ABSTRACT
INTRODUCTION: Lifestyle Medicine comprises six domains: diet, substance use, physical activity, stress management, social connection, and sleep. The comprehensive assessment of lifestyle is challenging, but the "Short Multidimensional Inventory on Lifestyle Evaluation" (SMILE) was developed to fill out this gap. In this paper, we describe the development and the psychometric properties (internal consistency, concurrent and convergent validity) of a shorter version of the SMILE among university students. METHODS: Data from a cross-sectional study including 369 students from 10 Brazilian universities were used. Considering a theoretical nomological net, we performed exploratory factor analysis to obtain the most parsimonious, interpretable and good-fitting model. RESULTS: The final model was called U-SMILE, comprised 24 items, and presented acceptable internal consistency (Cronbach's α = 0.73, McDonald's ω = 0.79). To evaluate the concurrent validity of the U-SMILE, we compared it to the original SMILE and found a high correlation between the instruments (Spearman's r= 0.94). Furthermore, we evaluated convergent validity by examining the U-SMILE correlation with the PHQ-9 (Spearman's r= -0.517), and GAD-7 (Spearman's r= -0.356), two validated instruments to screen for depression and anxiety, respectively. DISCUSSION: Our findings suggest that the U-SMILE is a valid instrument for assessing lifestyle among university students. We recommend that the use of U-SMILE to evaluate overall lifestyle scores rather than individual domain scores. Finally, we discuss the importance of clarifying the definitions of lifestyle and related constructs in future research.
ABSTRACT
Background: Lifestyle Medicine (LM) aims to address six main behavioral domains: diet/nutrition, substance use (SU), physical activity (PA), social relationships, stress management, and sleep. Digital Health Interventions (DHIs) have been used to improve these domains. However, there is no consensus on how to measure lifestyle and its intermediate outcomes aside from measuring each behavior separately. We aimed to describe (1) the most frequent lifestyle domains addressed by DHIs, (2) the most frequent outcomes used to measure lifestyle changes, and (3) the most frequent DHI delivery methods. Methods: We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-ScR) Extension for Scoping Reviews. A literature search was conducted using MEDLINE, Cochrane Library, EMBASE, and Web of Science for publications since 2010. We included systematic reviews and meta-analyses of clinical trials using DHI to promote health, behavioral, or lifestyle change. Results: Overall, 954 records were identified, and 72 systematic reviews were included. Of those, 35 conducted meta-analyses, 58 addressed diet/nutrition, and 60 focused on PA. Only one systematic review evaluated all six lifestyle domains simultaneously; 1 systematic review evaluated five lifestyle domains; 5 systematic reviews evaluated 4 lifestyle domains; 14 systematic reviews evaluated 3 lifestyle domains; and the remaining 52 systematic reviews evaluated only one or two domains. The most frequently evaluated domains were diet/nutrition and PA. The most frequent DHI delivery methods were smartphone apps and websites. Discussion: The concept of lifestyle is still unclear and fragmented, making it hard to evaluate the complex interconnections of unhealthy behaviors, and their impact on health. Clarifying this concept, refining its operationalization, and defining the reporting guidelines should be considered as the current research priorities. DHIs have the potential to improve lifestyle at primary, secondary, and tertiary levels of prevention-but most of them are targeting clinical populations. Although important advances have been made to evaluate DHIs, some of their characteristics, such as the rate at which they become obsolete, will require innovative research designs to evaluate long-term outcomes in health.
