TY  - JOUR
AU  - Gabarron, Elia
AU  - Larbi, Dillys
AU  - Rivera-Romero, Octavio
AU  - Denecke, Kerstin
PY  - 2024
DA  - 2024/7/3
TI  - Human Factors in AI-Driven Digital Solutions for Increasing Physical Activity: Scoping Review
JO  - JMIR Hum Factors
SP  - e55964
VL  - 11
KW  - machine learning
KW  - ML
KW  - artificial intelligence
KW  - AI
KW  - algorithm
KW  - algorithms
KW  - predictive model
KW  - predictive models
KW  - predictive analytics
KW  - predictive system
KW  - practical model
KW  - practical models
KW  - deep learning
KW  - human factors
KW  - physical activity
KW  - physical exercise
KW  - healthy living
KW  - active lifestyle
KW  - exercise
KW  - physically active
KW  - digital health
KW  - mHealth
KW  - mobile health
KW  - app
KW  - apps
KW  - application
KW  - applications
KW  - digital technology
KW  - digital intervention
KW  - digital interventions
KW  - smartphone
KW  - smartphones
KW  - PRISMA
AB  - Background: Artificial intelligence (AI) has the potential to enhance physical activity (PA) interventions. However, human factors (HFs) play a pivotal role in the successful integration of AI into mobile health (mHealth) solutions for promoting PA. Understanding and optimizing the interaction between individuals and AI-driven mHealth apps is essential for achieving the desired outcomes. Objective: This study aims to review and describe the current evidence on the HFs in AI-driven digital solutions for increasing PA. Methods: We conducted a scoping review by searching for publications containing terms related to PA, HFs, and AI in the titles and abstracts across 3 databases—PubMed, Embase, and IEEE Xplore—and Google Scholar. Studies were included if they were primary studies describing an AI-based solution aimed at increasing PA, and results from testing the solution were reported. Studies that did not meet these criteria were excluded. Additionally, we searched the references in the included articles for relevant research. The following data were extracted from included studies and incorporated into a qualitative synthesis: bibliographic information, study characteristics, population, intervention, comparison, outcomes, and AI-related information. The certainty of the evidence in the included studies was evaluated using GRADE (Grading of Recommendations Assessment, Development, and Evaluation). Results: A total of 15 studies published between 2015 and 2023 involving 899 participants aged approximately between 19 and 84 years, 60.7% (546/899) of whom were female participants, were included in this review. The interventions lasted between 2 and 26 weeks in the included studies. Recommender systems were the most commonly used AI technology in digital solutions for PA (10/15 studies), followed by conversational agents (4/15 studies). User acceptability and satisfaction were the HFs most frequently evaluated (5/15 studies each), followed by usability (4/15 studies). Regarding automated data collection for personalization and recommendation, most systems involved fitness trackers (5/15 studies). The certainty of the evidence analysis indicates moderate certainty of the effectiveness of AI-driven digital technologies in increasing PA (eg, number of steps, distance walked, or time spent on PA). Furthermore, AI-driven technology, particularly recommender systems, seems to positively influence changes in PA behavior, although with very low certainty evidence. Conclusions: Current research highlights the potential of AI-driven technologies to enhance PA, though the evidence remains limited. Longer-term studies are necessary to assess the sustained impact of AI-driven technologies on behavior change and habit formation. While AI-driven digital solutions for PA hold significant promise, further exploration into optimizing AI’s impact on PA and effectively integrating AI and HFs is crucial for broader benefits. Thus, the implications for innovation management involve conducting long-term studies, prioritizing diversity, ensuring research quality, focusing on user experience, and understanding the evolving role of AI in PA promotion. 
SN  - 2292-9495
UR  - https://humanfactors.jmir.org/2024/1/e55964
UR  - https://doi.org/10.2196/55964
DO  - 10.2196/55964
ID  - info:doi/10.2196/55964
ER  -