Background
Despite New Year’s resolutions to exercise and eat well to lose excess weight, most individuals are not able to follow through with their plan despite their best intentions [
1]. In fact, only ~ 20% of adults who are overweight successfully lose weight [
2]. Indeed, more than two thirds of American adults are overweight or obese, which is mostly due to human behavior and environment, not genetics [
3,
4]. This situation incurs increased morbidity and soaring health care costs [
5,
6]. Similarly, while individuals are cognizant of the importance of saving for retirement, actual savings rates in the U.S. represent only ~ 5% of disposable income [
7]. Moreover, the median retirement savings account balance for the working-age American household is $3000, and remains alarmingly low ($12,000) even for those nearing retirement age [
8].
Both health and wealth outcomes require a self-regulatory effort in the form of goal setting, planning, overcoming hurdles and resisting immediate impulses [
9,
10]. Goal setting (e.g., losing weight) and controlling emotions is required to preempt immediate gratification urges when temptation arises [
9,
10]. For example, when primed to eat chocolate cake an individual with a weight loss goal who has a high degree of self-regulation will likely opt for an apple rather than eating the cake to achieve their goal [
11,
12].. In the psychological literature, while both constructs are frequently used interchangeably, self-regulation generally refers to intentional and deliberate actions by which individuals plan, monitor and alter their cognitions, emotions, and behaviors in the service of long term goals [
13,
14]. Self-control, more specifically, refers to suppressing, overcoming or channeling acute impulses to the extent that they interfere with valued long term goals [
14].
In economics, health and wealth decisions are viewed as inter-temporal decisions between myopic and far-sighted choices [
15]. That is, individuals with ‘near sighted’ (impatient) preferences will devalue future rewards (known as delayed discounting), such as future health benefits, over present day immediately gratifying choices (e.g., eating ice-cream) [
11,
16‐
18]. Devaluing greater future benefits often occurs since the future rewards are not salient at present [
19]. Individuals able to delay immediate gratifying behavior are regarded as having patient time preferences, which has been linked in the literature to less impulsive behavior [
20]. Whereas traditional micro-economic theory regarded time preference as stable over time, more recent modeling integrates insights from psychology by acknowledging that time preferences could change over time, particularly when in a visceral state (e.g., when angry or hungry) [
21‐
23]. For example, if one’s goal is to adhere to a healthful diet yet social and environmental cues prime them to consume energy dense foods, their a priori goal will often not be met. This ‘scenario’, where the a priori goal is not met, is regarded as an inconsistent time preference and reflects a self-control problem [
24].
Both fields (psychology and economics) concur that the ability to prioritize ‘should’ (e.g., walking on the treadmill) over ‘want’ (e.g., sitting on the couch watching TV) behaviors to achieve higher levels goals (e.g., chronic disease prevention), is indicative of higher levels of self-control or self-regulation [
25,
26]. Henceforth, in the current study we use the term ‘self-regulation’. High self-regulation has been linked to behaviors that enhance health and well-being, such as educational attainment, monetary savings, and obesity prevention [
27‐
30]. In the health domain, for example, a study by Stoklosa et al. (2018), among a national sample of U.S. adults, observed that higher self-regulation is linked to reduced obesity levels among adults and their children [
21]. Moreover, Fan and Jin (2014) found that individuals who are obese exhibit lower levels of self-regulation than their normal weight counterparts, and that lower self-regulation is associated with unhealthful eating and physical inactivity [
31]. In the specific case of self-regulation in financial decision making, research by Gubler and Pierce (2014) observed that employees who saved for retirement exhibited better blood test scores [
32]. Similarly, Israel et al. (2014) found that higher credit scores, arguably indicative of financial prudence, were predictive of lower cardiovascular disease risk, irrespective of income [
33].
The focal point of these studies, however, was on individuals with high self-regulation rather than the cross-domain behavior of those with lower levels. Furthermore, these studies have insufficiently focused on low-income individuals at increased risk for obesity and other chronic diseases, with less financial resources, and less access to medical care [
34]. To this end, the current study focuses on weight loss and financial behaviors of low-income adults who were historically overweight. That is, at one point in life participants ceased maintaining normal weight status (henceforth, historically overweight). Thus, the present study aims to describe the relationship between long term weight loss (LTWL) success and monetary savings among adults who have been historically overweigh.
Results
The socio-demographic characteristics (weighted) of the study sample are presented in Table
1. Briefly, the mean age of participants was 36.5 years (SE = 0.35), and 48.6% were women. Slightly less than half (48.5%) were non-Hispanic whites, 28.4% were Hispanic, and 17.4% were non-Hispanic black. Furthermore, 14.4% were college graduates, and 84.6% had an annual household income of less than $45,000.
Table 1
Descriptive Characteristics (Weighted) of Sample: NHANES 2007–2014 (n = 1994)
Gender |
Women | 48.65% | 0.01 |
Age (years): mean (SE) | 36.50 | 0.35 |
Race/Ethnicity |
Non-Hispanic White | 48.54% | 0.03 |
Non-Hispanic Black | 17.38% | 0.02 |
Hispanic | 28.41% | 0.02 |
Other | 5.67% | 0.01 |
Marital Status |
Married | 43.32% | 0.02 |
Widow | 0.91% | 0.00 |
Divorced/Separated | 16.07% | 0.01 |
Never married | 25.69% | 0.02 |
College Graduate | 14.45% | 0.01 |
Household Size: mean (SE) | 3.803 | 0.05 |
Annual Household Income (U.S. Dollars)- categories |
< $20,000.00 | 31.62% | 0.01 |
$20,000.00–$44,999.00 | 53.00% | 0.01 |
$45,000.00–$74,999.00 | 9.78% | 0.01 |
≥ $75,000.00 | 5.60% | 0.01 |
Long-term Weight Loss Maintenance (0.00–9.99%) | 71.85% | 0.01 |
Long-term Weight Loss Maintenance (10.00–19.99%) | 20.34% | 0.01 |
Long-term Weight Loss Maintenance (≥20.00%) | 7.81% | 0.01 |
Self-reported Health Status |
Excellent | 8.47% | 0.01 |
Very good | 27.74% | 0.01 |
Good | 44.73% | 0.01 |
Fair | 17.33% | 0.01 |
Poor | 1.72% | 0.00 |
Current Smoker | 40.30% | 0.02 |
Diet Goal | 56.44% | 0.01 |
Total Savings (U.S. Dollars) |
< $500.00 | 72.61% | 0.02 |
$501.00–$5000.00 | 13.12% | 0.01 |
> $5000.00 | 14.28% | 0.02 |
Multivariable analysis, presented in Table
2, reveals that in comparison to the reference group (< 10% LTWL), a significant inverse relationship was found between the ≥20% LTWL category and total savings. That is, individuals in the highest LTWL category were 45% less likely to save (95%CI = 0.34–0.91) than the reference group. This relationship was not observed for the lower, 10–19.99% LTWL category (OR = 0.92; 95%CI 0.69–1.23). In addition, having a diet goal, college graduation, and a higher annual income were each independently and positively related to increased savings. Specifically, participants with a diet goal were 1.46 times more likely to save (95%CI 1.13–1.90), college graduates were 2.95 times more likely to save (95%CI 1.89–4.60), and those in the highest income strata were 6.22 times more likely to save (95%CI 2.84–13.61).
Table 2
Long-term Weight Loss and Monetary Savings: ordered logistic regressiona
Long-term Weight Loss-categories (< 10% reference) |
10.00–19.99% Weight Loss | 0.92 0.69–1.23 |
≥ 20.00% Weight Loss | 0.55* 0.34–0.91 |
Diet Goal | 1.46* 1.13–1.90 |
College Graduate | 2.95* 1.89–4.60 |
Annual Household Income (<$20,000 reference) |
$20,000.00–$44,999.00 | 1.42 0.99–2.12 |
$45,000.00–$74,999.00 | 3.04* 1.88–4.91 |
$ ≥ 75,000.00 | 6.22* 2.84–13.61 |
Discussion
The inverse relationship observed between LTWL and monetary savings might stem from the fact that in this study sample participants were historically overweight, which could indicate lower overall self-regulation [
45,
46]. Thus, unlike individuals with high self-regulation who are able to control their thoughts, goals and behaviors across domains, [
47] those with lower levels might be able to exert self-control in some domains but not in others. This finding might be explained by the significant cognitive-affective effort exerted by the group in the process of losing 20% (or more) of their maximum weight, [
12] which did not leave enough “mental resources” deployed when it came to financial decision-making. This might also be compounded by the fact that low-income participants experience a preponderance of adverse life events, which assail mental resources and detrimentally affect decision-making [
48‐
50]. Theses suppositions are preliminary, however, and should be substantiated in study samples which contain additional information pertaining to adverse life events, psychological mechanisms, social and environmental variables, as well as more elaborate data pertaining to spending and saving behaviors.
Nonetheless, these findings are novel and important since they illustrate how decisions in different domains are interrelated. These decisions, namely to lose weight and save money, have paramount health and welfare implications. The field of public health has traditionally focused on social determinants of health, that is, how one’s socio-demographic and economic status affects health [
51]. Evidence clearly points to the fact that individuals with low-incomes have higher prevalence rates of obesity, type 2 diabetes, cardiovascular diseases, and premature death, while having less access to quality medical care in comparison to their higher income counterparts [
52‐
54]. The increased risk for chronic morbidity and mortality among individuals with low incomes likely stems from a higher rate of ‘behavioral risk factors’, such as physical inactivity and unhealthful eating leading to obesity, which is due to living in environments conducive to these behaviors [
55]. Research, however, has rarely assessed whether health behaviors are related to monetary savings, both sharing a common underlying factor of self-regulation.
It should be noted that as with the Gubler and Pierce (2014) study, a direct measure of self-regulation was not available in the present study. However, analysis reveals that individuals possessing a diet goal were significantly more likely to save money. While a diet goal leads to goal directed action in the eating domain, [
12] it also might be related to achieving higher level goals in other domains. Hence, having a diet goal might be a proxy for higher overall self-regulation versus lower self-regulation among those without a diet goal in this sample [
35,
41‐
43]. This assumption, however, needs to be substantiated in future research. Additionally, the NHANES dataset is cross-sectional, which precludes determining a temporal relationship. Moreover, whereas LTWL is based on an individual’s weight at three time points, the exact timeframe in which the financial savings occurred is not clear. Further, the monetary saving behavior was based on either individual or household savings, which does not necessarily reflect an individual’s decision alone. Additionally, the monetary saving variable cannot be expressed as a percent of total income due the way this variable and the annual house income variable are constructed in the survey. Nonetheless, annual household income is adjusted for in multivariable analysis. While we adjusted for self-reported health status and excluded participants who were underweight (as a proxy for underlying medical conditions) and with heart disease and stroke, it could still be possible that weight loss occurred as a result of a medical condition. Finally, examining decision making in low-income samples has important health, welfare and policy implications, yet the analytic sample utilized in this study is not representative of the U.S. population at large.
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