Engagement in electronic health (eHealth) and mobile health (mHealth) behavior change interventions is thought to be important for intervention effectiveness, though what constitutes engagement and how it enhances efficacy has been somewhat unclear in the literature. Recently published detailed definitions and conceptual models of engagement have helped to build consensus around a definition of engagement and improve our understanding of how engagement may influence effectiveness. This work has helped to establish a clearer research agenda. However, to test the hypotheses generated by the conceptual modules, we need to know how to measure engagement in a valid and reliable way. The aim of this viewpoint is to provide an overview of engagement measurement options that can be employed in eHealth and mHealth behavior change intervention evaluations, discuss methodological considerations, and provide direction for future research. To identify measures, we used snowball sampling, starting from systematic reviews of engagement research as well as those utilized in studies known to the authors. A wide range of methods to measure engagement were identified, including qualitative measures, self-report questionnaires, ecological momentary assessments, system usage data, sensor data, social media data, and psychophysiological measures. Each measurement method is appraised and examples are provided to illustrate possible use in eHealth and mHealth behavior change research. Recommendations for future research are provided, based on the limitations of current methods and the heavy reliance on system usage data as the sole assessment of engagement. The validation and adoption of a wider range of engagement measurements and their thoughtful application to the study of engagement are encouraged.

SummaryFruit and vegetable (FV) intake has been proposed to protect against obesity. The purpose of this paper was to assess the FV consumption to adiposity relationship. Twenty-three publications were included. Inclusion criteria: longitudinal or experimental designs; FV intake tested in relation to adiposity; child, adolescent or adult participants; published in English-language peer-reviewed journals. Exclusion criteria: dietary pattern and cross-sectional designs; participants with health concerns. Experimental studies found increased FV consumption (in conjunction with other behaviours) contributed to reduced adiposity among overweight or obese adults, but no association was shown among children. Longitudinal studies among overweight adults found greater F and/or V consumption was associated with slower weight gain, but only half of child longitudinal studies found a significant inverse association. Limitations in methods prevented a thorough examination of the role of increased FV intake alone or mechanisms of effect. An inverse relationship between FV intake and adiposity among overweight adults appears weak; this relationship among children is unclear. Research needs to clarify the nature of, and mechanisms for, the effects of FV consumption on adiposity. Whether increases in FV intake in isolation from lower caloric intake or increased physical activity will result in declines or slower growth in adiposity remains unclear.

Purpose Many dietary factors have either pro- or anti-inflammatory properties. We previously developed a dietary inflammatory index (DII) to assess the inflammatory potential of diet. In this study we conducted a construct validation of the DII based on data from a food frequency questionnaire and three inflammatory biomarkers in a subsample of 2,567 postmenopausal women in the Women’s Health Initiative Observational Study. Methods We used multiple linear and logistic regression models, controlling for potential confounders, to test whether baseline DII predicted concentrations of interleukin-6 (IL-6), high-sensitivity C-reactive protein (hs-CRP), tumor necrosis factor alpha receptor 2 (TNFα-R2), or an overall biomarker score combining all three inflammatory biomarkers. Results The DII was associated with the four biomarkers with beta estimates (95%CI) comparing the highest with lowest DII quintiles as follows: IL-6: 1.26 (1.15, 1.38), Ptrend<0.0001; TNFα-R2: 81.43 (19.15, 143.71), Ptrend=0.004; dichotomized hs-CRP (odds ratio for higher versus lower hs-CRP): 1.30 (0.97, 1.67), Ptrend=0.34); and the combined inflammatory biomarker score: 0.26 (0.12, 0.40), Ptrend=0.0001. Conclusion The DII was significantly associated with inflammatory biomarkers. Construct validity of the DII indicates its utility for assessing the inflammatory potential of diet and for expanding its use to include associations with common chronic diseases in future studies.

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Limited conclusions may be drawn regarding the best method to involve parents in changing child diet to promote health. However, direct methods show promise and warrant further research.