A Mobile Health Solution Complementing Psychopharmacology-Supported Smoking Cessation: Randomized Controlled Trial

Laura Carrasco-Hernandez, Francisco Jódar-Sánchez, Francisco Núñez-Benjumea, Jesús Moreno Conde, Marco Mesa González, Antón Civit-Balcells, Santiago Hors-Fraile, Carlos Luis Parra-Calderón, Panagiotis D Bamidis, Francisco Ortega-Ruiz, Laura Carrasco-Hernandez, Francisco Jódar-Sánchez, Francisco Núñez-Benjumea, Jesús Moreno Conde, Marco Mesa González, Antón Civit-Balcells, Santiago Hors-Fraile, Carlos Luis Parra-Calderón, Panagiotis D Bamidis, Francisco Ortega-Ruiz

Abstract

Background: Smoking cessation is a persistent leading public health challenge. Mobile health (mHealth) solutions are emerging to improve smoking cessation treatments. Previous approaches have proposed supporting cessation with tailored motivational messages. Some managed to provide short-term improvements in smoking cessation. Yet, these approaches were either static in terms of personalization or human-based nonscalable solutions. Additionally, long-term effects were neither presented nor assessed in combination with existing psychopharmacological therapies.

Objective: This study aimed to analyze the long-term efficacy of a mobile app supporting psychopharmacological therapy for smoking cessation and complementarily assess the involved innovative technology.

Methods: A 12-month, randomized, open-label, parallel-group trial comparing smoking cessation rates was performed at Virgen del Rocío University Hospital in Seville (Spain). Smokers were randomly allocated to a control group (CG) receiving usual care (psychopharmacological treatment, n=120) or an intervention group (IG) receiving psychopharmacological treatment and using a mobile app providing artificial intelligence-generated and tailored smoking cessation support messages (n=120). The secondary objectives were to analyze health-related quality of life and monitor healthy lifestyle and physical exercise habits. Safety was assessed according to the presence of adverse events related to the pharmacological therapy. Per-protocol and intention-to-treat analyses were performed. Incomplete data and multinomial regression analyses were performed to assess the variables influencing participant cessation probability. The technical solution was assessed according to the precision of the tailored motivational smoking cessation messages and user engagement. Cessation and no cessation subgroups were compared using t tests. A voluntary satisfaction questionnaire was administered at the end of the intervention to all participants who completed the trial.

Results: In the IG, abstinence was 2.75 times higher (adjusted OR 3.45, P=.01) in the per-protocol analysis and 2.15 times higher (adjusted OR 3.13, P=.002) in the intention-to-treat analysis. Lost data analysis and multinomial logistic models showed different patterns in participants who dropped out. Regarding safety, 14 of 120 (11.7%) IG participants and 13 of 120 (10.8%) CG participants had 19 and 23 adverse events, respectively (P=.84). None of the clinical secondary objective measures showed relevant differences between the groups. The system was able to learn and tailor messages for improved effectiveness in supporting smoking cessation but was unable to reduce the time between a message being sent and opened. In either case, there was no relevant difference between the cessation and no cessation subgroups. However, a significant difference was found in system engagement at 6 months (P=.04) but not in all subsequent months. High system appreciation was reported at the end of the study.

Conclusions: The proposed mHealth solution complementing psychopharmacological therapy showed greater efficacy for achieving 1-year tobacco abstinence as compared with psychopharmacological therapy alone. It provides a basis for artificial intelligence-based future approaches.

Trial registration: ClinicalTrials.gov NCT03553173; https://ichgcp.net/clinical-trials-registry/NCT03553173.

International registered report identifier (irrid): RR2-10.2196/12464.

Keywords: behavioral change; health recommender systems; mHealth; randomized controlled trial; smoking cessation.

Conflict of interest statement

Conflicts of Interest: SHF is a product manager at Salumedia Tecnologías SLU, the company with exploitation rights to the digital therapeutic solution used in the So-Lo-Mo study (DigiQuit). He contributed to technical data extraction for the precision, time-to-open the messages, and perceived quality metrics and their analysis. He was not involved in the clinical trial design, execution, or analysis of the resulting clinical data. Some institutions of all the other authors (University of Seville, the Aristotle University of Thessaloniki, and the Servicio Andaluz de Salud as a legal representative institution for the Virgen del Rocío University Hospital) signed an exploitation agreement with Salumedia Tecnologías SLU to benefit from DigiQuit commercialization. This agreement was elaborated and signed before publishing the present results but after the trial was finished and its results were generated.

©Laura Carrasco-Hernandez, Francisco Jódar-Sánchez, Francisco Núñez-Benjumea, Jesús Moreno Conde, Marco Mesa González, Antón Civit-Balcells, Santiago Hors-Fraile, Carlos Luis Parra-Calderón, Panagiotis D Bamidis, Francisco Ortega-Ruiz. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 27.04.2020.

Figures

Figure 1
Figure 1
CONSORT diagram of the study.
Figure 2
Figure 2
Structure of app features.
Figure 3
Figure 3
Efficacy evolution during the 1-year follow-up.
Figure 4
Figure 4
Evolution of health recommender system precision and number of users providing ratings by subgroup. M: month; precision_p: precision calculated by dividing the number of hits (messages rated as “like”) by the total number of sent messages; precision_p_n: precision calculated by dividing the number of hits (messages rated as “like” and “neutral”) by the total number of sent messages.
Figure 5
Figure 5
Mean time between a message being sent and opened. M: month.
Figure 6
Figure 6
Evolution of user engagement. M: month.

References

    1. WHO . WHO Global Report On Trends In Prevalence Of Tobacco Smoking, 2015. Geneva: World Health Organization; 2015.
    1. Goodchild M, Nargis N, Tursan d'Espaignet E. Global economic cost of smoking-attributable diseases. Tob Control. 2017 Jan 30;27(1):58–64. doi: 10.1136/tobaccocontrol-2016-053305.
    1. Cahill K, Stevens S, Perera R, Lancaster T. Pharmacological interventions for smoking cessation: an overview and network meta-analysis. Cochrane Database Syst Rev. 2013 May 31;(5):CD009329. doi: 10.1002/14651858.CD009329.pub2.
    1. Reid RD, Pritchard G, Walker K, Aitken D, Mullen K, Pipe AL. Managing smoking cessation. CMAJ. 2016 Dec 06;188(17-18):E484–E492. doi: 10.1503/cmaj.151510.
    1. Cahill K, Lindson-Hawley N, Thomas K, Fanshawe T, Lancaster T. Nicotine receptor partial agonists for smoking cessation. Cochrane Database Syst Rev. 2016 May 09;(5):CD006103. doi: 10.1002/14651858.CD006103.pub7.
    1. Condon LA, Coulson NS. Behavior Change Research and Theory. Cambirdge, Massachusetts: Elsevier; 2017. Chapter 2 - Designing and Delivering Interventions for Health Behavior Change in Adolescents Using Multitechnology Systems: From Identification of Target Behaviors to Implementation; pp. 27–45.
    1. Elbert SP, Dijkstra A, Oenema A. A Mobile Phone App Intervention Targeting Fruit and Vegetable Consumption: The Efficacy of Textual and Auditory Tailored Health Information Tested in a Randomized Controlled Trial. J Med Internet Res. 2016 Jun 10;18(6):e147. doi: 10.2196/jmir.5056.
    1. Zhao J, Freeman B, Li M. Can Mobile Phone Apps Influence People's Health Behavior Change? An Evidence Review. J Med Internet Res. 2016 Oct 31;18(11):e287. doi: 10.2196/jmir.5692.
    1. Teasdale N, Elhussein A, Butcher F, Piernas C, Cowburn G, Hartmann-Boyce J, Saksena R, Scarborough P. Systematic review and meta-analysis of remotely delivered interventions using self-monitoring or tailored feedback to change dietary behavior. Am J Clin Nutr. 2018 Feb 01;107(2):247–256. doi: 10.1093/ajcn/nqx048.
    1. Whittaker R, McRobbie H, Bullen C, Rodgers A, Gu Y. Mobile phone-based interventions for smoking cessation. Cochrane Database Syst Rev. 2016 Apr 10;4:CD006611. doi: 10.1002/14651858.CD006611.pub4.
    1. Brug J, Glanz K, Van Assema P, Kok G, van Breukelen GJ. The impact of computer-tailored feedback and iterative feedback on fat, fruit, and vegetable intake. Health Educ Behav. 1998 Aug 30;25(4):517–31. doi: 10.1177/109019819802500409.
    1. Dijkstra A, De Vries H, Roijackers J, van Breukelen G. Tailoring information to enhance quitting in smokers with low motivation to quit: Three basic efficacy questions. Health Psychology. 1998;17(6):513–519. doi: 10.1037/0278-6133.17.6.513.
    1. Dijkstra A. The Psychology of Tailoring-Ingredients in Computer-Tailored Persuasion. Social Pers Psych Compass. 2008 Mar;2(2):765–784. doi: 10.1111/j.1751-9004.2008.00081.x.
    1. Enwald HP, Huotari MA. Preventing the obesity epidemic by second generation tailored health communication: an interdisciplinary review. J Med Internet Res. 2010 Jun 28;12(2):e24. doi: 10.2196/jmir.1409.
    1. Kamel Ghalibaf A, Nazari E, Gholian-Aval M, Tara M. Comprehensive overview of computer-based health information tailoring: a systematic scoping review. BMJ Open. 2019 Jan 09;9(1):e021022. doi: 10.1136/bmjopen-2017-021022.
    1. Kreuter M, Farrell D, Olevitch L, Brennan L. Customizing communication with computer technology. In: Routledge , editor. Tailored health messages. Mahwah, New Jersey: Lawrence Erlbaum Associates, Inc; 2000.
    1. Cupertino AP, Cartujano-Barrera F, Ramírez M, Rodríguez-Bolaños R, Thrasher JF, Pérez-Rubio G, Falfán-Valencia R, Ellerbeck EF, Reynales-Shigematsu LM. A Mobile Smoking Cessation Intervention for Mexico (Vive sin Tabaco... ¡Decídete!): Single-Arm Pilot Study. JMIR Mhealth Uhealth. 2019 Apr 25;7(4):e12482. doi: 10.2196/12482.
    1. Bamidis P, Paraskevopoulos E, Konstantinidis E, Spachos D, Billis A. Multimodal e-health services for smoking cessation and public health: the smokefreebrain project approach. Proceedings of the 16th World Congress on Medical and Health Informatics; MEDINFO 2017: Precision Healthcare through Informatics; August 2017; Hangzhou. 2017. pp. 5–9.
    1. Prochaska JO, Velicer WF. The transtheoretical model of health behavior change. Am J Health Promot. 1997 Aug 26;12(1):38–48. doi: 10.4278/0890-1171-12.1.38.
    1. Hors-Fraile S, Nuñes Benjumea FJ, Carrasco Hernández L, Ruiz FO, Fernandez-Luque L. Design of two combined health recommender systems for tailoring messages in a smoking cessation app. arXiv preprint; International Workshop on Engendering Health with RecSys co-located with ACM RecSys; 2016; Boston, Massachusetts. 2016. p. 160807192.
    1. Hors-Fraile S, de Vries H, Malwade S, Luna-Perejon F, Amaya C, Civit A, Schneider F, Bamidis P, Syed-Abdul S, Li Y. Opening the Black Box: Explaining the Process of Basing a Health Recommender System on the I-Change Behavioral Change Model. IEEE Access. 2019;7:176525–176540. doi: 10.1109/access.2019.2957696.
    1. Hors-Fraile S, Rivera-Romero O, Schneider F, Fernandez-Luque L, Luna-Perejon F, Civit-Balcells A, de Vries H. Analyzing recommender systems for health promotion using a multidisciplinary taxonomy: A scoping review. Int J Med Inform. 2018 Jun;114:143–155. doi: 10.1016/j.ijmedinf.2017.12.018.
    1. Jódar-Sánchez F, Carrasco Hernández L, Núñez-Benjumea FJ, Mesa González MA, Moreno Conde J, Parra Calderón CL, Fernandez-Luque L, Hors-Fraile S, Civit A, Bamidis P, Ortega-Ruiz F. Using the Social-Local-Mobile App for Smoking Cessation in the SmokeFreeBrain Project: Protocol for a Randomized Controlled Trial. JMIR Res Protoc. 2018 Dec 06;7(12):e12464. doi: 10.2196/12464.
    1. Hors-Fraile S, Schneider F, Fernandez-Luque L, Luna-Perejon F, Civit A, Spachos D, Bamidis P, de Vries H. Tailoring motivational health messages for smoking cessation using an mHealth recommender system integrated with an electronic health record: a study protocol. BMC Public Health. 2018 Jun 05;18(1):698. doi: 10.1186/s12889-018-5612-5.
    1. Heatherton TF, Kozlowski LT, Frecker RC, Fagerström KO. The Fagerström Test for Nicotine Dependence: a revision of the Fagerström Tolerance Questionnaire. Br J Addict. 1991 Sep;86(9):1119–27. doi: 10.1111/j.1360-0443.1991.tb01879.x.
    1. Richmond RL, Kehoe LA, Webster IW. Multivariate models for predicting abstention following intervention to stop smoking by general practitioners. Addiction. 1993 Aug;88(8):1127–35. doi: 10.1111/j.1360-0443.1993.tb02132.x.
    1. Pearce MS, Hayes L, Newcastle Heart Project. Newcastle Thousand Families Study Self-reported smoking status and exhaled carbon monoxide: results from two population-based epidemiologic studies in the North of England. Chest. 2005 Sep;128(3):1233–8. doi: 10.1378/chest.128.3.1233.
    1. Pérez-Trullén A, Bartolomé CB, Banegas JR. [New perspectives in the diagnosis and evolution of the consumption of tobacco: markers of susceptibility and damage] Med Clin (Barc) 2006 Apr 29;126(16):628–31. doi: 10.1157/13087722.
    1. Vilagut G, Ferrer M, Rajmil L, Rebollo P, Permanyer-Miralda G, Quintana JM, Santed R, Valderas JM, Ribera A, Domingo-Salvany A, Alonso J. [The Spanish version of the Short Form 36 Health Survey: a decade of experience and new developments] Gac Sanit. 2005 Apr;19(2):135–50. doi: 10.1157/13074369.
    1. Ramos-Goñi JM, Craig BM, Oppe M, Ramallo-Fariña Y, Pinto-Prades JL, Luo N, Rivero-Arias O. Handling Data Quality Issues to Estimate the Spanish EQ-5D-5L Value Set Using a Hybrid Interval Regression Approach. Value Health. 2018 May;21(5):596–604. doi: 10.1016/j.jval.2017.10.023.
    1. Booth M. Assessment of physical activity: an international perspective. Res Q Exerc Sport. 2000 Jun 13;71 Suppl 2(sup2):114–20. doi: 10.1080/02701367.2000.11082794.
    1. Cavelaars M, Rousseau J, Parlayan C, de Ridder S, Verburg A, Ross R, Visser G, Rotte A, Azevedo R, Boiten J, Meijer GA, Belien JA, Verheul H. OpenClinica. J Clin Bioinformatics. 2015;5(Suppl 1):S2. doi: 10.1186/2043-9113-5-s1-s2.
    1. Pu P, Chen L, Hu R, editors A user-centric evaluation framework for recommender systems. RecSys '11: Fifth ACM Conference on Recommender Systems; October 2011; Chicago, Illinois. ACM Digital Library; 2011. Oct, pp. 157–164.
    1. de Vries H. An Integrated Approach for Understanding Health Behavior; The I-Change Model as an Example. PBSIJ. 2017 Mar 9;2(2) doi: 10.19080/pbsij.2017.02.555585.
    1. Jamshidian M, Jalal S. Tests of homoscedasticity, normality, and missing completely at random for incomplete multivariate data. Psychometrika. 2010 Dec 3;75(4):649–674. doi: 10.1007/s11336-010-9175-3.
    1. Breiman L. Random forests. Machine learning. 2001;45(1):5–32. doi: 10.1023/A:1010933404324. doi: 10.1023/A:1010933404324.
    1. Fox J, Monette G. Generalized Collinearity Diagnostics. Journal of the American Statistical Association. 1992 Mar;87(417):178–183. doi: 10.1080/01621459.1992.10475190.
    1. Garrison KA, Pal P, Rojiani R, Dallery J, O'Malley SS, Brewer JA. A randomized controlled trial of smartphone-based mindfulness training for smoking cessation: a study protocol. BMC Psychiatry. 2015 Apr 14;15(1):83. doi: 10.1186/s12888-015-0468-z.
    1. Heffner JL, Vilardaga R, Mercer LD, Kientz JA, Bricker JB. Feature-level analysis of a novel smartphone application for smoking cessation. Am J Drug Alcohol Abuse. 2015 Jan 14;41(1):68–73. doi: 10.3109/00952990.2014.977486.
    1. Ubhi HK, Michie S, Kotz D, Wong WC, West R. A mobile app to aid smoking cessation: preliminary evaluation of SmokeFree28. J Med Internet Res. 2015 Jan 16;17(1):e17. doi: 10.2196/jmir.3479.
    1. Germeroth LJ, Levine MD. Postcessation weight gain concern as a barrier to smoking cessation: Assessment considerations and future directions. Addict Behav. 2018 Jan;76:250–257. doi: 10.1016/j.addbeh.2017.08.022.
    1. Goldenberg M, Danovitch I, IsHak WW. Quality of life and smoking. Am J Addict. 2014 Sep 25;23(6):540–62. doi: 10.1111/j.1521-0391.2014.12148.x.
    1. Fu S, Dao M, Wong CK, Yu W. SF-6D utility scores of smokers and ex-smokers with or without respiratory symptoms attending primary care clinics. Health Qual Life Outcomes. 2019 Mar 15;17(1):48. doi: 10.1186/s12955-019-1115-z.
    1. Almario CV. The Effect of Digital Health Technology on Patient Care and Research. Gastroenterol Hepatol (N Y) 2017 Jul;13(7):437–439.
    1. Kathleen FH, Young-Il K, Meifang C, Rekha R, Maria P, Rajani SS, Thomas KH, William CB. Web-Based Intervention for Transitioning Smokers From Inpatient to Outpatient Care: An RCT. Am J Prev Med. 2016 Oct;51(4):620–9. doi: 10.1016/j.amepre.2016.04.008.
    1. Taylor G, Dalili M, Semwal M, Civljak M, Sheikh A, Car J. Internet-based interventions for smoking cessation. Cochrane Database Syst Rev. 2017 Sep 04;9:CD007078. doi: 10.1002/14651858.CD007078.pub5.
    1. Baltrunas L, Ludwig B, Peer S, Ricci F. Context relevance assessment and exploitation in mobile recommender systems. Pers Ubiquit Comput. 2011 Jun 21;16(5):507–526. doi: 10.1007/s00779-011-0417-x.
    1. BinDhim NF, McGeechan K, Trevena L. Smartphone Smoking Cessation Application (SSC App) trial: a multicountry double-blind automated randomised controlled trial of a smoking cessation decision-aid 'app'. BMJ Open. 2018 Jan 21;8(1):e017105. doi: 10.1136/bmjopen-2017-017105.
    1. Garrison K, Pal P, O'Malley SS, Pittman B, Gueorguieva R, Rojiani R, Scheinost D, Dallery J, Brewer JA. Craving to Quit: A Randomized Controlled Trial of Smartphone App-Based Mindfulness Training for Smoking Cessation. Nicotine Tob Res. 2020 Mar 16;22(3):324–331. doi: 10.1093/ntr/nty126.
    1. Baskerville NB, Struik LL, Guindon GE, Norman CD, Whittaker R, Burns C, Hammond D, Dash D, Brown KS. Effect of a Mobile Phone Intervention on Quitting Smoking in a Young Adult Population of Smokers: Randomized Controlled Trial. JMIR Mhealth Uhealth. 2018 Oct 23;6(10):e10893. doi: 10.2196/10893.
    1. Hassandra M, Lintunen T, Hagger MS, Heikkinen R, Vanhala M, Kettunen T. An mHealth App for Supporting Quitters to Manage Cigarette Cravings With Short Bouts of Physical Activity: A Randomized Pilot Feasibility and Acceptability Study. JMIR Mhealth Uhealth. 2017 May 26;5(5):e74. doi: 10.2196/mhealth.6252.
    1. McClure JB, Anderson ML, Bradley K, An LC, Catz SL. Evaluating an Adaptive and Interactive mHealth Smoking Cessation and Medication Adherence Program: A Randomized Pilot Feasibility Study. JMIR Mhealth Uhealth. 2016 Aug 03;4(3):e94. doi: 10.2196/mhealth.6002.
    1. Whittaker R, McRobbie H, Bullen C, Rodgers A, Gu Y, Dobson R. Mobile phone text messaging and app-based interventions for smoking cessation. Cochrane Database Syst Rev. 2019 Oct 22;10:CD006611. doi: 10.1002/14651858.CD006611.pub5.
    1. Cremonesi P, Koren Y, Turrin R, editors Performance of recommender algorithms on top-n recommendation tasks. Fourth ACM Conference on Recommender Systems; 2010; Barcelona, Spain. RecSys '10; 2010. Sep, pp. 39–46.
    1. Hernández del Olmo F, Gaudioso E. Evaluation of recommender systems: A new approach. Expert Systems with Applications. 2008 Oct;35(3):790–804. doi: 10.1016/j.eswa.2007.07.047.
    1. Ricci F, Rokach L, Shapira B, Kantor P. Recommender Systems Handbook. Boston, Massachusetts: Springer; 2011. Introduction to recommender systems handbook; pp. 1–35.
    1. Schröder G, Thiele M, Lehner W. Setting goals and choosing metrics for recommender system evaluations. The RecSys 2011 Workshops - Decisions@RecSys 2011 and UCERSTI-2; UCERSTI2 Workshop at the 5th ACM conference on recommender systems; 2011; Chicago. 2011. Oct 23, pp. 78–85.
    1. Shani G, Gunawardana A. Recommender systems handbook. Boston, Massachusetts: Springer; 2011. Evaluating recommendation systems; pp. 257–297.
    1. Bennett J, Lanning S. The netflix prize. Proceedings of KDD Cup and Workshop 2007; KDD Cup and Workshop; 2007; San Jose, California. 2007. Aug 12,
    1. Li S, Karahanna E. Online Recommendation Systems in a B2C E-Commerce Context: A Review and Future Directions. JAIS. 2015 Feb;16(2):72–107. doi: 10.17705/1jais.00389.
    1. Schafer J, Konstan J, Riedl J. E-commerce recommendation applications. Data mining and knowledge discovery. 2001 Jan;5(1-2):115–153. doi: 10.1007/978-1-4615-1627-9_6.
    1. Herlocker JL, Konstan JA, Terveen LG, Riedl JT. Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 2004 Jan;22(1):5–53. doi: 10.1145/963770.963772.
    1. Ge M, Delgado-Battenfeld C, Jannach D. Beyond accuracy: evaluating recommender systems by coverage and serendipity. RecSys '10: Proceedings of the fourth ACM conference on Recommender systems; The fourth ACM conference on Recommender systems; 2010; Barcelona, Spain. 2010. Sep, pp. 257–260.
    1. McMillan B, Hickey E, Patel MG, Mitchell C. Quality assessment of a sample of mobile app-based health behavior change interventions using a tool based on the National Institute of Health and Care Excellence behavior change guidance. Patient Educ Couns. 2016 Mar;99(3):429–435. doi: 10.1016/j.pec.2015.10.023.
    1. Stoyanov SR, Hides L, Kavanagh DJ, Zelenko O, Tjondronegoro D, Mani M. Mobile app rating scale: a new tool for assessing the quality of health mobile apps. JMIR Mhealth Uhealth. 2015 Mar 11;3(1):e27. doi: 10.2196/mhealth.3422.
    1. Lewis TL, Wyatt JC. App Usage Factor: A Simple Metric to Compare the Population Impact of Mobile Medical Apps. J Med Internet Res. 2015 Aug 19;17(8):e200. doi: 10.2196/jmir.4284.
    1. Grundy QH, Wang Z, Bero LA. Challenges in Assessing Mobile Health App Quality: A Systematic Review of Prevalent and Innovative Methods. Am J Prev Med. 2016 Dec;51(6):1051–1059. doi: 10.1016/j.amepre.2016.07.009.
    1. McKay FH, Cheng C, Wright A, Shill J, Stephens H, Uccellini M. Evaluating mobile phone applications for health behaviour change: A systematic review. J Telemed Telecare. 2016 Oct 18;24(1):22–30. doi: 10.1177/1357633x16673538.
    1. Singh K, Drouin K, Newmark L, Rozenblum R, Lee J, Landman A. Developing a framework for evaluating the patient engagement, quality, and safety of mobile health applications. Issue Brief, Commonw Fund Pub. 2016 Feb 18;5:11. doi: 10.15868/socialsector.25066.
    1. Yardley L, Spring BJ, Riper H, Morrison LG, Crane DH, Curtis K, Merchant GC, Naughton F, Blandford A. Understanding and Promoting Effective Engagement With Digital Behavior Change Interventions. Am J Prev Med. 2016 Nov;51(5):833–842. doi: 10.1016/j.amepre.2016.06.015.
    1. Klasnja P, Consolvo S, Pratt W. How to evaluate technologies for health behavior change in HCI research. CHI '11: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems; The SIGCHI conference on human factors in computing systems; 2011; Vancouver, Canada. 2011. May, pp. 3063–3072.
    1. Smith W, Ploderer B, Wadley G, Webber S, Borland R. Trajectories of engagementdisengagement with a story-based smoking cessation app. CHI '17: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems; CHI '17: CHI Conference on Human Factors in Computing Systems; May, 2017; Denver, Colorado, USA. 2017. May, pp. 3045–3056.
    1. Scherer EA, Ben-Zeev D, Li Z, Kane JM. Analyzing mHealth Engagement: Joint Models for Intensively Collected User Engagement Data. JMIR Mhealth Uhealth. 2017 Jan 12;5(1):e1. doi: 10.2196/mhealth.6474.
    1. Taki S, Lymer S, Russell CG, Campbell K, Laws R, Ong K, Elliott R, Denney-Wilson E. Assessing User Engagement of an mHealth Intervention: Development and Implementation of the Growing Healthy App Engagement Index. JMIR Mhealth Uhealth. 2017 Jun 29;5(6):e89. doi: 10.2196/mhealth.7236.
    1. O'Connor S, Hanlon P, O'Donnell CA, Garcia S, Glanville J, Mair FS. Understanding factors affecting patient and public engagement and recruitment to digital health interventions: a systematic review of qualitative studies. BMC Med Inform Decis Mak. 2016 Sep 15;16(1):120. doi: 10.1186/s12911-016-0359-3.
    1. Eysenbach G. The law of attrition. J Med Internet Res. 2005 Mar 31;7(1):e11. doi: 10.2196/jmir.7.1.e11.
    1. Kohl LF, Crutzen R, de Vries NK. Online prevention aimed at lifestyle behaviors: a systematic review of reviews. J Med Internet Res. 2013 Jul 16;15(7):e146. doi: 10.2196/jmir.2665.
    1. Anscombe J, Bacardit A. AT Kearney Insights. Chicago, Illinois: AT Kearney; 2011. [2016-02-20]. Is Mobile the Cure for the Rising Costs of Healthcare? .
    1. Iacoviello BM, Steinerman JR, Klein DB, Silver TL, Berger AG, Luo SX, Schork NJ. Clickotine, A Personalized Smartphone App for Smoking Cessation: Initial Evaluation. JMIR Mhealth Uhealth. 2017 Apr 25;5(4):e56. doi: 10.2196/mhealth.7226.
    1. Owen JE, Jaworski BK, Kuhn E, Makin-Byrd KN, Ramsey KM, Hoffman JE. mHealth in the Wild: Using Novel Data to Examine the Reach, Use, and Impact of PTSD Coach. JMIR Ment Health. 2015 Mar 25;2(1):e7. doi: 10.2196/mental.3935.
    1. Bricker JB, Copeland W, Mull KE, Zeng EY, Watson NL, Akioka KJ, Heffner JL. Single-arm trial of the second version of an acceptance & commitment therapy smartphone application for smoking cessation. Drug Alcohol Depend. 2017 Jan 01;170:37–42. doi: 10.1016/j.drugalcdep.2016.10.029.
    1. Short CE, DeSmet A, Woods C, Williams SL, Maher C, Middelweerd A, Müller AM, Wark PA, Vandelanotte C, Poppe L, Hingle MD, Crutzen R. Measuring Engagement in eHealth and mHealth Behavior Change Interventions: Viewpoint of Methodologies. J Med Internet Res. 2018 Nov 16;20(11):e292. doi: 10.2196/jmir.9397.
    1. Okoshi T, Tsubouchi K, Taji M, Ichikawa T, Tokuda H. Attention and engagement-awareness in the wild: A large-scale study with adaptive notifications. 2017 IEEE International Conference on Pervasive Computing and Communications, PerCom 2017; March, 2017; Big Island, United States. 2017. Mar 17, pp. 100–110.
    1. Schwanda V, Ibara S, Reynolds L, Cosley D. Side effects and gateway tools: advocating a broader look at evaluating persuasive systems. CHI '11: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems; CHI '11: CHI Conference on Human Factors in Computing Systems; May, 2011; Vancouver, Canada. Side effectsgateway tools: advocating a broader look at evaluating persuasive systems. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems; 2011. May, pp. 345–348.
    1. Clawson J, Pater J, Miller A, Mynatt E, Mamykina L. No longer wearing: investigating the abandonment of personal health-tracking technologies on craigslist. UbiComp '15: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing; UbiComp '15: The 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing; September, 2015; Osaka, Japan. 2015. Sep, pp. 647–658.
    1. Epstein D, Caraway M, Johnston C, Ping A, Fogarty J, Munson S. Beyond Abandonment to Next Steps: Understanding and Designing for Life after Personal Informatics Tool Use. CHI '16: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems; CHI'16: CHI Conference on Human Factors in Computing Systems; May 2016; San Jose, California, USA. 2016. May, pp. 1109–1113.

Source: PubMed

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