine the effectiveness of an intervention and time-varying moderators of those effects. Microrandomized trials are an efficient study design that can be used to determine which intervention components to include in just-in-time adaptive interventions and to optimize their decision rules while maintaining the strength of causal inference associated with traditional randomized controlled trials.
Collapse
Affiliation(s)
- Jessica R Golbus
- Division of Cardiovascular Diseases, Department of Internal Medicine (J.R.G., B.K.N.), University of Michigan, Ann Arbor
| | - Walter Dempsey
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor (W.D.)
| | - Elizabeth A Jackson
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Alabama at Birmingham (E.A.J.)
| | - Brahmajee K Nallamothu
- Division of Cardiovascular Diseases, Department of Internal Medicine (J.R.G., B.K.N.), University of Michigan, Ann Arbor
- Michigan Integrated Center for Health Analytics and Medical Prediction (MiCHAMP), Division of Cardiovascular Diseases, Department of Internal Medicine, University of Michigan (B.K.N.)
- The Center for Clinical Management and Research, Ann Arbor VA Medical Center, MI (B.K.N.)
| | - Predrag Klasnja
- School of Information (P.K.), University of Michigan, Ann Arbor
- Kaiser Permanente Washington Health Research Institute, Seattle (P.K.)
| |
Collapse
|
32
|
Hekler E, Tiro JA, Hunter CM, Nebeker C. Precision Health: The Role of the Social and Behavioral Sciences in Advancing the Vision. Ann Behav Med 2020; 54:805-826. [PMID: 32338719 PMCID: PMC7646154 DOI: 10.1093/abm/kaaa018] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND In 2015, Collins and Varmus articulated a vision for precision medicine emphasizing molecular characterization of illness to identify actionable biomarkers to support individualized treatment. Researchers have argued for a broader conceptualization, precision health. Precision health is an ambitious conceptualization of health, which includes dynamic linkages between research and practice as well as medicine, population health, and public health. The goal is a unified approach to match a full range of promotion, prevention, diagnostic, and treatment interventions to fundamental and actionable determinants of health; to not just address symptoms, but to directly target genetic, biological, environmental, and social and behavioral determinants of health. PURPOSE The purpose of this paper is to elucidate the role of social and behavioral sciences within precision health. MAIN BODY Recent technologies, research frameworks, and methods are enabling new approaches to measure, intervene, and conduct social and behavioral science research. These approaches support three opportunities in precision health that the social and behavioral sciences could colead including: (a) developing interventions that continuously "tune" to each person's evolving needs; (b) enhancing and accelerating links between research and practice; and (c) studying mechanisms of change in real-world contexts. There are three challenges for precision health: (a) methods of knowledge organization and curation; (b) ethical conduct of research; and (c) equitable implementation of precision health. CONCLUSIONS Precision health requires active coleadership from social and behavioral scientists. Prior work and evidence firmly demonstrate why the social and behavioral sciences should colead with regard to three opportunity and three challenge areas.
Collapse
Affiliation(s)
- Eric Hekler
- Department of Family Medicine and Public Health, School of Medicine, UC San Diego, La Jolla, CA, USA
- Center for Wireless and Population Health Systems, Qualcomm Institute, UC San Diego, La Jolla, CA, USA
- Design Lab, UC San Diego, La Jolla, CA, USA
| | - Jasmin A Tiro
- Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Harold C. Simmons Comprehensive Cancer Center, Dallas, TX, USA
| | - Christine M Hunter
- Office of Behavioral and Social Sciences Research, National Institutes of Health, Bethesda, MD, USA
| | - Camille Nebeker
- Department of Family Medicine and Public Health, School of Medicine, UC San Diego, La Jolla, CA, USA
- Center for Wireless and Population Health Systems, Qualcomm Institute, UC San Diego, La Jolla, CA, USA
- Design Lab, UC San Diego, La Jolla, CA, USA
| |
Collapse
|
33
|
Liao P, Klasnja P, Murphy S. Off-Policy Estimation of Long-Term Average Outcomes with Applications to Mobile Health. J Am Stat Assoc 2020; 116:382-391. [PMID: 33814653 PMCID: PMC8014957 DOI: 10.1080/01621459.2020.1807993] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 03/03/2020] [Accepted: 08/03/2020] [Indexed: 10/23/2022]
Abstract
Due to the recent advancements in wearables and sensing technology, health scientists are increasingly developing mobile health (mHealth) interventions. In mHealth interventions, mobile devices are used to deliver treatment to individuals as they go about their daily lives. These treatments are generally designed to impact a near time, proximal outcome such as stress or physical activity. The mHealth intervention policies, often called just-in-time adaptive interventions, are decision rules that map a individual's current state (e.g., individual's past behaviors as well as current observations of time, location, social activity, stress and urges to smoke) to a particular treatment at each of many time points. The vast majority of current mHealth interventions deploy expert-derived policies. In this paper, we provide an approach for conducting inference about the performance of one or more such policies using historical data collected under a possibly different policy. Our measure of performance is the average of proximal outcomes over a long time period should the particular mHealth policy be followed. We provide an estimator as well as confidence intervals. This work is motivated by HeartSteps, an mHealth physical activity intervention.
Collapse
Affiliation(s)
- Peng Liao
- Department of Statistics, University of Michigan
| | | | | |
Collapse
|
34
|
Kroska EB, Hoel S, Victory A, Murphy SA, McInnis MG, Stowe ZN, Cochran A. Optimizing an Acceptance and Commitment Therapy Microintervention Via a Mobile App With Two Cohorts: Protocol for Micro-Randomized Trials. JMIR Res Protoc 2020; 9:e17086. [PMID: 32965227 PMCID: PMC7542401 DOI: 10.2196/17086] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 07/28/2020] [Accepted: 08/03/2020] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Given gaps in the treatment of mental health, brief adaptive interventions have become a public health imperative. Transdiagnostic interventions may be particularly appropriate given high rates of medical comorbidity and the broader reach of transdiagnostic therapies. One such approach utilized herein is acceptance and commitment therapy (ACT), which is focused on increasing engagement with values, awareness, and openness to internal experiences. ACT theory posits that experiential avoidance is at the center of human suffering, regardless of diagnosis, and, as such, seeks to reduce unworkable experiential avoidance. OBJECTIVE Our objective is to provide the rationale and protocol for examining the safety, feasibility, and effectiveness of optimizing an ACT-based intervention via a mobile app among two disparate samples, which differ in sociodemographic characteristics and symptom profiles. METHODS Twice each day, participants are prompted via a mobile app to complete assessments of mood and activity and are then randomly assigned to an ACT-based intervention or not. These interventions are questions regarding engagement with values, awareness, and openness to internal experiences. Participant responses are recorded. Analyses will examine completion of assessments, change in symptoms from baseline assessment, and proximal change in mood and activity. A primary outcome of interest is proximal change in activity (eg, form and function of behavior and energy consumed by avoidance and values-based behavior) following interventions as a function of time, symptoms, and behavior, where we hypothesize that participants will focus more energy on values-based behaviors. Analyses will be conducted using a weighted and centered least squares approach. Two samples will run concurrently to assess the capacity of optimizing mobile ACT in populations that differ widely in their clinical presentation and sociodemographic characteristics: individuals with bipolar disorder (n=30) and distressed first-generation college students (n=50). RESULTS Recruitment began on September 10, 2019, for the bipolar sample and on October 5, 2019, for the college sample. Participation in the study began on October 18, 2019. CONCLUSIONS This study examines an ACT-based intervention among two disparate samples. Should ACT demonstrate feasibility and preliminary effectiveness in each sample, a large randomized controlled trial applying ACT across diagnoses and demographics would be indicated. The public health implications of such an approach may be far-reaching. TRIAL REGISTRATION ClinicalTrials.gov NCT04098497; https://clinicaltrials.gov/ct2/show/NCT04098497; ClinicalTrials.gov NCT04081662; https://clinicaltrials.gov/ct2/show/NCT04081662. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/17086.
Collapse
Affiliation(s)
- Emily B Kroska
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA, United States
| | - Sydney Hoel
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
| | - Amanda Victory
- Department of Psychiatry, University of Michigan-Ann Arbor, Ann Arbor, MI, United States
| | - Susan A Murphy
- Department of Statistics, Harvard University, Cambridge, MA, United States
| | - Melvin G McInnis
- Department of Psychiatry, University of Michigan-Ann Arbor, Ann Arbor, MI, United States
| | - Zachary N Stowe
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
| | - Amy Cochran
- Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI, United States
- Department of Math, University of Wisconsin-Madison, Madison, WI, United States
| |
Collapse
|
35
|
Qian T, Klasnja P, Murphy SA. Linear mixed models with endogenous covariates: modeling sequential treatment effects with application to a mobile health study. Stat Sci 2020; 35:375-390. [PMID: 33132496 PMCID: PMC7596885 DOI: 10.1214/19-sts720] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Mobile health is a rapidly developing field in which behavioral treatments are delivered to individuals via wearables or smartphones to facilitate health-related behavior change. Micro-randomized trials (MRT) are an experimental design for developing mobile health interventions. In an MRT the treatments are randomized numerous times for each individual over course of the trial. Along with assessing treatment effects, behavioral scientists aim to understand between-person heterogeneity in the treatment effect. A natural approach is the familiar linear mixed model. However, directly applying linear mixed models is problematic because potential moderators of the treatment effect are frequently endogenous-that is, may depend on prior treatment. We discuss model interpretation and biases that arise in the absence of additional assumptions when endogenous covariates are included in a linear mixed model. In particular, when there are endogenous covariates, the coefficients no longer have the customary marginal interpretation. However, these coefficients still have a conditional-on-the-random-effect interpretation. We provide an additional assumption that, if true, allows scientists to use standard software to fit linear mixed model with endogenous covariates, and person-specific predictions of effects can be provided. As an illustration, we assess the effect of activity suggestion in the HeartSteps MRT and analyze the between-person treatment effect heterogeneity.
Collapse
Affiliation(s)
- Tianchen Qian
- Department of Statistics, Harvard University, Cambridge, MA 02138
- School of Information, University of Michigan, Ann Arbor, MI 48109
| | - Predrag Klasnja
- Department of Statistics, Harvard University, Cambridge, MA 02138
- School of Information, University of Michigan, Ann Arbor, MI 48109
| | - Susan A Murphy
- Department of Statistics, Harvard University, Cambridge, MA 02138
- School of Information, University of Michigan, Ann Arbor, MI 48109
| |
Collapse
|
36
|
Carpenter SM, Menictas M, Nahum-Shani I, Wetter DW, Murphy SA. Developments in Mobile Health Just-in-Time Adaptive Interventions for Addiction Science. CURRENT ADDICTION REPORTS 2020; 7:280-290. [PMID: 33747711 PMCID: PMC7968352 DOI: 10.1007/s40429-020-00322-y] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
PURPOSE OF REVIEW Addiction is a serious and prevalent problem across the globe. An important challenge facing intervention science is how to support addiction treatment and recovery while mitigating the associated cost and stigma. A promising solution is the use of mobile health (mHealth) just-in-time adaptive interventions (JITAIs), in which intervention options are delivered in situ via a mobile device when individuals are most in need. RECENT FINDINGS The present review describes the use of mHealth JITAIs to support addiction treatment and recovery, and provides guidance on when and how the micro-randomized trial (MRT) can be used to optimize a JITAI. We describe the design of five mHealth JITAIs in addiction and three MRT studies, and discuss challenges and future directions. SUMMARY This review aims to provide guidance for constructing effective JITAIs to support addiction treatment and recovery.
Collapse
Affiliation(s)
| | | | - Inbal Nahum-Shani
- Institute for Social Research, University of Michigan, Ann Arbor, MI
| | - David W. Wetter
- Huntsman Cancer Institute and the University of Utah, Salt Lake City, UT
| | | |
Collapse
|
37
|
Inan OT, Tenaerts P, Prindiville SA, Reynolds HR, Dizon DS, Cooper-Arnold K, Turakhia M, Pletcher MJ, Preston KL, Krumholz HM, Marlin BM, Mandl KD, Klasnja P, Spring B, Iturriaga E, Campo R, Desvigne-Nickens P, Rosenberg Y, Steinhubl SR, Califf RM. Digitizing clinical trials. NPJ Digit Med 2020; 3:101. [PMID: 32821856 PMCID: PMC7395804 DOI: 10.1038/s41746-020-0302-y] [Citation(s) in RCA: 151] [Impact Index Per Article: 37.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 06/19/2020] [Indexed: 01/31/2023] Open
Abstract
Clinical trials are a fundamental tool used to evaluate the efficacy and safety of new drugs and medical devices and other health system interventions. The traditional clinical trials system acts as a quality funnel for the development and implementation of new drugs, devices and health system interventions. The concept of a "digital clinical trial" involves leveraging digital technology to improve participant access, engagement, trial-related measurements, and/or interventions, enable concealed randomized intervention allocation, and has the potential to transform clinical trials and to lower their cost. In April 2019, the US National Institutes of Health (NIH) and the National Science Foundation (NSF) held a workshop bringing together experts in clinical trials, digital technology, and digital analytics to discuss strategies to implement the use of digital technologies in clinical trials while considering potential challenges. This position paper builds on this workshop to describe the current state of the art for digital clinical trials including (1) defining and outlining the composition and elements of digital trials; (2) describing recruitment and retention using digital technology; (3) outlining data collection elements including mobile health, wearable technologies, application programming interfaces (APIs), digital transmission of data, and consideration of regulatory oversight and guidance for data security, privacy, and remotely provided informed consent; (4) elucidating digital analytics and data science approaches leveraging artificial intelligence and machine learning algorithms; and (5) setting future priorities and strategies that should be addressed to successfully harness digital methods and the myriad benefits of such technologies for clinical research.
Collapse
Affiliation(s)
- O. T. Inan
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
| | - P. Tenaerts
- Clinical Trials Transformation Initiative, Duke University, Durham, NC 27708 USA
| | - S. A. Prindiville
- Coordinating Center for Clinical Trials, Office of the Director, National Cancer Institute at the National Institutes of Health, Bethesda, MD 20892 USA
| | - H. R. Reynolds
- School of Medicine, New York University, New York, NY 10003 USA
| | - D. S. Dizon
- The Lifespan Cancer Institute, Brown University, Providence, RI 02912 USA
| | - K. Cooper-Arnold
- National, Heart, Lung and Blood Institute at the National Institutes of Health, Bethesda, MD 20892 USA
- Present Address: Fortira at AstraZeneca, Gaithersburg, MD 20877 USA
| | - M. Turakhia
- VA Palo Alto Health Care System and the Center for Digital Health, Stanford University, Stanford, CA 94305 USA
| | - M. J. Pletcher
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94143 USA
| | - K. L. Preston
- Intramural Research Program of the National Institute on Drug Abuse at the National Institutes of Health, Baltimore, MD 21224 USA
| | - H. M. Krumholz
- The Center for Outcomes Research, Yale New Haven Hospital, Yale University, New Haven, CT 06510 USA
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT 06510 USA
- Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut 06510 USA
| | - B. M. Marlin
- College of Information and Computer Sciences, University of Massachusetts at Amherst, Amherst, MA 01003 USA
| | - K. D. Mandl
- Computational Health Informatics Program at Boston Children’s Hospital, Departments of Biomedical Informatics and Pediatrics, Harvard Medical School, Boston, MA 02115 USA
| | - P. Klasnja
- School of Information, University of Michigan, Ann Arbor, MI 48109 USA
| | - B. Spring
- Northwestern University Feinberg School of Medicine, Chicago, IL 60611 USA
| | - E. Iturriaga
- National Heart, Lung, and Blood Institute at the National Institutes of Health, Bethesda, MD 20892 USA
| | - R. Campo
- National Heart, Lung, and Blood Institute at the National Institutes of Health, Bethesda, MD 20892 USA
| | - P. Desvigne-Nickens
- National Heart, Lung, and Blood Institute at the National Institutes of Health, Bethesda, MD 20892 USA
| | - Y. Rosenberg
- National Heart, Lung, and Blood Institute at the National Institutes of Health, Bethesda, MD 20892 USA
| | - S. R. Steinhubl
- Scripps Research Translational Institute, La Jolla, CA 92037 USA
| | - R. M. Califf
- School of Medicine, Duke University, Durham, NC 27710 USA
- Verily Life Sciences and Google Health, South San Francisco, CA 94080 USA
| |
Collapse
|
38
|
Bidargaddi N, Schrader G, Klasnja P, Licinio J, Murphy S. Designing m-Health interventions for precision mental health support. Transl Psychiatry 2020; 10:222. [PMID: 32636358 PMCID: PMC7341865 DOI: 10.1038/s41398-020-00895-2] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2019] [Revised: 05/25/2020] [Accepted: 05/28/2020] [Indexed: 12/02/2022] Open
Abstract
Mobile health (m-Health) resources are emerging as a significant tool to overcome mental health support access barriers due to their ability to rapidly reach and provide support to individuals in need of mental health support. m-Health provides an approach to adapt and initiate mental health support at precise moments, when they are most likely to be effective for the individual. However, poor adoption of mental health apps in the real world suggests that new approaches to optimising the quality of m-Health interventions are critically needed in order to realise the potential translational benefits for mental health support. The micro-randomised trial is an experimental approach for optimising and adapting m-Health resources. This trial design provides data to construct and optimise m-Health interventions. The data can be used to inform when and what type of m-Health interventions should be initiated, and thus serve to integrate interventions into daily routines with precision. Here, we illustrate this approach in a case study, review implementation issues that need to be considered while conducting an MRT, and provide a checklist for mental health m-Health intervention developers.
Collapse
Affiliation(s)
- N Bidargaddi
- College of Medicine & Public Health, Flinders University, Adelaide, Australia.
| | - G Schrader
- College of Medicine & Public Health, Flinders University, Adelaide, Australia
| | - P Klasnja
- School of Information, University of Michigan, Ann Arbor, MI, USA
| | - J Licinio
- Departments of Psychiatry, Pharmacology, Medicine and Neuroscience & Physiology, State University of New York Upstate Medical University, Syracuse, New York, USA
| | - S Murphy
- Departments of Statistics & Computer Science, Harvard University, Boston, MA, USA
| |
Collapse
|
39
|
Klasnja P, Smith S, Seewald NJ, Lee A, Hall K, Luers B, Hekler EB, Murphy SA. Efficacy of Contextually Tailored Suggestions for Physical Activity: A Micro-randomized Optimization Trial of HeartSteps. Ann Behav Med 2020; 53:573-582. [PMID: 30192907 DOI: 10.1093/abm/kay067] [Citation(s) in RCA: 96] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND HeartSteps is an mHealth intervention that encourages regular walking via activity suggestions tailored to the individuals' current context. PURPOSE We conducted a micro-randomized trial (MRT) to evaluate the efficacy of HeartSteps' activity suggestions to optimize the intervention. METHODS We conducted a 6-week MRT with 44 adults. Contextually tailored suggestions could be delivered up to five times per day at user-selected times. At each of these five times, for each participant on each day of the study, HeartSteps randomized whether to provide an activity suggestion, and, if so, whether to provide a walking or an antisedentary suggestion. We used a centered and weighted least squares method to analyze the effect of suggestions on the 30-min step count following suggestion randomization. RESULTS Averaging over study days and types of activity suggestions, delivering a suggestion versus no suggestion increased the 30-min step count by 14% (p = .06), 35 additional steps over the 253-step average. The effect was not evenly distributed in time. Providing any type of suggestion versus no suggestion initially increased the step count by 66% (167 steps; p < .01), but this effect diminished over time. Averaging over study days, delivering a walking suggestion versus no suggestion increased the average step count by 24% (59 steps; p = .02). This increase was greater at the start of study (107% or 271 additional steps; p < .01), but decreased over time. Antisedentary suggestions had no detectable effect on the 30-min step count. CONCLUSION Contextually tailored walking suggestions are a promising way of initiating bouts of walking throughout the day. CLINICAL TRIAL INFORMATION This study was registered on ClinicalTrials.gov number NCT03225521.
Collapse
Affiliation(s)
- Predrag Klasnja
- Kaiser Permanente Washington Health Research Institute, Minor Ave, Suite, Seattle, WA, USA.,School of Information, University of Michigan, Ann Arbor, MI, USA
| | - Shawna Smith
- Insitute for Social Research, University of Michigan, Ann Arbor, MI, USA.,School of Medicine, University of Michigan, Ann Arbor, MI, USA
| | | | - Andy Lee
- School of Information, University of Michigan, Ann Arbor, MI, USA
| | - Kelly Hall
- Department of Statistics, University of Michigan, Ann Arbor, MI, USA
| | - Brook Luers
- Department of Statistics, University of Michigan, Ann Arbor, MI, USA
| | - Eric B Hekler
- School of Medicine, University of California San Diego, San Diego, CA, USA
| | - Susan A Murphy
- Department of Statistics, Harvard University, Cambridge, MA, USA
| |
Collapse
|
40
|
Dempsey W, Liao P, Kumar S, Murphy SA. THE STRATIFIED MICRO-RANDOMIZED TRIAL DESIGN: SAMPLE SIZE CONSIDERATIONS FOR TESTING NESTED CAUSAL EFFECTS OF TIME-VARYING TREATMENTS. Ann Appl Stat 2020; 14:661-684. [PMID: 33868539 DOI: 10.1214/19-aoas1293] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Technological advancements in the field of mobile devices and wearable sensors have helped overcome obstacles in the delivery of care, making it possible to deliver behavioral treatments anytime and anywhere. Here, we discuss our work on the design of a mobile health smoking cessation intervention study with the goal of assessing whether reminders, delivered at times of stress, result in a reduction/prevention of stress in the near-term, and whether this effect changes with time in study. Multiple statistical challenges arose in this effort, leading to the development of the stratified micro-randomized trial design. In these designs, each individual is randomized to treatment repeatedly at times determined by predictions of risk. These risk times may be impacted by prior treatment. We describe the statistical challenges and detail how they can be met.
Collapse
|
41
|
Li S, Psihogios AM, McKelvey ER, Ahmed A, Rabbi M, Murphy S. Microrandomized trials for promoting engagement in mobile health data collection: Adolescent/young adult oral chemotherapy adherence as an example. CURRENT OPINION IN SYSTEMS BIOLOGY 2020; 21:1-8. [PMID: 32832738 PMCID: PMC7437990 DOI: 10.1016/j.coisb.2020.07.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Long-term engagement with mobile health (mHealth) apps can provide critical data for improving empirical models for real-time health behaviors. To learn how to improve and maintain mHealth engagement, micro-randomized trials (MRTs) can be used to optimize different engagement strategies. In MRTs, participants are sequentially randomized, often hundreds or thousands of times, to different engagement strategies or treatments. The data gathered are then used to decide which treatment is optimal in which context. In this paper, we discuss an example MRT for youth with cancer, where we randomize different engagement strategies to improve self-reports on factors related to medication adherence. MRTs, moreover, can go beyond improving engagement, and we reference other MRTs to address substance abuse, sedentary behavior, and so on.
Collapse
Affiliation(s)
- Shuang Li
- Department of Statistics, Harvard University
| | - Alexandra M. Psihogios
- The Children’s Hospital of Philadelphia
- Perelman School of Medicine, University of Pennsylvania
| | | | | | | | | |
Collapse
|
42
|
Haug S, Paz Castro R, Scholz U, Kowatsch T, Schaub MP, Radtke T. Assessment of the Efficacy of a Mobile Phone-Delivered Just-in-Time Planning Intervention to Reduce Alcohol Use in Adolescents: Randomized Controlled Crossover Trial. JMIR Mhealth Uhealth 2020; 8:e16937. [PMID: 32452818 PMCID: PMC7284414 DOI: 10.2196/16937] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 01/27/2020] [Accepted: 03/24/2020] [Indexed: 01/20/2023] Open
Abstract
Background Interventions to reduce alcohol use typically include several elements, such as information on the risks of alcohol consumption, planning for sensible drinking, and training of protective behavioral strategies. However, the effectiveness of these individual intervention elements within comprehensive programs has not been addressed so far, but it could provide valuable insights for the development of future interventions. Just-in-time interventions provided via mobile devices are intended to help people make healthy decisions in the moment and thus could influence health behavior. Objective The aim of this study was to test the proximal effects of a mobile phone–delivered, just-in-time planning intervention to reduce alcohol use in adolescents who reported recent binge drinking. The efficacy of this individual intervention element was tested within a comprehensive intervention program to reduce problem drinking in adolescents. Methods The study had an AB/BA crossover design, in which participants were randomly allocated to (1) a group receiving the planning intervention (A) in period 1 and assessment only (B) in period 2 or (2) a group receiving assessment only (B) in period 1 and the planning intervention (A) in period 2. The planning intervention included a text message to choose one of two predetermined if-then plans to practice sensible drinking with friends or when going out and a prompt to visualize the chosen plan. There was a washout period of at least 1 week between period 1 and period 2. Results Out of 633 program participants who recently binge drank, 136 (21.5%) were receptive in both periods of time and provided data on the proximal outcome, which was the number of alcoholic drinks consumed with friends or when going out. After the planning intervention, the number of alcoholic drinks consumed was approximately one standard drink lower compared with the finding without the intervention (P=.01). Conclusions A mobile phone–delivered, just-in-time, if-then planning intervention to practice sensible drinking with friends or when going out is effective in reducing alcohol consumption among adolescents who report recent binge drinking. Based on the relatively low percentage of participants with self-reported receptivity for the planning intervention, measures to increase the population impact of similar planning interventions should be implemented and tested in future trials. Trial Registration ISRCTN Registry ISRCTN52150713; http://www.isrctn.com/ISRCTN52150713
Collapse
Affiliation(s)
- Severin Haug
- Swiss Research Institute for Public Health and Addiction, University of Zurich, Zurich, Switzerland
| | - Raquel Paz Castro
- Swiss Research Institute for Public Health and Addiction, University of Zurich, Zurich, Switzerland
| | - Urte Scholz
- Applied Social and Health Psychology, University of Zurich, Zurich, Switzerland
| | - Tobias Kowatsch
- Center for Digital Health Interventions, Institute of Technology Management, University of St Gallen, St Gallen, Switzerland.,Center for Digital Health Interventions, Department of Technology, Management, and Economics, ETH Zurich, Zurich, Switzerland
| | - Michael Patrick Schaub
- Swiss Research Institute for Public Health and Addiction, University of Zurich, Zurich, Switzerland
| | - Theda Radtke
- Health, Work & Organizational Psychology, School of Psychology and Psychotherapy, Witten/Herdecke University, Witten/Herdecke, Germany
| |
Collapse
|
43
|
Liao P, Greenewald K, Klasnja P, Murphy S. Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activity. PROCEEDINGS OF THE ACM ON INTERACTIVE, MOBILE, WEARABLE AND UBIQUITOUS TECHNOLOGIES 2020; 4:18. [PMID: 34527853 PMCID: PMC8439432 DOI: 10.1145/3381007] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
With the recent proliferation of mobile health technologies, health scientists are increasingly interested in developing just-in-time adaptive interventions (JITAIs), typically delivered via notifications on mobile devices and designed to help users prevent negative health outcomes and to promote the adoption and maintenance of healthy behaviors. A JITAI involves a sequence of decision rules (i.e., treatment policies) that take the user's current context as input and specify whether and what type of intervention should be provided at the moment. In this work, we describe a reinforcement learning (RL) algorithm that continuously learns and improves the treatment policy embedded in the JITAI as data is being collected from the user. This work is motivated by our collaboration on designing an RL algorithm for HeartSteps V2 based on data collected HeartSteps V1. HeartSteps is a physical activity mobile health application. The RL algorithm developed in this work is being used in HeartSteps V2 to decide, five times per day, whether to deliver a context-tailored activity suggestion.
Collapse
|
44
|
Standardized Effect Sizes for Preventive Mobile Health Interventions in Micro-randomized Trials. PREVENTION SCIENCE : THE OFFICIAL JOURNAL OF THE SOCIETY FOR PREVENTION RESEARCH 2020; 20:100-109. [PMID: 29318443 DOI: 10.1007/s11121-017-0862-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Mobile Health (mHealth) interventions are behavioral interventions that are accessible to individuals in their daily lives via a mobile device. Most mHealth interventions consist of multiple intervention components. Some of the components are "pull" components, which require individuals to access the component on their mobile device at moments when they decide they need help. Other intervention components are "push" components, which are initiated by the intervention, not the individual, and are delivered via notifications or text messages. Micro-randomized trials (MRTs) have been developed to provide data to assess the effects of push intervention components on subsequent emotions and behavior. In this paper, we review the micro-randomized trial design and provide an approach to computing a standardized effect size for these intervention components. This effect size can be used to compare different push intervention components that may be included in an mHealth intervention. In addition, a standardized effect size can be used to inform sample size calculations for future MRTs. Here, the standardized effect size is a function of time because the push notifications can occur repeatedly over time. We illustrate this methodology using data from an MRT involving HeartSteps, an mHealth intervention for physical activity as part of the secondary prevention of heart disease.
Collapse
|
45
|
Nahum-Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz K, Tewari A, Murphy SA. Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support. Ann Behav Med 2019; 52:446-462. [PMID: 27663578 PMCID: PMC5364076 DOI: 10.1007/s12160-016-9830-8] [Citation(s) in RCA: 857] [Impact Index Per Article: 171.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023] Open
Abstract
Background The just-in-time adaptive intervention (JITAI) is an intervention design aiming to provide the right type/amount of support, at the right time, by adapting to an individual's changing internal and contextual state. The availability of increasingly powerful mobile and sensing technologies underpins the use of JITAIs to support health behavior, as in such a setting an individual's state can change rapidly, unexpectedly, and in his/her natural environment. Purpose Despite the increasing use and appeal of JITAIs, a major gap exists between the growing technological capabilities for delivering JITAIs and research on the development and evaluation of these interventions. Many JITAIs have been developed with minimal use of empirical evidence, theory, or accepted treatment guidelines. Here, we take an essential first step towards bridging this gap. Methods Building on health behavior theories and the extant literature on JITAIs, we clarify the scientific motivation for JITAIs, define their fundamental components, and highlight design principles related to these components. Examples of JITAIs from various domains of health behavior research are used for illustration. Conclusions As we enter a new era of technological capacity for delivering JITAIs, it is critical that researchers develop sophisticated and nuanced health behavior theories capable of guiding the construction of such interventions. Particular attention has to be given to better understanding the implications of providing timely and ecologically sound support for intervention adherence and retention.
Collapse
Affiliation(s)
- Inbal Nahum-Shani
- Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Shawna N Smith
- Division of General Medicine, Department of Internal Medicine and Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Bonnie J Spring
- Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Linda M Collins
- TheMethodology Center andDepartment ofHuman Development & Family Studies, Penn State, State College, PA, USA
| | - Katie Witkiewitz
- Department of Psychology, University of New Mexico, Albuquerque, NM, USA
| | - Ambuj Tewari
- Department of Statistics and Department of EECS, University of Michigan, Ann Arbor, MI, USA
| | - Susan A Murphy
- Department of Statistics, and Institute for Social Research,University of Michigan, Ann Arbor, MI, USA
| |
Collapse
|
46
|
Hiremath SV, Amiri AM, Thapa-Chhetry B, Snethen G, Schmidt-Read M, Ramos-Lamboy M, Coffman DL, Intille SS. Mobile health-based physical activity intervention for individuals with spinal cord injury in the community: A pilot study. PLoS One 2019; 14:e0223762. [PMID: 31613909 PMCID: PMC6793862 DOI: 10.1371/journal.pone.0223762] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 09/26/2019] [Indexed: 01/05/2023] Open
Abstract
Low levels of physical activity (PA) and high levels of sedentary behavior in individuals with spinal cord injury (SCI) have been associated with secondary conditions such as pain, fatigue, weight gain, and deconditioning. One strategy for promoting regular PA is to provide people with an accurate estimate of everyday PA level. The objective of this research was to use a mobile health-based PA measurement system to track PA levels of individuals with SCI in the community and provide them with a behavior-sensitive, just-in-time-adaptive intervention (JITAI) to improve their PA levels. The first, second, and third phases of the study, each with a duration of one month, involved collecting baseline PA levels, providing near-real-time feedback on PA level (PA Feedback), and providing PA Feedback with JITAI, respectively. PA levels in terms of energy expenditure in kilocalories, and minutes of light- and moderate- or vigorous-intensity PA were assessed by an activity monitor during the study. Twenty participants with SCI took part in this research study with a mean (SD) age of 39.4 (12.8) years and 12.4 (12.5) years since injury. Sixteen participants completed the study. Sixteen were male, 16 had paraplegia, and 12 had complete injury. Within-participant comparisons indicated that only two participants had higher energy expenditure (>10%) or lower energy expenditure (<-10%) during PA Feedback with JITAI compared to the baseline. However, eleven participants (69.0%) had higher light- and/or moderate-intensity PA during PA Feedback with JITAI compared to the baseline. To our knowledge, this is the first study to test a PA JITAI for individuals with SCI that responds automatically to monitored PA levels. The results of this pilot study suggest that a sensor-enabled mobile JITAI has potential to improve PA levels of individuals with SCI. Future research should investigate the efficacy of JITAI through a clinical trial.
Collapse
Affiliation(s)
- Shivayogi V. Hiremath
- Department of Health and Rehabilitation Sciences, Temple University, Philadelphia, Pennsylvania, United States of America
- Department of Electrical and Computer Engineering, Temple University, Philadelphia, Pennsylvania, United States of America
- * E-mail:
| | - Amir Mohammad Amiri
- Department of Health and Rehabilitation Sciences, Temple University, Philadelphia, Pennsylvania, United States of America
- Department of Biomedical Engineering, Widener University, Chester, Pennsylvania, United States of America
| | - Binod Thapa-Chhetry
- Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts, United States of America
- Department of Health Sciences, Bouvé College of Health Sciences, Northeastern University, Boston, Massachusetts, United States of America
| | - Gretchen Snethen
- Department of Health and Rehabilitation Sciences, Temple University, Philadelphia, Pennsylvania, United States of America
| | - Mary Schmidt-Read
- Magee Rehabilitation Hospital, Jefferson Health, Philadelphia, Pennsylvania, United States of America
| | - Marlyn Ramos-Lamboy
- MossRehab, Einstein Healthcare Network, Philadelphia, Pennsylvania, United States of America
| | - Donna L. Coffman
- Department of Epidemiology and Biostatistics, Temple University, Philadelphia, Pennsylvania, United States of America
| | - Stephen S. Intille
- Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts, United States of America
- Department of Health Sciences, Bouvé College of Health Sciences, Northeastern University, Boston, Massachusetts, United States of America
| |
Collapse
|
47
|
Kim J, Marcusson-Clavertz D, Yoshiuchi K, Smyth JM. Potential benefits of integrating ecological momentary assessment data into mHealth care systems. Biopsychosoc Med 2019; 13:19. [PMID: 31413726 PMCID: PMC6688314 DOI: 10.1186/s13030-019-0160-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2019] [Accepted: 07/28/2019] [Indexed: 01/03/2023] Open
Abstract
The advancement of wearable/ambulatory technologies has brought a huge change to data collection frameworks in recent decades. Mobile health (mHealth) care platforms, which utilize ambulatory devices to collect naturalistic and often intensively sampled data, produce innovative information of potential clinical relevance. For example, such data can inform clinical study design, recruitment approach, data analysis, and delivery of both "traditional" and novel (e.g., mHealth) interventions. We provide a conceptual overview of how data measured continuously or repeatedly via mobile devices (e.g., smartphone and body sensors) in daily life could be fruitfully used within a mHealth care system. We highlight the potential benefits of integrating ecological momentary assessment (EMA) into mHealth platforms for collecting, processing, and modeling data, and delivering and evaluating novel interventions in everyday life. Although the data obtained from EMA and related approaches may hold great potential benefits for mHealth care system, there are also implementation challenges; we briefly discuss the challenges to integrating EMA into mHealth care system.
Collapse
Affiliation(s)
- Jinhyuk Kim
- Department of Informatics, Shizuoka University, 3-5-1 Johoku, Naka-ku, Hamamatsu, Shizuoka, 432-8011 Japan
- Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA USA
| | - David Marcusson-Clavertz
- Department of Psychology, Lund University, Lund, Sweden
- Department of Psychology, Ludwig Maximilian University of Munich, Munich, Germany
| | - Kazuhiro Yoshiuchi
- Department of Stress Sciences and Psychosomatic Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Joshua M. Smyth
- Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA USA
- Department of Medicine, Hershey Medical Center and The Pennsylvania State University, Hershey, PA USA
| |
Collapse
|
48
|
Seewald NJ, Smith SN, Lee AJ, Klasnja P, Murphy SA. Practical Considerations for Data Collection and Management in Mobile Health Micro-randomized Trials. STATISTICS IN BIOSCIENCES 2019; 11:355-370. [PMID: 31462937 PMCID: PMC6713230 DOI: 10.1007/s12561-018-09228-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Revised: 10/10/2018] [Accepted: 12/14/2018] [Indexed: 11/24/2022]
Abstract
There is a growing interest in leveraging the prevalence of mobile technology to improve health by delivering momentary, contextualized interventions to individuals' smartphones. A just-in-time adaptive intervention (JITAI) adjusts to an individual's changing state and/or context to provide the right treatment, at the right time, in the right place. Micro-randomized trials (MRTs) allow for the collection of data which aid in the construction of an optimized JITAI by sequentially randomizing participants to different treatment options at each of many decision points throughout the study. Often, this data is collected passively using a mobile phone. To assess the causal effect of treatment on a near-term outcome, care must be taken when designing the data collection system to ensure it is of appropriately high quality. Here, we make several recommendations for collecting and managing data from an MRT. We provide advice on selecting which features to collect and when, choosing between "agents" to implement randomization, identifying sources of missing data, and overcoming other novel challenges. The recommendations are informed by our experience with HeartSteps, an MRT designed to test the effects of an intervention aimed at increasing physical activity in sedentary adults. We also provide a checklist which can be used in designing a data collection system so that scientists can focus more on their questions of interest, and less on cleaning data.
Collapse
Affiliation(s)
- Nicholas J Seewald
- University of Michigan, Department of Statistics, 311 West Hall, 1085 South University Ave, Ann Arbor, MI, 48109,
| | - Shawna N Smith
- University of Michigan, Departments of Psychiatry and General Medicine
| | | | | | - Susan A Murphy
- Harvard University, Departments of Statistics and Computer Science
| |
Collapse
|
49
|
Luckett DJ, Laber EB, Kahkoska AR, Maahs DM, Mayer-Davis E, Kosorok MR. Estimating Dynamic Treatment Regimes in Mobile Health Using V-learning. J Am Stat Assoc 2019; 115:692-706. [PMID: 32952236 DOI: 10.1080/01621459.2018.1537919] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The vision for precision medicine is to use individual patient characteristics to inform a personalized treatment plan that leads to the best possible health-care for each patient. Mobile technologies have an important role to play in this vision as they offer a means to monitor a patient's health status in real-time and subsequently to deliver interventions if, when, and in the dose that they are needed. Dynamic treatment regimes formalize individualized treatment plans as sequences of decision rules, one per stage of clinical intervention, that map current patient information to a recommended treatment. However, most existing methods for estimating optimal dynamic treatment regimes are designed for a small number of fixed decision points occurring on a coarse time-scale. We propose a new reinforcement learning method for estimating an optimal treatment regime that is applicable to data collected using mobile technologies in an out-patient setting. The proposed method accommodates an indefinite time horizon and minute-by-minute decision making that are common in mobile health applications. We show that the proposed estimators are consistent and asymptotically normal under mild conditions. The proposed methods are applied to estimate an optimal dynamic treatment regime for controlling blood glucose levels in patients with type 1 diabetes.
Collapse
Affiliation(s)
- Daniel J Luckett
- Department of Biostatistics, University of North Carolina at Chapel Hill
| | - Eric B Laber
- Department of Statistics, North Carolina State University
| | - Anna R Kahkoska
- Department of Nutrition, University of North Carolina at Chapel Hill
| | | | | | - Michael R Kosorok
- Department of Biostatistics, University of North Carolina at Chapel Hill
| |
Collapse
|
50
|
Arigo D, Jake-Schoffman DE, Wolin K, Beckjord E, Hekler EB, Pagoto SL. The history and future of digital health in the field of behavioral medicine. J Behav Med 2019; 42:67-83. [PMID: 30825090 PMCID: PMC6644720 DOI: 10.1007/s10865-018-9966-z] [Citation(s) in RCA: 89] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Accepted: 08/23/2018] [Indexed: 01/03/2023]
Abstract
Since its earliest days, the field of behavioral medicine has leveraged technology to increase the reach and effectiveness of its interventions. Here, we highlight key areas of opportunity and recommend next steps to further advance intervention development, evaluation, and commercialization with a focus on three technologies: mobile applications (apps), social media, and wearable devices. Ultimately, we argue that future of digital health behavioral science research lies in finding ways to advance more robust academic-industry partnerships. These include academics consciously working towards preparing and training the work force of the twenty first century for digital health, actively working towards advancing methods that can balance the needs for efficiency in industry with the desire for rigor and reproducibility in academia, and the need to advance common practices and procedures that support more ethical practices for promoting healthy behavior.
Collapse
Affiliation(s)
- Danielle Arigo
- Department of Psychology, Rowan University, Glassboro, USA.
| | | | | | - Ellen Beckjord
- Population Health and Clinical Affairs, University of Pittsburgh Medical Center Health Plan, Pittsburgh, USA
| | - Eric B Hekler
- Department of Family Medicine Public Health, Center for Wireless and Population Health Systems, University of California, San Diego, San Diego, USA
| | - Sherry L Pagoto
- Department of Allied Health Sciences, Institute for Collaboration in Health, Interventions, and Policy, Center for mHealth and Social Media, University of Connecticut, Storrs, USA
| |
Collapse
|