PMID- 37575427
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230815
IS  - 1664-1078 (Print)
IS  - 1664-1078 (Electronic)
IS  - 1664-1078 (Linking)
VI  - 14
DP  - 2023
TI  - A theory-based and data-driven approach to promoting physical activity through 
      message-based interventions.
PG  - 1200304
LID - 10.3389/fpsyg.2023.1200304 [doi]
LID - 1200304
AB  - OBJECTIVE: We investigated how physical activity can be effectively promoted with 
      a message-based intervention, by combining the explanatory power of theory-based 
      structural equation modeling with the predictive power of data-driven artificial 
      intelligence. METHODS: A sample of 564 participants took part in a two-week 
      message intervention via a mobile app. We measured participants' regulatory 
      focus, attitude, perceived behavioral control, social norm, and intention to 
      engage in physical activity. We then randomly assigned participants to four 
      message conditions (gain, non-loss, non-gain, loss). After the intervention 
      ended, we measured emotions triggered by the messages, involvement, deep 
      processing, and any change in intention to engage in physical activity. RESULTS: 
      Data analysis confirmed the soundness of our theory-based structural equation 
      model (SEM) and how the emotions triggered by the messages mediated the influence 
      of regulatory focus on involvement, deep processing of the messages, and 
      intention. We then developed a Dynamic Bayesian Network (DBN) that incorporated 
      the SEM model and the message frame intervention as a structural backbone to 
      obtain the best combination of in-sample explanatory power and out-of-sample 
      predictive power. Using a Deep Reinforcement Learning (DRL) approach, we then 
      developed an automated, fast-profiling strategy to quickly select the best 
      message strategy, based on the characteristics of each potential respondent. 
      Finally, the fast-profiling method was integrated into an AI-based chatbot. 
      CONCLUSION: Combining the explanatory power of theory-driven structural equation 
      modeling with the predictive power of data-driven artificial intelligence is a 
      promising strategy to effectively promote physical activity with message-based 
      interventions.
CI  - Copyright (c) 2023 Catellani, Biella, Carfora, Nardone, Brischigiaro, Manera and 
      Piastra.
FAU - Catellani, Patrizia
AU  - Catellani P
AD  - Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy.
FAU - Biella, Marco
AU  - Biella M
AD  - Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy.
FAU - Carfora, Valentina
AU  - Carfora V
AD  - Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy.
FAU - Nardone, Antonio
AU  - Nardone A
AD  - University of Pavia - Istituti Clinici Scientifici Maugeri IRCCS - 
      Neurorehabilitation and Spinal Units, Pavia, Italy.
FAU - Brischigiaro, Luca
AU  - Brischigiaro L
AD  - Istituti Clinici Scientifici Maugeri IRCCS - Psychology Unit, Pavia, Italy.
FAU - Manera, Marina Rita
AU  - Manera MR
AD  - Istituti Clinici Scientifici Maugeri IRCCS - Psychology Unit, Pavia, Italy.
FAU - Piastra, Marco
AU  - Piastra M
AD  - Department of Industrial, Computer and Biomedical Engineering, University of 
      Pavia, Pavia, Italy.
LA  - eng
PT  - Journal Article
DEP - 20230727
PL  - Switzerland
TA  - Front Psychol
JT  - Frontiers in psychology
JID - 101550902
PMC - PMC10415075
OTO - NOTNLM
OT  - artificial intelligence
OT  - framing
OT  - message intervention
OT  - physical activity
OT  - regulatory focus
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/08/14 06:41
MHDA- 2023/08/14 06:42
PMCR- 2023/07/27
CRDT- 2023/08/14 04:29
PHST- 2023/04/04 00:00 [received]
PHST- 2023/07/12 00:00 [accepted]
PHST- 2023/08/14 06:42 [medline]
PHST- 2023/08/14 06:41 [pubmed]
PHST- 2023/08/14 04:29 [entrez]
PHST- 2023/07/27 00:00 [pmc-release]
AID - 10.3389/fpsyg.2023.1200304 [doi]
PST - epublish
SO  - Front Psychol. 2023 Jul 27;14:1200304. doi: 10.3389/fpsyg.2023.1200304. 
      eCollection 2023.