Associations of addiction-like eating with personal relative deprivation, compensatory health beliefs, and body mass index from the life history perspective

Abstract

Background

The rising global overweight/obesity rate has led to an increase of research attention in one of its antecedents: addiction-like eating (AE). Under the framework of life history (LH) theory, the present study aimed at examining not only the association between AE and body mass index (BMI) but also the effects of LH strategy on AE, via two cognitive mediators (i.e., compensatory health beliefs [CHB] and personal relative deprivation [PRD]).

Method

A convenience sample of 660 Chinese adults (45.9% males, M_age=33.33, SD = 7.62, Range = 19–69 years) was recruited to participate in an anonymous online survey.

Results

Bivariate correlations revealed significant positive association between AE and BMI (r = 0.12, p < 0.001) and negative association between slow LH strategy and AE (r = − 0.38, p < 0.001). Path analyses revealed both significant direct (β=−0.15, 95%CI [− 0.23, − 0.06]) and indirect effects (via CHB and PRD) of LH strategy on AE (β=−0.06, 95%CI [− 0.10, − 0.03] and β=−0.16, 95%CI [− 0.20, − 0.11]). Additional model testing provided preliminary evidence that AE fully mediated the association between LH strategy and BMI (β=−0.03, 95%CI [− 0.06, − 0.01]). The indirect effect of LH strategy on BMI, via CHB/PRD and AE, serially, was also significant (β=−0.02, 95%CI [− 0.03, − 0.01] and β=−0.04, 95%CI [− 0.06, − 0.02]).

Discussion

Our study provided additional empirical support regarding the capacity of LH theory to shed light on the mechanisms underlying behavioral addiction. This study was the first of its kind to examine how LH strategy may stimulate resource-related cognitive beliefs, which contribute to addictive behaviors and associated outcomes. Findings have useful implications regarding future research directions and interventions targeting AE and weight control.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40337-025-01356-1.

Plain language summary

Addiction-like eating refers to maladaptive eating patterns that involve a strong sensitivity to unhealthy foods and reduced ability to control food consumption, often leading to higher risk of overweight and obesity. This study explored what psychological factors might influence addiction-like eating and body weight, using the life history theory approach, which explains how people make decisions based on their environment and resources. Based on self-report data, the study findings suggest that people who tend to invest resources in a slower development pattern for better quality and long-term goals over quantity and immediate gratification would be associated with lower levels of addiction-like eating and in turn healthier body weight. They are also less likely to believe they can “make up” for unhealthy behaviors later with healthy or “compensatory” behaviors as well as to feel unfairly deprived when compared to others in socially advantaged positions. Such belief and frustrated feeling may increase risk for addiction-like eating and overweight. This study emphasizes the importance of reshaping psychological beliefs to help manage maladaptive eating behaviors and support healthier weight outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40337-025-01356-1.

The changes in food composition and the availability of highly palatable foods in recent decades have resulted in a drastic increase in the overweight/obesity rate [1], constituting a global pandemic of obesity [2]. Extensive evidence has revealed how overweight and obesity are associated with the cause of various negative health consequences (e.g., heart disease, diabetes) [1], revealing an urgent need to curb this trend by examining its underlying psychological mechanisms [3]. Recent research attention has focused on one of the common antecedents of obesity/overweight: addiction-like eating (AE) [4], which is an addictive eating pattern, involving both heightened responsivity towards appetitive rewards (i.e., high sensitivity to cues of unhealthy food) and diminished inhibitory control over food consumption [5], closely associated with high body mass index (BMI) [6, 7]. The present study hence takes the life history (LH) perspective to understand the psychological mechanisms underlying AE and overweight/obesity.

LH theory and addiction-like eating

LH theory is an evolutionary theory explaining how organisms arrange limited time, energy, and other resources to face life tasks and environmental conditions [8–10]. According to LH theory, individuals will pursue different LH strategies (ranging from fast to slow on a continuum), when allocating their resources (e.g., food), for maximizing their fitness in the changing environment. LH strategies are expressed as distinct patterns of psychological features and behavioral tendencies [11]. If one’s childhood environment is highly unpredictable and harsh, individuals tend to adopt a fast LH strategy, i.e., resource allocation to “here and now” (e.g., early mating, early reproduction, immediate gratification, and impulsive acts) [12, 13]. In contrast, a consistent and stable childhood environment will result in individuals adopting a slow LH strategy, involving a more future-oriented control over one’s use of resources (e.g., delay reproduction, fewer offspring, and delayed gratification) [14].

The adaptations of fast/slow LH strategies are aimed to achieve reproductive success, which are not inherently positive or negative for health and wellbeing [8]. However, striving for reproduction can result in a series of socially undesirable traits (e.g., risk-taking and aggression) [12], which have been commonly used to explain the development of psychopathology among slow/fast LH strategists [14]. As for addictions, seeking immediate gratification and risk-taking tendencies aroused from adopting fast LH strategy are common traits for behavioral addictions [15], as addictive behaviors can bring immediate satisfaction and/or relief from unpleasant situations. Research has revealed consistent associations between fast LH strategy and addictive behaviors, including gaming [16], smartphone use [17, 18], and food addiction [19]. While AE shares substantial overlap with food addiction [6], their distinction may lie in AE’s unique aspect of inhibitory control [7]. Notably, no previous study has tested AE’s association with LH strategies; however, a study has revealed that priming cues of environmental harshness can induce intake of highly caloric foods [20], which may prolong sustainability for fast LH strategists in difficult environments with uncertain food supply [21].

Cognitive beliefs related to resource allocation and availability as mediators

LH theory also presumes that adopting specific LH strategy necessarily shapes individuals’ cognitions, such as perception and interpretation of risk and/or time, leading to specific behavioral patterns [22]. Such cognitions often reflect their preferences regarding allocating resources to various life tasks (e.g., survival and personal growth) [23]. For example, fast LH strategists tend to express poorer competence expectancy to engage in responsible gambling like setting bet limits [24]. Although cognitive beliefs are associated with vulnerability to both eating disorders [25] and addictive disorders (e.g., gambling disorder) [26], further research is warranted to understand their roles underlying LH strategy and individuals’ susceptibility to maladaptive eating behaviors, including AE.

Compensatory Health Beliefs (CHB). Health is a socially desirable long-term goal [27] deserving persistent investment of resources to achieve, but it often conflicts with more immediate, rewarding goals. CHB are beliefs that the negative consequences of unhealthy behaviors (e.g., highly caloric food intake) can be compensated by engaging in healthy behavior later [28]. Such beliefs will often be activated in our daily lives during motivational conflicts [29], biasing them to take unhealthy actions. Given that people with fast LH strategy tend to seek immediate gratification [8], they are likely to allocate resources to unhealthy but psychologically rewarding behaviors with a mindset of taking subsequent compensatory actions. CHB help justify or rationalize unhealthy decisions and avoid guilt feelings [28], particularly among fast LH strategists who prefer immediately satisfying behavior. CHB, consistently and positively associated with unhealthy behaviors (e.g., bad sleep patterns [30] and unhealthy eating [31]), may also heighten AE risks because palatable food consumption is highly rewarding, leading people to repeatedly engage in it despite negative health effects [32].

Personal Relative Deprivation (PRD). Resource allocation plays crucial roles in LH theory, but it is unknown how LH strategy is associated with perceptions of relative resource availability (e.g., abundant or scarce when comparing to others). PRD is defined as the subjective experience of frustration arising from making upward social comparisons [33]. PRD is associated with low socioeconomic status (SES) [34], which is a common indicator of environmental harshness [35] and correlate of fast LH strategy [36]. Therefore, fast LH strategists may be likely to develop PRD as they would perceive their resources as insufficient compared to others. In addition, as they tend to face more social and emotional difficulties when growing up in adverse environments [10], they may be more likely to experience frustrations or distress when socially comparing to others with more resources. Moreover, deprived individuals are more likely to compensate their feelings by engaging in more resource-seeking behaviors [35], which is not necessarily domain-specific and can activate an overlapping motivation for food intake [37], resulting in a higher risk of addiction to food. Indeed, research has demonstrated that relative deprivation is associated with increasing portion size [38] and consumption of immediately rewarding food [39].

The present study

The present study aims to apply LH theory to AE, by first examining the associations among LH strategy, AE, and BMI, and then evaluating the mediating mechanisms of two resource-related cognitive beliefs underlying their associations, if any. The findings would provide crucial insights into whether and how the LH perspective may contribute to the development of maladaptive eating patterns and associated consequences. The following hypotheses are proposed:

Hypothesis 1

AE is positively associated with higher levels of BMI.

Hypothesis 2

Slow LH strategy is negatively associated with AE.

Hypothesis 3

Slow LH strategy is negatively associated with (a) CHB and (b) PRD.

Hypothesis 4

(a) CHB and (b) PRD are positively associated with AE.

Hypothesis 5

(a) CHB and (b) PRD mediate the association between slow LH strategy and AE.

Methods

Participants and procedures

Between May 2024 and July 2024, a cross-sectional survey was conducted using convenience sampling via a Chinese online survey platform WenJuanXing. After obtaining informed consent from participants, they were instructed to finish an anonymous online survey. To ensure response quality, the current study embedded two attention checks, asking participants to select certain responses. A total of 935 participants completed the survey, but 275 cases were removed due to unreasonably short completion time and/or failing attention checks, resulting in 660 participants for the final sample. The final sample consisted of 303 males (45.9%) and 357 females (54.1%), ranging in age from 19 to 69 (M_age = 33.33, SD = 7.62). This study was approved by the ethics review panel of the department of psychology at the university affiliated by the corresponding author (Ref#: DPSY/2023-23).

Measures

LH Strategies. The current study employed the 8-item Cognitive-Emotional Traits of Life-history Strategy Scale [40] to measure the LH strategies of our Chinese participants. The scale was developed based on existing self-administered questionnaires for LH strategies, such as the 20-item Mini-K scale [41] and the 42-item version of the Arizona Life History Battery (K-SF-42; [42]). In consideration of both the cognitive and emotional components of LH strategies, satisfactory validity was demonstrated by its large correlation (i.e., r > 0.69) with the Mini-K scale [40]. A sample item is, “I like to make plans for the future.” Participants responded to items on a 7-point Likert scale, in which 1 = strongly disagree and 7 = strongly agree, with higher summative scores indicating slower LH strategies. The internal reliability of the scale was acceptable, α = 0.77.

Addiction-like Eating (AE). The 15-item Addiction-Like Eating Behaviors Scale (AEBS) was used to measure participants’ AE [4]. The study employed a Chinese version of AEBS, which has been shown to be a valid and reliable assessment tool among Chinese adults [43]. Participants responded to a 5-point Likert scale, in which 1 = Never and 5 = Always. A sample item is, “I eat a lot of high fat/sugar foods.” Higher scores indicated higher risk of AE. Cronbach’s α for this scale was 0.93 in this study.

Compensatory Health Beliefs (CHB). Compensatory health beliefs were measured by the Chinese version of the Compensatory Health Beliefs Scale (CHBS) [28], which has been validated among Chinese adults [44]. Participants were asked to respond to statements (e.g., “Eating dessert can be made up for by skipping the main dish.”), using a 5-point Likert scale, ranging from 1 = not at all to 5 = very much, with higher scores indicating higher levels of CHBs. The internal reliability of this scale was good (α = 0.86).

Personal Relative Deprivation (PRD). Participants degree of PRD was assessed using Callan et al.’s [45] 4-item Personal Relative Deprivation Scale (PRDS). An example item is, “I feel resentful when I see how prosperous other people seem to be.” The PRDS has been successfully employed in previous research [46]. Participants were instructed to respond to a 7-point Likert scale, ranging from 1 = strongly disagree to 7 = strongly agree, with higher mean scores indicating higher levels of PRD. The Cronbach’s α of this scale was 0.63.

Demographic Variables. Participants also reported their gender (1 = male, 2 = female), age (years), and height (m) and weight (kg) for BMI calculation (kg/m²). According to the BMI classification for Chinese participants [47, 48], BMI less than 18.5 was classified as underweight; 18.5 to 24.0 as normal weight; 24.0 to 28.0 as overweight; and above 28.0 as obese.

Statistical analysis

Descriptive and correlational analyses were conducted in SPSS26. Internal consistency was assessed using Cronbach’s α, of which > 0.70 was considered acceptable [49]. Lavaan package in R [50] was used to conduct path analysis. According to the recommendations of 1:20 N:q ratio by Jackson [51], the resultant sample size was sufficient for conducting the hypothesized path analysis model with 15 parameters. The default maximum likelihood (ML) estimator was used. The goodness of fit of the hypothesized path model (Fig. 1) was evaluated using the standards of the comparative fit index (CFI) ≥ 0.90 [52], the Tucker-Lewis index (TLI) ≥ 0.90, the root mean square error of approximation (RMSEA) < 0.08, and standardized root mean square residual (SRMR) < 0.08 [53]. The significance of direct and indirect effects of the hypothesized pathways were estimated using 95% confidence interval (95%CI), calculated by bootstrapping approach with 5000 samples. Besides the hypothesized path analysis, we also examined an additional path analysis with BMI as the outcome and was not part of the original hypotheses. As such, these results should be interpreted with caution and considered exploratory. To ensure the robustness of our findings, we also conducted sensitivity analyses examining alternative models (i.e., controlling both gender and age across all proposed variables) for all tested models.

Fig. 1.

Hypothesized path model of the present study

Results

Descriptive analysis and bivariate correlations

Descriptive statistics and bivariate correlations are shown in Table 1. Among the 660 participants, 85 (12.9%) were classified as underweight, 433 (65.6%) as normal weight, 126 (19.1%) as overweight, and 16 (2.4%) as obese.

Table 1.

Descriptive and bivariate correlations (N = 660)

Variables	M	SD	1	2	3	4	5	6	7
1. LH strategy	43.78	6.10	1
2. AE	33.24	10.21	−0.38***	1
3. CHB	43.43	9.88	−0.18***	0.46***	1
4. PRD	3.39	0.97	−0.52***	0.45***	0.18***	1
5. BMI	21.72	2.92	0.01	0.12***	−0.002	0.03	1
6. Age	33.33	7.62	0.16***	−0.16**	−0.12**	−0.12**	0.26***	1
7. Gender	−	−	−0.11**	0.09*	0.06	0.04	−0.43***	−0.18***	1

Note: LH = Life History; AE = Addiction-like Eating; CHB = Compensatory Health Beliefs; PRD = Personal Relative Deprivation. ^*p < 0.05, ^**p < 0.01, ^***p < 0.001

Correlational findings revealed a significant positive association between AE and BMI (r = 0.12, p < 0.001). LH strategy also displayed significant negative correlations with AE (r = − 0.38, p < 0.001), CHB (r = − 0.18, p < 0.001), and PRD (r = − 0.52, p < 0.001). Both CHB and PRD were positively correlated with AE (r = 0.45 and 0.46, p < 0.001, respectively).

As for the demographic variables, age was positively associated with LH strategy (r = 0.16, p < 0.001) and BMI (r = 0.26, p < 0.001) and negatively correlated with AE (r = − 0.16, p < 0.001), PRD (r = − 0.12, p < 0.01), and CHB (r = − 0.12, p < 0.01); whereas gender was found to be negatively associated with LH strategy and BMI (r = − 0.11 and − 0.43, p < 0.01), and positively with AE (r = 0.09, p < 0.05).

Mediation testing by path analysis

For testing the hypothesized mediating effects of the two cognitive beliefs (i.e., CHB and PRD) on the relationship between LH strategy and AE, a partial mediation model (Fig. 2a) was examined using path analysis. The model revealed a satisfactory model fit, χ²(14) = 572.74, CFI = 0.993, TLI = 0.967, RMSEA = 0.045, SRMR = 0.021. All hypothesized pathways were significant (p < 0.001).

Fig. 2.

The hypothesized path model (2a) and additional path model (2b) with standardized estimates

Note: ^***p < 0.001. The effects of the demographic variables (i.e., gender and age), which showed significant correlations with the variables involved, were controlled

The indirect/direct effects are displayed in Table 2. Significant direct/indirect effects were observed of slow LH strategy on AE (β=−0.15, 95%CI [− 0.23, − 0.06]) via CHB (β=−0.06, 95%CI [− 0.10, − 0.03]) and PRD (β=−0.16, 95%CI [− 0.20, − 0.11]). Sensitivity analysis controlling both gender and age across all variables revealed poorer model fit, indicating the original hypothesized model to be superior (see Supplementary Material 1).

Table 2.

Pathways of the hypothesized and additional path models (N = 660)

Path	β	95% CI (Lower, Upper)	Significance
Hypothesized path model
Direct effect of LH strategy on AE	−0.15	(− 0.23, − 0.06)	Significant
Indirect effect of LH strategy on AE via CHB	−0.06	(− 0.10, − 0.03)	Significant
Indirect effect of LH strategy on AE via PRD	−0.16	(− 0.20, − 0.11)	Significant
Additional path model
Direct effect of LH strategy on BMI	−0.002	(− 0.09, 0.08)	Non-significant
Indirect effect of LH strategy on BMI via AE	−0.03	(− 0.06, − 0.01)	Significant
Indirect effect of LH strategy on BMI via CHB	0.01	(− 0.004, 0.03)	Non-significant
Indirect effect of LH strategy on BMI via PRD	0.01	(− 0.03, 0.06)	Non-significant
Indirect effect of LH strategy on BMI via CHB and AE	−0.02	(− 0.03, − 0.01)	Significant
Indirect effect of LH strategy on BMI via PRD and AE	−0.04	(− 0.06, − 0.02)	Significant

Note: LH = Life History; AE = Addiction-like Eating; CHB = Compensatory Health Beliefs; PRD = Personal Relative Deprivation

Additional path analysis on BMI

Considering the positive association between AE and BMI, as well as the significant mediating roles of both CHB and PRD on the relationship between slow LH strategy and AE, another path analysis was conducted to explore whether LH strategy was associated with BMI via a serial mediation of CHB/PRD and AE. The partial and serial mediation model (Fig. 2b) revealed a satisfactory model fit, χ²(20) = 772.71, CFI = 0.995, TLI = 0.965, RMSEA = 0.045, SRMR = 0.018. All its path coefficients were statistically significant (p < 0.001), except the direct path from LH strategy to BMI (β=−0.002, p = 0.95) and that from either CHB or PRD to BMI (β=−0.06 and − 0.03 respectively, p > 0.05).

The significant indirect/direct effects are displayed in Table 2. No significant direct effect was found between slow LH strategy and BMI (β=−0.002, 95%CI [− 0.09, 0.08]), but the indirect effect of slow LH strategy on BMI via AE was significant (β=−0.03, 95%CI [− 0.06, − 0.01]). Significant indirect effects from slow LH strategy to BMI were also revealed via CHB and AE (β=−0.02, 95%CI [− 0.03, − 0.01]), and via PRD and AE (β=−0.04, 95%CI [− 0.06, − 0.02]). The indirect effects from slow LH strategy on BMI via CHB and PRD were nonsignificant. The sensitivity analysis, which accounted for both gender and age across all variables, showed a poorer model fit, suggesting that the original additional model performed better (see Supplementary Material 2).

Discussion

Despite the dramatic rise in China’s overweight/obesity prevalence (from 14.7% in 2002 to 34.8% in 2022) [54, 55], research on AE remains nascent, with only one prior study on AE, in which an AE assessment was validated and an association between AE and perceived weight status was reported [43]. In response to the heightened levels of overweight/obesity worldwide and relatively limited knowledge of Chinese people’s AE, the present study examined and supported the positive association between AE and BMI among Chinese adults, and also revealed two cognitive mechanisms (i.e., CHB and PRD) contributing to the link between LH strategies and their AE/BMI, from the LH perspective. Specifically, our data elucidated how slow LH strategy might weaken certain resource-related beliefs, resulting in a lower risk of AE and potentially overweight.

Supporting Hypothesis 1, the present study was the first to demonstrate a significant positive association between AE and BMI among Chinese adults, which is consistent with previous findings from other cultural groups (e.g., Italians and Canadians) [6, 7]. Our findings shed light on the potentially detrimental impact of AE on physical wellbeing among Chinese populations, suggesting an urgent need to address its antecedents, LH strategies and cognitive beliefs, in potential intervention and prevention programs targeting AE.

Aligned with Hypothesis 2, we also demonstrated significant negative associations between slow LH strategy and AE. These results were consistent with previous findings, as slow LH strategy was found to be associated with addictive eating (e.g., food addiction) [19] and other behavioral addictions [17]. According to LH theory, individuals with slow LH strategy are less likely to seek immediate gratification by food consumption, which can in turn decrease one’s vulnerability to AE. From the perspective of those with slow LH strategy, the present environment is stable and there are sufficient resources [9]; they, thus, tend to focus less on immediate resource acquisition to prolong their sustainability in the environment. Consequently, they are less prone to engage in addictive eating, showing less preference for high-caloric foods that can provide immediate energy [20], which lowers their risk of developing AE.

In line with Hypotheses 3a and 4a, slow LH strategy was found to be negatively correlated to CHB, and CHB to be positively associated with AE in this study. Path analysis further revealed CHB to be a significant cognitive mediator between the effect of slow LH strategy on AE (Hypothesis 5a). The present study was the first to shed light on the cognitive mechanism of CHB in the context of LH strategy in its association with subsequent AE, which is consistent with previous literature showing CHB’s associations with behavioral addictions (e.g., smartphone addiction) [30], supporting the notion that CHB can be closely associated with addictive behaviors. Indeed, the tendency to prefer later gratification adopted by slow LH strategists can result in less activations of CHB regarding food consumption, as they are less likely to allocate resources to engaging in unhealthy but immediately rewarding behaviors. Given that the outcome of activating CHBs can be neutralized as long as subsequent compensatory behaviors are performed (e.g., exercise or dieting) [28], even when CHB is activated for slow LH strategists, they are more likely to engage in compensatory behaviors as they tend to be future-oriented and more concerned about long-term health impacts [8]; thus, this mechanism in combination with a slow LH strategy may result in less excessive food consumption and lower one’s risk for AE.

In addition to shedding light on the role of CHB as a mediator between LH strategy and AE, our results also provided empirical support for our postulations that LH strategy would be consistently associated with PRD (Hypothesis 3b), and PRD with AE (Hypothesis 4b). PRD was also found to serve as a mediator between these two variables (Hypothesis 5b). From the LH theory perspective, we found that LH strategy was indeed highly associated with perceived resource availability; and building on existing studies [33, 56, 57], our findings also provided additional empirical evidence of the role of PRD on addiction. Given that slow LH strategy is commonly associated with high SES [36], those who adopt a slow LH strategy may be less likely to encounter feelings of dissatisfaction and frustration triggered by comparing oneself to others with high SES, due to perceiving themselves to have sufficient resources to be distributed. They are hence less likely to activate a resource-seeking tendency (i.e., PRD) as a compensatory act, as PRD involves experiencing a gap between what individuals have and what they think they deserve [58]. Consequently, individuals with slow LH strategy and lower levels of PRD will be less likely to engage in addictive behaviors (i.e., AE) when immediate resources are available, lowering their vulnerability to AE.

Our results also demonstrated significant indirect effects with small effect sizes of slow LH strategy on BMI via not only AE alone, but also via CHB/PRD and AE serially, suggesting the notion that LH strategies can have an impact on psychopathology, or even potentially on subsequent physical wellbeing, via cognitive mediators. Indeed, as reviewed by Caldwell and Sayer [59], slow LH strategy, which is shaped by a stable childhood environment, will often affect individuals’ cognitions to be more future rather than present oriented. This alteration can be viewed as an adaptive mechanism to achieve long-term success in a low-stress environment and will typically reduce the risk of excessive eating (e.g., eating in the absence of hunger cues) and result in healthier weight outcomes. The small effect sizes of indirect effects on BMI can also be attributed to the complicated interactions between genetic, psychological, and environmental factors on obesity development, which has been argued by previous review to play significant roles influencing the impacts of unpredictable life events on obesity [60]. Nevertheless, this finding provides additional practical insights for intervention programs targeting not only AE, but also potentially overweight/obesity, through targeting and neutralizing resource-related cognitions, especially among at-risk populations who adopt fast LH strategy.

Some limitations should be addressed. Firstly, the sample was collected by convenience sampling, which could limit the generalizability to the larger population of Chinese adults. Future research may benefit from utilizing a large-scale sample across means/platforms to further validate the generalizability of the findings. Secondly, responses from participants were collected by self-report data, which can lead to potential biases. For instance, previous research has revealed self-report height/weight resulting in misclassification of obesity among Chinese populations [61], at least partially explaining the lower overweight/obesity rate in our study compared to a recent large-scale study of Chinese participants (34.8% overweight and 14.1% obese) [55]. We thus recommend future studies assessing weight/height using anthropometric measurements. Thirdly, the cross-sectional design of the present study could not test the predictive effects of the proposed variables. Considering recent study has shown a bidirectional relationship between LH strategy and psychological variables (i.e., need frustrations) [62], future research could employ a longitudinal design to explore the temporal/bidirectional relationships among LH strategy, cognitive beliefs, AE, and BMI.

Despite limitations, our findings have important theoretical implications for utilizing LH approach to explain addictive psychopathology and practical implications for designing intervention programs targeting AE, and potentially, overweight/obesity. Building on previous literature, the present research provided additional theoretical insights regarding the application of LH strategies on behavioral addictions and highlighted the mediating roles of resource-related cognitions in these associations. In terms of practical implications, given the significant mediating effects of CHB and PRD on the relationship between LH strategy and AE, or even potentially affecting subsequent BMI, future research regarding AE can further explore how psychological interventions (e.g., cognitive behavioral therapy) may be useful in neutralizing the pernicious effects of cognitions (e.g., CHB and PRD) that are related to LH strategy, especially among at-risk populations who grow up in an unpredictable and harsh environment.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

AE

Addiction-like Eating

BMI

Body Mass Index

LH

Life History

CHB

Compensatory Health Beliefs

PRD

Personal Relative Deprivation

ML

Maximum Likelihood

CFI

Comparative Fit Index

TLI

Tucker-Lewis Index

RMSEA

Root Mean Square Error of Approximation

SRMR

Standardized Root Mean Square Residual

CI

Confidence Interval

Author contributions

HL: Conceptualization, Methodology, Formal analysis, Data curation, Writing-Original draft, Writing-Reviewing and Editing. BBC: Writing-Reviewing and Editing. HZ: Methodology. HMY: Writing-Reviewing and Editing. AMSW: Funding acquisition, Supervision, Conceptualization, and Writing-Reviewing and Editing. All authors contributed to and approved the final manuscript.

Funding

This study was supported by research grants of the University of Macau ([MYRG-GRG2023-00074-FSS] and [MYRG-GRG2024-00136-FSS])

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

This study was approved by the ethics review panel of the department of psychology at the university affiliated with the corresponding author (the reference number: DPSY/2023-23). Informed consent was obtained from all individual participants included in the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.