Published on in Vol 4, No 3 (2016): Jul-Sept

A Context-Sensing Mobile Phone App (Q Sense) for Smoking Cessation: A Mixed-Methods Study

A Context-Sensing Mobile Phone App (Q Sense) for Smoking Cessation: A Mixed-Methods Study

A Context-Sensing Mobile Phone App (Q Sense) for Smoking Cessation: A Mixed-Methods Study

Journals

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. Vinci C. Cognitive Behavioral and Mindfulness-Based Interventions for Smoking Cessation: a Review of the Recent Literature. Current Oncology Reports 2020;22(6) View
  16. Figueiras M, Neto D. Challenges in “Tailoring” Adjustment. European Psychologist 2019;24(1):1 View
  17. 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
  18. 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
  19. 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
  20. 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
  21. Walsh J, Groarke J. Integrating Behavioral Science With Mobile (mHealth) Technology to Optimize Health Behavior Change Interventions. European Psychologist 2019;24(1):38 View
  22. 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
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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
  31. Vinci C, Haslam A, Lam C, Kumar S, Wetter D. The use of ambulatory assessment in smoking cessation. Addictive Behaviors 2018;83:18 View
  32. 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
  33. 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
  34. Cornet V, Holden R. Systematic review of smartphone-based passive sensing for health and wellbeing. Journal of Biomedical Informatics 2018;77:120 View
  35. 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
  36. 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
  37. 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
  38. 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
  39. 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
  40. 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
  41. 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
  42. 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
  43. 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
  44. 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
  45. 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
  46. 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
  47. Abo-Tabik M, Benn Y, Costen N. Are Machine Learning Methods the Future for Smoking Cessation Apps?. Sensors 2021;21(13):4254 View
  48. 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
  49. 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
  50. 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
  51. Tack C. A model of integrated remote monitoring and behaviour change for osteoarthritis. BMC Musculoskeletal Disorders 2021;22(1) View
  52. 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
  53. 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
  54. 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
  55. 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
  56. 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
  57. 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
  58. 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
  59. 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
  60. 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
  61. 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
  62. 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
  63. 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
  64. 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 View
  65. 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
  66. Shevchenko Y, Reips U. Geofencing in location-based behavioral research: Methodology, challenges, and implementation. Behavior Research Methods 2023 View
  67. 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
  68. 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
  69. 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
  70. 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
  71. 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
  72. 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
  73. 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

Books/Policy Documents

  1. Castro L, Rodríguez M, Martínez F, Rodríguez L, Andrade Á, Cornejo R. Intelligent Data Sensing and Processing for Health and Well-Being Applications. View
  2. Ferguson S, Jahnel T, Elliston K, Shiffman S. The Cambridge Handbook of Research Methods in Clinical Psychology. View
  3. . The Cambridge Handbook of Research Methods in Clinical Psychology. View
  4. Yardley L, Morrison L, Muller I, Bradbury K. The Handbook of Behavior Change. View
  5. Terhorst Y, Knauer J, Baumeister H. Digital Phenotyping and Mobile Sensing. View
  6. Pramanik H, Pal A, Kirtania M, Chakravarty T, Ghose A. Smartphone-Based Detection Devices. View
  7. Rao D, Shiyanbola O. Contemporary Research Methods in Pharmacy and Health Services. View
  8. Nahum-Shani I, Wetter D, Murphy S. Digital Therapeutics for Mental Health and Addiction. View
  9. Meijer E, Mansour M. Digital Respiratory Healthcare. View
  10. Togawa R, Legaspi R, Nishimura Y, Miyamamoto A, Yang B, Sugisaki E, Ikeda K, Kobayashi N, Naruse Y. Persuasive Technology. View