ORCID Profile
0000-0002-7727-366X
Current Organisation
University of Tasmania
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Publisher: Wiley
Date: 19-07-2017
DOI: 10.1111/BJHP.12259
Abstract: This study aims at testing predictions derived from temporal self-regulation theory (TST) in relation to discretionary food choices (snacks). TST combines a motivational sphere of influence (cognitions and temporal valuations resulting in intentions) with a momentary sphere (encompassing social and physical environmental cues). This dual approach differs from current health behaviour theories, but can potentially improve our understanding of the interplay of personal and environmental factors in health behaviour self-regulation. A mixed event-based and time-based (Ecological Momentary Assessment) study in 61 adults aged between 18 and 64, with a BMI range between 18.34 and 39.78 (M = 25.66, SD = 4.82) over two weeks. Participants recorded their food and drink intake for two weeks in real time using electronic diaries. Participants also responded to non-consumption assessments at random intervals throughout each day. Momentary cues (in idual, situational, and environmental factors) were assessed both during food logs and non-consumption assessments. Motivational factors, past behaviour, and trait self-regulation were assessed during baseline. Multilevel logistic regression analyses showed that across all snack types, environmental cues and negative affect were associated with an increased likelihood of snacking. Perceiving a cost of healthy eating to occur before eating was associated with an increased likelihood of snacking, whereas intentions and self-regulation were not. Discretionary food intake is largely guided by momentary cues, and motivational-level factors, such as intention and self-regulation, are less important in the initiation of discretionary food intake. Statement of contribution What is already known on this subject? Overweight and obesity are a result of prolonged periods of energy imbalance between energy intake and expenditure (Hill & Peters, ). One of the key behavioural determinants of energy imbalances results from food intake, specifically from discretionary food choices (snacking). Temporal self-regulation theory (Hall & Fong, ) takes into account both deliberate and momentary influences on health behaviour, which is especially relevant to exploring the drivers of snacking. What does this study add? Offers new insight into the application of TST in explaining momentary eating behaviours. Snacking initiation is guided by momentary cues, not person-level factors. Dietary interventions should acknowledge the momentary cues that are associated with snacking.
Publisher: Informa UK Limited
Date: 11-2019
Publisher: American Psychological Association (APA)
Date: 04-2017
DOI: 10.1037/HEA0000439
Abstract: In idual eating behavior is a risk factor for obesity and highly dependent on internal and external cues. Many studies also suggest that the food environment (i.e., food outlets) influences eating behavior. This study therefore examines the momentary food environment (at the time of eating) and the role of cues simultaneously in predicting everyday eating behavior in adults with overweight and obesity. Intensive longitudinal study using ecological momentary assessment (EMA) over 14 days in 51 adults with overweight and obesity (average body mass index = 30.77 SD = 4.85) with a total of 745 participant days of data. Multiple daily assessments of eating (meals, high- or low-energy snacks) and randomly timed assessments. Cues and the momentary food environment were assessed during both assessment types. Random effects multinomial logistic regression shows that both internal (affect) and external (food availability, social situation, observing others eat) cues were associated with increased likelihood of eating. The momentary food environment predicted meals and snacking on top of cues, with a higher likelihood of high-energy snacks when fast food restaurants were close by (odds ratio [OR] = 1.89, 95% confidence interval [CI] = 1.22, 2.93) and a higher likelihood of low-energy snacks in proximity to supermarkets (OR = 2.29, 95% CI = 1.38, 3.82). Real-time eating behavior, both in terms of main meals and snacks, is associated with internal and external cues in adults with overweight and obesity. In addition, perceptions of the momentary food environment influence eating choices, emphasizing the importance of an integrated perspective on eating behavior and obesity prevention. (PsycINFO Database Record
Publisher: JMIR Publications Inc.
Date: 22-07-2020
DOI: 10.2196/15948
Abstract: It has been observed that eating is influenced by the presence and availability of food. Being aware of the presence of food in the environment may enable mobile health (mHealth) apps to use geofencing techniques to determine the most appropriate time to proactively deliver interventions. To date, however, studies on eating typically rely on self-reports of environmental contexts, which may not be accurate or feasible for issuing mHealth interventions. This study aimed to compare the subjective and geographic information system (GIS) assessments of the momentary food environment to explore the feasibility of using GIS data to predict eating behavior and inform geofenced interventions. In total, 72 participants recorded their food intake in real-time for 14 days using an ecological momentary assessment approach. Participants logged their food intake and responded to approximately 5 randomly timed assessments each day. During each assessment, the participants reported the number and type of food outlets nearby. Their electronic diaries simultaneously recorded their GPS coordinates. The GPS data were later overlaid with a GIS map of food outlets to produce an objective count of the number of food outlets within 50 m of the participant. Correlations between self-reported and GIS counts of food outlets within 50 m were only of a small size (r=0.17 P .001). Logistic regression analyses revealed that the GIS count significantly predicted eating similar to the self-reported counts (area under the curve for the receiver operating characteristic curve [AUC-ROC] self-report=0.53, SE 0.00 versus AUC-ROC 50 m GIS=0.53, SE 0.00 P=.41). However, there was a significant difference between the GIS-derived and self-reported counts of food outlets and the self-reported type of food outlets (AUC-ROC self-reported outlet type=0.56, SE 0.01 P .001). The subjective food environment appears to predict eating better than objectively measured food environments via GIS. mHealth apps may need to consider the type of food outlets rather than the raw number of outlets in an in idual’s environment.
Publisher: Wiley
Date: 04-01-2021
DOI: 10.1111/BJHP.12505
Abstract: ‘Comfort eating’ has been used to explain real‐world food choices, suggesting that in iduals 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. 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. 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. 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.
Publisher: JMIR Publications Inc.
Date: 21-08-2019
Abstract: t has been observed that eating is influenced by the presence and availability of food. Being aware of the presence of food in the environment may enable mobile health (mHealth) apps to use geofencing techniques to determine the most appropriate time to proactively deliver interventions. To date, however, studies on eating typically rely on self-reports of environmental contexts, which may not be accurate or feasible for issuing mHealth interventions. his study aimed to compare the subjective and geographic information system (GIS) assessments of the momentary food environment to explore the feasibility of using GIS data to predict eating behavior and inform geofenced interventions. n total, 72 participants recorded their food intake in real-time for 14 days using an ecological momentary assessment approach. Participants logged their food intake and responded to approximately 5 randomly timed assessments each day. During each assessment, the participants reported the number and type of food outlets nearby. Their electronic diaries simultaneously recorded their GPS coordinates. The GPS data were later overlaid with a GIS map of food outlets to produce an objective count of the number of food outlets within 50 m of the participant. orrelations between self-reported and GIS counts of food outlets within 50 m were only of a small size ( i r /i =0.17 i P /i & .001). Logistic regression analyses revealed that the GIS count significantly predicted eating similar to the self-reported counts (area under the curve for the receiver operating characteristic curve [AUC-ROC] self-report=0.53, SE 0.00 versus AUC-ROC 50 m GIS=0.53, SE 0.00 i P /i =.41). However, there was a significant difference between the GIS-derived and self-reported counts of food outlets and the self-reported type of food outlets (AUC-ROC self-reported outlet type=0.56, SE 0.01 i P /i & .001). he subjective food environment appears to predict eating better than objectively measured food environments via GIS. mHealth apps may need to consider the type of food outlets rather than the raw number of outlets in an in idual’s environment.
Publisher: Elsevier BV
Date: 11-2020
No related grants have been discovered for Katherine Elliston.