User Models for Personalized Physical Activity Interventions: Scoping Review

Abstract

BackgroundFitness devices have spurred the development of apps that aim to motivate users, through interventions, to increase their physical activity (PA). Personalization in the interventions is essential as the target users are diverse with respect to their activity levels, requirements, preferences, and behavior.ObjectiveThis review aimed to (1) identify different kinds of personalization in interventions for promoting PA among any type of user group, (2) identify user models used for providing personalization, and (3) identify gaps in the current literature and suggest future research directions.MethodsA scoping review was undertaken by searching the databases PsycINFO, PubMed, Scopus, and Web of Science. The main inclusion criteria were (1) studies that aimed to promote PA; (2) studies that had personalization, with the intention of promoting PA through technology-based interventions; and (3) studies that described user models for personalization.ResultsThe literature search resulted in 49 eligible studies. Of these, 67% (33/49) studies focused solely on increasing PA, whereas the remaining studies had other objectives, such as maintaining healthy lifestyle (8 studies), weight loss management (6 studies), and rehabilitation (2 studies). The reviewed studies provide personalization in 6 categories: goal recommendation, activity recommendation, fitness partner recommendation, educational content, motivational content, and intervention timing. With respect to the mode of generation, interventions were found to be semiautomated or automatic. Of these, the automatic interventions were either knowledge-based or data-driven or both. User models in the studies were constructed with parameters from 5 categories: PA profile, demographics, medical data, behavior change technique (BCT) parameters, and contextual information. Only 27 of the eligible studies evaluated the interventions for improvement in PA, and 16 of these concluded that the interventions to increase PA are more effective when they are personalized.ConclusionsThis review investigates personalization in the form of recommendations or feedback for increasing PA. On the basis of the review and gaps identified, research directions for improving the efficacy of personalized interventions are proposed. First, data-driven prediction techniques can facilitate effective personalization. Second, use of BCTs in automated interventions, and in combination with PA guidelines, are yet to be explored, and preliminary studies in this direction are promising. Third, systems with automated interventions also need to be suitably adapted to serve specific needs of patients with clinical conditions. Fourth, previous user models focus on single metric evaluations of PA instead of a potentially more effective, holistic, and multidimensional view. Fifth, with the widespread adoption of activity monitoring devices and mobile phones, personalized and dynamic user models can be created using available user data, including users’ social profile. Finally, the long-term effects of suc...

A Qualitative Study toward Technologies for Active and Healthy Aging: A Thematic Analysis of Perspectives among Primary, Secondary, and Tertiary End Users

“…In closing, another significant aspect that emerged from the results is the push for personalization (Theme 6), since a “one size fits all” approach is not suited to a diverse set of users [ 80 ]. The need is to adapt content and functionalities to the aims, behaviors, preferences, context, and lifestyle of the intended user.…”

Trends in Persuasive Technologies for Physical Activity and Sedentary Behavior: A Systematic Review

“…Ghanvatkar et al [ 23 ] offered a scoping review of 48 studies to address the use of a personalization strategy for PA interventions, to recognize the different types of personalization, and to identify the user models employed for delivering personalization. Their review covered only the studies that implemented a personalization strategy in the design of the PT for PA regardless of the use of other persuasive strategies.…”

Management of Childhood Obesity—Time to Shift from Generalized to Personalized Intervention Strategies

“…A 3-month personalized PA intervention using an internet-based program showed significant effects on psychosocial health and PA level in adolescents [ 114 ]. Evidence consistently indicates that when compared to generalized programs, personalized technology-based PA interventions are more effective at modifying health behaviors [ 115 ]. Results from a study on adolescents with obesity or diabetes show that 16 weeks of personalized exercise (based upon baseline fitness level of participants)—with parental support and ongoing motivation—can improve PA level and result in a sense of personal health [ 116 ].…”