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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] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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
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Haag D, Carrozzo E, Pannicke B, Niebauer J, Blechert J. Within-person association of volitional factors and physical activity: Insights from an ecological momentary assessment study. Psychol Sport Exerc 2023; 68:102445. [PMID: 37665897 DOI: 10.1016/j.psychsport.2023.102445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 03/28/2023] [Accepted: 04/24/2023] [Indexed: 09/06/2023]
Abstract
OBJECTIVE Current health behavior models of physical activity (PA) suggest that not all PA intentions are translated into actual PA behavior, resulting in a significant intention-behavior gap (IBG) of almost 50%. These models further suggest that higher self-efficacy and specific planning can aid in decreasing this gap. However, as most evidence stems from between-person (trait level), questionnaire-based research, it is unclear how large short-term IBGs are, how self-efficacy and planning covary within-persons across time and whether they similarly predict smaller IBGs. It is likely that day-to-day changes in circumstances and barriers affect these variables thus the applicability of theoretical models is uncertain. Here, within-person prospective analyses of ecological momentary assessment (EMA) data can provide insights. METHODS 35 healthy participants (aged 23-67) completed four EMA-based questionnaires every day for three weeks. Each prompt assessed PA (retrospectively, "since the last EMA prompt"); PA intentions, planning specificity, self-efficacy, and intrinsic motivation (prospectively, "until the next EMA prompt") and momentary affect. Generalized logistic mixed-effect modeling was used to test predictors of PA. RESULTS Across the 2341 answered EMA prompts, PA intentions were not enacted in 25% of the episodes (IBG). In episodes with given intentions, PA likelihood increased with higher levels of self-efficacy, planning specificity, and intrinsic motivation. The latter two also positively predicted PA duration and intensity. CONCLUSIONS Short-term intention behavior gaps seem to be smaller than what is known from more long-term studies, most likely as individuals can anticipate the actual circumstances of PA. Further, current health behavior models show validity in explaining within-person dynamics in IBGs across time. Knowing the relevance of planning specificity, self-efficacy and intrinsic motivation for day-to-day variations in PA enactment can inform respective real-time mHealth interventions for facilitating PA.
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Affiliation(s)
- David Haag
- Ludwig Boltzmann Institute for Digital Health and Prevention, Salzburg, Austria; Department of Psychology, Paris-Lodron-University of Salzburg, Salzburg, Austria; Center for Cognitive Neuroscience, Paris-Lodron-University of Salzburg, Salzburg, Austria; Digital Health Information Systems, Center for Health & Bioresources, AIT Austrian Institute of Technology GmbH, Graz, Austria.
| | - Eleonora Carrozzo
- Ludwig Boltzmann Institute for Digital Health and Prevention, Salzburg, Austria
| | - Björn Pannicke
- Department of Psychology, Paris-Lodron-University of Salzburg, Salzburg, Austria; Center for Cognitive Neuroscience, Paris-Lodron-University of Salzburg, Salzburg, Austria
| | - Josef Niebauer
- Ludwig Boltzmann Institute for Digital Health and Prevention, Salzburg, Austria; University Institute of Sports Medicine, Prevention and Rehabilitation, Paracelsus Medical University, Salzburg, Austria
| | - Jens Blechert
- Department of Psychology, Paris-Lodron-University of Salzburg, Salzburg, Austria; Center for Cognitive Neuroscience, Paris-Lodron-University of Salzburg, Salzburg, Austria
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Oikonomidi T, Ravaud P, LeBeau J, Tran VT. A systematic scoping review of just-in-time, adaptive interventions finds limited automation and incomplete reporting. J Clin Epidemiol 2023; 154:108-116. [PMID: 36521653 DOI: 10.1016/j.jclinepi.2022.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 11/17/2022] [Accepted: 12/08/2022] [Indexed: 12/14/2022]
Abstract
OBJECTIVES To describe the degree of automation in just-in-time, adaptive interventions (JITAIs) assessed in randomized controlled trials (RCTs) in any medical specialty, and to assess the completeness of intervention reporting. STUDY DESIGN AND SETTING Systematic scoping review-we searched PubMed, PsycINFO, and Web of Science, from 1 January 2019 to 2 March 2021, for reports of RCTs assessing JITAIs. We assessed whether study reports included the minimum information required to replicate the interventions based on JITAI frameworks. We described JITAIs according to their automation level using an established framework (partially, highly, or fully automated), and care workload distribution (requiring work from patients, health care professionals [HCPs], both, or neither). RESULTS We included 88 JITAIs (62%, n = 55 supported chronic illness management and 12%, n = 11 supported health behavior change). Overall, 77% (n = 68) of JITAIs were missing some information required to replicate the intervention (e.g., n = 38, 43% inadequately reported the algorithm used to select intervention components). Only fifteen (17%) JITAIs were fully automated and did not require additional work from HCPs nor patients. Of the remaining JITAIs, 36% required work from both patients and HCPs, and 47% required work from either patients or HCPs. CONCLUSION Most JITAIs are not fully automated and require work from the HCPs and patients.
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Affiliation(s)
- Theodora Oikonomidi
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), F-75004 Paris, France; Clinical Epidemiology Unit, Hôtel-Dieu Hospital, Assistance Publique-Hôpitaux de Paris, (AP-HP), 75004 Paris, France.
| | - Philippe Ravaud
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), F-75004 Paris, France; Clinical Epidemiology Unit, Hôtel-Dieu Hospital, Assistance Publique-Hôpitaux de Paris, (AP-HP), 75004 Paris, France; Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Jonathan LeBeau
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), F-75004 Paris, France; Clinical Epidemiology Unit, Hôtel-Dieu Hospital, Assistance Publique-Hôpitaux de Paris, (AP-HP), 75004 Paris, France
| | - Viet-Thi Tran
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), F-75004 Paris, France; Clinical Epidemiology Unit, Hôtel-Dieu Hospital, Assistance Publique-Hôpitaux de Paris, (AP-HP), 75004 Paris, France
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Gönül S, Namlı T, Coşar A, Toroslu İH. A reinforcement learning based algorithm for personalization of digital, just-in-time, adaptive interventions. Artif Intell Med 2021; 115:102062. [PMID: 34001322 DOI: 10.1016/j.artmed.2021.102062] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 03/04/2021] [Accepted: 03/29/2021] [Indexed: 01/13/2023]
Abstract
Suboptimal health related behaviors and habits; and resulting chronic diseases are responsible for majority of deaths globally. Studies show that providing personalized support to patients yield improved results by preventing and/or timely treatment of these problems. Digital, just-in-time and adaptive interventions are mobile phone-based notifications that are being utilized to support people wherever and whenever necessary in coping with their health problems. In this research, we propose a reinforcement learning-based mechanism to personalize interventions in terms of timing, frequency and preferred type(s). We simultaneously employ two reinforcement learning models, namely intervention-selection and opportune-moment-identification; capturing and exploiting changes in people's long-term and momentary contexts respectively. While the intervention-selection model adapts the intervention delivery with respect to type and frequency, the opportune-moment-identification model tries to find the most opportune moments to deliver interventions throughout a day. We propose two accelerator techniques over the standard reinforcement learning algorithms to boost learning performance. First, we propose a customized version of eligibility traces for rewarding past actions throughout an agent's trajectory. Second, we utilize the transfer learning method to reuse knowledge across multiple learning environments. We validate the proposed approach in a simulated experiment where we simulate four personas differing in their daily activities, preferences on specific intervention types and attitudes towards the targeted behavior. Our experiments show that the proposed approach yields better results compared to the standard reinforcement learning algorithms and successfully capture the simulated variations associated with the personas.
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Affiliation(s)
- Suat Gönül
- SRDC Corp., Silikon Blok Kat: 1 No: 16 SRDC Teknokent ODTÜ, Ankara, Turkey.
| | - Tuncay Namlı
- SRDC Corp., Silikon Blok Kat: 1 No: 16 SRDC Teknokent ODTÜ, Ankara, Turkey
| | - Ahmet Coşar
- Department of Computer Engineering, Middle East Technical University, Orta Doğu Teknik Üniversitesi Universiteler Mah. Dumlupinar Blv. No:1 06800, Ankara Turkey
| | - İsmail Hakkı Toroslu
- Department of Computer Engineering, Middle East Technical University, Orta Doğu Teknik Üniversitesi Universiteler Mah. Dumlupinar Blv. No:1 06800, Ankara Turkey
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Seewald NJ, Smith SN, Lee AJ, Klasnja P, Murphy SA. Practical Considerations for Data Collection and Management in Mobile Health Micro-randomized Trials. Stat Biosci 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] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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
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Goldstein SP, Evans BC, Flack D, Juarascio A, Manasse S, Zhang F, Forman EM. Return of the JITAI: Applying a Just-in-Time Adaptive Intervention Framework to the Development of m-Health Solutions for Addictive Behaviors. Int J Behav Med 2018; 24:673-682. [PMID: 28083725 DOI: 10.1007/s12529-016-9627-y] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
PURPOSE Lapses are strong indicators of later relapse among individuals with addictive disorders, and thus are an important intervention target. However, lapse behavior has proven resistant to change due to the complex interplay of lapse triggers that are present in everyday life. It could be possible to prevent lapses before they occur by using m-Health solutions to deliver interventions in real-time. METHOD Just-in-time adaptive intervention (JITAI) is an intervention design framework that could be delivered via mobile app to facilitate in-the-moment monitoring of triggers for lapsing, and deliver personalized coping strategies to the user to prevent lapses from occurring. An organized framework is key for successful development of a JITAI. RESULTS Nahum-Shani and colleagues (2014) set forth six core elements of a JITAI and guidelines for designing each: distal outcomes, proximal outcomes, tailoring variables, decision points, decision rules, and intervention options. The primary aim of this paper is to illustrate the use of this framework as it pertains to developing a JITAI that targets lapse behavior among individuals following a weight control diet. CONCLUSION We will detail our approach to various decision points during the development phases, report on preliminary findings where applicable, identify problems that arose during development, and provide recommendations for researchers who are currently undertaking their own JITAI development efforts. Issues such as missing data, the rarity of lapses, advantages/disadvantages of machine learning, and user engagement are discussed.
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Affiliation(s)
- Stephanie P Goldstein
- Department of Psychology, Drexel University, 3141 Chestnut Street, Suite 119, Philadelphia, PA, 19104, USA.
| | - Brittney C Evans
- Department of Psychology, Drexel University, 3141 Chestnut Street, Suite 119, Philadelphia, PA, 19104, USA
| | - Daniel Flack
- Department of Psychology, Drexel University, 3141 Chestnut Street, Suite 119, Philadelphia, PA, 19104, USA
| | - Adrienne Juarascio
- Department of Psychology, Drexel University, 3141 Chestnut Street, Suite 119, Philadelphia, PA, 19104, USA
| | - Stephanie Manasse
- Department of Psychology, Drexel University, 3141 Chestnut Street, Suite 119, Philadelphia, PA, 19104, USA
| | - Fengqing Zhang
- Department of Psychology, Drexel University, 3141 Chestnut Street, Suite 119, Philadelphia, PA, 19104, USA
| | - Evan M Forman
- Department of Psychology, Drexel University, 3141 Chestnut Street, Suite 119, Philadelphia, PA, 19104, USA
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Wagner B, Liu E, Shaw SD, Iakovlev G, Zhou L, Harrington C, Abowd G, Yoon C, Kumar S, Murphy S, Spring B, Nahum-Shani I. ewrapper: Operationalizing engagement strategies in mHealth. Proc ACM Int Conf Ubiquitous Comput 2017; 2017:790-798. [PMID: 29362728 PMCID: PMC5776015 DOI: 10.1145/3123024.3125612] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
The advancement of digital technologies particularly in the domain of mobile health (mHealth) holds great promise in the promotion of health behavior. However, keeping users engaged remains a central challenge. This paper proposes a novel approach to address this issue by supplementing existing and future mHealth applications with an engagement wrapper - a collection of engagement strategies integrated into a single, coherent model. The engagement wrapper is operationalized within the format of an ambient display on the lock screen of mobile devices.
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Spruijt-Metz D, Hekler E, Saranummi N, Intille S, Korhonen I, Nilsen W, Rivera DE, Spring B, Michie S, Asch DA, Sanna A, Salcedo VT, Kukakfa R, Pavel M. Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research. Transl Behav Med 2015; 5:335-46. [PMID: 26327939 PMCID: PMC4537459 DOI: 10.1007/s13142-015-0324-1] [Citation(s) in RCA: 163] [Impact Index Per Article: 18.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Adverse and suboptimal health behaviors and habits are responsible for approximately 40 % of preventable deaths, in addition to their unfavorable effects on quality of life and economics. Our current understanding of human behavior is largely based on static "snapshots" of human behavior, rather than ongoing, dynamic feedback loops of behavior in response to ever-changing biological, social, personal, and environmental states. This paper first discusses how new technologies (i.e., mobile sensors, smartphones, ubiquitous computing, and cloud-enabled processing/computing) and emerging systems modeling techniques enable the development of new, dynamic, and empirical models of human behavior that could facilitate just-in-time adaptive, scalable interventions. The paper then describes concrete steps to the creation of robust dynamic mathematical models of behavior including: (1) establishing "gold standard" measures, (2) the creation of a behavioral ontology for shared language and understanding tools that both enable dynamic theorizing across disciplines, (3) the development of data sharing resources, and (4) facilitating improved sharing of mathematical models and tools to support rapid aggregation of the models. We conclude with the discussion of what might be incorporated into a "knowledge commons," which could help to bring together these disparate activities into a unified system and structure for organizing knowledge about behavior.
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Affiliation(s)
- Donna Spruijt-Metz
- />University of Southern California, 635 Downey Way, Suite 305 Building Code: VPD 3332, Los Angeles, CA 90089-3332 USA
| | | | | | | | | | - Wendy Nilsen
- />National Institutes of Health, Bethesda, MD USA
| | | | | | | | - David A. Asch
- />Wharton School, University of Pennsylvania, Philadelphia, PA USA
| | - Alberto Sanna
- />Scientific Institute Hospital San Raffaelle, Milano, Italy
| | | | | | - Misha Pavel
- />VTT Technical Research Centre of Finland, Espoo, Finland
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