Selecting and describing control conditions in mobile health randomized controlled trials: a proposed typology

Abstract

Hundreds of randomized controlled trials (RCTs) have tested the efficacy of mobile health (mHealth) tools for a wide range of mental and behavioral health outcomes. These RCTs have used a variety of control condition types which dramatically influence the scientific inferences that can be drawn from a given study. Unfortunately, nomenclature across mHealth RCTs is inconsistent and meta-analyses commonly combine control conditions that differ in potentially important ways. We propose a typology of control condition types in mHealth RCTs. We define 11 control condition types, discuss key dimensions on which they differ, provide a decision tree for selecting and identifying types, and describe the scientific inferences each comparison allows. We propose a five-tier comparison strength gradation along with four simplified categorization schemes. Lastly, we discuss unresolved definitional, ethical, and meta-analytic issues related to the categorization of control conditions in mHealth RCTs.

Fig. 1. Eleven-category typology decision tree.

mHealth mobile health, EBT evidence-based treatment (i.e., frontline intervention), TAU treatment-as-usual. Color coding reflects comparison strength ranging from low (Class V, red) to high (Class I, darker green). The choice of control condition type must ultimately be guided by the scientific question motivating a given study, which may be strongly influenced by a variety of factors including the stage of research (i.e., Class V comparisons may be very appropriate at early stages in the development of an intervention)^,,.