Published on in Vol 4, No 3 (2016): Jul-Sept
Journals
- Bernardes-Souza B, Patruz Ananias De Assis Pires F, Madeira G, Felício Da Cunha Rodrigues T, Gatzka M, Heppt M, Omlor A, Enk A, Groneberg D, Seeger W, von Kalle C, Berking C, Corrêa P, Suhre J, Alfitian J, Assis A, Brinker T. Facial-Aging Mobile Apps for Smoking Prevention in Secondary Schools in Brazil: Appearance-Focused Interventional Study. JMIR Public Health and Surveillance 2018;4(3):e10234 View
- Bertz J, Epstein D, Preston K. Combining ecological momentary assessment with objective, ambulatory measures of behavior and physiology in substance-use research. Addictive Behaviors 2018;83:5 View
- Kwasnicka D, Inauen J, Nieuwenboom W, Nurmi J, Schneider A, Short C, Dekkers T, Williams A, Bierbauer W, Haukkala A, Picariello F, Naughton F. Challenges and solutions for N-of-1 design studies in health psychology. Health Psychology Review 2019;13(2):163 View
- Abo-Tabik M, Costen N, Darby J, Benn Y. Towards a Smart Smoking Cessation App: A 1D-CNN Model Predicting Smoking Events. Sensors 2020;20(4):1099 View
- Juarascio A, Parker M, Lagacey M, Godfrey K. Just‐in‐time adaptive interventions: A novel approach for enhancing skill utilization and acquisition in cognitive behavioral therapy for eating disorders. International Journal of Eating Disorders 2018;51(8):826 View
- Yin K, Laranjo L, Tong H, Lau A, Kocaballi A, Martin P, Vagholkar S, Coiera E. Context-Aware Systems for Chronic Disease Patients: Scoping Review. Journal of Medical Internet Research 2019;21(6):e10896 View
- Huh J, Cerrada C, Dzubur E, Dunton G, Spruijt-Metz D, Leventhal A. Effect of a mobile just-in-time implementation intention intervention on momentary smoking lapses in smoking cessation attempts among Asian American young adults. Translational Behavioral Medicine 2021;11(1):216 View
- Choi W, Park S, Kim D, Lim Y, Lee U. Multi-Stage Receptivity Model for Mobile Just-In-Time Health Intervention. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2019;3(2):1 View
- Perski O, Blandford A, Ubhi H, West R, Michie S. Smokers’ and drinkers’ choice of smartphone applications and expectations of engagement: a think aloud and interview study. BMC Medical Informatics and Decision Making 2017;17(1) View
- Hébert E, Ra C, Alexander A, Helt A, Moisiuc R, Kendzor D, Vidrine D, Funk-Lawler R, Businelle M. A Mobile Just-in-Time Adaptive Intervention for Smoking Cessation: Pilot Randomized Controlled Trial. Journal of Medical Internet Research 2020;22(3):e16907 View
- Di Matteo D, Fotinos K, Lokuge S, Yu J, Sternat T, Katzman M, Rose J. The Relationship Between Smartphone-Recorded Environmental Audio and Symptomatology of Anxiety and Depression: Exploratory Study. JMIR Formative Research 2020;4(8):e18751 View
- McQuoid J, Thrul J, Ling P. A geographically explicit ecological momentary assessment (GEMA) mixed method for understanding substance use. Social Science & Medicine 2018;202:89 View
- Businelle M, Walters S, Mun E, Kirchner T, Hébert E, Li X. Reducing Drinking Among People Experiencing Homelessness: Protocol for the Development and Testing of a Just-in-Time Adaptive Intervention. JMIR Research Protocols 2020;9(4):e15610 View
- Chatterjee S, Moreno A, Lizotte S, Akther S, Ertin E, Fagundes C, Lam C, Rehg J, Wan N, Wetter D, Kumar S. SmokingOpp. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2020;4(1):1 View
- Vinci C. Cognitive Behavioral and Mindfulness-Based Interventions for Smoking Cessation: a Review of the Recent Literature. Current Oncology Reports 2020;22(6) View
- Figueiras M, Neto D. Challenges in “Tailoring” Adjustment. European Psychologist 2019;24(1):1 View
- Allan J, McMinn D, Powell D. Tracking snacking in real time: Time to look at individualised patterns of behaviour. Nutrition and Health 2019;25(3):179 View
- Brinker T, Alfitian J, Seeger W, Groneberg D, Von Kalle C, Enk A, Herth F, Kreuter M, Bauer C, Gatzka M, Suhre J. A Face-Aging Smoking Prevention/Cessation Intervention for Nursery School Students in Germany: An Appearance-Focused Interventional Study. International Journal of Environmental Research and Public Health 2018;15(8):1656 View
- Carpenter S, Menictas M, Nahum-Shani I, Wetter D, Murphy S. Developments in Mobile Health Just-in-Time Adaptive Interventions for Addiction Science. Current Addiction Reports 2020;7(3):280 View
- Cosco T, Firth J, Vahia I, Sixsmith A, Torous J. Mobilizing mHealth Data Collection in Older Adults: Challenges and Opportunities. JMIR Aging 2019;2(1):e10019 View
- Walsh J, Groarke J. Integrating Behavioral Science With Mobile (mHealth) Technology to Optimize Health Behavior Change Interventions. European Psychologist 2019;24(1):38 View
- Wray T, Pérez A, Celio M, Carr D, Adia A, Monti P. Exploring the Use of Smartphone Geofencing to Study Characteristics of Alcohol Drinking Locations in High‐Risk Gay and Bisexual Men. Alcoholism: Clinical and Experimental Research 2019;43(5):900 View
- Attwood S, Parke H, Larsen J, Morton K. Using a mobile health application to reduce alcohol consumption: a mixed-methods evaluation of the drinkaware track & calculate units application. BMC Public Health 2017;17(1) View
- Elliston K, Schüz B, Albion T, Ferguson S. Comparison of Geographic Information System and Subjective Assessments of Momentary Food Environments as Predictors of Food Intake: An Ecological Momentary Assessment Study. JMIR mHealth and uHealth 2020;8(7):e15948 View
- Kruse G, Park E, Shahid N, Abroms L, Haberer J, Rigotti N. Combining Real-Time Ratings With Qualitative Interviews to Develop a Smoking Cessation Text Messaging Program for Primary Care Patients. JMIR mHealth and uHealth 2019;7(3):e11498 View
- Vilardaga R, Casellas-Pujol E, McClernon J, Garrison K. Mobile Applications for the Treatment of Tobacco Use and Dependence. Current Addiction Reports 2019;6(2):86 View
- Zhai D, Schiavone G, Van Diest I, Vrieze E, DeRaedt W, Van Hoof C. Ambulatory Smoking Habits Investigation based on Physiology and Context (ASSIST) using wearable sensors and mobile phones: protocol for an observational study. BMJ Open 2019;9(9):e028284 View
- Pulantara I, Parmanto B, Germain A. Clinical Feasibility of a Just-in-Time Adaptive Intervention App (iREST) as a Behavioral Sleep Treatment in a Military Population: Feasibility Comparative Effectiveness Study. Journal of Medical Internet Research 2018;20(12):e10124 View
- Pulantara I, Parmanto B, Germain A. Development of a Just-in-Time Adaptive mHealth Intervention for Insomnia: Usability Study. JMIR Human Factors 2018;5(2):e21 View
- Wright W, Rafferty A, Winterbauer N, Locklear K, Tucker‐McLaughlin M. Geofencing: Mobile Technology as a Health Promotion Tool to Raise Awareness of a Dental Clinic in Rural North Carolina. The Journal of Rural Health 2021;37(3):667 View
- Vinci C, Haslam A, Lam C, Kumar S, Wetter D. The use of ambulatory assessment in smoking cessation. Addictive Behaviors 2018;83:18 View
- Suchting R, Hébert E, Ma P, Kendzor D, Businelle M. Using Elastic Net Penalized Cox Proportional Hazards Regression to Identify Predictors of Imminent Smoking Lapse. Nicotine & Tobacco Research 2019;21(2):173 View
- Blank M, Hoek J, George M, Gendall P, Conner T, Thrul J, Ling P, Langlotz T. An Exploration of Smoking-to-Vaping Transition Attempts Using a “Smart” Electronic Nicotine Delivery System. Nicotine & Tobacco Research 2019;21(10):1339 View
- Cornet V, Holden R. Systematic review of smartphone-based passive sensing for health and wellbeing. Journal of Biomedical Informatics 2018;77:120 View
- McClure E, Tomko R, Carpenter M, Treiber F, Gray K. Acceptability and compliance with a remote monitoring system to track smoking and abstinence among young smokers. The American Journal of Drug and Alcohol Abuse 2018;44(5):561 View
- Garnett C, Crane D, West R, Brown J, Michie S. The development of Drink Less: an alcohol reduction smartphone app for excessive drinkers. Translational Behavioral Medicine 2019;9(2):296 View
- Schick R, Kelsey T, Marston J, Samson K, Humphris G. MapMySmoke: feasibility of a new quit cigarette smoking mobile phone application using integrated geo-positioning technology, and motivational messaging within a primary care setting. Pilot and Feasibility Studies 2018;4(1) View
- Jones A, Crawford J, Rose A, Christiansen P, Cooke R. Regret Me Not: Examining the Relationship between Alcohol Consumption and Regrettable Experiences. Substance Use & Misuse 2020;55(14):2379 View
- Juarascio A, Hunt R, Lantz Lesser E, Engel S, Pisetsky E, Peterson C, Wonderlich S. Enhancing Integrative Cognitive‐Affective Therapy with ecological momentary interventions: A pilot trial. European Eating Disorders Review 2021;29(1):152 View
- Koslovsky M, Hébert E, Businelle M, Vannucci M. A Bayesian time-varying effect model for behavioral mHealth data. The Annals of Applied Statistics 2020;14(4) View
- Naughton F, Brown C, High J, Notley C, Mascolo C, Coleman T, Barton G, Shepstone L, Sutton S, Prevost A, Crane D, Greaves F, Hope A. Randomised controlled trial of a just-in-time adaptive intervention (JITAI) smoking cessation smartphone app: the Quit Sense feasibility trial protocol. BMJ Open 2021;11(4):e048204 View
- Rao D, Shiyanbola O. Best practices for conducting and writing mixed methods research in social pharmacy. Research in Social and Administrative Pharmacy 2022;18(1):2184 View
- Lekkas D, Jacobson N. Using artificial intelligence and longitudinal location data to differentiate persons who develop posttraumatic stress disorder following childhood trauma. Scientific Reports 2021;11(1) View
- Wright A, Browne J, Skiest H, Bhiku K, Baker J, Cather C. The relationship between conventional clinical assessments and momentary assessments of symptoms and functioning in schizophrenia spectrum disorders: A systematic review. Schizophrenia Research 2021;232:11 View
- Daniëls N, Hochstenbach L, van Zelst C, van Bokhoven M, Delespaul P, Beurskens A. Factors That Influence the Use of Electronic Diaries in Health Care: Scoping Review. JMIR mHealth and uHealth 2021;9(6):e19536 View
- Koch E, Moukhtarian T, Skirrow C, Bozhilova N, Asherson P, Ebner-Priemer U. Using e-diaries to investigate ADHD – State-of-the-art and the promising feature of just-in-time-adaptive interventions. Neuroscience & Biobehavioral Reviews 2021;127:884 View
- Abo-Tabik M, Benn Y, Costen N. Are Machine Learning Methods the Future for Smoking Cessation Apps?. Sensors 2021;21(13):4254 View
- Xu X, Mankoff J, Dey A. Understanding practices and needs of researchers in human state modeling by passive mobile sensing. CCF Transactions on Pervasive Computing and Interaction 2021;3(4):344 View
- Engelhard M, D'Arcy J, Oliver J, Kozink R, McClernon F. Prediction of Smoking Risk From Repeated Sampling of Environmental Images: Model Validation. Journal of Medical Internet Research 2021;23(11):e27875 View
- Moczygemba L, Thurman W, Tormey K, Hudzik A, Welton-Arndt L, Kim E. GPS Mobile Health Intervention Among People Experiencing Homelessness: Pre-Post Study. JMIR mHealth and uHealth 2021;9(11):e25553 View
- Tack C. A model of integrated remote monitoring and behaviour change for osteoarthritis. BMC Musculoskeletal Disorders 2021;22(1) View
- Perski O, Hébert E, Naughton F, Hekler E, Brown J, Businelle M. Technology‐mediated just‐in‐time adaptive interventions (JITAIs) to reduce harmful substance use: a systematic review. Addiction 2022;117(5):1220 View
- Thrul J, Howe C, Devkota J, Alexander A, Allen A, Businelle M, Hébert E, Heffner J, Kendzor D, Ra C, Gordon J. A Scoping Review and Meta-analysis of the Use of Remote Biochemical Verification Methods of Smoking Status in Tobacco Research. Nicotine and Tobacco Research 2023;25(8):1413 View
- Felemban S, Vazquez P, Balbaied T, Moore E. Lab-on-a-Chip Electrochemical Immunosensor Array Integrated with Microfluidics: Development and Characterisation. Electrochem 2022;3(4):570 View
- Wolkowicz N, Peltier M, Wemm S, MacLean R. Subjective stress and alcohol use among young adult and adult drinkers: Systematic review of studies using Intensive Longitudinal Designs. Drug and Alcohol Dependence Reports 2022;3:100039 View
- Van Emmenis M, Jamison J, Kassavou A, Hardeman W, Naughton F, A'Court C, Sutton S, Eborall H. Patient and practitioner views on a combined face-to-face and digital intervention to support medication adherence in hypertension: a qualitative study within primary care. BMJ Open 2022;12(2):e053183 View
- Hoving C, de Ruijter D, Smit E. Using tailored eHealth programmes to stimulate primary health care professionals’ lifestyle counselling guideline adherence - Lessons learned from the STAR project. Patient Education and Counseling 2023;109:107621 View
- Lee U, Jung G, Ma E, Kim J, Kim H, Alikhanov J, Noh Y, Kim H. Toward Data-Driven Digital Therapeutics Analytics: Literature Review and Research Directions. IEEE/CAA Journal of Automatica Sinica 2023;10(1):42 View
- Luken A, Desjardins M, Moran M, Mendelson T, Zipunnikov V, Kirchner T, Naughton F, Latkin C, Thrul J. Using Smartphone Survey and GPS Data to Inform Smoking Cessation Intervention Delivery: Case Study. JMIR mHealth and uHealth 2023;11:e43990 View
- Zhang M, Wolters M, O'Connor S, Wang Y, Doi L. Smokers’ user experience of smoking cessation apps: A systematic review. International Journal of Medical Informatics 2023;175:105069 View
- Serre F, Moriceau S, Donnadieu L, Forcier C, Garnier H, Alexandre J, Dupuy L, Philip P, Levavasseur Y, De Sevin E, Auriacombe M. The Craving-Manager smartphone app designed to diagnose substance use/addictive disorders, and manage craving and individual predictors of relapse: a study protocol for a multicenter randomized controlled trial. Frontiers in Psychiatry 2023;14 View
- Tobin K, Heidari O, Volpi C, Sodder S, Duncan D. Use of geofencing interventions in population health research: a scoping review. BMJ Open 2023;13(8):e069374 View
- Perski O, Li K, Pontikos N, Simons D, Goldstein S, Naughton F, Brown J. Classification of Lapses in Smokers Attempting to Stop: A Supervised Machine Learning Approach Using Data From a Popular Smoking Cessation Smartphone App. Nicotine and Tobacco Research 2023;25(7):1330 View
- Dowling N, Rodda S, Merkouris S. Applying the Just-In-Time Adaptive Intervention Framework to the Development of Gambling Interventions. Journal of Gambling Studies 2023;40(2):717 View
- Naughton F, Hope A, Siegele-Brown C, Grant K, Barton G, Notley C, Mascolo C, Coleman T, Shepstone L, Sutton S, Prevost A, Crane D, Greaves F, High J. An Automated, Online Feasibility Randomized Controlled Trial of a Just-In-Time Adaptive Intervention for Smoking Cessation (Quit Sense). Nicotine and Tobacco Research 2023;25(7):1319 View
- Shevchenko Y, Reips U. Geofencing in location-based behavioral research: Methodology, challenges, and implementation. Behavior Research Methods 2023;56(7):6411 View
- Chen J, Chu J, Marsh S, Shi T, Bullen C. Smartphone App-Based Interventions to Support Smoking Cessation in Smokers with Mental Health Conditions: A Systematic Review. Psych 2023;5(4):1077 View
- Xu Z, Smit E. Using a complexity science approach to evaluate the effectiveness of just-in-time adaptive interventions: A meta-analysis. DIGITAL HEALTH 2023;9 View
- Jackson K, Meisel M, Sokolovsky A, Chen K, Barnett N. Detecting and Understanding Social Influence During Drinking Situations: Protocol for a Bluetooth-Based Sensor Feasibility and Acceptability Study. JMIR Research Protocols 2024;13:e50650 View
- Zhou S, Levinson A, Zhang X, Portz J, Moore S, Gore M, Ford K, Li Q, Bull S. A Pilot Study and Ecological Model of Smoking Cues to Inform Mobile Health Strategies for Quitting Among Low-Income Smokers. Health Promotion Practice 2021;22(6):850 View
- Pater J, Phelan C, Cornet V, Ahmed R, Colletta S, Hess E, Kerrigan C, Toscos T. User-Centered Design of a Mobile App to Support Peer Recovery in a Clinical Setting. Proceedings of the ACM on Human-Computer Interaction 2021;5(CSCW1):1 View
- Naughton F, Hope A, Siegele-Brown C, Grant K, Notley C, Colles A, West C, Mascolo C, Coleman T, Barton G, Shepstone L, Prevost T, Sutton S, Crane D, Greaves F, High J. A smoking cessation smartphone app that delivers real-time ‘context aware’ behavioural support: the Quit Sense feasibility RCT. Public Health Research 2024:1 View
- Horvath M, Pittman B, O’Malley S, Grutman A, Khan N, Gueorguieva R, Brewer J, Garrison K. Smartband-based smoking detection and real-time brief mindfulness intervention: findings from a feasibility clinical trial. Annals of Medicine 2024;56(1) View
- Perski O, Kale D, Leppin C, Okpako T, Simons D, Goldstein S, Hekler E, Brown J, Or C. Supervised machine learning to predict smoking lapses from Ecological Momentary Assessments and sensor data: Implications for just-in-time adaptive intervention development. PLOS Digital Health 2024;3(8):e0000594 View
- Hsu T, Whelan P, Gandrup J, Armitage C, Cordingley L, McBeth J. Personalized interventions for behaviour change: A scoping review of just‐in‐time adaptive interventions. British Journal of Health Psychology 2025;30(1) View
Books/Policy Documents
- Castro L, Rodríguez M, Martínez F, Rodríguez L, Andrade Á, Cornejo R. Intelligent Data Sensing and Processing for Health and Well-Being Applications. View
- Ferguson S, Jahnel T, Elliston K, Shiffman S. The Cambridge Handbook of Research Methods in Clinical Psychology. View
- . The Cambridge Handbook of Research Methods in Clinical Psychology. View
- Yardley L, Morrison L, Muller I, Bradbury K. The Handbook of Behavior Change. View
- Terhorst Y, Knauer J, Baumeister H. Digital Phenotyping and Mobile Sensing. View
- Pramanik H, Pal A, Kirtania M, Chakravarty T, Ghose A. Smartphone-Based Detection Devices. View
- Rao D, Shiyanbola O. Contemporary Research Methods in Pharmacy and Health Services. View
- Nahum-Shani I, Wetter D, Murphy S. Digital Therapeutics for Mental Health and Addiction. View
- Meijer E, Mansour M. Digital Respiratory Healthcare. View
- Togawa R, Legaspi R, Nishimura Y, Miyamamoto A, Yang B, Sugisaki E, Ikeda K, Kobayashi N, Naruse Y. Persuasive Technology. View