This paper is in the following e-collection/theme issue:

Mobile Health (mhealth) (2941) mHealth for Data Collection and Research (955) mHealth for Wellness, Behavior Change and Prevention (2872) Smoking Cessation (557) Ecological Momentary Assessment (EMA) (311)

Published on in Vol 18, No 10 (2016): October

Using Intensive Longitudinal Data Collected via Mobile Phone to Detect Imminent Lapse in Smokers Undergoing a Scheduled Quit Attempt

Using Intensive Longitudinal Data Collected via Mobile Phone to Detect Imminent Lapse in Smokers Undergoing a Scheduled Quit Attempt

Using Intensive Longitudinal Data Collected via Mobile Phone to Detect Imminent Lapse in Smokers Undergoing a Scheduled Quit Attempt

Authors of this article:

Michael S Businelle1, 2 Author Orcid Image ;   Ping Ma3 Author Orcid Image ;   Darla E Kendzor1, 2 Author Orcid Image ;   Summer G Frank2 Author Orcid Image ;   David W Wetter4 Author Orcid Image ;   Damon J Vidrine1, 2 Author Orcid Image
Article Authors Cited by (49) Tweetations (30) Metrics

Journals

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  4. 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
  5. 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
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  11. Waring J, Hébert E, Alexander A, Kendzor D, Businelle M. Evaluating the influences of social support and smoking cues on daily smoking abstinence among socioeconomically disadvantaged adults. Addictive Behaviors 2020;100:106107 View
  12. Reingle Gonzalez J, Businelle M, Kendzor D, Staton M, North C, Swartz M. Using mHealth to Increase Treatment Utilization Among Recently Incarcerated Homeless Adults (Link2Care): Protocol for a Randomized Controlled Trial. JMIR Research Protocols 2018;7(6):e151 View
  13. Businelle M, Ma P, Kendzor D, Frank S, Vidrine D, Wetter D. An Ecological Momentary Intervention for Smoking Cessation: Evaluation of Feasibility and Effectiveness. Journal of Medical Internet Research 2016;18(12):e321 View
  14. Preston K, Kowalczyk W, Phillips K, Jobes M, Vahabzadeh M, Lin J, Mezghanni M, Epstein D. Before and after: craving, mood, and background stress in the hours surrounding drug use and stressful events in patients with opioid-use disorder. Psychopharmacology 2018;235(9):2713 View
  15. Nandy R, Nandy K, Hébert E, Businelle M, Walters S. Identifying Behaviors Predicting Early Morning Emotions by Observing Permanent Supportive Housing Residents: An Ecological Momentary Assessment. JMIR Mental Health 2019;6(2):e10186 View
  16. Houghton L, Howland R, McDonald J. Mobilizing Breast Cancer Prevention Research Through Smartphone Apps: A Systematic Review of the Literature. Frontiers in Public Health 2019;7 View
  17. 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
  18. Cambron C, Hopkins P, Burningham C, Lam C, Cinciripini P, Wetter D. Socioeconomic status, mindfulness, and momentary associations between stress and smoking lapse during a quit attempt. Drug and Alcohol Dependence 2020;209:107840 View
  19. Nakajima M, Lemieux A, Fiecas M, Chatterjee S, Sarker H, Saleheen N, Ertin E, Kumar S, al'Absi M. Using novel mobile sensors to assess stress and smoking lapse. International Journal of Psychophysiology 2020;158:411 View
  20. Shim Y, Scotney V, Tay L. Conducting mobile-enabled ecological momentary intervention research in positive psychology: key considerations and recommended practices. The Journal of Positive Psychology 2022;17(5):708 View
  21. Acorda D, Businelle M, Santa Maria D. Perceived Impacts, Acceptability, and Recommendations for Ecological Momentary Assessment Among Youth Experiencing Homelessness: Qualitative Study. JMIR Formative Research 2021;5(4):e21638 View
  22. Walters S, Businelle M, Suchting R, Li X, Hébert E, Mun E. Using machine learning to identify predictors of imminent drinking and create tailored messages for at-risk drinkers experiencing homelessness. Journal of Substance Abuse Treatment 2021;127:108417 View
  23. Garey L, Hébert E, Mayorga N, Chavez J, Shepherd J, Businelle M, Zvolensky M. Evaluating the feasibility and acceptability of a mobile‐based health technology for smoking cessation: Mobile Anxiety Sensitivity Program. British Journal of Clinical Psychology 2022;61(S1):111 View
  24. Abo-Tabik M, Benn Y, Costen N. Are Machine Learning Methods the Future for Smoking Cessation Apps?. Sensors 2021;21(13):4254 View
  25. Santa Maria D, Padhye N, Businelle M, Yang Y, Jones J, Sims A, Lightfoot M. Efficacy of a Just-in-Time Adaptive Intervention to Promote HIV Risk Reduction Behaviors Among Young Adults Experiencing Homelessness: Pilot Randomized Controlled Trial. Journal of Medical Internet Research 2021;23(7):e26704 View
  26. Benson L, Ra C, Hébert E, Kendzor D, Oliver J, Frank-Pearce S, Neil J, Businelle M. Quit Stage and Intervention Type Differences in the Momentary Within-Person Association Between Negative Affect and Smoking Urges. Frontiers in Digital Health 2022;4 View
  27. Perski O, Kwasnicka D, Kale D, Schneider V, Szinay D, ten Hoor G, Asare B, Verboon P, Powell D, Naughton F, Keller J. Within‐person associations between psychological and contextual factors and lapse incidence in smokers attempting to quit: A systematic review and meta‐analysis of ecological momentary assessment studies. Addiction 2023;118(7):1216 View
  28. 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
  29. Potter L, Schlechter C, Nahum‐Shani I, Lam C, Cinciripini P, Wetter D. Socio‐economic status moderates within‐person associations of risk factors and smoking lapse in daily life. Addiction 2023;118(5):925 View
  30. Patterson J, Macisco J, Glasser A, Wermert A, Nemeth J, Ahmed S. Psychosocial factors influencing smoking relapse among youth experiencing homelessness: A qualitative study. PLOS ONE 2022;17(7):e0270665 View
  31. Coughlin L, Bonar E, Walton M, Fernandez A, Duguid I, Nahum-Shani I. New Directions for Motivational Incentive Interventions for Smoking Cessation. Frontiers in Digital Health 2022;4 View
  32. Nahum-Shani I, Potter L, Lam C, Yap J, Moreno A, Stoffel R, Wu Z, Wan N, Dempsey W, Kumar S, Ertin E, Murphy S, Rehg J, Wetter D. The mobile assistance for regulating smoking (MARS) micro-randomized trial design protocol. Contemporary Clinical Trials 2021;110:106513 View
  33. Garey L, Zvolensky M, Gallagher M, Vujanovic A, Kendzor D, Stephens L, Cheney M, Cole A, Kezbers K, Matoska C, Robison J, Montgomery A, Zappi C, Businelle M. A Smartphone-Based Intervention for Anxiety and Depression in Racially and Ethnically Diverse Adults (EASE): Protocol for a Randomized Controlled Trial. JMIR Research Protocols 2022;11(12):e40713 View
  34. van Amsterdam J, van den Brink W. The effect of alcohol use on smoking cessation: A systematic review. Alcohol 2023;109:13 View
  35. Gajos J, Hawes E, Chana S, Mrug S, Wolford-Clevenger C, Businelle M, Carpenter M, Cropsey K. Daily adherence to nicotine replacement therapy in low-income smokers: The role of gender, negative mood, motivation, and self-efficacy. Addictive Behaviors 2023;138:107543 View
  36. Berge J, Trofholz A, Aqeel M, Norderud K, Tate A, Fertig A, Loth K, Mendenhall T, Neumark-Sztainer D. A three-arm randomized controlled trial using ecological momentary intervention, community health workers, and video feedback at family meals to improve child cardiovascular health: the Family Matters study design. BMC Public Health 2023;23(1) View
  37. 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
  38. Potter L, Nahum-Shani I, Wetter D. Editorial: Digital technology for tobacco control: Novel data collection, study designs, and interventions. Frontiers in Digital Health 2023;5 View
  39. West A, Correll L, Kirschner J. Reducing tobacco and nicotine use among women in treatment for substance use disorder: evaluation of the knit to quit program. Arts & Health 2024:1 View
  40. Businelle M, Benson L, Hébert E, Neil J, Kendzor D, Frank-Pearce S, Kezbers K, Vidrine D, Gaur A. Project phoenix: Pilot randomized controlled trial of a smartphone-delivered intervention for people who are not ready to quit smoking. Drug and Alcohol Dependence 2024;260:111351 View
  41. Businelle M, Perski O, Hébert E, Kendzor D. Mobile Health Interventions for Substance Use Disorders. Annual Review of Clinical Psychology 2024;20(1):49 View
  42. Businelle M, Becerra J, Witten C, Chen S, Kezbers K, Beebe L, Kendzor D. Smartphone-Based Smoking Cessation Intervention (OKquit) for Oklahoma Tobacco Helpline Users: Protocol for a Randomized Controlled Trial. JMIR Research Protocols 2024;13:e56827 View
  43. Businelle M, Hébert E, Shi D, Benson L, Kezbers K, Tonkin S, Piper M, Qian T. Investigating Best Practices for Ecological Momentary Assessment: Nationwide Factorial Experiment. Journal of Medical Internet Research 2024;26:e50275 View
  44. White J, Toussaert S, Raiff B, Salem M, Chiang A, Crane D, Warrender E, Lyles C, Abroms L, Westmaas J, Thrul J. Evaluating the Impact of a Game (Inner Dragon) on User Engagement Within a Leading Smartphone App for Smoking Cessation: Randomized Controlled Trial. Journal of Medical Internet Research 2024;26:e57839 View
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