JMIR Publications

ABSTRACT

Background:

Traditional psychological theories are inadequate to fully leverage the potential of smartphones and improve effectiveness of physical activity (PA) and sedentary behaviour (SB) change interventions. Future interventions need to consider dynamic models taken from other disciplines such as engineering (e.g., control systems). It is unclear the extent to which such dynamic models have been incorporated in the development of interventions for PA and SB.

Objective:

This review aims to quantify the number of studies that have used dynamic models to develop smartphone-based interventions to promote PA and reduce SB, describe their features, and where possible evaluate their effectiveness.

Methods:

Databases including PubMed, PsychINFO, IEEE Xplore, Cochrane and SCOPUS were searched from inception to 15 May, 2019 using terms related to mobile health, dynamic models, SB and PA. Included studies involved: PA or SB interventions involving human adults; either developed or evaluated, integrated psychological theory with dynamic theories; used smartphones for the intervention delivery; the interventions were either adaptive or JITAIs; included randomized controlled trials (RCTs), pilot RCTs, quasi-experimental, and pre-post study designs; and published from 2000 onwards. Outcomes included general characteristics, dynamic model, theory/construct integrated, and measured SB and PA behaviours. Data were synthesized narratively. There was limited scope for a meta-analysis because of the variability in the study results.

Results:

A total of 1087 publications were screened, with 11 publications describing 8 studies included in the review. All studies targeted PA and four included SB as well. Social Cognitive Theory (SCT) was the major psychological theory upon which the studies were based. Behavioral Intervention Technology, Control Systems, Computational Agent Model, Explit-Explore strategy, Behavioural Analytic Algorithm, and Dynamic Decision Network were dynamic models used by included studies. Effectiveness results for quasi-experimental studies involved reduced SB (one study; p=.08), increased light PA (one study; p=.002), walking steps (two studies; p=.057 and p<.001), walking time (one study; p=.02), moderate-to-vigorous PA (two studies; p=.08 and p=.81), and non-walking exercise time (one study; p=.31). RCT studies showed increased walking steps (one study; p=.003) and walking time (one study; p=.055). To measure activity, five studies used built-in smartphone sensors (i.e. accelerometers), three of which used phone’s GPS as well, and three studies used wearable activity trackers.

Conclusions:

To our knowledge, this is the first systematic review to report on smartphone-based studies to reduce SB and promote PA with a focus on integrated dynamic models. Current findings highlight the scarcity of dynamic model-based smartphone studies to reduce SB or to promote PA. However, the limited number of studies incorporating these models show promising findings. Future research is required to assess the effectiveness of dynamic models to promote PA and reduce SB. Clinical Trial: International Prospective Register of Systematic Reviews (PROSPERO) CRD42020139350; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=139350