Body Mass Index and stimulus control: Results from a real-world study of eating behaviour

Characteristics of smartphone-based dietary assessment tools: a systematic review

Smartphones have become popular in assessing eating behaviour in real-life and real-time. This systematic review provides a comprehensive overview of smartphone-based dietary assessment tools, focusing on how dietary data is assessed and its completeness ensured. Seven databases from behavioural, social and computer science were searched in March 2020. All observational, experimental or intervention studies and study protocols using a smartphone-based assessment tool for dietary intake were included if they reported data collected by adults and were published in English. Out of 21,722 records initially screened, 117 publications using 129 tools were included. Five core assessment features were identified: photo-based assessment (48.8% of tools), assessed serving/ portion sizes (48.8%), free-text descriptions of food intake (42.6%), food databases (30.2%), and classification systems (27.9%). On average, a tool used two features. The majority of studies did not implement any features to improve completeness of the records. This review provides a comprehensive overview and framework of smartphone-based dietary assessment tools to help researchers identify suitable assessment tools for their studies. Future research needs to address the potential impact of specific dietary assessment methods on data quality and participants' willingness to record their behaviour to ultimately improve the quality of smartphone-based dietary assessment for health research.

Measuring Food-Related Attentional Bias

Objective: Food-related attentional bias has been defined as the tendency to give preferential attention to food-related stimuli. Attentional bias is of interest as studies have found that increased attentional bias is associated with obesity; others, however, have not. A possible reason for mixed results may be that there is no agreed upon measure of attentional bias: studies differ in both measurement and scoring of attentional bias. Additionally, little is known about the stability of attentional bias over time. The present study aims to compare attentional bias measures generated from commonly used attentional bias tasks and scoring protocols, and to test re-test reliability.

Methods: As part of a larger study, 69 participants (67% female) completed two food-related visual probe tasks at baseline: lexical (words as stimuli), and pictorial (pictures as stimuli). Reaction time bias scores (attentional bias scores) for each task were calculated in three different ways: by subtracting the reaction times for the trials where probes replaced (1) neutral stimuli from the trials where the probes replaced all food stimuli, (2) neutral stimuli from the trials where probes replaced high caloric food stimuli, and (3) neutral stimuli from low caloric food stimuli. This resulted in three separate attentional bias scores for each task. These reaction time results were then correlated. The pictorial visual probe task was administered a second time 14-days later to assess test-retest reliability.

Results: Regardless of the scoring use, lexical attentional bias scores were minimal, suggesting minimal attentional bias. Pictorial task attentional bias scores were larger, suggesting greater attentional bias. The correlation between the various scores was relatively small (r = 0.13–0.20). Similarly, test-retest reliability for the pictorial task was poor regardless of how the test was scored (r = 0.20–0.41).

Conclusion: These results suggest that at least some of the variation in findings across attentional bias studies could be due to differences in the way that attentional bias is measured. Future research may benefit from either combining eye-tracking measurements in addition to reaction times.

Comfort eating: An observational study of affect in the hours immediately before, and after, snacking

OBJECTIVE

'Comfort eating' has been used to explain real-world food choices, suggesting that individuals are drawn to energy-dense ('unhealthy') snacks when experiencing negative affect. However, this concept has rarely been studied, particularly in real-world settings. Similarly, the effects of snacking on subsequent affect are also poorly understood. The present study aimed to examine the association between affect and snacking in daily life.

METHODS

One hundred and forty-one adults recorded their food intake in real time for ~14 days using a study issued mobile phone. Participants also responded to randomly timed assessments. During both types of assessments, participants indicated their current level of affect. By anchoring off snacking events, the trajectory of affect in the hours leading up to - and following - snacking was explored.

RESULTS

In the three hours leading up to a healthy snack, affect was stable. In contrast, affect fell during the hours leading up to an unhealthy snack. The interaction between snack type and time was significant. A similar, but opposite, pattern was seen following snacking: where affect decreased after unhealthy snacking, affect increased following healthy snack intake.

CONCLUSION

The findings are consistent with the hypothesis of comfort eating, with unhealthy snacking being preceded by worsening affect. Unhealthy snacking did not, however, lead to affect improvements afterwards, which questions the 'effectiveness' of comfort eating. The intake of healthy snacks however was associated with positive affective experiences. These findings could function as a component of interventions aiming at improving dietary behaviours.