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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] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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
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2
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Schneider S, Junghaenel DU, Smyth JM, Fred Wen CK, Stone AA. Just-in-time adaptive ecological momentary assessment (JITA-EMA). Behav Res Methods 2024; 56:765-783. [PMID: 36840916 PMCID: PMC10450096 DOI: 10.3758/s13428-023-02083-8] [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: 02/06/2023] [Indexed: 02/26/2023]
Abstract
Interest in just-in-time adaptive interventions (JITAI) has rapidly increased in recent years. One core challenge for JITAI is the efficient and precise measurement of tailoring variables that are used to inform the timing of momentary intervention delivery. Ecological momentary assessment (EMA) is often used for this purpose, even though EMA in its traditional form was not designed specifically to facilitate momentary interventions. In this article, we introduce just-in-time adaptive EMA (JITA-EMA) as a strategy to reduce participant response burden and decrease measurement error when EMA is used as a tailoring variable in JITAI. JITA-EMA builds on computerized adaptive testing methods developed for purposes of classification (computerized classification testing, CCT), and applies them to the classification of momentary states within individuals. The goal of JITA-EMA is to administer a small and informative selection of EMA questions needed to accurately classify an individual's current state at each measurement occasion. After illustrating the basic components of JITA-EMA (adaptively choosing the initial and subsequent items to administer, adaptively stopping item administration, accommodating dynamically tailored classification cutoffs), we present two simulation studies that explored the performance of JITA-EMA, using the example of momentary fatigue states. Compared with conventional EMA item selection methods that administered a fixed set of questions at each moment, JITA-EMA yielded more accurate momentary classification with fewer questions administered. Our results suggest that JITA-EMA has the potential to enhance some approaches to mobile health interventions by facilitating efficient and precise identification of momentary states that may inform intervention tailoring.
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Affiliation(s)
- Stefan Schneider
- Dornsife Center for Self-Report Science & Center for Economic and Social Research, University of Southern California, 635 Downey Way, Los Angeles, CA, 90089-3332, USA.
- Department of Psychology, University of Southern California, Los Angeles, CA, USA.
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA.
| | - Doerte U Junghaenel
- Dornsife Center for Self-Report Science & Center for Economic and Social Research, University of Southern California, 635 Downey Way, Los Angeles, CA, 90089-3332, USA
- Department of Psychology, University of Southern California, Los Angeles, CA, USA
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Joshua M Smyth
- Biobehavioral Health and Medicine, Pennsylvania State University, State College, PA, USA
| | - Cheng K Fred Wen
- Dornsife Center for Self-Report Science & Center for Economic and Social Research, University of Southern California, 635 Downey Way, Los Angeles, CA, 90089-3332, USA
| | - Arthur A Stone
- Dornsife Center for Self-Report Science & Center for Economic and Social Research, University of Southern California, 635 Downey Way, Los Angeles, CA, 90089-3332, USA
- Department of Psychology, University of Southern California, Los Angeles, CA, USA
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3
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Azizoddin DR, DeForge SM, Baltazar A, Edwards RR, Allsop M, Tulsky JA, Businelle MS, Schreiber KL, Enzinger AC. Development and pre-pilot testing of STAMP + CBT: an mHealth app combining pain cognitive behavioral therapy and opioid support for patients with advanced cancer and pain. Support Care Cancer 2024; 32:123. [PMID: 38252172 PMCID: PMC11088794 DOI: 10.1007/s00520-024-08307-7] [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] [Received: 08/29/2023] [Accepted: 01/05/2024] [Indexed: 01/23/2024]
Abstract
PURPOSE We developed and piloted a mobile health app to deliver cognitive behavioral therapy for pain (pain-CBT), remote symptom monitoring, and pharmacologic support for patients with pain from advanced cancer. METHODS Using an iterative process of patient review and feedback, we developed the STAMP + CBT app. The app delivers brief daily lessons from pain-CBT and pain psychoeducation, adapted for advanced cancer. Daily surveys assess physical symptoms, psychological symptoms, opioid utilization and relief. Just-in-time adaptive interventions generate tailored psychoeducation in response. We then conducted a single-arm pilot feasibility study at two cancer centers. Patients with advanced cancer and chronic pain used the app for 2 or 4 weeks, rated its acceptability and provided feedback in semi-structured interviews. Feasibility and acceptability were defined as ≥ 70% of participants completing ≥ 50% of daily surveys, and ≥ 80% of acceptability items rated ≥ 4/5. RESULTS Fifteen participants (female = 9; mean age = 50.3) tested the app. We exceeded our feasibility and accessibility benchmarks: 73% of patients completed ≥ 50% of daily surveys; 87% of acceptability items were rated ≥ 4/5. Participants valued the app's brevity, clarity, and salience, and found education on stress and pain to be most helpful. The app helped participants learn pain management strategies and decrease maladaptive thoughts. However, participants disliked the notification structure (single prompt with one snooze), which led to missed content. CONCLUSION The STAMP + CBT app was an acceptable and feasible method to deliver psychological/behavioral treatment with pharmacologic support for cancer pain. The app is being refined and will be tested in a larger randomized pilot study. TRN: NCT05403801 (05/06/2022).
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Affiliation(s)
- Desiree R Azizoddin
- Health Promotion Research Center, Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
- Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, MA, USA.
| | - Sara M DeForge
- Health Promotion Research Center, Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Ashton Baltazar
- Health Promotion Research Center, Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Robert R Edwards
- Department of Anesthesiology, Perioperative, and Pain Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Matthew Allsop
- Academic Unit of Palliative Care, Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| | - James A Tulsky
- Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Michael S Businelle
- Health Promotion Research Center, Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Kristin L Schreiber
- Department of Anesthesiology, Perioperative, and Pain Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Andrea C Enzinger
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
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4
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Dowling NA, Rodda SN, Merkouris SS. Applying the Just-In-Time Adaptive Intervention Framework to the Development of Gambling Interventions. J Gambl Stud 2023:10.1007/s10899-023-10250-x. [PMID: 37659031 DOI: 10.1007/s10899-023-10250-x] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/19/2023] [Indexed: 09/05/2023]
Abstract
Just-In-Time Adaptive Interventions (JITAIs) are emerging "push" mHealth interventions that provide the right type, timing, and amount of support to address the dynamically-changing needs for each individual. Although JITAIs are well-suited to the delivery of interventions for the addictions, few are available to support gambling behaviour change. We therefore developed GamblingLess: In-The-Moment and Gambling Habit Hacker, two smartphone-delivered JITAIs that differ with respect to their target populations, theoretical underpinnings, and decision rules. We aim to describe the decisions, methods, and tools we used to design these two treatments, with a view to providing guidance to addiction researchers who wish to develop JITAIs in the future. Specifically, we describe how we applied a comprehensive, organising scientific framework to define the problem, define just-in-time in the context of the identified problem, and formulate the adaptation strategies. While JITAIs appear to be a promising design in addiction intervention science, we describe several key challenges that arose during development, particularly in relation to applying micro-randomised trials to their evaluation, and offer recommendations for future research. Issues including evaluation considerations, integrating on-demand intervention content, intervention optimisation, combining active and passive assessments, incorporating human facilitation, adding cost-effectiveness evaluations, and redevelopment as transdiagnostic interventions are discussed.
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Affiliation(s)
- Nicki A Dowling
- School of Psychology, Deakin University, Geelong, Australia.
- Melbourne Graduate School of Education, University of Melbourne, Parkville, Australia.
| | - Simone N Rodda
- School of Psychology, Deakin University, Geelong, Australia
- Department of Psychology and Neuroscience, Auckland University of Technology, Auckland, New Zealand
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5
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Yang MJ, Sutton SK, Hernandez LM, Jones SR, Wetter DW, Kumar S, Vinci C. A Just-In-Time Adaptive intervention (JITAI) for smoking cessation: Feasibility and acceptability findings. Addict Behav 2023; 136:107467. [PMID: 36037610 PMCID: PMC10246550 DOI: 10.1016/j.addbeh.2022.107467] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [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] [Received: 03/07/2022] [Revised: 08/11/2022] [Accepted: 08/18/2022] [Indexed: 02/03/2023]
Abstract
Smoking cessation treatments that are easily accessible and deliver intervention content at vulnerable moments (e.g., high negative affect) have great potential to impact tobacco abstinence. The current study examined the feasibility and acceptability of a multi-component Just-In-Time Adaptive Intervention (JITAI) for smoking cessation. Daily smokers interested in quitting were consented to participate in a 6-week cessation study. Visit 1 occurred 4 days pre-quit, Visit 2 was on the quit day, Visit 3 occurred 3 days post-quit, Visit 4 was 10 days post-quit, and Visit 5 was 28 days post-quit. During the first 2 weeks (Visits 1-4), the JITAI delivered brief mindfulness/motivational strategies via smartphone in real-time based on negative affect or smoking behavior detected by wearable sensors. Participants also attended 5 in-person visits, where brief cessation counseling (Visits 1-4) and nicotine replacement therapy (Visits 2-5) were provided. Outcomes were feasibility and acceptability; biochemically-confirmed abstinence was also measured. Participants (N = 43) were 58.1 % female (AgeMean = 49.1, mean cigarettes per day = 15.4). Retention through follow-up was high (83.7 %). For participants with available data (n = 38), 24 (63 %) met the benchmark for sensor wearing, among whom 16 (67 %) completed at least 60 % of strategies. Perceived ease of wearing sensors (Mean = 5.1 out of 6) and treatment satisfaction (Mean = 3.6 out of 4) were high. Biochemically-confirmed abstinence was 34 % at Visit 4 and 21 % at Visit 5. Overall, the feasibility of this novel multi-component intervention for smoking cessation was mixed but acceptability was high. Future studies with improved technology will decrease participant burden and better detect key intervention moments.
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Affiliation(s)
- Min-Jeong Yang
- Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, FL, United States
| | - Steven K Sutton
- Department of Psychology, University of South Florida, Tampa, FL, United States; Department of Oncologic Sciences, University of South Florida, Tampa, FL, United States; Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, United States
| | - Laura M Hernandez
- Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, FL, United States
| | - Sarah R Jones
- Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, FL, United States
| | - David W Wetter
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, United States
| | - Santosh Kumar
- Department of Computer Science, University of Memphis, Memphis, TN, United States
| | - Christine Vinci
- Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, FL, United States; Department of Psychology, University of South Florida, Tampa, FL, United States; Department of Oncologic Sciences, University of South Florida, Tampa, FL, United States.
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6
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Wang S, Zhang C, Kröse B, van Hoof H. Optimizing Adaptive Notifications in Mobile Health Interventions Systems: Reinforcement Learning from a Data-driven Behavioral Simulator. J Med Syst 2021; 45:102. [PMID: 34664120 PMCID: PMC8523513 DOI: 10.1007/s10916-021-01773-0] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 09/20/2021] [Indexed: 11/19/2022]
Abstract
Mobile health (mHealth) intervention systems can employ adaptive strategies to interact with users. Instead of designing such complex strategies manually, reinforcement learning (RL) can be used to adaptively optimize intervention strategies concerning the user’s context. In this paper, we focus on the issue of overwhelming interactions when learning a good adaptive strategy for the user in RL-based mHealth intervention agents. We present a data-driven approach integrating psychological insights and knowledge of historical data. It allows RL agents to optimize the strategy of delivering context-aware notifications from empirical data when counterfactual information (user responses when receiving notifications) is missing. Our approach also considers a constraint on the frequency of notifications, which reduces the interaction burden for users. We evaluated our approach in several simulation scenarios using real large-scale running data. The results indicate that our RL agent can deliver notifications in a manner that realizes a higher behavioral impact than context-blind strategies.
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Affiliation(s)
- Shihan Wang
- Informatics Institute, University of Amsterdam, Amsterdam, Netherlands. .,Information and Computing Sciences, Utrecht University, Utrecht, Netherlands.
| | - Chao Zhang
- Department of Psychology, Utrecht University, Utrecht, Netherlands.,Human-Technology Interaction, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Ben Kröse
- Informatics Institute, University of Amsterdam, Amsterdam, Netherlands.,Digital Life, Amsterdam University of Applied Sciences, Amsterdam, Netherlands
| | - Herke van Hoof
- Informatics Institute, University of Amsterdam, Amsterdam, Netherlands
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Battalio SL, Conroy DE, Dempsey W, Liao P, Menictas M, Murphy S, Nahum-Shani I, Qian T, Kumar S, Spring B. Sense2Stop: A micro-randomized trial using wearable sensors to optimize a just-in-time-adaptive stress management intervention for smoking relapse prevention. Contemp Clin Trials 2021; 109:106534. [PMID: 34375749 PMCID: PMC8556307 DOI: 10.1016/j.cct.2021.106534] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [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] [Received: 04/09/2021] [Revised: 07/11/2021] [Accepted: 08/05/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND Relapse to smoking is commonly triggered by stress, but behavioral interventions have shown only modest efficacy in preventing stress-related relapse. Continuous digital sensing to detect states of smoking risk and intervention receptivity may make it feasible to increase treatment efficacy by adapting intervention timing. OBJECTIVE Aims are to investigate whether the delivery of a prompt to perform stress management behavior, as compared to no prompt, reduces the likelihood of (a) being stressed and (b) smoking in the subsequent two hours, and (c) whether current stress moderates these effects. STUDY DESIGN A micro-randomized trial will be implemented with 75 adult smokers who wear Autosense chest and wrist sensors and use the mCerebrum suite of smartphone apps to report and respond to ecological momentary assessment (EMA) questions about smoking and mood for 4 days before and 10 days after a quit attempt and to access a set of stress-management apps. Sensor data will be processed on the smartphone in real time using the cStress algorithm to classify minutes as probably stressed or probably not stressed. Stressed and non-stressed minutes will be micro-randomized to deliver either a prompt to perform a stress management exercise via one of the apps or no prompt (2.5-3 stress management prompts will be delivered daily). Sensor and self-report assessments of stress and smoking will be analyzed to optimize decision rules for a just-in-time adaptive intervention (JITAI) to prevent smoking relapse. SIGNIFICANCE Sense2Stop will be the first digital trial using wearable sensors and micro-randomization to optimize a just-in-time adaptive stress management intervention for smoking relapse prevention.
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Affiliation(s)
- Samuel L Battalio
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 N. Lakeshore Drive, Suite 1400, Chicago, IL 60611, United States of America
| | - David E Conroy
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 N. Lakeshore Drive, Suite 1400, Chicago, IL 60611, United States of America; Department of Kinesiology, Penn State University, 266 Recreation Building, University Park, PA 16802, United States of America
| | - Walter Dempsey
- Survey Research Center, University of Michigan, 426 Thompson Street, Room 2464, Ann Arbor, MI 48106, United States of America
| | - Peng Liao
- Department of Statistics, Harvard University, Science Center 400 Suite, One Oxford Street, Cambridge, MA 02138, United States of America
| | - Marianne Menictas
- Department of Statistics, Harvard University, Science Center 400 Suite, One Oxford Street, Cambridge, MA 02138, United States of America
| | - Susan Murphy
- Department of Statistics, Harvard University, Science Center 400 Suite, One Oxford Street, Cambridge, MA 02138, United States of America
| | - Inbal Nahum-Shani
- Survey Research Center, University of Michigan, 426 Thompson Street, Room 2464, Ann Arbor, MI 48106, United States of America
| | - Tianchen Qian
- Department of Statistics, University of California, Irvine, Irvine, CA 92697, United States of America
| | - Santosh Kumar
- Department of Computer Science, University of Memphis, 319 Dunn Hall, Memphis, TN 38152, United States of America
| | - Bonnie Spring
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 N. Lakeshore Drive, Suite 1400, Chicago, IL 60611, United States of America.
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Huh J, Lee KJ, Roldan W, Castro Y, Kshirsagar S, Rastogi P, Kim I, Miller KA, Cockburn M, Yip J. Making of Mobile SunSmart: Co-designing a Just-in-Time Sun Protection Intervention for Children and Parents. Int J Behav Med 2021; 28:768-778. [PMID: 33846955 PMCID: PMC8041475 DOI: 10.1007/s12529-021-09987-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/26/2021] [Indexed: 12/02/2022]
Abstract
Background In this study, we describe a participatory design process to develop a technology-based intervention for sun protection for children and their parents. Our methodology embraces and leverages the expert knowledge of the target users, children and their parents, about their sun protection practices to directly influence the design of our mobile just-in-time adaptive intervention (JITAI). The objectives of this paper are to describe our research procedures and summarize primary findings incorporated into developing our JITAI modules. Methods We conducted 3 rounds of iterative co-design workshops with design expert KidsTeam UW children (N: 11–12) and subject expert children and their parents from local communities in California (N: 22–48). Iteratively, we thematically coded the qualitative data generated by participants in the co-design sessions to directly inform design specifications. Results Three themes emerged: (1) preference for non-linear educational format with less structure,; (2) situations not conducive for prioritizing sun protection; and (3) challenges, barriers, and ambiguity relating to sun protection to protect oneself and one’s family. Based on the design ideas and iterative participant feedback, three categories of modules were developed: personalized and interactive data intake module, narrative-education module with augmented reality experiment, person/real-time tailored JITAI, and assessment modules. Conclusions This is one of the first projects that maximally engage children and parents as co-designers to build a technology to improve sun protection with iterative and intentional design principles. Our scalable approach to design a mobile JITAI to improve sun protection will lay the foundation for future public health investigators with similar endeavors. Supplementary Information The online version contains supplementary material available at 10.1007/s12529-021-09987-9.
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Affiliation(s)
- Jimi Huh
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA 90007, USA.
| | - Kung Jin Lee
- Information School, University of Washington, Seattle, WA, USA
| | - Wendy Roldan
- Human Centered Design & Engineering, University of Washington, Seattle, WA 98105, USA
| | - Yasmine Castro
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA 90007, USA
| | - Saurabh Kshirsagar
- School of Engineering, University of Southern California, Los Angeles, CA 90089, USA
| | - Pankhuri Rastogi
- School of Engineering, University of Southern California, Los Angeles, CA 90089, USA
| | - Ian Kim
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA 90007, USA
| | - Kimberly A Miller
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA 90007, USA.,Department of Dermatology, University of Southern California, Los Angeles, CA 90033, USA
| | - Myles Cockburn
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA 90007, USA.,Department of Dermatology, University of Southern California, Los Angeles, CA 90033, USA
| | - Jason Yip
- Information School, University of Washington, Seattle, WA, USA
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9
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Hébert ET, Suchting R, Ra CK, Alexander AC, Kendzor DE, Vidrine DJ, Businelle MS. Predicting the first smoking lapse during a quit attempt: A machine learning approach. Drug Alcohol Depend 2021; 218:108340. [PMID: 33092911 PMCID: PMC8496911 DOI: 10.1016/j.drugalcdep.2020.108340] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Revised: 09/11/2020] [Accepted: 09/26/2020] [Indexed: 11/17/2022]
Abstract
BACKGROUND Just-in-time adaptive interventions (JITAI) aim to prevent smoking lapse using tailored support delivered via mobile technology in the moments when it is most needed. Effective smoking cessation JITAI rely on the development of accurate decision rules that determine when someone is most likely to lapse. The primary goal of the present study was to identify the strongest predictors of first lapse among smokers undergoing a quit attempt. METHODS Smokers attending a clinic-based smoking cessation program (n = 74) were asked to complete ecological momentary assessments five times daily on study-provided smartphones for 4 weeks post-quit. A three-stage modeling process utilized Cox proportional hazards regression to examine time to lapse a function of 31 predictors. First, univariate models evaluated the relationship between each predictor and time to lapse. Second, the elastic net machine learning algorithm was used to select the best predictors. Third, backwards elimination further reduced the set of predictors to optimize parsimony. RESULTS Univariate models identified seven predictors significantly related to time to lapse. The elastic net algorithm retained five: perceived odds of smoking today, confidence in ability to avoid smoking, motivation to avoid smoking, urge to smoke, and cigarette availability. The reduced model demonstrated inadequate approximation to the non-penalized baseline model. CONCLUSIONS Accurate estimation of moments of high risk for smoking lapse remains an important goal in the development of JITAI. These results demonstrate the utility of exploratory data-driven approaches to variable selection. The results of this study can inform future JITAI by highlighting targets for intervention.
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Affiliation(s)
- Emily T Hébert
- University of Texas Health Science Center (UTHealth) School of Public Health, Austin, TX, United States.
| | - Robert Suchting
- UTHealth McGovern Medical School, Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Chaelin K Ra
- TSET Health Promotion Research Center, Oklahoma City, OK, United States
| | - Adam C Alexander
- TSET Health Promotion Research Center, Oklahoma City, OK, United States
| | - Darla E Kendzor
- TSET Health Promotion Research Center, Oklahoma City, OK, United States; Department of Family and Preventive Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
| | | | - Michael S Businelle
- TSET Health Promotion Research Center, Oklahoma City, OK, United States; Department of Family and Preventive Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
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10
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Fiedler J, Eckert T, Wunsch K, Woll A. Key facets to build up eHealth and mHealth interventions to enhance physical activity, sedentary behavior and nutrition in healthy subjects - an umbrella review. BMC Public Health 2020; 20:1605. [PMID: 33097013 PMCID: PMC7585171 DOI: 10.1186/s12889-020-09700-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 10/14/2020] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Electronic (eHealth) and mobile (mHealth) health interventions can provide a large coverage, and are promising tools to change health behavior (i.e. physical activity, sedentary behavior and healthy eating). However, the determinants of intervention effectiveness in primary prevention has not been explored yet. Therefore, the objectives of this umbrella review were to evaluate intervention effectiveness, to explore the impact of pre-defined determinants of effectiveness (i.e. theoretical foundations, behavior change techniques, social contexts or just-in-time adaptive interventions), and to provide recommendations for future research and practice in the field of primary prevention delivered via e/mHealth technology. METHODS PubMed, Scopus, Web of Science and the Cochrane Library were searched for systematic reviews and meta-analyses (reviews) published between January 1990 and May 2020. Reviews reporting on e/mHealth behavior change interventions in physical activity, sedentary behavior and/or healthy eating for healthy subjects (i.e. subjects without physical or physiological morbidities which would influence the realization of behaviors targeted by the respective interventions) were included if they also investigated respective theoretical foundations, behavior change techniques, social contexts or just-in-time adaptive interventions. Included studies were ranked concerning their methodological quality and qualitatively synthesized. RESULTS The systematic search revealed 11 systematic reviews and meta-analyses of moderate quality. The majority of original research studies within the reviews found e/mHealth interventions to be effective, but the results showed a high heterogeneity concerning assessment methods and outcomes, making them difficult to compare. Whereas theoretical foundation and behavior change techniques were suggested to be potential positive determinants of effective interventions, the impact of social context remains unclear. None of the reviews included just-in-time adaptive interventions. CONCLUSION Findings of this umbrella review support the use of e/mHealth to enhance physical activity and healthy eating and reduce sedentary behavior. The general lack of precise reporting and comparison of confounding variables in reviews and original research studies as well as the limited number of reviews for each health behavior constrains the generalization and interpretation of results. Further research is needed on study-level to investigate effects of versatile determinants of e/mHealth efficiency, using a theoretical foundation and additionally explore the impact of social contexts and more sophisticated approaches like just-in-time adaptive interventions. TRIAL REGISTRATION The protocol for this umbrella review was a priori registered with PROSPERO: CRD42020147902 .
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Affiliation(s)
- Janis Fiedler
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Engler-Bunte-Ring 15, 76131, Karlsruhe, Germany.
| | - Tobias Eckert
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Engler-Bunte-Ring 15, 76131, Karlsruhe, Germany
| | - Kathrin Wunsch
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Engler-Bunte-Ring 15, 76131, Karlsruhe, Germany
| | - Alexander Woll
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Engler-Bunte-Ring 15, 76131, Karlsruhe, Germany
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Abstract
PURPOSE OF REVIEW Adaptive behavioral interventions tailor the type or dose of intervention strategies to individuals over time to improve saliency and intervention efficacy. This review describes the unique characteristics of adaptive intervention designs, summarizes recent diabetes-related prevention studies, which used adaptive designs, and offers recommendations for future research. RECENT FINDINGS Eight adaptive intervention studies were reported since 2013 to reduce sedentary behavior or improve weight management in overweight or obese adults. Primarily, feasibility studies were conducted. Preliminary results suggest that just-in-time adaptive interventions can reduce sedentary behavior or increase minutes of physical activity through repeated prompts. A stepped-down weight management intervention did not increase weight loss compared to a fixed intervention. Other adaptive interventions to promote weight management are underway and require further evaluation. Additional research is needed to target a broader range of health-related behaviors, identify optimal decision points and dose for intervention, develop effective engagement strategies, and evaluate outcomes using randomized trials.
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Affiliation(s)
- Carla K Miller
- Department of Human Sciences/Human Nutrition, Ohio State University, 1787 Neil Ave., 325 Campbell Hall, Columbus, OH, 43210, USA.
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12
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Cerrada CJ, Dzubur E, Blackman KCA, Mays V, Shoptaw S, Huh J. Development of a Just-in-Time Adaptive Intervention for Smoking Cessation Among Korean American Emerging Adults. Int J Behav Med 2018; 24:665-672. [PMID: 28070868 DOI: 10.1007/s12529-016-9628-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [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: 11/29/2022]
Abstract
PURPOSE Cigarette smoking is a preventable risk factor that contributes to unnecessary lung cancer burden among Korean Americans and there is limited research on effective smoking cessation strategies for this population. Smartphone-based smoking cessation apps that leverage just-in-time adaptive interventions (JITAIs) hold promise for smokers attempting to quit. However, little is known about how to develop and tailor a smoking cessation JITAI for Korean American emerging adult (KAEA) smokers. METHOD This paper documents the development process of MyQuit USC according to design guidelines for JITAI. Our development process builds on findings from a prior ecological momentary assessment study by using qualitative research methods. Semi-structured interviews and a focus group were conducted to inform which intervention options to offer and the decision rules that dictate their delivery. RESULTS Qualitative findings highlighted that (1) smoking episodes are highly context-driven and that (2) KAEA smokers believe they need personalized cessation strategies tailored to different contexts. Thus, MyQuit USC operates via decision rules that guide the delivery of personalized implementation intentions, which are contingent on dynamic factors, to be delivered "just in time" at user-scheduled, high-risk smoking situations. CONCLUSION Through an iterative design process, informed by quantitative and qualitative formative research, we developed a smoking cessation JITAI tailored specifically for KAEA smokers. Further testing is under way to optimize future versions of the app with the most effective intervention strategies and decision rules. MyQuit USC has the potential to provide cessation support in real-world settings, when KAEAs need them the most.
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Affiliation(s)
- Christian Jules Cerrada
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, 2001 N. Soto Street, 3rd Floor, Los Angeles, CA, 90032, USA.
| | - Eldin Dzubur
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, 2001 N. Soto Street, 3rd Floor, Los Angeles, CA, 90032, USA
| | - Kacie C A Blackman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, 2001 N. Soto Street, 3rd Floor, Los Angeles, CA, 90032, USA
| | - Vickie Mays
- University of California, Los Angeles, CA, USA
| | | | - Jimi Huh
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, 2001 N. Soto Street, 3rd Floor, Los Angeles, CA, 90032, USA
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