TY - JOUR
T1 - A recommender system to quit smoking with mobile motivational messages
T2 - study protocol for a randomized controlled trial
AU - Hors-Fraile, Santiago
AU - Malwade, Shwetambara
AU - Spachos, Dimitris
AU - Fernandez-Luque, Luis
AU - Su, Chien-Tien
AU - Jeng, Wei-Li
AU - Syed-Abdul, Shabbir
AU - Bamidis, Panagiotis
AU - Li, Yu-Chuan (Jack)
PY - 2018/11/9
Y1 - 2018/11/9
N2 - BackgroundSmoking cessation is the most common preventative for an array of diseases, including lung cancer and chronic obstructive pulmonary disease. Although there are many efforts advocating for smoking cessation, smoking is still highly prevalent. For instance, in the USA in 2015, 50% of all smokers attempted to quit smoking, and only 5-7% of them succeeded - with slight deviation depending on external assistance. Previous studies show that computer-tailored messages which support smoking abstinence are effective. The combination of health recommender systems and behavioral-change theories is becoming increasingly popular in computer-tailoring. The objective of this study is to evaluate patients's smoking cessation rates by means of two randomized controlled trials using computer-tailored motivational messages. A group of 100 patients will be recruited in medical centers in Taiwan (50 patients in the intervention group, and 50 patients in the control group), and a group of 1000 patients will be recruited on-line (500 patients in the intervention group, and 500 patients in the control group). The collected data will be made available to the public in an open-source data portal.MethodsOur study will gather data from two sources. The first source is a clinical pilot in which a group of patients from two Taiwanese medical centers will be randomly assigned to either an intervention or a control group. The intervention group will be provided with a mobile app that sends motivational messages selected by a recommender system that takes the user profile (including gender, age, motivations, and social context) and similar users' opinions. For 6 months, the patients' smoking activity will be followed up, and confirmed as smoke-free by using a test that measures expired carbon monoxide and urinary cotinine levels. The second source will be a public pilot in which Internet users wanting to quit smoking will be able to download the same mobile app as used in the clinical pilot. They will be randomly assigned to a control group that receives basic motivational messages or to an intervention group, that receives personalized messages by the recommender system. For 6 months, patients in the public pilot will be assessed periodically with self-reported questionnaires.DiscussionThis study will be the first to use the I-Change behavioral-change model in combination with a health recommender system and will, therefore, provide relevant insights into computer-tailoring for smoking cessation. If our hypothesis is validated, clinical practice for smoking cessation would benefit from the use of our mobile solution.Trial registrationClinicalTrials.gov, ID: NCT03108651. Registered on 11 April 2017.
AB - BackgroundSmoking cessation is the most common preventative for an array of diseases, including lung cancer and chronic obstructive pulmonary disease. Although there are many efforts advocating for smoking cessation, smoking is still highly prevalent. For instance, in the USA in 2015, 50% of all smokers attempted to quit smoking, and only 5-7% of them succeeded - with slight deviation depending on external assistance. Previous studies show that computer-tailored messages which support smoking abstinence are effective. The combination of health recommender systems and behavioral-change theories is becoming increasingly popular in computer-tailoring. The objective of this study is to evaluate patients's smoking cessation rates by means of two randomized controlled trials using computer-tailored motivational messages. A group of 100 patients will be recruited in medical centers in Taiwan (50 patients in the intervention group, and 50 patients in the control group), and a group of 1000 patients will be recruited on-line (500 patients in the intervention group, and 500 patients in the control group). The collected data will be made available to the public in an open-source data portal.MethodsOur study will gather data from two sources. The first source is a clinical pilot in which a group of patients from two Taiwanese medical centers will be randomly assigned to either an intervention or a control group. The intervention group will be provided with a mobile app that sends motivational messages selected by a recommender system that takes the user profile (including gender, age, motivations, and social context) and similar users' opinions. For 6 months, the patients' smoking activity will be followed up, and confirmed as smoke-free by using a test that measures expired carbon monoxide and urinary cotinine levels. The second source will be a public pilot in which Internet users wanting to quit smoking will be able to download the same mobile app as used in the clinical pilot. They will be randomly assigned to a control group that receives basic motivational messages or to an intervention group, that receives personalized messages by the recommender system. For 6 months, patients in the public pilot will be assessed periodically with self-reported questionnaires.DiscussionThis study will be the first to use the I-Change behavioral-change model in combination with a health recommender system and will, therefore, provide relevant insights into computer-tailoring for smoking cessation. If our hypothesis is validated, clinical practice for smoking cessation would benefit from the use of our mobile solution.Trial registrationClinicalTrials.gov, ID: NCT03108651. Registered on 11 April 2017.
KW - Health recommender systems
KW - Smoking cessation
KW - Motivational
KW - Messages
KW - App
KW - Mobile
KW - Computer-tailoring
KW - Behavioral change
KW - PRIMARY-CARE
KW - CESSATION
KW - ABSTINENCE
KW - SMOKERS
KW - WEB
KW - PREVENTION
KW - PREDICTORS
KW - SYMPTOMS
KW - PROGRAM
KW - TOBACCO
U2 - 10.1186/s13063-018-3000-1
DO - 10.1186/s13063-018-3000-1
M3 - Article
C2 - 30413176
SN - 1745-6215
VL - 19
JO - Trials
JF - Trials
M1 - 618
ER -