Hors Fraile, Santiago

English

Smoking  has  several  harmful  effects  on  our  health  and  affects  our  organs,  leading  to  the incidence  of  many  life-threatening  diseases.  Furthermore,  it  is  one  of  the  most  preventable causes of death. Despite its detrimental effect on our health, quitting smoking is challenging due  to  the  tobacco  addictive  chemicals  and  humans’  psychological  dependency  on  it. Nonetheless,  there  are  different  approaches  to  support  people  willing  to  stop  smoking.  One method  is  eHealth  computer  tailoring,  which  helps  personalize  feedback  given  to  smokers based on psychological models of behavioral change based on pre-defined if-then-else rules. These methods showed to generate positive results in terms of high abstinence rates and cost-effectiveness. However, new innovative solutions are available to improve the eHealth methods for  smoking  cessation  further.  One  of  those  methods  is  related  to  recommender  systems technology.  Recommender  systems  are  AI  algorithms  that  can  select  the  most  relevant  item (such as a piece of text, book, movie, or product) from a set of items for each user. Depending on  the  type  of  recommender  system,  relevance  is  determined  considering  different  methods and  variables.  A  commonly  used  method  for  calculating  relevance  is  the  “collective intelligence” approach. This approach uses algorithms to generate a user profile for each user (e.g., using demographic variables) and calculate how relevant a specific item is based on the given relevance of that item for users with similar user profiles.These systems can learn from user feedback over time in that the users rate the relevance of the recommended items, which helps  train  the  system  for  making  future  recommendations.  For  decades,  the  scientific community has explored the relevance of these systems in other fields such as leisure (movie recommendations on Netflix)  and e-commerce (product recommendations on Amazon). Due to  their  potential  and  proven  effectiveness  in  other  fields  but  limited  application  in  the healthcare sector, which began onlya few years ago, studying how these systems can be applied for smoking cessation is crucial.In  Chapter  2  of  this  dissertation,  we  have  conducted  a  scoping  review  to  assess  the  existing knowledge and research gaps using recommender systems in healthcare,also known as health recommender systems (HRSs). We assessed their technical and healthcare aspects through this review.  Based  on  its  results,  we  then  generated  a  new  taxonomy  for  these  types  of  systems. Next,  we  provided  a  detailed  description  of  a  health  recommender  system  (HRS)  design process with collective intelligence grounded in behavioral science for smoking cessation using the  I-Change  model  as  an  example.  In  Chapter  3,  we  explained  all  the  steps  and  the  system design, including algorithm components, messages creation, and user interface design, to help interested stakeholders better understand such systems, which would provide inspiration and a basis  for  future  studies.  Furthermore,  we  performed  an  assessment  study  to  test  the  created HRS using collective intelligence in a real-world setting with a follow-up period of six months. The control condition was a simpler version of the created HRS in this assessment, except for the collective intelligence component. In Chapter 4, we reported the protocolof this study and analyzed  the  actual  results  regarding  the  appreciation,  engagement,  dropouts,  and  smoking abstinence generated by the system (Chapter 5). Chapter 1 provides a general introduction to the problem associated with smoking cessation. First,  it  introduces  different  existing  support  approaches,  focusing  on  the  ones  related  to behavioral  change  and  their  application  in  computer-tailored  interventions.  Then,  it  presents the  recommender  system  technology  and  its  different  types  that  exist  as  an option  for facilitating   computer-tailored   interventions.   Further,   it   highlights   the   appreciation   and engagement metrics, which are the factors that complement abstinence for intervention success. 
Chapter  2  contains  a  scoping  review  that  provides  an  analysis  of  the  state-of-the-art  HRS, identifying  the  research  gaps  and  the  elements  that  should  be  improved  when  applying  this technology  to  the  healthcare  sector.  From  this  study,  we  identified  that  the  collaborative filtering  technique  was  the  most-used  information  filtering  method.  However,  it  was  also observed that there is a lack of applying behavioral change theories and factors in HRS studies. Furthermore,  these  studies  neither  implemented  the  principles  of  tailoring  nor  assessed  their (cost)-effectiveness. Therefore, a taxonomy was proposed to facilitate consistent classification and better comprehension of these systems. This taxonomy included the domain of the study (e.g., the type of population, country, therapeutic area), the methodology and procedures of the study  (the  duration,  number  of  users,  outcomes),  health  behavior  change  factors  (e.g.,  self-efficacy,  social  influence,  attitudes),  and  the  technical  aspects  required  to  understand  the algorithm (e.g., recommendation technology, profile generation techniques).Chapter 3 provides a multidisciplinary and comprehensive description of the design process of an HRS for supporting smoking cessation that uses collective intelligence in combination with the I-Change behavioral change model. This detailed description contributed to help reveal the process of how an HRS can be built to support behavioral change interventions. This process had not been disclosed in detail before, and this lack of transparency  can act as a barrier for behavioral change researchers in using HSR technology. The new system was built based on a previous HRS that utilized a mobile app to support smokers trying to stay abstinent by sending them motivational messages. First, we identified the areas that needed improvements based on the app’s usage data. Then, we implemented relevant changes to our new system design (e.g., increasing the granularity of the possible user feedback from three options to five options). Our final mobile app was supposed to be more streamlined and usable thanthe first version. The generated  HRS  was  a  hybrid  algorithm  with  a  knowledge-based  step  and  a  collaborative-filtering step in  cascade. It used 58 variables to compute the similarity formula for  choosing recommendations; from the total, 47 were related to the determinants of the I-Change model. Altogether, 331 motivational messages were created, and ten different health communication methods were considered for their design. Chapter 4 explains the protocol to be followed to assess the system created in Chapter 3. This protocol included the description of a clinical pilot and a public pilot. We used the latter one to analyze the HRS in this dissertation. Chapter  5  presents,  discusses  and  reflects  on  the  results  obtained  from  the  public  pilot.  The public  pilot  was  a  double-blinded  experiment.  Those  smokers  who  can  read  English  or Mandarin  and  download  a  mobile  app  from  the  Internet  were  eligible  to  participate.  After creating their account and answering questions relevant to their user profile (e.g., name, age, gender, level of addiction, and motivation to quit), they can set a quitting day to start receiving personalized motivational text messages via the mobile app. Smokers were randomly allocated to the group where such  messages were generated by the new HRS, which was described  in Chapter 3, or to the group associated with a simpler version of the algorithm, without collective intelligence (using only the knowledge-based step), selected and sent these messages. A total of 371 participants were eligible to be part of the study analysis. Smokers were followed up for six  months,  starting  from  their  quitting  day,  and  were  asked  weekly  about  their  smoking abstinence through a voluntary question in the app.Moreover, we measured their message appreciation and engagement. The attributes (factors) considered as possible indicators of differences in the study outcomes included the motivation to  quit,  nicotine  dependence,  age,  gender,  and  completion  of  the  extended  user  profile questionnaire.  They  were  studied  as  potential  covariates  in  the  statistical  analysis.  No 
statistically significant differences were found neither for the analysis on available data of the 7D-PP  abstinence  averaging  the  abstinence  reports  across  the  study  nor  for  the  penalized imputation analysisof both the 7D-PP abstinence averaging the abstinence reports across the study  and  the  7D-PP  considering  only  the  last  available  abstinence  report.  However,  the analysis on available data for the 7D-PP considering only the last available abstinence reportshowed  lower  abstinence  rates  in  the  HRS  using  collective  intelligence.  Also,  the  results showed  that  the  HRS  using  collective  intelligence  did  not  have  statistically  significant differences  for  message  appreciation,  number  of  rated  messages,  and  number  of  quitting attempts.  However,  the  collective  intelligence  algorithm  performed  worse  regarding  the number  of  abstinence  reports  and  active  days.  The  sub-group  analysis  showed  that  the completion  of  the  extended  user  profile  did  significantly  impact  the  engagement  of  the participants  reducing  the  number  of  dropouts  in  both  groups  and  increasing  the  number  of quitting   attempts   in   participants   who   received   messages   selected   with   the   collective intelligence. Finally, Chapter 6 provides a general discussion of the main findings and conclusions of all the studies   presented   in   this   dissertation   (from   chapters   2–5).   It   also   contains   the   main methodological considerations for this dissertation, such as the strengths and limitations, risks, reflections for practice, and the impact of this thesis on the scientific community. In conclusion, the studies presented in this dissertation showed that although HRSs are gaining traction in the healthcare sector, they are still novel, with underreported details and suboptimal application, as they do not take advantage of the behavioral change theories. However, we have shown that they can be used as an alternative approach to traditional tailoring for behavioral interventions by embedding behavioral science in the design of theseemergent systems. We compared the HRSs  with  and  without  collective  intelligence  technology  for  a  trial  for  smoking  cessation, measuring their performance in real-life conditions. The results showed that despite showing some positive results in terms of engagement –number of quitting attempts -when completing the  extended  user  profile,  the  HRS  using  collective  intelligence  did  not  manage  to  improve smoking behavior, appreciation, and engagement compared to the other HRS. In addition, some of  the  engagement  and  abstinence  metrics  led  to  worse  results.  Furthermore,  although  we achieved better smoking cessation outcomes than quitting  cold  turkey or with brief  clinician advice, our HRS did not improve the abstinence rates achieved by other approaches in smoking cessation, such as traditional computer tailoring. Further, it is still unclear why the theoretical potential of collective intelligence did not provide the expected benefits in our study. Therefore, future  research  is  needed  to  find  out  how  HRS-based  interventions,  using  or  not  using  the collective  intelligence  technology,  can  be  improved  to  achieve  better  outcomes  in  terms  of behavioral change

idUS. Depósito de Investigación Universidad de Sevilla

Health Recommender Systems for Behavior Change: Exploring their Potential for Smoking Cessation

https://idus.us.es/bitstream/11441/133408/1/Hors%20Fraile%2c%20Santiago%20Thesis.pdf

Health Recommender Systems for Behavior Change: Exploring their Potential for Smoking Cessation

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idUS. Depósito de Investigación Universidad de Sevilla