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Article

Identifying Dietary Triggers Among Individuals with Overweight and Obesity: An Ecological Momentary Assessment Study

by
Han Shi Jocelyn Chew
1,2,*,
Rakhi Vashishtha
2,
Ruochen Du
3,
Yan Xin Liaw
1 and
Ayelet Gneezy
2,4
1
Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
2
Behaviour and Implementation Science Interventions, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
3
Biostatistics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
4
Rady School of Management, University of California, San Diego, CA 117599, USA
*
Author to whom correspondence should be addressed.
Nutrients 2025, 17(3), 481; https://doi.org/10.3390/nu17030481
Submission received: 4 December 2024 / Revised: 17 January 2025 / Accepted: 24 January 2025 / Published: 29 January 2025
(This article belongs to the Section Nutrition and Obesity)
Browse Figure
Versions Notes

Abstract

:
Background/Objectives: Excess adiposity, affecting 43% of the global adult population, is a major contributor to cardiometabolic diseases. Lifestyle behaviours, specifically dietary habits, play a key role in weight management. Real-time assessment methods such as Ecological Momentary Assessment (EMA) provide context-rich data that reduce recall bias and offer insights into dietary triggers and lapses. This study examines dietary triggers among adults with excess adiposity in Singapore using EMA, focusing on factors influencing dietary adherence and lapses. Methods: A total of 250 participants with a BMI ≥ 23 kg/m2 were recruited to track dietary habits for one week, at least three times a day, using the Eating Behaviour Lapse Inventory Survey Singapore (eBLISS) embedded within the Eating Trigger Response Inhibition Program (eTRIP© V.1) smartphone app. Logistic regression analysis was used to identify predictors of dietary adherence. Results: Of the 4708 responses, 76.4% of the responses were indicative of adherence to dietary plans. Non-adherence was primarily associated with food accessibility and negative emotions (stress, nervousness, and sadness). Factors such as meals prepared by domestic helpers and self-preparation were significantly associated with adherence. Negative emotions and premenstrual syndrome were identified as significant predictors of dietary lapses. Conclusions: EMA offers valuable insights into dietary behaviours by identifying real-time triggers for dietary lapses. Future interventions can utilise technology-driven approaches to predict and prevent lapses, potentially improving adherence and weight management outcomes.

Graphical Abstract

1. Introduction

In 2022, overweight and obesity (henceforth known as excess adiposity) affected 43% of the global adult population [1], translating to a projected increase to 3.29% of the global gross domestic product (GDP) by 2060 [2]. Fuelled by factors such as economic growth, urbanisation, sedentary lifestyles, and dietary changes, excess adiposity increases individuals’ risks of cardiometabolic diseases such as diabetes, hypertension, hyperlipidaemia, stroke, and fatty liver disease, leading to morbidities and mortalities [3,4,5]. While genetics have been shown to play a significant role in excess adiposity, lifestyle behaviours, especially dietary habits, are crucial predictors of body weight and cardiometabolic disease risk [6].
The food frequency questionnaire (FFQ), 24-h diet recall, and dietary records are the most frequently used methods to assess dietary habits but are prone to recall, aggregation, and social desirability biases [7,8]. In the context of dietary habits, research has shown that individuals often hold “self-favouring” perceptions of their eating behaviours, believing that their lapses are less significant or more justified than those of others [9,10,11]. Ecological Momentary Assessment (EMA), defined as the real-time collection of data in participants’ natural environments through repeated sampling over time, [12] has been increasingly popular over the past few decades for tracking various aspects of eating behaviours, including diet choices, meal frequency, and contextual factors. EMA offers significant advantages over traditional assessment techniques by providing immediate and context-rich data, [12] which reduces recall bias and participant burden, resulting in more accurate accounts of daily eating behaviours [13]. In addition to identifying dietary lapses in real time, EMA can detect dietary triggers that traditional methods may miss [14,15,16]. For example, studies utilising EMA have identified common triggers such as negative psychological states like loneliness [17,18], food cravings [19], the presence and variety of palatable foods [14,20], food marketing [21], or the influence of peers and friends [22].
In culturally diverse settings, EMA could potentially reveal how unique cultural factors such as traditional food preferences influence dietary behaviours and body weight. Previous EMA studies conducted in Singapore have found that dietary food patterns and food choices vary substantially among different ethnicities [23,24,25,26,27], potentially explaining the inter-ethnic differences in obesity rates among Singapore residents [28]. However, there is currently limited knowledge on the types of dietary lapse triggers that adults face, especially for those living in multi-ethnic societies, where food is an integral part of the local culture. Therefore, we examine the moment-by-moment specific dietary triggers among adults with excess adiposity in Singapore using EMA.

2. Materials and Methods

This study is conducted as part of a larger research project [29]. We recruited 250 individuals with a body mass index (BMI) ≥ 23 kg/m2 from the public as well as a specialist obesity management centre. Participants were asked to track their dietary habits over one week through the eating Trigger Response Inhibition Program version 1 (eTRIP© V.1), a smartphone app developed to collect data on individual eating habits, specifically food logs and dietary triggers (i.e., triggers leading to dietary lapses—eating at an unplanned time, an unplanned portion size, or a food item). Based on our previous study, we identified six broad categories of dietary triggers—pre-meal activities, meal companion(s), ease of food accessibility, emotions, physiological conditions, and time of day [30]. These triggers informed the design of the Eating Behaviour Lapse Inventory Survey Singapore (eBLISS), which is embedded in eTRIP© V1 [30]. eBLISS has undergone validation by an expert panel of 10 multidisciplinary healthcare professional team members and is designed to identify real-time dietary triggers experienced by people with excess adiposity through repeated sampling of one’s eating behaviour at least three times daily (breakfast, lunch, and dinner) (manuscript under review). Our findings are reported according to the STROBE checklist (Appendix A).

2.1. Daily User Engagement with eTRIP© V.1

At three user-specified mealtimes, predetermined by each participant at the outset of the study and consistently adhered to throughout, the app prompted participants to photograph their meals and respond to a series of questions. Unique dietary lapse triggers were identified as part of the development of the eBLISS and were categorised into seven domains, namely (1) place; (2) emotions; (3) physiological state; (4) eating company; (5) food provider; (6) activity before eating; and (7) number of hours of sleep (manuscript under review). These questions included programmed response options, as well as self-reported yes-or-no questions assessing adherence to one’s diet plan (“Are you adhering to your diet plan?”). If the available responses did not accurately reflect their experience, participants had the option to provide free-text responses for each dietary trigger question. Each check-in required approximately 1–2 min to complete. The participants were prompted three times a day before each of the three meals that locals commonly consume to prevent prompt fatigue. Participants were able to enter their responses for snacks voluntarily.

2.2. Data Analysis

All statistical analyses were conducted using the IBM SPSS Statistics 29 [31] with a two-tailed significance level set at 0.05. Free text responses were coded independently by two study team members (HSJC and MS). Responses to multiple-choice questions were dichotomised into two categories, and the frequency and percentage of responses for each category were calculated. A series of univariate generalised estimating equations were conducted to compare responses of adherence and non-adherence for each dietary trigger. Due to the increased risk of type 1 errors arising from the multiple comparisons, Bonferroni adjustment was used such that a p-value of 0.001 was needed to indicate significant difference. Relative risks of dietary triggers influencing dietary adherence responses were also calculated.

3. Results

Descriptive sociodemographic characteristics of study participants are reported elsewhere [29]. A total of 4708 check-in responses were recorded, representing a 97.7% completion rate of which 1109 and 3599 were identified as non-adherent and adherent to dietary plans, respectively (Table 1). The most common reason for non-adherence was the inability to avoid eating a certain food (55.4%) rather than meal portion and timing. Moreover, 5 out of 48 dietary triggers were significantly different between the two groups but non-significant after Bonferroni adjustment. These includes the place of meal (home/residence); company (alone and with colleagues/peers); ease of obtaining food (self-prepared); and activity prior to eating (travelling).
Adjusting for cluster effect, the age-, sex- and BMI-adjusted relative risk of each dietary trigger contributing to dietary adherence is shown below (Table 2). Only self-preparing food and travelling were significant predictors of dietary adherence responses.

4. Discussion

Our findings revealed that 74% of responses were indicative of adherence to diet plans. Consistent with prior research, the inability to resist unintended food types rather than food portion or meal timing was the most common reason for non-adherence [18]. We found significant differences between responses of dietary adherence and non-adherence for dietary triggers, namely place of meal (home/residence); company (alone and with colleagues/peers); ease of obtaining food (self-prepared); and activity before eating (travelling). Instances where individuals prepared their food were 5% more likely to have a dietary adherent response. Instances where individuals travelled before eating were 7% less likely to have a dietary adherent response. Understanding the factors that facilitate adherence, such as avoiding large portion sizes, sticking to planned mealtimes, and avoiding unintended food, provides insights that could be used in the design of future interventions.
Surprisingly, while emotional eating is well recognised to influence dietary non-adherence and weight gain [32], we did not find significant differences between dietary adherence responses for emotions. This could be because individuals have different tendencies to be influenced by different dietary triggers, highlighting that each individual has personalised dietary triggers that require personalised interventions to overcome [33,34]. For example, by using EMA, timely behavioural interventions could increase one’s awareness of an impending event of emotional eating, potentially preventing a dietary lapse event and eventual weight gain [35]. Therefore, when we controlled for a cluster effect arising from different individual response rates, we could have also controlled for such individual differences that statistically dampened the differences at the instance-based level. Nevertheless, we showed that self-preparing food and travelling were significant predictors of dietary adherence that can be generalised. Mills et al. found that people who frequently consume home-cooked meals tend to have a better diet [36]. When travelling, individuals often need to eat outside the home which has been linked to higher energy intake and poorer diet quality [37]. Nago suggested that frequent eating outside the home was positively associated with an increased risk of overweight and obesity [38]. An increasing degree of eating out-of-home was also correlated with a rise in BMI [39]. Furthermore, a study found that pre-meal screen-time activities did not increase subjective appetite and food intake in girls, which is consistent with our results [40].
Contrary to some studies [17,18,23,41,42], our study corroborates that perceived hunger may not be different between adherent and non-adherent individuals, suggesting that non-adherence is likely influenced by other examined factors [43]. More research is warranted to examine the reasons for this discrepancy. Among physiological factors, only premenstrual syndrome (PMS) was negatively associated with dietary adherence. To our knowledge, no other study has previously documented this finding; although, one study suggested that overweight women increase their dietary intake during the pre-menstruation phase [44].
Dietary habits including eating speed, meal frequency, meal timing, diet quantity, and diet quality have been shown to play a crucial role in weight management [45]. By understanding dietary lapse triggers, individuals can improve their eating habits and enhance their weight loss efforts [46]. Forman found that dietary lapse frequency at baseline was inversely correlated with early and overall weight loss [18]. Crochiere identified that momentary increases in urges to deviate from one’s eating plan, cravings, alcohol consumption, and tiredness, as well as a decline in confidence related to meeting dietary goals and planning food intake, were the predictors of dietary lapses [47]. However, adopting and maintaining healthy dietary habits remain challenging as they require a strong level of effortful self-regulation over dietary habits of which episodes are initiated by environmental triggers that could be arrested just-in-time when identified [48]. In other words, even if one is determined to change their dietary habits, they are likely to lapse into maladaptive behaviours before a new healthier dietary habit is established.

4.1. Strengths and Limitations

This study had several strengths, including its focus on a multi-ethnic sample and the use of EMA to prevent biases such as recall bias. However, this study was limited by the 1-week timeframe of observation, limiting the potential for more insights to be generated. Like other studies, dietary adherence was also self-reported, limiting the accuracy of our findings due to social desirability bias.

4.2. Future Directions

Future research can expand upon our findings and address the noted limitations. Qualitative research could offer meaningful insights into the triggers of lapses and the coping strategies of those who successfully adhere to their diet plan. Investigating the role of other lifestyle factors, such as physical activity, mindfulness, and sleep, in mediating motivation’s influence on dietary adherence is also crucial. Longitudinal studies could shed light on the dynamics of dietary adherence, identifying patterns and predictors of dietary lapses over time. Furthermore, future studies could explore how different triggers identified through EMA impact weight loss outcomes among obese individuals. Understanding the relationship between these triggers and EMA predictions can inform targeted interventions to reduce lapses and promote sustained dietary adherence.

5. Conclusions

In sum, we found that non-adherence was primarily associated with food accessibility and negative emotions (stress, nervousness, and sadness). Factors such as meals prepared by domestic helpers and self-preparation were significantly associated with adherence. Negative emotions and premenstrual syndrome were identified as significant predictors of dietary lapses. Our study underscores the potential of technology-driven, tailored, in-the-moment approaches in predicting and preventing dietary lapses. By tracking the dietary triggers and delivering specific coping skills designed to avert lapses, these interventions can significantly enhance dietary adherence. In the future, more sophisticated passive wearable sensors which could measure heart rate during different emotional states would be used to capture changes in the dietary triggers in a continuous manner over time.

Author Contributions

H.S.J.C.: Conceptualisation, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing—Original Draft, Writing—Review and Editing, Visualisation, Supervision, Project administration, Funding acquisition; R.V.: Validation, Writing—Original Draft; Y.X.L.: Writing—Original Draft; R.D.: Validation, Software, formal analysis; A.G.: Validation, Writing—Review and Editing. All authors have read and agreed to the published version of this manuscript.

Funding

This study was funded by the National University Singapore (NUS) start-up fund.

Institutional Review Board Statement

Ethics approval was granted by the National Healthcare Group (NHG DSRB Ref: 2020/01439; approved on 18 April 2022).

Informed Consent Statement

Informed consent was obtained from all individual participants included in this study.

Data Availability Statement

The data presented in this study are available upon reasonable request from the corresponding author. The data are not publicly available due to privacy reasons.

Acknowledgments

We thank Manish Sridhar for his contribution to the coding of the participants’ free-text responses.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. STROBE Statement—checklist of items that should be included in reports of observational studies.
Table A1. STROBE Statement—checklist of items that should be included in reports of observational studies.
Item NoRecommendationPage
No
Title and abstract1(a) Indicate the study’s design with a commonly used term in the title or the abstract1
(b) Provide in the abstract an informative and balanced summary of what was done and what was found1
Introduction
Background/rationale2Explain the scientific background and rationale for the investigation being reported1–2
Objectives3State specific objectives, including any prespecified hypotheses2
Methods
Study design4Present key elements of study design early in the paper2
Setting5Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data collection2–3
Participants6(a) Cohort study—Give the eligibility criteria, and the sources and methods of selection of participants. Describe methods of follow-up
Case-control study—Give the eligibility criteria, and the sources and methods of case ascertainment and control selection. Give the rationale for the choice of cases and controls
Cross-sectional study—Give the eligibility criteria, and the sources and methods of selection of participants		2
(b) Cohort study—For matched studies, give matching criteria and number of exposed and unexposed
Case-control study—For matched studies, give matching criteria and the number of controls per case		-
Variables7Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if applicable3
Data sources/measurement8 *For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe comparability of assessment methods if there is more than one group3
Bias9Describe any efforts to address potential sources of bias-
Study size10Explain how the study size was arrived at2
Quantitative variables11Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and why3
Statistical methods12(a) Describe all statistical methods, including those used to control for confounding3
(b) Describe any methods used to examine subgroups and interactions3
(c) Explain how missing data were addressed-
(d) Cohort study—If applicable, explain how loss to follow-up was addressed
Case-control study—If applicable, explain how matching of cases and controls was addressed
Cross-sectional study—If applicable, describe analytical methods taking account of sampling strategy		-
(e) Describe any sensitivity analyses3
Results
Participants13 *(a) Report numbers of individuals at each stage of study—eg numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up, and analysed3
(b) Give reasons for non-participation at each stage3
(c) Consider use of a flow diagram-
Descriptive data14 *(a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and potential confounders3
(b) Indicate number of participants with missing data for each variable of interest3
(c) Cohort study—Summarise follow-up time (eg, average and total amount)Table 1
Outcome data15 *Cohort study—Report numbers of outcome events or summary measures over time3
Case-control study—Report numbers in each exposure category, or summary measures of exposure
Cross-sectional study—Report numbers of outcome events or summary measures
Main results16(a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, 95% confidence interval). Make clear which confounders were adjusted for and why they were included6
(b) Report category boundaries when continuous variables were categorized-
(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period-
Other analyses17Report other analyses done—eg analyses of subgroups and interactions, and sensitivity analysesTable 2
Discussion
Key results18Summarise key results with reference to study objectives7–8
Limitations19Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and magnitude of any potential bias8
Interpretation20Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidence8–9
Generalisability21Discuss the generalisability (external validity) of the study results8
Other information
Funding22Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based9
* Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies. Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at https://journals.plos.org/plosmedicine/, Annals of Internal Medicine at http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe-statement.org.

References

  1. World Health Organization. Obesity and Overweight. 2024. Available online: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight#:~:text=In%202022%2C%202.5%20billion%20adults%20%2818%20years%20and,children%20under%20the%20age%20of%205%20were%20overweight (accessed on 12 December 2024).
  2. Obesity, W. Economic Impact of Overweight and Obesity. 2024. Available online: https://data.worldobesity.org/economic-impact-new/#:~:text=If%20current%20trends%20continue%2C%20using%20data%20from%20161,to%20rise%20to%20%3E%203%25%20of%20GDP%20globally. (accessed on 12 December 2024).
  3. Bailey-Davis, L.; Wood, G.C.; Benotti, P.; Cook, A.; Dove, J.; Mowery, J.; Ramasamy, A.; Iyer, N.N.; Smolarz, B.G.; Kumar, N.; et al. Impact of Sustained Weight Loss on Cardiometabolic Outcomes. Am. J. Cardiol. 2022, 162, 66–72. [Google Scholar] [CrossRef] [PubMed]
  4. Boushey, C.; Ard, J.; Bazzano, L.; Heymsfield, S.; Mayer-Davis, E.; Sabaté, J.; Snetselaar, L.; Van Horn, L.; Schneeman, B.; English, L.K.; et al. USDA Nutrition Evidence Systematic Reviews. Dietary Patterns and All-Cause Mortality: A Systematic Review; USDA Nutrition Evidence Systematic Review: Alexandria, VA, USA, 2020.
  5. Kinoshita, K.; Ozato, N.; Yamaguchi, T.; Sudo, M.; Yamashiro, Y.; Mori, K.; Ishida, M.; Katsuragi, Y.; Sasai, H.; Yasukawa, T.; et al. Association of sedentary behaviour and physical activity with cardiometabolic health in Japanese adults. Sci. Rep. 2022, 12, 2262. [Google Scholar] [CrossRef] [PubMed]
  6. Weihrauch-Blüher, S.; Schwarz, P.; Klusmann, J.-H. Childhood obesity: Increased risk for cardiometabolic disease and cancer in adulthood. Metabolism 2019, 92, 147–152. [Google Scholar] [CrossRef]
  7. Naska, A.; Lagiou, A.; Lagiou, P. Dietary assessment methods in epidemiological research: Current state of the art and future prospects. F1000Research 2017, 6, 926. [Google Scholar] [CrossRef]
  8. Shim, J.-S.; Oh, K.; Kim, H.C. Dietary assessment methods in epidemiologic studies. Epidemiol. Health 2014, 36, e2014009. [Google Scholar] [CrossRef] [PubMed]
  9. Sproesser, G.; Klusmann, V.; Schupp, H.T.; Renner, B. Self-other differences in perceiving why people eat what they eat. Front. Psychol. 2017, 8, 209. [Google Scholar] [CrossRef]
  10. Polivy, J.; Herman, C.P.; Trottier, K.; Sidhu, R. Who are you trying to fool: Does weight underreporting by dieters reflect self-protection or self-presentation? Health Psychol. Rev. 2014, 8, 319–338. [Google Scholar] [CrossRef] [PubMed]
  11. Vartanian, L.R.; Reily, N.M.; Spanos, S.; Herman, C.P.; Polivy, J. Evidence of a self-serving bias in people’s attributions for their food intake. Appetite 2024, 201, 107583. [Google Scholar] [CrossRef]
  12. Shiffman, S.; Stone, A.A.; Hufford, M.R. Ecological Momentary Assessment. Annu. Rev. Clin. Psychol. 2008, 4, 1–32. [Google Scholar] [CrossRef]
  13. Schembre, S.M.; Liao, Y.; O’Connor, S.G.; Hingle, M.D.; Shen, S.-E.; Hamoy, K.G.; Huh, J.; Dunton, G.F.; Weiss, R.; A Thomson, C.; et al. Mobile Ecological Momentary Diet Assessment Methods for Behavioral Research: Systematic Review. JMIR mHealth uHealth 2018, 6, e11170. [Google Scholar] [CrossRef] [PubMed]
  14. Goldstein, S.P.; Dochat, C.; Schumacher, L.M.; Manasse, S.M.; Crosby, R.D.; Thomas, J.G.; Butryn, M.L.; Forman, E.M. Using ecological momentary assessment to better understand dietary lapse types. Appetite 2018, 129, 198–206. [Google Scholar] [CrossRef]
  15. Wahl, D.R.; Villinger, K.; Blumenschein, M.; König, L.M.; Ziesemer, K.; Sproesser, G.; Schupp, H.T.; Renner, B. Why we eat what we eat: Assessing dispositional and in-the-moment eating motives by using ecological momentary assessment. JMIR mHealth and uHealth 2020, 8, e13191. [Google Scholar] [CrossRef]
  16. Mason, T.B.; Smith, K.E.; Crosby, R.D.; Engel, S.G.; Wonderlich, S.A. Examination of momentary maintenance factors and eating disorder behaviors and cognitions using ecological momentary assessment. Eat. Disord. 2021, 29, 42–55. [Google Scholar] [CrossRef] [PubMed]
  17. Goldschmidt, A.B.; Crosby, R.D.; Cao, L.; Engel, S.G.; Durkin, N.; Beach, H.M.; Berg, K.C.; Wonderlich, S.A.; Crow, S.J.; Peterson, C.B. Ecological momentary assessment of eating episodes in obese adults. Psychosom. Med. 2014, 76, 747–752. [Google Scholar] [CrossRef] [PubMed]
  18. Forman, E.M.; Schumacher, L.M.; Crosby, R.; Manasse, S.M.; Goldstein, S.P.; Butryn, M.L.; Wyckoff, E.P.; Graham Thomas, J. Ecological Momentary Assessment of Dietary Lapses Across Behavioral Weight Loss Treatment: Characteristics, Predictors, and Relationships with Weight Change. Ann. Behav. Med. 2017, 51, 741–753. [Google Scholar] [CrossRef] [PubMed]
  19. Roefs, A.; Boh, B.; Spanakis, G.; Nederkoorn, C.; Lemmens, L.; Jansen, A. Food craving in daily life: Comparison of overweight and normal-weight participants with ecological momentary assessment. J. Hum. Nutr. Diet. 2019, 32, 765–774. [Google Scholar] [CrossRef] [PubMed]
  20. Thomas, J.G.; Doshi, S.; Crosby, R.D.; Lowe, M.R. Ecological momentary assessment of obesogenic eating behavior: Combining person-specific and environmental predictors. Obesity 2011, 19, 1574–1579. [Google Scholar] [CrossRef] [PubMed]
  21. Boyland, E.; Spanakis, P.; O’Reilly, C.; Christiansen, P. Associations between everyday exposure to food marketing and hunger and food craving in adults: An ecological momentary assessment study. Appetite 2024, 196, 107241. [Google Scholar] [CrossRef] [PubMed]
  22. Grenard, J.L.; Stacy, A.W.; Shiffman, S.; Baraldi, A.N.; MacKinnon, D.P.; Lockhart, G.; Kisbu-Sakarya, Y.; Boyle, S.; Beleva, Y.; Koprowski, C.; et al. Sweetened drink and snacking cues in adolescents. A study using ecological momentary assessment. Appetite 2013, 67, 61–73. [Google Scholar] [CrossRef]
  23. Carels, R.A.; Hoffman, J.; Collins, A.; Raber, A.C.; Cacciapaglia, H.; O’Brien, W.H. Ecological momentary assessment of temptation and lapse in dieting. Eat. Behav. 2001, 2, 307–321. [Google Scholar] [CrossRef] [PubMed]
  24. Carels, R.A.; Douglass, O.M.; Cacciapaglia, H.M.; O’Brien, W.H. An ecological momentary assessment of relapse crises in dieting. J. Consult. Clin. Psychol. 2004, 72, 341. [Google Scholar] [CrossRef] [PubMed]
  25. Patel, K.A.; Schlundt, D.G. Impact of moods and social context on eating behavior. Appetite 2001, 36, 111–118. [Google Scholar] [CrossRef] [PubMed]
  26. Schlundt, D.G.; Sbrocco, T.; Bell, C. Identification of high-risk situations in a behavioral weight loss program: Application of the relapse prevention model. Int. J. Obes. 1989, 13, 223–234. [Google Scholar]
  27. Bennett, G.; Bardon, L.A.; Gibney, E.R. A comparison of dietary patterns and factors influencing food choice among ethnic groups living in one locality: A systematic review. Nutrients 2022, 14, 941. [Google Scholar] [CrossRef]
  28. Lee, Y.S.; Biddle, S.; Chan, M.F.; Cheng, A.; Cheong, M.; Chong, Y.S.; Foo, L.L.; Lee, C.H.; Lim, S.C.; Ong, W.S.; et al. Health promotion board–ministry of health clinical practice guidelines: Obesity. Singap. Med. J. 2016, 57, 292. [Google Scholar] [CrossRef]
  29. Chew, H.S.J.; Chew, N.W.; Loong, S.S.E.; Lim, S.L.; Tam, W.S.W.; Chin, Y.H.; Chao, A.M.; Dimitriadis, G.K.; Gao, Y.; So, J.B.Y.; et al. Effectiveness of an Artificial Intelligence-Assisted App for Improving Eating Behaviors: Mixed Methods Evaluation. J. Med. Internet Res. 2024, 26, e46036. [Google Scholar] [CrossRef] [PubMed]
  30. Chew, H.S.J.; Gao, Y.; Shabbir, A.; Lim, S.L.; Geetha, K.; Kim, G.; Chong, C.S.; Lomanto, D.; So, B.Y.J. Personal motivation, self-regulation barriers and strategies for weight loss in people with overweight and obesity: A thematic framework analysis. Public Health Nutr. 2022, 25, 2426–2435. [Google Scholar] [CrossRef] [PubMed]
  31. International Business Machines Corporation. IBM SPSS Statistics for Windows, Version 29.0.2.0; International Business Machines Corporation: Armonk, NY, USA, 2023. [Google Scholar]
  32. Chew, H.S.J.; Lau, S.T.; Lau, Y. Weight-Loss interventions for improving emotional eating among adults with high body mass index: A systematic review with Meta-Analysis and Meta-Regression. Eur. Eat. Disord. Rev. 2022, 30, 304–327. [Google Scholar] [CrossRef]
  33. Ulusoy-Gezer, H.G.; Rakıcıoğlu, N. The Future of Obesity Management through Precision Nutrition: Putting the Individual at the Center. Curr. Nutr. Rep. 2024, 13, 455–477. [Google Scholar] [CrossRef]
  34. Gibson, A.A.; Sainsbury, A. Strategies to Improve Adherence to Dietary Weight Loss Interventions in Research and Real-World Settings. Behav. Sci. 2017, 7, 44. [Google Scholar] [CrossRef]
  35. Ahlich, E.; Goldstein, S.P.; Thomas, J.G. Characterizing emotional eating: Ecological momentary assessment with person-specific modeling. Appetite 2023, 183, 106476. [Google Scholar] [CrossRef] [PubMed]
  36. Mills, S.; Brown, H.; Wrieden, W.; White, M.; Adams, J. Frequency of eating home cooked meals and potential benefits for diet and health: Cross-sectional analysis of a population-based cohort study. Int. J. Behav. Nutr. Phys. Act. 2017, 14, 109. [Google Scholar] [CrossRef] [PubMed]
  37. Landais, E.; Miotto-Plessis, M.; Bene, C.; Maitre d’Hotel, E.; Truong, M.T.; Somé, J.W.; Verger, E.O. Consumption of food away from home in low- and middle-income countries: A systematic scoping review. Nutr. Rev. 2023, 81, 727–754. [Google Scholar] [CrossRef]
  38. Nago, E.S.; Lachat, C.K.; Dossa, R.A.; Kolsteren, P.W. Association of out-of-home eating with anthropometric changes: A systematic review of prospective studies. Crit. Rev. Food Sci. Nutr. 2014, 54, 1103–1116. [Google Scholar] [CrossRef] [PubMed]
  39. Bhutani, S.; Schoeller, D.A.; Walsh, M.C.; McWilliams, C. Frequency of Eating Out at Both Fast-Food and Sit-Down Restaurants Was Associated with High Body Mass Index in Non-Large Metropolitan Communities in Midwest. Am. J. Health Promot. 2018, 32, 75–83. [Google Scholar] [CrossRef]
  40. Totosy de Zepetnek, J.O.; Pollard, D.; Welch, J.M.; Rossiter, M.; Faghih, S.; Bellissimo, N. Pre-meal screen-time activities increase subjective emotions, but not food intake in young girls. Appetite 2017, 111, 32–37. [Google Scholar] [CrossRef]
  41. Chatterjee, A.; Gerdes, M.W.; Martinez, S.G. Identification of risk factors associated with obesity and overweight—A machine learning overview. Sensors 2020, 20, 2734. [Google Scholar] [CrossRef]
  42. McKee, H.C.; Ntoumanis, N.; Taylor, I.M. An ecological momentary assessment of lapse occurrences in dieters. Ann. Behav. Med. 2014, 48, 300–310. [Google Scholar] [CrossRef]
  43. Stinson, E.J.; Piaggi, P.; Votruba, S.B.; Venti, C.; Lovato-Morales, B.; Engel, S.; Krakoff, J.; Gluck, M.E. Is dietary nonadherence unique to obesity and weight loss? Results from a randomized clinical trial. Obesity 2020, 28, 2020–2027. [Google Scholar] [CrossRef] [PubMed]
  44. Cross, G.B.; Marley, J.; Miles, H.; Willson, K. Changes in nutrient intake during the menstrual cycle of overweight women with premenstrual syndrome. Br. J. Nutr. 2001, 85, 475–482. [Google Scholar] [CrossRef]
  45. Kim, J.Y. Optimal Diet Strategies for Weight Loss and Weight Loss Maintenance. J. Obes. Metab. Syndr. 2021, 30, 20–31. [Google Scholar] [CrossRef] [PubMed]
  46. Spro Chwyl, C.; Crochiere, R.J.; Forman, E.M. The role of everyday activities on likelihood of dietary lapse: An ecological momentary assessment study. J. Behav. Med. 2023, 46, 532–539. [Google Scholar] [CrossRef] [PubMed]
  47. Crochiere, R.J.; Abber, S.R.; Taylor, L.C.; Sala, M.; Schumacher, L.M.; Goldstein, S.P.; Forman, E.M. Momentary predictors of dietary lapse from a mobile health weight loss intervention. J. Behav. Med. 2022, 45, 324–330. [Google Scholar] [CrossRef] [PubMed]
  48. Lowe, M.R. Self-regulation of energy intake in the prevention and treatment of obesity: Is it feasible? Obes. Res. 2003, 11 (Suppl. S10), 44S–59S. [Google Scholar] [CrossRef]
Table 1. Response count (%) and comparison of each dietary trigger.
Table 1. Response count (%) and comparison of each dietary trigger.
Dietary TriggerTotal Number of Responses, Count (%) (N = 4708)Responses of Non-Adherence to Diet Plan, Count (%) (n = 1109)Responses of Adherence to Diet Plan, Count (%) (n = 3599)p-Value
Place
 Home/residence2884 (61.3)628 (56.6)2256 (62.7)0.046 *
 Work819 (17.4)216 (19.5)603 (16.8)0.171
 Hawker centre/coffeeshop/restaurant/café/food stall795 (16.9)204 (18.4)591 (16.4)0.728
 On the go78 (1.7)17 (1.5)61 (1.7)0.528
 Family’s/friend’s house57 (1.2)21 (1.9)36 (1.0)0.078
 School45 (1)13 (1.2)32 (0.9)0.707
 Event/place of worship/exercise studio/park30 (0.6)10 (0.9)20 (0.6)0.343
Emotions
 Neutral3288 (69.8)726 (65.4)2562 (71.2)0.175
 Happy1285 (27.3)330 (29.7)955 (26.5)0.149
 Stressed/nervous/restless/anxious/worry425 (9)125 (11.3)300 (8.3)0.495
 Bored307 (6.5)86 (7.7)221 (6.1)0.841
 Sad86 (1.8)30 (2.7)56 (1.6)0.173
 Irritated/annoyed/frustrated/angry19 (0.4)4 (0.4)15 (0.4)0.858
Physiological state
 Hungry1153 (24.5)281 (25.3)872 (24.2)0.775
 Tired827 (17.6)213 (19.2)614 (17.1)0.661
 Cold111 (2.4)19 (1.7)92 (2.6)0.222
 Premenstrual syndrome79 (1.7)28 (2.5)51 (1.4)0.332
 Sick38 (0.8)9 (0.8)29 (0.8)0.566
 Full21 (0.4)3 (0.3)18 (0.5)0.586
 Warm3 (0.1)1 (0.1)2 (0.1)0.399
Company
 Alone2244 (47.7)482 (43.4)1762 (48.9)0.047 *
 With family/friends1692 (35.9)402 (36.2)1290 (35.8)0.999
 With colleagues/peers420 (8.9)118 (10.6)302 (8.4)0.034 *
Ease of obtaining food
 Self-prepared1432 (30.4)280 (25.2)1152 (32)0.007 **
 Provided by family1186 (25.2)299 (26.9)887 (24.6)0.958
 Takeaway934 (19.8)233 (21)701 (19.5)0.129
 Self-bought849 (18)213 (19.2)636 (17.7)0.476
 Provided by colleagues/peers151 (3.2)40 (3.6)111 (3.1)0.451
 Provided by friends114 (2.4)42 (3.8)72 (2)0.09
 Provided by domestic helper42 (0.9)2 (0.2)40 (1.1)0.969
Activity prior to eating
 Social media1163 (24.7)264 (23.8)899 (25)0.489
 Working774 (16.4)169 (15.2)605 (16.8)0.174
 Resting/relaxing/watching tv/videos545 (11.6)151 (13.6)394 (10.9)0.390
 Physical activity518 (11)112 (10.1)406 (11.3)0.238
 Eating305 (6.5)71 (6.4)234 (6.5)0.411
 Travelling274 (5.8)80 (7.2)194 (5.4)0.008 *
 Sleeping267 (5.7)48 (4.3)219 (6.1)0.061
 Shopping227 (4.8)64 (5.8)163 (4.5)0.108
 Attending class/reading/studying158 (3.4)47 (4.2)111 (3.1)0.22
 Showering/washing up/preparing to go out140 (3)31 (2.8)109 (3)0.731
 Chatting104 (2.2)29 (2.6)75 (2.1)0.933
 Special occasion95 (2)22 (2)73 (2)0.745
 Preparing a meal42 (0.9)10 (0.9)32 (0.9)0.289
 Gaming25 (0.5)3 (0.3)22 (0.6)0.342
 Caregiving16 (0.3)1 (0.1)15 (0.4)0.973
 Smoking16 (0.3)5 (0.5)11 (0.3)0.677
 Religious activity7 (0.1)2 (0.2)5 (0.1)0.922
Adherence (“I planned to:”)
 I am following my diet plan3599 (76.4)0 (0)3599 (100)-
 Avoid eating this food615 (13.1)615 (55.4)0 (0)-
 Avoid eating this portion271 (5.8)271 (24.4)0 (0)-
 Avoid eating at this time223 (4.7)223 (20.1)0 (0)-
 Sleep7.07.17.00.458
Note: * p-value ≤ 0.05; ** p-value ≤ 0.01.
Table 2. Relative risk of dietary triggers on dietary adherence response.
Table 2. Relative risk of dietary triggers on dietary adherence response.
Dietary TriggersRelative Risk95% Confidence Intervalp-Value
Place
Home/residence1.000.97, 1.050.721
Company
Alone1.010.97, 1.060.574
With colleagues/peers0.940.87, 1.010.084
Ease of obtaining food
Self-prepared1.051.01, 1.090.04 *
Activity prior to eating
Travelling0.930.87, 0.990.03 *
Note: CI = confidence interval; * p-value ≤ 0.05.
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Chew, H.S.J.; Vashishtha, R.; Du, R.; Liaw, Y.X.; Gneezy, A. Identifying Dietary Triggers Among Individuals with Overweight and Obesity: An Ecological Momentary Assessment Study. Nutrients 2025, 17, 481. https://doi.org/10.3390/nu17030481

AMA Style

Chew HSJ, Vashishtha R, Du R, Liaw YX, Gneezy A. Identifying Dietary Triggers Among Individuals with Overweight and Obesity: An Ecological Momentary Assessment Study. Nutrients. 2025; 17(3):481. https://doi.org/10.3390/nu17030481

Chicago/Turabian Style

Chew, Han Shi Jocelyn, Rakhi Vashishtha, Ruochen Du, Yan Xin Liaw, and Ayelet Gneezy. 2025. "Identifying Dietary Triggers Among Individuals with Overweight and Obesity: An Ecological Momentary Assessment Study" Nutrients 17, no. 3: 481. https://doi.org/10.3390/nu17030481

APA Style

Chew, H. S. J., Vashishtha, R., Du, R., Liaw, Y. X., & Gneezy, A. (2025). Identifying Dietary Triggers Among Individuals with Overweight and Obesity: An Ecological Momentary Assessment Study. Nutrients, 17(3), 481. https://doi.org/10.3390/nu17030481

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