JMIR Publications

ABSTRACT

Background:

Fitness devices have spurred the development of applications that not only monitor physical activity (PA) but also aim to motivate users, through interventions, to increase their PA. Personalization in the interventions is essential as the target users are diverse with respect to their activity levels, requirements, preferences, and behaviour.

Objective:

This review aims 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.

Methods:

A scoping review was undertaken by searching the databases PsycINFO, PubMed, Scopus and Web of Science databases. The main inclusion criteria were: 1) studies that aimed to promote PA among target users as at least one of their objectives; 2) studies that had user personalization, with the intention of promoting PA (e.g., activity recommendations or motivational messages) through technology-based interventions; 3) studies that described user models for personalization.

Results:

The literature search resulted in 49 eligible studies. Of these, 67% (33 studies) focused solely on increasing PA, while the remaining studies had other objectives, such as maintaining healthy lifestyle (8 studies), weight loss management (6 studies), and rehabilitation (2 studies) as well. The reviewed studies provide personalization in six categories related to recommendation and feedback: 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 semi-automated, 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 five categories: PA profile, demographics, medical data, behaviour 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.

Conclusions:

This scoping review investigates personalization of technology-based interventions, in the form of recommendations or feedback for increasing PA. Based on 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 of automated and semi-automated interventions. 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 smartphones, personalized and dynamic user models can be created using available user data, including users’ social profile. Finally, the long-term effects of such interventions, as well as the technology medium used for the interventions, need to be evaluated rigorously.