Effects of Acetaminophen on Pain Response Among Overweight or Obese Women Exposed to Weight Stigmatization
Conditions: Overweight, Obesity, Healthy Adults
Sponsor: Ohio State University

 
 
 
Effects of Acetaminophen on Pain Response among Overweight or Obese Women Exposed to 
Weight Stigmatization 
 
RESEARCH PROTOCOL 
 
By 
Jacob David Landers, M.A. 
Graduate Program in Psychology 
 
The Ohio State University 
2019 
 
Faculty Advisor and Principal Investigator: Dr. Charles F. Emery 
 

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Chapter 1: Introduction 
 
Pain is widely prevalent, with recent estimates suggesting that approximately 50 million 
U.S. adults and 30-50% of the world population suffer from chronic pain (Barcellos de Souza et 
al., 2017; Dahlhamer et al., 2018). Recent analysis of a 2012 U.S. National Health Interview 
Survey revealed that an estimated 126 million American adults experienced pain during the prior 
three months (Nahin, 2015). Pain may be secondary to several chronic conditions and diseases 
(e.g., cancer-related pain, chronic neuropathic pain), but, in some cases, chronic pain may be 
considered a disease itself (e.g., low back pain, fibromyalgia; Treede et al., 2019). Chronic pain 
is associated with several negative outcomes such as restrictions in mobility and daily activities, 
anxiety, depression, poor perceived health, reduced quality of life, and increased risk for 
dependence on opioids (Dahlhamer et al., 2018).  
 
Pain is a subjective experience that involves both physiological and psychological 
processes. Experiences of pain, in turn, influence reactions to new episodes of pain. From an 
evolutionary perspective, learning from previous pain experiences may help individuals avoid 
potentially dangerous objects in their environment, cope with new pain more effectively, and 
maintain good health (Linton & Shaw, 2011). For these reasons, the psychological processes 
involved in pain hold value for human survival. Thus, eliminating pain from the human 
experience is not an adaptive clinical goal. However, pain that is insufficiently managed, such as 
chronic pain, may contribute to unnecessary or exacerbated suffering. Therefore, there is 
increased need to develop more effective interventions for pain management (Tompkins, 
Hobelmann, & Compton, 2017).  
 
Chronic pain is positively associated with body weight such that individuals with 
overweight and obesity are disproportionately affected by pain relative to individuals with 

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normal weight (Okifuji & Hare, 2015). Indeed, results from a recent multi-state study revealed 
that pain complaints were more common as body mass index (BMI) increased, and that 
individuals with obesity are four times more likely to complain about pain compared to 
individuals without obesity (Hitt, McMillen, Thornton-Neaves, Koch, & Cosby, 2007). While 
efficacious pain management interventions may be utilized to help individuals cope with chronic 
pain, recent evidence suggests that these may not always be as effective for individuals with 
higher BMI. For example, Sellinger and colleagues (2010) found that individuals with obesity 
were less likely than individuals without obesity to benefit from a cognitive behavioral treatment 
for low back pain. Thus, obesity is associated with exacerbated pain, and may complicate the 
treatment of pain. 
Obesity 
 
Obesity is a chronic medical condition of excessive or abnormal fat accumulation in 
adipose tissue (Garrow, 1981) commonly defined using BMI, which is an approximation of 
percentage body fat using the ratio of one’s body weight (in kilograms) to height (in meters 
squared). A BMI of 30 or greater is classified as obese (U.S. National Library of Medicine, 
2015). There is growing public health concern regarding obesity; 2014 estimates from the World 
Health Organization revealed that 39% of adults worldwide were classified as overweight (BMI 
³ 25 and < 30), while 13% of adults met criteria for obesity. The prevalence of obesity in the 
United States is rising at an even more alarming rate. A recent, population-based study of 9,000 
adults found that 34% of the sample was overweight, and an additional 35% met criteria for 
obesity (Ogden, Carrol, Kit, & Flegal, 2014). Increased prevalence of obesity contributed to 
higher average medical costs, estimated at $1,000 more per year than costs for individuals with 

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normal weight (Finkelstein, Trogdon, Cohen, & Dietz, 2009), in addition to increased risk of 
physical problems such as pain. 
Physical Pain and Obesity 
Several studies have investigated the relationship of obesity with specific forms of 
chronic pain. Studies have documented links between obesity and a variety of pain diagnoses 
including low back pain and abdominal pain (Wright et al., 2010). A 2014 review by Chai and 
colleagues revealed that individuals with obesity are at greater risk for experiencing headaches, 
especially chronic headaches. Other research reveals that obesity may be a risk factor for pelvic 
and neuropathic pains (Okifuji & Hare, 2015). Studies among individuals with pain conditions 
provide support for the connection between pain and obesity. For example, in studies of patients 
with fibromyalgia, a significant proportion is overweight or obese (Okifuji, Bradshaw, & Olson, 
2009; Okifuji, Donaldson, Barck, & Fine, 2010; Yunus, Arslan, & Aldag, 2002). Yoo and 
colleagues (2014) found greater total fat mass and less total lean mass among individuals 
reporting musculoskeletal pain than among those not reporting pain.  
The relationship between obesity and pain may be bidirectional such that obesity not only 
contributes to pain, but also is a product of pain. Several longitudinal studies have revealed 
greater risk for pain among individuals with obesity. For example, Mork, Holtermann, and 
Nilsen (2013) followed over 30,000 people in Norway for over ten years and found that those 
with obesity, especially those who were inactive, were more likely to develop chronic arm pain. 
Other, larger longitudinal studies have found that obesity is a risk factor for future multisite 
musculoskeletal pain (e.g., low back, neck, knees; Haukka, Ojajarvi, Takala, Viikari-Juntura, & 
Leino-Arjas, 2012; Heuch, Heuch, Hagen, & Zwart, 2013). In support of obesity as a product of 
pain, common effects of chronic pain such as poor sleep and sedentary lifestyle are linked to 

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weight gain among chronic pain patients (Okifuji & Hare, 2015). Individuals treated for chronic 
pain via medication may experience unpleasant side-effects that can contribute to weight gain 
such as mood changes, difficulty sleeping, and opioid-induced androgen deficiency. A study of 
self-reported causes of weight gain among pre-bariatric surgery patients highlighted chronic pain 
as one of the major reasons patients believed they had developed obesity (Ferguson et al., 2013). 
While weight loss may attenuate pain, pain also may interfere with successful weight loss in 
ways such as limiting one’s ability to engage in physical activity (Masheb et al., 2015). 
Several mechanisms have been postulated to help explain the relationship between 
obesity and pain, including structural and biochemical factors, as well as metabolic and 
inflammatory processes. 
Structural and Mechanical Factors 
 
Obesity is thought to contribute to chronic pain via negative effects of greater weight on 
joints and the spine. In support of this link, prior research has documented a positive relationship 
between BMI and defective change in knee cartilage which, in turn, may produce pain (Ding, 
Cicuttini, Scott, Cooley, & Jones, 2005). Individuals with obesity also produce greater spinal 
disk compression force when lifting objects than do individuals with normal weight, which may 
help explain the relation of obesity to greater structural damage in the back (Okifuji & Hare, 
2015; Singh, Park, Hwang, & Levy, 2014). Changes in body posture also are potentially 
implicated in the relationship between pain and obesity as they are common among individuals 
with obesity (Fabris de Souza et al., 2005). Structural abnormalities of the spine may lead to poor 
ambulation, and by extension muscular deconditioning (Narouze & Souzdalnitski, 2015). In fact, 
obesity in combination with low back pain may negatively affect gait more than obesity alone 
(Cimonlin et al., 2011). 
 

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Metabolic and Inflammatory Mechanisms 
 
Adipose tissue is metabolically active and, like other endocrine organs in the body, 
produces and releases adipokines and proinflammatory cytokines. It has been suggested that 
these endocrine changes may act as chemical mediators in the relationship between obesity and 
pain (Okifuji & Hare, 2015; Ronti, Lupattelli, & Mannarino, 2006). Indeed, prior studies have 
found that obesity is associated with elevated levels of inflammatory markers such as 
interleukin-6 (IL-6), interleukin-1 (IL-1), C-reactive protein (CRP), and tumor necrosis factor-
alpha (TNF-a), as well as elevated levels of macrophages which are known to be associated with 
inflammation (Wellen & Hotamisligil, 2003). These data are consistent with the assertion that 
obesity is associated with chronic low-grade inflammation, which may in turn contribute to pain. 
High concentrations of CRP have been found to increase odds of experiencing low back pain, 
especially among individuals with obesity (Briggs, Givens, Schmitt, & Taylor, 2013). In 
addition, obesity is associated with greater potential for experiencing migraines, and 
inflammatory factors appear to be a mechanism (Bigal & Rapaport, 2012). Achieving weight 
loss through activities such as aerobic exercise may help individuals better cope with migraine 
via reduction of inflammatory markers that are associated with both obesity and migraine (Bigal 
& Lipton, 2006; Bigal & Rapaport, 2012). In fact, a recent study of women with obesity and 
chronic migraines who received bariatric surgery found that headache frequency and impairment 
from headaches decreased in the months following surgery, and that reduced inflammation may 
have contributed to the change (Novak et al., 2011).  
 
Many studies of obesity have focused on the role of leptin, a hormone that is both 
synthesized and secreted primarily by adipocytes. Obesity is associated with higher leptin levels 
(Considine, 2005). The primary role of leptin is to signal energy intake and energy stores in the 

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body to the central nervous system, allowing the hypothalamus to help maintain stable body 
weight. Elevated leptin levels have been found in individuals with obesity and osteoarthritis; and 
leptin appeared to contribute to additional inflammation and general joint damage, both of which 
may exacerbate pain (Vuolteenaho, Koskinen, & Moilanen, 2014).  
 
Dietary factors are associated with both inflammatory markers and leptin. Excessive food 
consumption, especially excessive consumption of saturated fatty acids, is associated with 
growth of adipose tissue, greater low-grade systemic inflammation, and leptin resistance. As a 
result, concentrations of leptin may increase, triggering inflammation and, by extension, 
promotion of joint damage that contributes to the experience of pain (Narouze & Souzdalnitski, 
2015; Penninx et al., 2004). 
 
Other hormone-like substances of relevance for obesity are the prostaglandins, a type of 
eicosanoid, or acid metabolite, formed through cyclooxegenase (COX) or a lipoxygenase 
metabolic pathway. Prostaglandins perform a variety of functions including, but not limited to, 
mediating the inflammatory response and regulating the lipolysis of adipose tissue. Research has 
shown that biosynthesis of prostaglandins is increased in acutely inflamed tissue (Ricciotti & 
FitzGerald, 2011), and that obesity is associated with overproduction of prostaglandins, thereby 
interfering with lipolysis of adipose tissue (Curtis-Prior, 1975). High levels of pro-inflammatory 
prostaglandins and cytokines have been observed in bones and joints that are overloaded due to 
elevated body weight (McVinnie, 2013). Thus, excess prostaglandin production may be evident 
in the adipose tissue growth and elevated inflammation characteristic of obesity. 
 
Prostaglandin E2 (PGE!) is of particular interest because it is a lipid mediator produced 
by the activity of COX that contributes to inflammatory pain and may have a significant impact 
on the processing of pain signals (Kawabata, 2011). Research has demonstrated that anti-

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inflammatory drugs inhibiting COX, such as ibuprofen and acetaminophen, are effective in 
reducing the production of prostaglandins such as PGE!. Reduced production of PGE! is 
associated with diminished inflammatory pain in various groups of patients with chronic pain, 
including rheumatoid arthritis, osteoarthritis, and migraine (Kawabata, 2011).  
Studies often determine prostaglandin levels from urine samples, but Benedetti and 
colleagues (2014) have successfully measured short-term change in prostaglandins, including 
PGE!, via salivary analysis among individuals exposed to a high-altitude environment where 
hypobaric hypoxia headache is more likely. Thus, systemic prostaglandin production can be 
measured to evaluate prostaglandin levels over time. 
Emotional Pain, Stigma, and Obesity 
While physical pain is typically characterized as negative sensations associated with 
disease, condition, or injury, emotional pain may be considered a sustained, distressing, and 
difficult to tolerate experience related to the self or perceived abilities (Meerwijk & Weiss, 2011; 
Merskey & Bogduk, 1994). Emotional pain, like physical pain, may signal a threat to well-being; 
therefore, acute emotional pain may signal a disruption in psychological state that helps 
individuals recognize a need to shift focus onto restoring well-being (Meerwijk & Weiss, 2011). 
However, chronic emotional pain results when adequate levels of self-efficacy, meaning, or 
social support cannot be maintained. Emotional pain also may result from negative evaluation of 
self and/or of social relationships with others (Athey & Overholser, 2016; Eisenberger & 
Lieberman, 2004). Both chronic physical pain and chronic emotional pain are related to feelings 
of hopelessness and incompetence (Jackson et al., 2014; Mee et al., 2011). Emotional pain also 
may occur in the context of conditions with heightened rates of physical pain; for example, 

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higher rates of depression are observed in patients with musculoskeletal conditions who report 
high levels of physical pain (Linton et al., 2011).  
Stigma is a common type of emotional pain with a social basis (i.e., social pain). Indeed, 
stigma may be considered a social process whereby an individual is assigned negative attributes 
based on possession of an undesirable characteristic (Goffman, 1963). Stigma commonly has 
been studied in a number of contexts (e.g., mental health, race), but substantial research has 
addressed weight stigma in the context of overweight and obesity. Weight stigma is a form of 
social rejection, which itself is positively associated with physical pain (Eisenberger, Lieberman, 
& Williams, 2003). Many western societies, including the United States, tend to view overweight 
and obesity as an undesirable characteristic because these body types are not consistent with the 
socially-defined concepts of beauty. Indeed, several studies have documented individuals with 
obesity as being rated less attractive than individuals with average weight (Puhl & Heuer, 2009). 
Among individuals with overweight or obesity, weight stigma may be triggered by exposure to 
stigmatizing experiences via images or social interactions reminding the individual that their 
weight is not considered to be socially acceptable.  
 
Three forms of stigma have been identified in the realm of weight stigma: experienced 
weight stigma, perceived weight stigma, and internalized weight stigma. Experienced weight 
stigma is defined as a clear act of weight-related discrimination towards an individual, such as 
being denied entry to an event due to excess weight. Perceived weight stigma is operationalized 
by the individual feeling discriminated against due to a weight-related comment or action by 
another, regardless of whether or not it was the intention of the speaker (e.g., doctor informing 
their patient that their BMI is categorized as overweight). Internalized weight stigma refers to the 
degree to which an individual believes negative weight-related attributes and/or stereotypes (e.g., 

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laziness, lack of intelligence, moral weakness) are true and applicable to themselves. These 
internalized attributes and stereotypes often may be negative attributes and stereotypes ascribed 
by society to individuals with higher body weights (Puhl & Heuer, 2009).  
 Recent studies have highlighted the prevalence of internalized weight stigma in 
particular, and its negative impacts among individuals with overweight or obesity. In a sample of 
over 3,500 adults with BMIs ranging from underweight to obese, approximately 20% endorsed 
elevated levels of internalized weight stigma as measured by the Modified Weight Bias 
Internalization Scale (Puhl, Himmelstein, & Quinn, 2018), with those with higher BMIs 
endorsing greater internalization. Additionally, 52% of individuals recruited from an obesity-
specific source, the Obesity Action Coalition (OAC), endorsed elevated internalized weight 
stigma scores, while only 18-20% of the non-obesity-specific sources endorsed elevated 
internalized weight stigma scores. The authors noted their results suggest a considerable portion 
of the population may be at risk for weight bias internalization, especially if they share certain 
characteristics often endorsed by the OAC subsample such as higher BMI, greater exposure to 
weight stigma, and attempting to lose weight.  
In an online experimental study concerning experiences and attitudes related to having 
excess weight, 260 community-residing adults with overweight or obesity were randomized to 
one of two induction conditions: experience or internalization of weight bias (Pearl & Puhl, 
2016). Participants in both groups began by reading a vignette describing a scenario in which an 
employee was denied a promotion at work due to being overweight. Following the vignette, 
participants completed two factual questions about vignette content to ensure they read it, and 
those who answered at least one question incorrectly were excluded from the study. 
Additionally, participants were asked to provide a rating on a seven-point scale of how qualified 

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and deserving the employee was of the promotion they were denied, with higher (greater 
number) ratings indicating greater perceived qualification and deservedness. Participants who 
reported ratings of one to three were excluded from the study, while those reporting ratings of 
four or greater were permitted to proceed as such ratings established that the participant 
perceived the employee to have experienced discrimination. Following these questions, 
participants in the experience condition read that the employee was upset due to their unfair 
treatment and were asked to write two to three sentences about a time when they experienced 
unfair treatment due to their weight. Participants randomized to the internalization condition read 
that the employee blamed themselves for being denied the promotion and felt worthless, as 
though they would not amount to anything due to their weight. Participants were then asked to 
write two to three sentences about a time they had similar thoughts and feelings about 
themselves due to their weight. Thus, although both conditions were designed to induce negative 
affect, the former encouraged the participant to focus on a triggering external event while the 
latter encouraged the participant to focus on personal thoughts and feelings. In the final portion 
of the online study, participants completed a series of self-report measures assessing affect, self-
esteem, and weight bias internalization. Results indicated that the internalization condition was 
associated with more negative outcomes than the experience condition, including greater 
negative affect, less positive affect, lower self-esteem, and greater weight bias internalization. 
Thus, weight bias internalization is associated with negative affect and lower self-esteem among 
individuals with overweight or obesity, especially when focus is directed to weight stigma-
related thoughts and feelings about the self. 
Research suggests that there are significant physical and psychological risks associated 
with weight stigma, including increased risk of depression, lower self-esteem, greater anxiety, 

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poorer body image, and suicidality (Schvey, Puhl, & Brownell, 2011). Weight stigma is also 
associated with unhealthy eating behaviors, such as eating in secrecy, refusing to diet, losing 
sense of control over eating, and binge eating. Weight stigma also may interfere with one’s 
motivation to engage in behaviors that would facilitate weight loss such as exercise and general 
physical activity. Indeed, among individuals enrolled in behavioral weight loss programs, greater 
weight stigma is associated with less energy expenditure during exercise, greater caloric intake, 
greater program attrition, and less weight loss (Carels et al., 2009). Although the mechanisms in 
these relationships are unknown, Carels and colleagues postulate that negative implicit and 
explicit weight-related attitudes held by those attempting to lose weight, negative affect, low 
self-efficacy, and/or low social support may be relevant. To further illustrate the problematic 
relationship between obesity and weight stigma, Tomiyama (2014) constructed the cyclic 
obesity/weight-based stigma (COBWEBS) model. This model specifically illustrates a negative, 
cyclical interaction between obesity and weight stigma. Tomiyama theorized that a positive 
feedback loop exists in this cycle such that weight stigma, an inherently negative emotional 
experience, contributes to weight gain through increased stress. The model illustrates how 
experiencing the stress of weight stigma is associated with behavioral (e.g., increased eating; 
Adam & Epel, 2007), biochemical (e.g., increased cortisol secretion; Schvey, Puhl, & Brownell, 
2014; Tomiyama et al., 2014), cognitive (e.g., reduced executive control; Major, Eliezer, & 
Rieck, 2012), and physiological (e.g., sensitization to food reward; Adam & Epel, 2007) 
reactions that may directly or indirectly contribute to weight gain and impair weight loss, thus 
increasing likelihood of encountering future experiences that may trigger weight-based stigma 
(Tomiyama, 2014). 

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Recent research has examined the relationship of bodily pain to obesity and weight 
stigma. Pearl and colleagues (2016) found a positive relationship between internalized weight 
stigma and self-reported bodily pain among individuals with obesity (mean BMI = 42.2±12.1) 
seeking treatment for binge eating disorder. Recent research by Olson and colleagues (2018) has 
built on this finding in a sample of overweight and obese women enrolled in a weight loss 
intervention addressing body image. Results of their study revealed a positive relationship 
between BMI and bodily pain. In addition, both perceived and internalized weight stigma were 
associated with bodily pain. Among the overweight (BMI 25-29.9) subsample, perceived weight 
stigma mediated the relationship between BMI and bodily pain, but internalized weight stigma 
did not.  
Pain Processing in the Brain 
 
Often individuals who experience emotional pain such as social rejection describe their 
pain with words that are commonly used to describe physical pain (e.g., “hurt,” “wounded;” 
DeWall et al., 2010). Interestingly, commonalities between physical and emotional pain may 
extend beyond descriptive terminology. It has been suggested that the neurobiological systems 
through which physical pain is processed may be the same mechanisms through which emotional 
pain is processed (Eisenberger & Lieberman, 2004).  
 
Contemporary research has utilized neuroimaging techniques to evaluate the 
commonalities between emotional and physical pain. These studies have implicated the dorsal 
anterior cingulate cortex (dACC) and anterior insula. Increased activation is observed in these 
brain areas during both emotional (e.g., social rejection, social loss) and physical pain 
experiences (DeWall et al., 2010). In nonhuman primate research, lesions to the dACC and 
ventral anterior cingulate cortex are associated with reduced distress vocalizations after 

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separating animals from their caregiver or social group, whereas stimulation of these brain areas 
elicits greater distress vocalizations. Similar observations have been made in human subject 
studies. Increased activity of the dACC and anterior insula is observed among individuals 
following social rejection during a virtual ball-tossing game (Eisenberger et al., 2003); and 
bereaved individuals viewing pictures of the deceased exhibit increased activation in the dACC 
and anterior insula (O’Conner et al., 2008) 
Thus, the literature supports the assertion that there are shared pathways involved in 
emotional/social and physical pain processing. In turn, it is possible that emotional pain, such as 
social rejection or weight stigma, could potentiate or exacerbate the experience of physical pain. 
Thus, methods for alleviating physical pain may be useful in addressing emotional pain such as 
social rejection or weight stigma. 
Pain Management 
Given the prevalence of chronic pain and its association with several negative physical 
and psychological health outcomes, identifying and disseminating effective pain management 
methods is of utmost importance. The goal of pain management is to modulate the experience of 
pain or response to pain using one or more approaches (e.g., pharmacological, behavioral) in 
order to reduce the pain as much as possible, or to improve self-efficacy in pain management 
(Takai, Yamamoto-Mitani, & Suzuki, 2015).  
Several psychological strategies have been identified that are effective for pain 
management in a variety of conditions. Biofeedback and mindfulness-based stress reduction are 
two examples of strategies that may be delivered on their own to aid in pain management, but 
also are commonly delivered in conjunction with other forms of pain management. Broadly, 
these approaches help the patient react to, and cope with, physical pain and any accompanying 

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emotional pain in a more adaptive manner (Hylands-White, Duarte, & Raphael, 2017). Cognitive 
behavioral therapy (CBT) is a psychological approach to pain management that encourages 
patients to re-conceptualize their experience of pain symptoms in order to develop more realistic 
perceptions, adaptive behaviors, and positive functioning. Patients are encouraged to exert 
control over pain symptoms by employing cognitive and behavioral skills. Although CBT does 
not usually reduce pain per se, it is associated with improved self-efficacy and quality of life, and 
reduced distress (Hylands-White, Duarte, & Raphael, 2017). 
Many researchers have studied the effectiveness of pharmacological therapies for chronic 
pain. This approach is an effective pain management strategy for many individuals with chronic 
pain, but adverse side-effects of long-term pharmacological therapy use have been reported 
(Finnerup, Sindrup, & Jensen, 2010; Planton & Edlund, 2010). Despite these risks, 
pharmacological therapy is still often used as a stand-alone treatment for chronic pain or in 
conjunction with other pain management methods. In treating pain with pharmacology, 
physicians may follow a system that matches, or is similar to, the World Health Organization 
(WHO) analgesic ladder (Ventadridda, Saita, Ripamonti, & De Conno, 1985). This system was 
initially developed for treatment of cancer pain, but can be applied to most pain conditions, and 
suggests that analgesic medications should be administered with increasing potency until the 
individual achieves pain relief (World Health Organization, 2015). It is suggested that pain relief 
efforts begin with administration of non-steroidal anti-inflammatory drugs, which share 
similarities with acetaminophen in that they produce pain relief via reductions in inflammation 
and products of inflammation (e.g., prostaglandins). Should this not achieve pain relief, weak 
opioids may be considered, with strong opioids serving as a last resort. Additionally, 
corticosteroids such as prednisone and dexamethasone may be used as adjuvant analgesics due to 

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their anti-inflammatory properties. Corticosteroids have been found to provide pain relief in a 
variety of conditions such as neuropathic and bone pains (Vyvey, 2010). 
One of the most popular analgesics for acute and chronic pain in the United States is 
acetaminophen, the active ingredient in Tylenol. An estimated 23% of adults in the U.S. 
consume a drug that contains acetaminophen on a weekly basis (Kaufman, Kelly, Rosenberg, 
Anderson, & Mitchell, 2002). The effectiveness of acetaminophen in treating pain has been 
documented in several randomized controlled trials among patients with chronic or acute pain 
(e.g., arthritic, postoperative), as well as among adults with experimentally-induced pain (e.g., 
cold pressor; Mischkowski, Crocker, & Way, 2016). The mechanism through which 
acetaminophen provides pain reduction is uncertain, but it is widely accepted that pain reduction 
occurs via mechanisms in the central nervous system such as the dACC and anterior insula 
(DeWall et al., 2010) which are known to play a role in emotional, and particularly social, pain. 
Due to the association of pain with obesity, several pain management approaches have 
been tested for their effectiveness in individuals with obesity. Weight reduction via diet and 
exercise have been associated with improved quality of life and reduced bodily pain in patients 
with obesity suffering from fibromyalgia (Senna, Sallam, Ashour, & Elarman, 2012), and weight 
reduction has been associated with diminished pain and improved physical performance among 
patients with obesity suffering from knee osteoarthritis (Messier et al., 2004). Physical 
rehabilitation via physical exercise (including aquatic therapy) has facilitated reductions in pain 
(Narouze & Souzdalnitski, 2015). Other strategies noted to reduce pain among individuals with 
obesity include acupuncture, CBT, and patient education regarding adaptive lifestyle changes to 
facilitate weight loss and better pain management (Narouze & Souzdalnitski, 2015). 
Pharmacotherapy also may be utilized to help patients with obesity manage pain. Medications 

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such as gabapentin or pregabalin may help patients with obesity and pain maintain activity levels 
to help prevent significant weight gain (Cabrera et al., 2012). Duloxetine also may help patients 
with obesity cope with pain from fibromyalgia, chronic musculoskeletal pain, and peripheral 
neuropathic pain from diabetes (Narouze & Souzdalnitski, 2015). Long-term opioid use is not 
recommended for patients with obesity due to lack of efficacy and safety concerns. Additionally, 
while adjuvant corticosteroids may assist with pain relief, common and problematic side-effects 
include increased appetite and weight gain (Vyvey, 2010). 
Acetaminophen and Emotional Pain 
 
Research on pain medications such as acetaminophen has traditionally examined physical 
pain, but recent studies documenting the overlap in physical and emotional pain processing have 
led to examining the effects of acetaminophen on emotional pain. An early study provided 
evidence that acetaminophen reduces social pain among healthy undergraduate students (DeWall 
et al., 2010) who were randomly assigned to receive 1,000mg of acetaminophen daily (500mg 
pill upon waking and 500mg pill one hour before bedtime) for three weeks, or placebo pills to be 
taken for the same amount of time in the first of two experiments. At the end of each day during 
the three-week period, participants rated total social pain experienced during that day. Results 
indicated that social pain decreased over the three-week span for those taking acetaminophen, 
but ratings did not change in the placebo group. In the second experiment conducted by DeWall 
and collages (2010), participants were randomly assigned to receive 2,000mg of either 
acetaminophen or placebo in pill form for three weeks, after which they completed a social 
exclusion task while undergoing an fMRI scan. Results indicated that participants who were 
randomly assigned to the acetaminophen condition exhibited less activity in the dACC and 

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anterior insula in response to social exclusion relative to participants assigned to the placebo 
condition (DeWall et al., 2010).  
Whereas DeWall and colleagues studied only social pain, a more recent study examined 
the influence of acetaminophen on both positive and negative affect among undergraduate 
students (Durso, Luttrell, & Way, 2015) who were randomized to receive either an acute dose of 
acetaminophen (1,000mg) or placebo via liquid vehicle. After waiting 60 minutes, participants 
provided their emotional reactions to a series of pleasant and unpleasant images. Participants in 
the acetaminophen condition rated unpleasant images less negatively, but also rated positive 
images less positively, compared to those in the placebo condition. Those in the acetaminophen 
condition also rated images less emotionally stimulating than those in the placebo condition. 
Thus, the results suggest that acetaminophen may exert a blunting effect on processing of 
emotionally-provocative stimuli. 
Other Psychological Outcomes 
More recent research has sought to examine effects of acetaminophen on other 
psychological variables and processes. Roberts and colleagues (2017) investigated the impact of 
a 1000mg dose of acetaminophen versus a placebo on distrust, a common feature of borderline 
personality disorder (BPD) that can contribute to the interpersonal difficulties often observed 
among individuals with the disorder. Results revealed that distrust remained high among 
individuals with elevated BPD features who were administered a placebo, but increased trust was 
observed among those who were administered acetaminophen.  Empathy also has been a focus of 
acetaminophen research, based on functional magnetic resonance imaging data suggesting that 
the brain areas (e.g., ACC, anterior insula) activated when experiencing pain are also activated 
when observing others experiencing pain (Lamm, Decety, & Singer, 2011). Mischkowski, 

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Crocker, and Way (2016) found that 1,000mg of acetaminophen reduced empathy for another’s 
pain. Participants were administered acetaminophen or a placebo, after which they read a 
passage about someone experiencing social or physical pain, observed an individual being 
ostracized, or observed someone receiving a painful noise blast. Those administered 
acetaminophen reported less empathy in response to pain observed in another or pain they read 
about another receiving. When participants were exposed to a painful noise blast themselves, 
acetaminophen was associated with lower ratings of unpleasantness compared to the placebo 
condition. 
Thus, acetaminophen has been associated with decreased social pain and reduced 
negative affect (DeWall et al., 2010; Durso et al., 2015). Such findings may be particularly 
relevant for individuals with overweight or obesity who are more likely to experience the social 
pain of weight stigma. However, no study has explored the effects of acetaminophen on the 
emotional pain of weight stigma among individuals with overweight or obesity. Although 
previous research has demonstrated that the relationship between BMI and physical pain is 
mediated by perceived weight stigma among women with overweight (Olson et al., 2018), the 
sample was relatively small (total n = 61; overweight n = 26). Therefore, it is important to 
replicate this finding, as well as to further investigate in a larger sample the relevance of 
internalized weight stigma as a mediator in the BMI-bodily pain relationship. Additionally, the 
possible role of an analgesic such as acetaminophen in moderating the relationship among BMI, 
weight stigma, and bodily pain has not been explored.  
Current Studies 
 
Two studies are proposed. Study one will evaluate the influence of a weight stigma 
induction on inflammation, pain, and distress among women with overweight and obesity, and 

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whether that effect is moderated by consuming acetaminophen. This study also will evaluate the 
relationship between BMI and pain, the degree to which the relationship is mediated by weight 
stigma, and the degree to which acetaminophen moderates the weight stigma-pain relationship. 
Study two will evaluate whether inflammation, pain, and distress are equivalent among three 
groups exposed to a weight stigma induction: women with overweight and obesity following an 
acute dose of acetaminophen, women with overweight and obesity following an acute dose of a 
placebo solution, and women with normal weight following an acute dose of a placebo solution. 
Aims and hypotheses for both studies are described below, with hypotheses identified as 
‘primary’ or ‘secondary’. Primary hypotheses reflect essential research questions while 
secondary hypotheses are exploratory analyses considered to be less central to the objectives of 
the two studies. 
Study One 
Aim #1: Evaluate the interrelationship of variables including BMI, inflammatory 
markers, distress, pain, affect, weight stigma, exposure to weight-based stigmatizing 
experiences, impact of weight on quality of life, and health-related quality of life. 
 
Primary Hypotheses: 
 
1a) At baseline, BMI will be positively associated with prostaglandins, CRP, 
internalized weight stigma, perceived weight stigma, exposure to weight-based 
stigmatizing experiences, impact of weight on quality of life, pain, and 
negative affect; and negatively associated with health-related quality of life and 
positive affect. 
 
1b) At baseline, levels of inflammatory markers (prostaglandins, CRP) will be 
positively associated with pain, negative affect, internalized weight stigma, 

21 
 
perceived weight stigma, impact of weight on quality of life, and exposure to 
weight-based stigmatizing experiences; and negatively associated with health-
related quality of life and positive affect.  
Secondary Hypotheses: 
1c) The relationship between BMI and baseline pain will be mediated by 
experienced weight stigma (see Figure 1). 
1d) The relationship between BMI and baseline pain will be mediated by 
perceived weight stigma (see Figure 2). 
1e) The relationship between BMI and baseline pain will be mediated by 
internalized weight stigma (see Figure 3). 
 
1f) Effects of the weight stigma induction on the relationship between BMI and 
post-induction pain will be mediated by internalized weight stigma (see Figure 
4). 
i. In this mediation model, the relationship between internalized weight 
stigma and post-induction pain will be moderated by acute 
acetaminophen ingestion (see Figure 5). 
Due to the cross-sectional design of the study, exploratory analyses will be used to 
evaluate the three types of weight stigma (experienced, perceived, and internalized) as 
moderators in the relationship between BMI and baseline pain. Additionally, exploratory 
analyses will evaluate internalized weight stigma as a moderator in the relationship between BMI 
and post-induction pain, as well as the moderating effect of acute acetaminophen ingestion in 
this relationship (i.e., moderated moderation).  

22 
 
Aim #2: Evaluate differences in affect, physical pain, distress, and inflammatory 
markers among four groups: those exposed to the weight stigma induction with either 
(1) acetaminophen or (2) a placebo solution, and those exposed to the control 
induction with either (3) acetaminophen or (4) a placebo solution. 
Primary Hypotheses: 
2) Participants randomized to receive the weight stigma induction and placebo 
conditions will report greater physical pain, distress, and negative affect; lower 
positive affect; and higher levels of prostaglandins following completion of the 
induction compared to the other three groups. No differences are expected on 
these dimensions among the other three conditions. 
Study Two 
Aim #3: Evaluate differences in affect, physical pain, distress, and inflammatory 
markers among three groups exposed to a weight stigma induction: participants with 
overweight or obesity randomized to acetaminophen or a placebo solution, and a 
sample of adults with normal weight given a placebo solution. 
Primary Hypotheses: 
3a) Among the three groups post-induction, those with overweight or obesity 
randomized to the placebo condition will report greater pain, distress, negative 
affect, and prostaglandins; and lower positive affect than individuals with 
overweight or obesity randomized to acetaminophen or individuals with 
normal weight. No differences are expected in pain, distress, positive and 
negative affect, and prostaglandins between the overweight or 
obese/acetaminophen group versus the normal weight group. 

23 
 
Secondary Hypotheses: 
3b) Individuals in the overweight or obese groups will report greater baseline 
pain, distress, internalized weight stigma, perceived weight stigma, exposure to 
weight-based stigmatizing experiences, impact of weight on quality of life, and 
negative affect; and worse health-related quality of life and positive affect 
compared to participants with normal weight. The overweight or obese group 
also will demonstrate higher levels of prostaglandins and CRP at baseline 
compared to the normal weight group. 
 

24 
 
Chapter 2: Methods 
Participants and Recruitment 
 
160 women who are categorized as overweight (BMI 25-29.9) or obese (BMI ³ 30) will 
be recruited for study one and study two, and an additional 40 females with normal weight (BMI 
18.5-24.9) will be recruited for comparison in study two. Women will be the focus of study 
because women report more weight-related stigmatization than men and are more likely to 
internalize weight stigma (Boswell & White, 2015). Participants will be recruited from multiple 
sources: the NIH-funded ResearchMatch database, an internet site designed to help research 
investigators identify potentially eligible participants; StudySearch, an internet site coordinated 
by the Ohio State University Center for Clinical and Translational Science that allows 
community-residing individuals to identify research studies for which they may be eligible; and 
MyChart for Recruitment, another recruitment service coordinated by the Ohio State University 
Center for Clinical and Translational Science that uses an Honest Broker to identify potentially 
eligible participants among those enrolled in OSU’s medical MyChart system and sends those 
individuals a study advertisement. The Psychology 1100 Research Experience Program at Ohio 
State University will serve as another source of participants for both studies. Flyers advertising 
both studies will be placed in various locations on Ohio State University’s Columbus campus 
and in the surrounding Columbus community. An additional source of participants with 
overweight or obesity for study one will be women enrolled in the “Living Well” program, a 
behavioral weight management program offered at The Ohio State University Center for 
Wellness and Prevention. All eligible participants for both studies will be women who are at 
least 18 years of age. Overweight or obese participants will be stratified by age (18-40 years vs. 
over 40 years) and BMI (BMI = 25-29.9 vs. BMI ³ 30) for randomization into one of four study 

25 
 
conditions. Following recruitment of overweight or obese participants for studies one and two, a 
cohort of participants with normal weight will be recruited for study two.  
Procedures – Study One (Participants with Overweight or Obesity Only) 
Individuals who express interest in the study will be contacted via telephone to complete 
a brief, ten-minute prescreening assessment to evaluate eligibility for the study. Information 
concerning age and sex will be collected to determine eligibility. Information regarding 
pregnancy status also will be collected; those endorsing current pregnancy will be excluded due 
to possible harm of acetaminophen use. Additionally, information regarding vision deficits and 
reading level will be obtained; those reporting vision deficits that prevent reading (e.g., 
blindness) or reporting less than a ninth-grade reading level will be excluded due to the impact 
these factors would have on the individual’s ability to read study vignettes and questionnaires. 
The Patient Health Questionnaire – 9 will be administered to assess for depressive symptoms; 
individuals scoring 15 or greater (i.e., moderate/severe depressive symptoms) will be ruled 
ineligible due to the potential influence of these symptoms on their reaction to study vignettes 
and responses to questionnaire items. Other exclusionary criteria are risk factors associated with 
acetaminophen use such as currently taking a drug containing acetaminophen, history of liver 
disorder and/or alcohol abuse, and allergic reaction to acetaminophen (Mischkowski et al., 
2016). Individuals also will be asked for estimated height and weight measurements to ensure 
they are eligible for study one (i.e., BMI in the overweight or obese categories). Individuals 
reporting weights exceeding 400 pounds will be excluded due to limitations of the laboratory 
weight scale. Eligible women will be asked to refrain from consuming food for three hours prior 
to their participation in the study to facilitate drug absorption (Roberts et al., 2017). 

26 
 
Participants will have their height and weight measured by study personnel upon arrival 
to confirm the BMI estimate calculated in the prescreening. This information also will be used to 
confirm the participant’s stratification group for randomization. Following completion of 
physical measurements, participants will read and sign a study consent form. Participants will be 
given a copy of the consent form to keep for their own records. Following completion of study 
consent, study personnel will collect blood and saliva samples from participants for CRP and 
prostaglandin measurements, respectively. Next, participants will be led by study personnel to a 
room where they will complete the remainder of the experiment. Upon arrival at the room, 
participants will be randomized to receive either 10mL of acetaminophen (100 mg/mL) in liquid 
suspension, or 10mL of placebo (microcrystalline powder) in the same solution. The 
acetaminophen or placebo solution will be given to participants in plain cups and they will be 
instructed to ingest the solution as quickly as they can. Consistent with prior studies evaluating 
the effect of an acute dose of acetaminophen on psychological outcomes, participants will be 
required to wait 60 minutes after administration of the acetaminophen or placebo solution to 
allow sufficient time for absorption (Durso et al., 2015; Randles et al., 2013). Immediately 
following administration of the solution, participants will be given a packet of self-report 
measures that will require approximately 25 minutes to complete and that they may work on 
while waiting the required 60 minutes for drug absorption. After completion of self-report 
measures, participants will be asked to remain seated quietly until 60 minutes following 
administration of the solution. After 60 minutes has elapsed, participants will then be 
randomized to a weight stigma induction or a control induction.  
The internalized weight stigma induction for the current study was created by Pearl and 
Puhl (2016) and is referenced in the introduction portion of this proposal (see pages 10-11). The 

27 
 
weight stigma induction consists of two parts. First, participants will read a vignette describing a 
scenario in which an employee has been denied a promotion at their place of employment due to 
being overweight.  
After reading the vignette, the piece of paper on which it is typed will be taken from the 
participant who will then answer two factual questions to ensure they read the vignette. 
Participants answering one or both factual questions incorrectly will be excluded from study 
analyses. Participants also will be asked to rate on a seven-point scale how qualified and 
deserving the employee was of the promotion they were denied, with higher scores reflecting 
greater perceived qualification and deservedness. Next, participants will be given another piece 
of paper expressing that the employee felt stigmatized due to their weight. Participants will then 
be instructed to write 2-3 sentences describing a time when they may have had similar thoughts 
and feelings about themselves due to their weight, if there has been such a time.  
The goal of the control induction for the current study will be to mirror the procedures of 
the weight stigma induction but with material that is not likely to evoke positive or negative 
emotion. The control vignette only describes the procedures involved in a company’s promotion 
process. After reading the vignette, the paper on which it is typed will be taken from the 
participant. Next, participants will answer two factual questions about the vignette to ensure they 
read it. Participants answering one or both factual questions incorrectly will be excluded from 
study analyses. Additionally, participants will rate on a seven-point scale how qualified and 
deserving the employee was of the promotion, with higher rating reflecting greater perceived 
qualification and deservedness. Following completion of these items, participants will be given 
another piece of paper and instructed to write 2-3 sentences describing whether they believe the 
promotion process described in the vignette is reasonable and how they would change it, if at all. 

28 
 
Immediately following completion of the weight stigma induction or control induction, 
participants will complete a second packet of self-report measures. Following completion of self-
report measures, participants will be taken to the room where they began their visit for collection 
of a second saliva sample. Study personnel will then provide participants with study payment, 
inform them of the nature of the experiment, and debrief the participant. During debriefing, study 
personnel will ask the participant which solution (acetaminophen or placebo) they believed they 
consumed earlier in the session, and will record their answer. The visit will then be concluded. 
Table 1 illustrates the study conditions and participant groups included in study 1, and a flow 
diagram of the experimental sessions is shown in Figure 6. 
Procedures – Study Two (Participants with Normal Weight Only) 
Prescreening procedures for study two will be identical to study one but with one 
addition. Individuals recruited for the normal weight group will be screened for history of 
overweight or obesity. Those endorsing a history of overweight or obesity will be excluded from 
the study due to their weight history potentially affecting their perceptions of weight stigma. 
BMI will be calculated using participant height and weight estimates to ensure that these 
individuals are in the normal weight range (i.e., BMI 18.5-24.9). 
Procedures prior to the induction will be the same in study two as in study one, with one 
important exception. Rather than randomization to acetaminophen or placebo solutions, all 
participants with normal weight will receive the 1,000mg placebo solution. Participants will still 
wait 60 minutes before proceeding to the induction task, during which they will complete the 
same self-report measures as those completed by participants in study one at baseline. 
Participants in study two will not be randomized to a weight stigma induction or control 
induction; instead, all participants will be exposed to the weight stigma induction. All other 

29 
 
procedures during and after the induction will be the same for study two as in study one. Table 2 
illustrates the study conditions and participant groups included in study 2. 
Measures 
All participants in both studies will complete a demographic and medical history 
questionnaire which will include information regarding age, gender, marital status, race, level of 
education, current employment, health history, personal weight history, and relationships with 
individuals with overweight or obesity. 
The following objective and self-report measures will be completed by participants in 
both studies: 
Objective Measures: 
Body Mass Index (BMI): Height and weight of each participant will be recorded. An individual’s 
body mass index is a ratio of weight (in kilograms) divided by the square of height (in meters). It 
is a commonly used indicator of percentage of body fat. Height and weight will be measured 
with a portable stadiometer and a digital high-capacity scale.  
Prostaglandins: Prostaglandin level (PGE!) will be utilized as an objective marker of 
inflammation. Study personnel will first provide the participant with a small medicine cup of 
water which the participant will consume 10 minutes prior to saliva collection. During saliva 
collection, participants will be asked to provide saliva through a small straw which will funnel 
into a small test tube. The total amount of saliva needed is approximately half a teaspoon. 
Participants struggling to produce saliva may be encouraged to think about their favorite food or 
imagine tasting a lemon to increase saliva production. For saliva analysis, an assay similar to that 
used by Benedetti and colleagues (2014) will be used to measure prostaglandins per milliliter. 

30 
 
Prostaglandins will be measured twice: once upon arrival (baseline) and a second time at 
completion of the visit (post-induction). Salivary levels of prostaglandins will be measured using 
an ELISA kit from Cayman Chemical (Product No. 514010) and read on a Fisher Scientific 
Multiskan FC microplate reader. 
C-Reactive Protein (CRP): CRP is found in blood plasma and will serve as another objective 
marker of inflammation. Study personnel will begin by cleaning a finger on the participant’s 
non-dominant hand with an isopropyl alcohol wipe. Participants will then be asked to wave their 
hand to increase circulation to the area. Next, study personnel will use a contact-activated 
disposable lancet on the participant’s finger in order to collect a blood sample. The lancet is 
designed to deliver a controlled, uniform puncture that provides a combination of sufficient 
capillary blood flow and minimal injury. Five blood drops from the finger will be applied to a 
blood spot card. Study personnel will facilitate the production of these blood drops by rubbing 
down on the participant’s finger. Following collection of blood spots, the participant’s finger will 
again be cleaned with an isopropyl alcohol wipe, and a bandage will be applied to the finger. 
CRP will be measured at baseline only. CRP (in milligrams per deciliter) will be measured in 
dried blood spots using the Meso QuickPlex SQ 120 (Meso Scale Discovery, 1601 Research 
Boulevard, Rockville, MD) with V-Plex kits purchased from Meso Scale Discovery. The average 
intra-assay coefficient of variation is 2.9% and the average inter-assay (run-to-run) coefficient of 
variation is 7.9%. 
Primary Self-Report Measures 
The following self-report measures will be completed using Qualtrics Survey Software at 
baseline and post-induction, unless otherwise noted. Participants may request to complete hard 
copy forms of the self-report measures if they prefer. Of note, weight stigma-related measures 

31 
 
will appear first in the order of measures to minimize the influence of those measures on the 
induction to which the participant is randomized. Additionally, all post-induction measures will 
be administered in random order: 
Pain 
McGill Pain Questionnaire (MPQ; Melzack, 1975): The MPQ is a measure designed to assess 
present aspects of pain (Pain Rating Index) and pain intensity (Present Pain Intensity scale) 
among adults. The Pain Rating Index contains 78 pain descriptor items that are grouped into four 
major pain subscales: sensory, affective, evaluative, and miscellaneous. Each descriptor has a 
value score assigned to it, with higher scores representing more severe pain. The Present Pain 
Intensity scale measures magnitude of overall pain intensity experienced by an individual, and 
includes six levels: none (0), mild (1), discomforting (2), distressing (3), horrible (4), and 
excruciating (5). Total Pain Rating Index scores range from 0-78 and Present Pain Intensity 
scores range from 0-5; in both scales, higher scores reflect greater pain severity. The MPQ has 
demonstrated good validity, reliability, consistency, and usefulness in assessing aspects of pain 
and pain intensity (Katz & Melzack, 2011).  
Visual Analog Scale for Pain (VAS-Pain): The VAS-Pain is a unidimensional measure of 
physical pain intensity that has been used in a variety of adult populations. The continuous scale 
consists of a line 100 millimeters in length, anchored by two descriptors representing the 
extremes of pain (i.e., “no pain,” versus “my pain is as bad as it could possibly be”). Participants 
are asked to place a mark on the line to indicate their current level of pain. VAS-Pain scores are 
determined by using a ruler to measure the distance in millimeters between the “no pain” anchor 
and the participant’s mark. Scores range from 0-100, with higher scores indicating greater pain 

32 
 
intensity. The VAS-Pain demonstrates good test-retest reliability and construct validity. It is also 
sensitive to changes in pain assessed in intervals as short as one hour (Hawker et al., 2011).  
Stigma 
Stigma Situations Inventory (SSI; Myers & Rosen, 1999): The SSI is a 50-item questionnaire 
examining lifetime frequency of obesity-related experiences across eleven different categories: 
comments from children, from strangers, from family and from doctors; social exclusion; being 
stared at; loved ones being embarrassed by your size; negative assumptions that people make; 
physical barriers or obstacles; job discrimination; and physical violence. Respondents report 
frequency of each item on a scale of 0 (never) to 9 (daily). The mean of all items is typically 
used to measure overall frequency of weight stigma experiences, with greater values suggesting 
greater experience of weight stigma. The SSI has been utilized successfully as a measure of 
weight-based stigmatization, as well as consequences of those experiences (Vartanian, 2015). 
SSI will be measured once, prior to the induction task (baseline). 
Weight Bias Internalization Scale (WBIS; Durso & Latner, 2008): The WBIS is an 11-item scale 
measuring the degree to which individuals believe that anti-fat attitudes are relevant to the self. 
Items are answered on a 7-point likert scale ranging from 1=strongly disagree to 7=strongly 
agree, with total scores ranging from 11 to 77. The WBIS has demonstrated high internal 
consistency and strong correlation with measures of drive for thinness, body image concern, and 
self-esteem (Durso & Latner, 2008). WBIS will be measured once, prior to the induction task 
(baseline). 
Stigma Impact Scale (SIS; Fife & Wright, 2000): The SIS is a 24-item questionnaire that was 
originally developed to evaluate various facets of stigma and assess the impact of perceived 

33 
 
stigma among individuals with chronic illnesses. Each item is rated on a 5-point likert scale 
ranging from 0=not applicable to 4=strongly agree. The questionnaire includes four subscales 
representing social rejection, financial insecurity, internalized shame, and social isolation. Total 
scores range from 0 to 96. Subscale scores range from 0 to 36 for 32 social rejection, from 0 to 
12 for financial insecurity, from 0 to 20 for internalized shame, and from 0 to 28 for social 
isolation. The term ‘illness’ will be changed to ‘condition’ to increase applicability of item 
content for participants in this study. Previously published data from our lab has demonstrated 
high reliability when following this procedure (Cronbach’s alpha = 0.93; Olson et al., 2018). 
Quality of Life 
Medical Outcomes Survey, Short Form-36 (SF-36; Ware & Sherbourne, 1992): The SF-36 is a 
36-item measure of health-related quality of life. Two composite scores can be generated: the 
mental component score (MCS) and the physical component score (PCS). T-scores are generated 
for component scores. The physical component score is heavily weighted by four subscales 
assessing physical functioning, physical role functioning, general health, and bodily pain, the 
latter of which will be utilized as another measure of baseline pain in analyses. The mental 
component score is heavily weighted by four subscales assessing vitality, social role functioning, 
emotional role functioning, and mental health. Each component score is transformed into a 0-100 
scale with higher scores indicating better health-related quality of life. The SF-36 is an 
internationally-validated instrument for measuring health-related quality of life and has been 
utilized in a variety of sample types, including individuals with obesity (Callegari, Michelini, 
Sguazzin, Catona, & Klersy, 2005; Emery et al., 2015). SF-36 will be measured once, prior to 
the induction task (baseline). 

34 
 
Impact of Weight on Quality of Life-Lite (IWQOL-L; Kolotkin, Crosby, Kosloski, & Williams, 
2001): The IWQOL-Lite is a 31-item measure of obesity-related quality of life. Each item is 
rated on a five-point Likert scale. Total scores range from 31 to 155 with higher scores reflecting 
greater impact of weight on quality of life. Also, five subscales can be scored for the IWQOL-
Lite: physical function, self-esteem, sexual life, public distress, and work. The IWQOL-Lite has 
been tested in various samples, such as community-dwelling individuals with overweight or 
obesity, and has demonstrated good psychometric properties including high internal consistency, 
test-retest reliability, convergent validity, and discriminant validity (Kolotkin & Crosby, 2002). 
IWQOL will be measured once, prior to the induction task (baseline). 
Positive and Negative Affect Schedule (PANAS; Watson, Clark, & Tellegen, 1988): The PANAS 
is a 20-item measure of positive and negative affect. Each item is a single word reflecting an 
emotion (e.g., proud, excited, ashamed, upset) and respondents use a five-point Likert scale to 
indicate the degree to which they are experiencing each emotion during the present moment. 
Total scores for the negative affect and positive affect subscales range from 10-50. The PANAS 
subscales have demonstrated strong internal consistency and discriminant validity (Watson, 
Clark, & Tellegen, 1988). 
Visual Analog Scale for Distress (VAS-Distress): The VAS-Distress will be used to measure 
distress and will match the construction and scoring formats of the VAS-Pain. Instead of pain-
related anchors, the VAS-Distress will be anchored with “no distress” and “as distressed as I 
could possibly be” descriptors. 
Secondary Self-Report Measures 
The following self-report measures serve multiple purposes, including distraction to participants 

35 
 
following completion of the weight stigma-related measures at baseline, helping characterize the 
sample, helping identify relevant variables that may need to be included as control variables in 
study analyses, and/or serving as manipulation checks. Timing of administration is specified in 
the description of each measure. 
International Physical Activity Questionnaire – Short Form (IPAQ-SF; Craig et al., 2003): The 
IPAQ-SF measures physical activity and inactivity performed during the previous seven days. 
Respondents report the number of days, hours per day, and minutes per day in the last seven days 
they engaged in vigorous exercise, moderate exercise, walking, and sitting. The IPAQ-SF has 
demonstrated acceptable reliability and validity across diverse samples. IPAQ-SF will be 
measured once, prior to the induction task (baseline). 
Pittsburgh Sleep Quality Index (PSQI; Buysse et al., 1989): The PSQI is used to assess sleep 
behavioral (quality and disturbances) over a one-month interval. It has demonstrated strong 
internal consistency, and has shown good sensitivity and specificity in distinguishing good and 
poor sleepers. The PSQI yields a total score as well as the following seven subscales: subjective 
sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of 
sleep medications, and daytime dysfunction. PSQI will be measured once, prior to the induction 
task (baseline). 
Rosenberg Self-Esteem Scale (RSES; Rosenberg, 1965): The RSES is a 10-item measure used to 
assess overall self-esteem. Both positive and negative feelings about the self are measured. Items 
are answered on a Likert scale ranging from 0 (strongly agree) to 3 (strongly disagree), with 
higher scores indicating higher self-esteem. The scale has demonstrated good internal 
consistency. RSES will be measured once, prior to the induction task (baseline). 

36 
 
Hospital Anxiety and Depression Scale (HADS; Zigmond & Snaith, 1983): The HADS is a 14-
item measure developed to assess depression and anxiety among non-psychiatric medical 
patients. Respondents are asked to rate each item on a scale ranging from 0 (not at all) to 3 (most 
of the time). The HADS consists of a total distress score ranging from 0-42, and two subscales 
consisting of seven items each: anxiety (HADS-A; range 0-21) and depression (HADS-D; range 
0-21). Six items are reverse scored. In each subscale a score above 10 indicates clinically 
significant distress. The HADS is a validated measure with strong internal consistency in both 
overall and subscale measures (Bjelland, Dahl, Haug, & Neckelmann, 2002), and has been used 
in several studies involving overweight or obesity. HADS will be measured once, prior to the 
induction task (baseline). 
Binge Eating Scale (BES; Gormally, Black, Daston, & Rardin, 1982): The BES is a 16-item 
questionnaire designed to assess the behavioral (e.g., large amount of food consumed), as well as 
the cognitive and emotional (e.g., feeling out of control while eating), features of binge eating in 
adults with overweight or obesity. Respondents select one of three or four response options 
coded 0 to 2 or 3, respectively. Scores are summed and range from 0 to 46, with higher scores 
reflecting more severe binge eating problems. Scores greater than 17 reflect moderate binge 
eating severity, while scores greater than 26 reflect severe binge eating problems. The BES has 
demonstrated good internal consistency in a variety of samples with overweight or obesity 
(Cotter & Kelly, 2017). BES will be measured once, prior to the induction task (baseline). 
Need Threat Scale (NTS; van Beest & Williams, 2006): The NTS is a 12-item measure designed 
to assess one’s need for belonging, self-esteem, control, and meaningful existence, each of which 
represent a subscale of the measure. Respondents indicate how they are feeling right now using a 
five-point Likert scale ranging from “not at all” to “very much so.” The NTS has been used in 

37 
 
several studies on Cyberball-induced ostracism (e.g., Eisenberger et al., 2003) showing that such 
an induction threatens these needs. In addition to four subscale scores, a composite score is 
calculated by averaging responses across the 12 items. The NTS will be measured once, after the 
induction task (post-induction). 
Buckley Scale (BS; Buckley, Winkel, & Leary, 2004): The BS is a 12-item measure designed to 
assess feelings of happiness, anxiety, anger, sadness, and hurt feelings in the present moment. 
The scale is composed of five subscales for these five domains of emotion. Respondents indicate 
the degree to which they endorse a particular feeling using a seven-point Likert scale ranging 
from “not at all” to “extremely.” The BS will be measured twice, both before and after the 
induction task. 
Vividness of Recall: A single-item measure will be administered to assess vividness of 
participants’ recall of the instance when they themselves experienced weight stigmatization that 
they write about in the weight stigma induction. This single item is adapted from research by 
Valenti and colleagues (2011) and will be administered once, after the induction task (post-
induction). 
Sample Size Calculation 
 
Sample size for this study was determined using G-Power 3.1. No reported studies on 
effects of acetaminophen on weight stigma among individuals with normal weight, overweight, 
or obesity are available; therefore, studies comparing the effects of acetaminophen versus 
placebo on other emotional outcomes (i.e., affect, empathy for pain) were utilized to help 
determine sample size for the current study. Data from Durso and colleagues (2015) revealed a 
medium effect size (Cohen’s d = .55) for undergraduate students administered acetaminophen 

38 
 
(versus placebo) evaluating positive and negative images (as determined by the International 
Affective Picture System). Data from Mischkowski and colleagues (2016) also revealed a 
medium effect size (Hedge’s g = .45) in a study of empathic concern for others’ pain among 
undergraduate students administered acetaminophen versus placebo. Effect sizes from Durso et 
al. and Mischkowski et al. were transformed to Cohen’s F in order to complete a priori power 
analyses for an analysis of covariance with fixed effects, main effects, and interactions. These 
effect sizes were used to calculate power with an alpha of .05, one numerator degree of freedom, 
four groups, and two covariates, indicating a total sample size of 106 to 156 would result in 
power of .80. This sample size recommendation is consistent with the acetaminophen studies that 
informed the sample size calculations for Durso et al. and Mischkowski et al.; those studies 
recruited 30 to 50 participants per condition. 
 
Analyses also were conducted to estimate the effect size of the weight stigma induction. 
Because Pearl and Puhl (2016) compared WBIS scores after exposure to one of two weight 
stigma inductions, the effect size for the difference between the two inductions could not be 
utilized to determine sample size for the current study. Instead, the WBIS mean score after 
exposure to the internalizing weight stigma induction condition in their study was compared to 
baseline WBIS mean scores in other studies among participants with overweight and obesity 
(Durso, Latner, & Ciao, 2016; Lee & Dedrick, 2016; Pearl et al., 2018; Latner, Barile, Durso, & 
O’Brien, 2014) as well as similar, unpublished data from our lab. A range of effect sizes (f = 
.216 to .582) was revealed after calculating mean differences between baseline WBIS scores and 
the mean WBIS score from Pearl and Puhl (2016), with most indicating effect sizes at the higher 
end of the range. To obtain the most conservative estimate of sample size needed for the 
proposed study, one a priori power analysis was conducted for an analysis of covariance with 

39 
 
fixed effects, main effects, and interactions using an effect size of .216 with an alpha of .05, one 
numerator degree of freedom, four groups, and two covariates. The required total sample size 
was 171 in order to achieve a power of .80.  
 
Study 2 introduces individuals with normal weight. Therefore, additional analyses were 
conducted to estimate the effect of body weight category on emotional and psychological 
outcomes. Vieira and colleagues (2012) evaluated differences in several emotional and 
psychological variables among women with normal weight, overweight, class I obesity (BMI 30-
34.9), and class II obesity (BMI 35-39.9). Analyses revealed medium to strong effect sizes for 
greater weight-related quality of life (as measured by the IWQOL-Lite; f = .510), higher self-
esteem (as measured by the Rosenberg Self-Concept/Self-Esteem Scale (Rosenberg, 1965); f = 
.324), and lower body image dissatisfaction (as measured by the Body Image Assessment 
Questionnaire (Williamson, Davis, Bennett, Goreczny, & Gleaves, 1989); f = .813) among 
individuals with normal weight compared to those with overweight. Effect sizes were larger 
when comparing the normal weight category to class I and II obesity categories. In another 
study, experience and internalization of weight stigma were assessed among individuals with 
underweight or normal weight and among individuals with overweight or obesity (Lee, 
Gonzalez, Small, & Thompson, 2019). Medium effect sizes were detected for greater stigma 
internalization (as measured by the WBIS; f = .339) and stigmatizing experiences related to 
weight (as measured by the SSI; f = .339) among individuals with overweight or obesity 
compared to those with underweight or normal weight. A third study revealed a medium effect 
size for greater psychological well-being (as measured by Ryff’s assessment of psychological 
well-being (Ryff, 1989)) among women with normal weight compared to women with severe 
obesity (f = .286; Bookwala & Boyar, 2008). The aforementioned effect sizes were used in 

40 
 
conducting a priori power analyses for an analysis of covariance with fixed effects, main effects, 
and interactions with an alpha of .05, one numerator degree of freedom, three groups, and three 
covariates. The required total sample to achieve a power of .80 for study 2 ranged from 15 to 98. 
Therefore, the proposed sample size of 200 participants (40 per cell; 160 overweight and obese, 
40 normal weight) should result in sufficient power for studies 1 and 2. 
Data Analysis 
 
Statistical Analysis Software (SAS 9.3) will be used to complete all analyses. For study 
1, Pearson correlations and analysis of variance (ANOVA) will be used to evaluate the 
interrelationship of baseline variables (baseline prostaglandins and CRP, MPQ total score, total 
WBIS score, total SIS score, mean SSI score, total IWQOL score, SF-36 component scores and 
bodily pain subscale score, and the PANAS subscale scores) with demographic variables (age, 
race, BMI, and education). These analyses also will evaluate hypotheses 1a (BMI will be 
positively associated with prostaglandins, CRP, internalized weight stigma, perceived weight 
stigma, exposure to weight-based stigmatizing experiences, impact of weight on quality of life, 
pain, and negative affect; and negatively associated with health-related quality of life and 
positive affect) and 1b (prostaglandins and CRP will be positively associated with pain, 
internalized weight stigma, perceived weight stigma, exposure to weight-based stigmatizing 
experiences, impact of weight on quality of life, and negative affect; and negatively associated 
with health-related quality of life and positive affect). 
 
To evaluate hypotheses 1c (the relationship between BMI and baseline pain will be 
mediated by experienced weight stigma), 1d (the relationship between BMI and baseline pain 
will be mediated by perceived weight stigma), 1e (the relationship between BMI and baseline 
pain will be mediated by internalized weight stigma), and 1f (the relationship between BMI and 

41 
 
post-induction pain will be mediated by internalized weight stigma), the PROCESS (Hayes, 
2013) macro will be employed with 10,000 bootstrap samples and 95% confidence intervals. For 
hypotheses 1c-1e, PROCESS will be used to test SSI, SIS, and WBIS, respectively, as mediators 
in the relationship between BMI and baseline MPQ total score. PROCESS will be used again to 
test three identical models, with the exception that baseline SF-36 bodily pain subscale score will 
serve as the dependent variable. For hypothesis 1f, PROCESS will be used to test WBIS as a 
mediator in the relationship between BMI and post-induction VAS-Pain, controlling for baseline 
VAS-Pain, and again between BMI and post-induction MPQ total score, controlling for baseline 
MPQ total score. Hypothesis 1fi will be evaluated using PROCESS to test moderated mediation 
models in which the mediating relationships tested in hypothesis 1f are moderated by 
acetaminophen condition (acetaminophen or placebo). Also, ratings of the employee’s 
deservedness of the promotion will be entered as a covariate in all analyses for hypotheses 1f and 
1fi.  
 
The PROCESS macro also will be utilized to conduct exploratory moderation analyses. 
Specifically, eight simple moderation models will be used to assess potential conditional effects 
of experienced, perceived, and internalized weight stigma on the relationship between BMI and 
baseline pain outcome variables (baseline MPQ total score and SF-36 bodily pain subscale 
score), as well as post-induction pain outcome variables (post-induction MPQ total score and 
post-induction VAS-Pain). Additionally, two moderated mediation models will be used to assess 
the potential conditional effects of drug condition (acetaminophen or placebo) and internalized 
weight stigma on the relationship between BMI and post-induction pain outcome variables (post-
induction MPQ total score and post-induction VAS-Pain). Ratings of the employee’s 
deservedness of the promotion and baseline pain scores will be entered as covariates in 

42 
 
exploratory analyses involving post-induction pain outcomes. All moderation and moderated-
moderation analyses will be conducted utilizing 10,000 bootstrap samples. 
 
To evaluate hypothesis 2 (participants randomized to receive the weight stigma induction 
and placebo conditions will report greater physical pain, distress, and negative affect; lower 
positive affect; and higher levels of prostaglandins following completion of the induction 
compared to the other three groups), a series of analyses of covariance (ANCOVAs) will be 
conducted using post-induction measures (prostaglandins, positive and negative affect subscales 
of the PANAS, VAS-Pain, and VAS-Distress) as dependent variables, controlling for the 
relevant baseline measure and ratings of the employee’s deservedness of the promotion. Stigma 
condition (with two levels) and drug condition (with two levels) will be entered as class 
variables. Scheffe tests will be utilized to identify group differences in the presence of a 
significant interaction.  
 
 For study 2, a series of ANCOVAs will be conducted matching the format of hypothesis 
2 to test the hypotheses that, among the three groups exposed to the weight stigma induction 
(two overweight or obese from study 1 and one normal weight from study 2) those with 
overweight or obesity randomized to the placebo condition will report greater physical pain, 
distress, negative affect, and prostaglandins, and lower positive affect post-induction than 
individuals with overweight or obesity randomized to acetaminophen or individuals with normal 
weight. No differences are expected in physical pain, distress, positive and negative affect, and 
prostaglandins between the overweight or obese/acetaminophen group versus the normal weight 
group (hypothesis 3a). Having a relationship with someone with overweight or obesity also will 
be controlled for due to its potential impact on perceptions of weight stigma. The class variable 
will represent three conditions: the two overweight and obese groups exposed to the weight 

43 
 
stigma induction in study 1, and the normal weight group exposed to the weight stigma induction 
with placebo solution in study 2. Scheffe tests will be utilized to identify group differences in the 
presence of a significant main effect. To evaluate hypothesis 3b (those in the overweight or 
obese groups will report greater baseline bodily pain, internalized weight stigma, perceived 
weight stigma, exposure to weight-based stigmatizing experiences, impact of weight on quality 
of life, prostaglandins, and CRP; and worse health-related quality of life compared to 
participants with normal weight), data from the two overweight or obese groups exposed to the 
weight stigma induction will be combined into one group. Next, a series of ANOVAs will be 
used to compare the overweight/obese group and normal weight group on baseline variables 
(prostaglandins, CRP, MPQ total score, VAS-Pain and VAS-Distress scores, IWQOL total score, 
SF-36 component scores and bodily pain subscale, and PANAS subscales). Three ANCOVAs 
will be used to compare the same groups on baseline WBIS total score, baseline SIS total score, 
and baseline mean SSI score. Having a relationship with someone with overweight or obesity 
will be controlled for in these two analyses given its potential impact on these weight stigma-
related outcomes. 
Implications 
Results of this study will further explicate possible shared pathways by which physical 
and social pain are processed. Past research has documented change in prostaglandins in relation 
to inflammation, and the effect of analgesics on both. However, this study will contribute to 
understanding how prostaglandins such as PGE! change in response to the social pain of weight 
stigma. Additionally, results will contribute to knowledge on stigma, obesity, and 
pharmacological intervention by determining the degree to which acetaminophen may blunt the 
acute, negative effects of weight stigma among individuals with overweight or obesity. If an 

44 
 
effect is detected, results also will illustrate how prostaglandins change in response to 
acetaminophen in such a situation. If the evidence suggests that those with overweight or obesity 
administered acetaminophen have the same inflammatory, physical, and psychological responses 
to a weight stigma induction as individuals with normal weight, then this may provide rationale 
for further investigation into the mechanisms by which pharmacological interventions affect 
social pain, including weight stigma. 
An additional, potential implication of the current study is further understanding weight 
stigma as a mediator in the relationship between BMI and pain. Specifically, should perceived 
weight stigma mediate the relationship between BMI and bodily pain (hypothesis 1d), the model 
described by Olson and colleagues (2018) will be corroborated. Although the model was only 
evident in the subsample of overweight participants, the proposed study is more adequately 
powered to test the model with a larger sample. Thus, this study may provide further evidence of 
internalized weight stigma mediating the BMI-bodily pain relationship (hypothesis 1d), as well 
as test the model in the context of experimental changes in pain (hypothesis 1e). Should this 
model be supported, further investigation will be needed of the relationship between weight 
stigma and bodily pain among individuals with overweight or obesity. It also would provide 
further rationale to investigate effects of interventions aimed at reducing weight stigma among 
individuals with overweight or obesity and bodily pain. In addition to interventions that directly 
target weight stigma, interventions may provide benefit by modulating the relationship between 
weight stigma and bodily pain. If drug condition (i.e., acetaminophen or placebo) moderates the 
influence of internalized weight stigma on bodily pain (hypothesis 1fi), results will enhance 
understanding of the mechanisms by which obesity is associated with bodily pain, and possibly 
contribute to future research into the mechanisms of these relationships.  

45 
 
References 
Adam, T. C., & Epel, E. S. (2007). Stress, eating and the reward system. Physiology & Behavior, 
91(4), 449-458. 
Athey, A., & Overholser, J. (2016). Learning from physical pain to help with the management of 
emotional pain. Journal of Contemporary Psychotherapy, 46(3), 119-127. 
Barcellos de Souza, J., Grossman, E., Perissinotti, D. M. N., Oswaldo de Oliviera, J. Jr., 
Barreiros da Fonseca, P. R., & de Paula Posso, I. (2017). Prevalence of Chronic Pain, 
Treatments, Perception, and Interference on Life Activities: Brazilian Population-Based 
Survey. Pain Research and Management. Article ID: 4643830. 
Benedetti, F., Durando, J., & Vighetti, S. (2014). Nocebo and placebo modulation of hypobaric 
hypoxia headache involves the cyclooxegenase-prostaglandins pathway. Pain, 155, 921-
928. 
Bigal, M. E., & Lipton, R. B. (2006). Modifiable risk factors for migraine progression. 
Headache, 46, 1334-1343. 
Bigal, M. E., & Rapaport, A. M. (2012). Obesity and chronic daily headache. Current Pain and 
Headache Reports, 16, 101-109. 
Bjelland, I., Dahl, A. A., Haug, T. T., & Nickelmann, D. (2002). The validity of the Hospital 
Anxiety and Depression Scale: An updated literature review. Journal of Psychosomatic 
Research, 52(2), 69-77. 
Bookwala, J., & Boyar, J. (2008). Gender, excessive body weight, and psychological well-being 
in adulthood. Psychology of Women Quarterly, 32, 188-195. 
Boswell, R. G., & White, M. A. (2015). Gender differences in weight bias internalization and 
eating pathology in overweight individuals. Advances in Eating Disorders, 3(3), 259-268. 
Briggs, M. S., Givens, D. L., Schmitt, L. C., & Taylor, C. A. (2013). Relations of C-reactive 
protein and obesity to the prevalence and the odds of reporting low back pain. Archives of 
Physical Medicine and Rehabilitation, 94, 745-752. 
Buckley, K. E., Winkel, R. E., & Leary, M. R. (2004). Reactions to acceptance and rejection: 
Effects of level and sequence of relational evaluation. Journal of Experimental Social 
Psychology, 40, 14–28 

46 
 
Buysse, D. J., Reynolds, C. F., Monk, T. H., Berman, S. R., Kupfer, D. (1989). Pittsburgh Sleep 
Quality Index: A new instrument for psychiatric practice and research. Psychiatry 
Research, 28, 193-213. 
Cabrera, J., Emir, B., Dills, D., Murphy, T. K., Whalen, E., & Clair, A. (2012). Characterizing 
and understanding body weight patterns in patients treated with pregabalin. Current 
Medical Research and Opinion, 28, 1027-1037. 
Callegari, A., Michelini, I., Sguazzin, C., Catona, A., & Klersy, C. (2005). Efficacy of the SF-36 
questionnaire in identifying obese patients with psychological discomfort. Obesity 
Surgery, 15, 254-260. 
Carels, R. A., Young, K. M., Wott, C. B., Harper, J., Gumble, A., Oehlof, M. W., & Clayton, A. 
M. (2009). Weight bias and weight loss treatment outcomes in treatment-seeking adults. 
Annals of Behavioral Medicine, 37(3), 350-355. 
Chai, N. C., Scher, A. I., Moghekar, A., Bond, D. S., & Peterlin, B. L. (2014). Obesity and 
headache: Part I – A systematic review of the epidemiology of obesity and headache. 
Headache, 54(2), 219-234.  
Cimonlin, V., Vismara, L., Galli, M., Zaina, F., Negrini, S., & Capodaglio, P. (2011). Effects of 
obesity and chronic low back pain on gait. Journal of NeuroEngineering and 
Rehabilitation, 8, 55. 
Considine, R. V. (2005). Human leptin: An adipocyte hormone with weight-regulatory and 
endocrine functions. Seminars in Vascular Medicine, 5(1), 15-24. 
Cotter, E. W., & Kelly, N. R., (2017). Binge Eating Scale (BES). In Encyclopedia of Feeding 
and Eating Disorders (pp. 52-55). Singapore, Singapore: Springer Verlag. 
Craig, C. L., Marshall, A. L., Sjöström, M., Bauman, A. E., Booth, M. L., Ainsworth B. E., … & 
Oja, P. (2003). International physical activity questionnaire: 12-country reliability and 
validity. Medicine & Science in Sports & Exercise, 35(8), 1381-1395. 
Curtis-Prior, P. B. (1975). Prostaglandins and obesity. Lancet, 1(7912), 897-899. 
Dahlhamer, J., Lucas, J., Zelaya, C., Nahin, R., Mackey, S., DeBar, L., … & Helmick, C. (2018). 
Prevalence of chronic pain and high-impact chronic pain among adults – United States, 
2016. Morbidity and Mortality Weekly Report, 67(36), 1001-1006. 

47 
 
DeWall, C. N., MacDonald, G., Webster, G. D., Masten, C. L., Baumeister, R. F., Powell, C., … 
& Eisenberger, N. I. (2010). Acetaminophen reduces social pain: Behavioral and neural 
evidence. Psychological Science, 21(7), 931-937. 
Ding, C., Cicuttini, F., Scott, F., Cooley, H., & Jones, G. (2005). Knee structural alteration and 
BMI: A cross-sectional study. Obesity Research, 13(2), 350-361. 
Durso, L. E., & Latner, J. D. (2008). Understanding self-directed stigma: Development of the 
Weight Bias Internalization Scale. Obesity, 16(52), S80-S86. 
Durso, L. E., Latner, J. D., & Ciao, A. C. (2016). Weight bias internalization in treatment-
seeking overweight adults: Psychometric validation and associations with self-esteem, 
body image, and mood symptoms. Eating Behaviors, 21, 104-108. 
Durso, G. R. O., Luttrell, A., & Way, B. M. (2015). Over-the-counter relief from pain and 
pleasures alike: Acetaminophen blunts evaluation sensitivity to both negative and 
positive stimuli. Psychological Science, 26(6), 750-758. 
Eisenberger, N. L., & Lieberman, M. D. (2004). Why rejection hurts: A common neural alarm 
system for physical and social pain. Trends in Cognitive Sciences, 8(7), 294-300. 
Eisenberger, N. L., Lieberman, M. D., & Williams, K. D. (2003). Does rejection hurt? An fMRI 
study of social exclusion. Science, 302(5643), 290-292. 
Emery, C. F., Olson, K. L., Lee, V. S., Habash, D. L., Nasar, J. L., & Bodine, A. (2015). Home 
environment and psychosocial predictors of obesity status among community-residing 
men and women. International Journal of Obesity, 39, 1401-1407. 
Fabris de Souza, S. A., Faintuch, J., Valezi, A. C., Sant’Anna, A. F., Gama-Rodrigues, J. J., de 
Batista Fonseca, I. C., & de Melo, R. D. (2005). Postural changes in morbidly obese 
patients. Obesity Surgery, 15(7), 1013-1016. 
Ferguson, S., Al-Rehany, L., Tang, C., Gougeon, L., Warwick, K., & Madill, J. (2013). Self-
reported causes of weight gain: Among prebariatric surgery patients. Canadian Journal 
of Dietetic Practice and Research, 74(4), 189-192. 
Fife, B. L., & Wright, E. R. (2000). The dimensionality of stigma: A comparison of its impact on 
the self of persons with HIV/AIDS and cancer. Journal of Health and Social Behavior, 
41, 50-67.  

48 
 
Finkelstein, E. A., Trogdon, J. G., Cohen, W. C., & Dietz, W. (2009). Annual medical spending 
attributable to obesity: payer-and-service-specific estimates. Health Affairs, 28(5), W822-
W831. 
Finnerup, N. B., Sindrup, S. H. & Jensen, T. S. (2010). The evidence for pharmacological 
treatment of neuropathic pain. Pain, 150, 573–581. 
Garrow, J. (1981). Obesity and Related Diseases. London: Churchill Livingstone. 
Goffman, E. (1963). Stigma. Notes on the management of spoiled identity. New York: Simon and 
Shuster.  
Gormally, J., Black, S., Daston, S., & Rardin, D. (1982). The assessment of binge eating severity 
among obese persons. Addictive Behaviors, 7(1), 47-55. 
Haukka, E., Ojajarvi, A., Takala, E. P., Viikari-Juntura, E., & Leino-Arjas, P. (2012). Physical 
workload, leisure-time physical activity, obesity and smoking as predictors of multisite 
musculoskeletal pain. A 2-year prospective study of kitchen workers. Occupational & 
Environmental Medicine, 69(7), 485-492. 
Hawker, G. A., Mian, S., Kendzerska, T., & French, M. (2011). Measures of adult pain: Visual 
Analog Scale for Pain (VAS Pain), Numeric Rating Scale for Pain (NRS pain), McGill 
Pain Questionnaire (MPQ), Short-Form McGill Pain Questionnaire (SF-MPQ), Chronic 
Pain Grade Scale (CPGS), Short Form-36 Bodily Pain Scale (SF-36 BPS), and Measure 
of Intermittent and Constant Osteoarthritis Pain (ICOAP). Arthritis Care & 
Research, 63(S11), S240-S252.  
Hayes, A. F. (2013). Introduction to Mediation, Moderation, and Conditional Process Analysis: 
A Regression-Based Approach. New York, NY: The Guilford Press. 
Heuch, I., Heuch, I., Hagen, K., & Zwart, J. A. (2013). Body mass index as a risk factor for 
developing chronic low back pain: A follow-up in the Nord-Trondelag Health Study. 
Spine, 38(2), 133-139. 
Hitt, H. C., McMillen, R. C., Thornton-Neaves, T., Koch, K., & Cosby, A. G. (2007). 
Comorbidity of obesity and pain in a general population: Results from the Southern Pain 
Prevalence Study. Journal of Pain, 8(5), 430-436. 
Hylands-White, N., Duarte, R. V., & Raphael, J. H. (2017). An overview of treatment 
approaches for chronic pain management. Rheumatology International, 37, 29-42. 

49 
 
Jackson, T., Wang, Y., Wang, Y., & Fan, H. (2014). Self-efficacy and chronic pain outcomes: A 
meta-analytic review. Journal of Pain, 15(8), 800-814. 
Katz, J., & Melzack, R. (2011). The McGill Pain Questionnaire: Development, psychometric 
properties, and usefulness of the long form, short form, and short form-2. In: Turk, D. C., 
Melzack, R. (Eds.) Handbook of Pain Assessment, 3rd Edition, 45-66. New York, NY: 
Guilford Press. 
Kaufman, D. W., Kelly, J. P., Rosenberg, L., Anderson, T. E., & Mitchell, A. A. (2002). Recent 
patterns of medication use in the ambulatory adult population of the United States: The 
Slone Survey. Journal of the American Medical Association, 287(3), 337-344. 
Kawabata, A. (2011). Prostaglandin e2 and pain – An update. Biological and Pharmaceutical 
Bulletin, 34(8), 1170-1173. 
Kolotkin, R. L., & Crosby, R. D. (2002). Psychometric evaluation of the impact of weight on 
quality of life questionnaire (IWQOL-lite) in a community sample. Quality of Life 
Research, 11(2), 157-171. 
Kolotkin, R. L., Crosby, R. D., Kosloski, K. D., & Williams, G. R. (2001). Development of a 
brief measure to assess quality of life in obesity. Obesity Research, 9(2), 102- 111.  
Lamm, C., Decety, J., & Singer, T. (2011). Meta-analytic evidence for common and distinct 
neural networks associated with directly experienced pain and empathy for pain. 
Neuroimage, 54(3), 2492-2502. 
Latner, J. D., Barile, J. P., Durso, L. E., & O’Brien, K. S. (2014). Weight and health-related 
quality of life: The moderating role of weight discrimination and internalized weight bias. 
Eating Behaviors, 15(4), 586-590. 
Lee, M. S., & Dedrick, R. F. (2016). Weigh bias internalization scale: Psychometric properties 
using alternative weight status classification approaches. Body Image, 17, 25-29. 
Lee, M. S., Gonzalez, B. D., Small, B. J., & Thompson, J. K. (2019). Internalized weight bias 
and psychological wellbeing: An exploratory investigation of a preliminary model. PLOS 
One, 14(5). e0216324. 
Linton, S. J., Nicholas, M. K., MacDonald, S., Boersma, K., Bergbom, S., Maher, C., & 
Refshauge, K. (2011). The role of depression and catastrophizing in musculoskeletal 
pain. European Journal of Pain, 15(4), 416-422. 

50 
 
Linton, S. J., & Shaw, W. S. (2011). Impact of psychological factors in the experience of pain. 
Physical Therapy, 91(5), 700-711. 
Major, B., Eliezer, D., & Rieck, H. (2012). The psychological weight of weight stigma. Social 
Psychological and Personality Science, 3, 651-658. 
Masheb, R. M., Lutes, L. D., Kim, H. M., Holleman, R. G., Goodrich, D. E., Janney, C. A., … & 
Damschroder, L. J. (2015). Weight loss outcomes in patients with pain. Obesity, 23(9), 
1778-1784. 
McVinnie, D. S. (2013). Obesity and pain. British Journal of Pain, 7(4), 163-170. 
Mee, S., Bunney, B. G., Bunney, W. E., Hetrick, W., Potkin, S. G., & Reist, C. (2011). 
Assessment of psychological pain in major depressive episodes. Journal of Psychiatric 
Research, 45(11), 1504-1510. 
Meerwijk, E., & Weiss, S. (2011). Toward a unifying definition of psychological pain. Journal 
of Loss and Trauma, 16(5), 402-412. 
Melzack, R. (1975). The McGill Pain Questionnaire: Major properties and scoring methods. 
Pain, 1, 277-299. 
Merskey, H., & Bogduk, N. (1994). Classification of chronic pain: Descriptions of chronic pain 
syndromes and definitions of pain terms. Pain. Seattle: IASP Press. 
Messier, S. P., Loeser, R. F., Miller, G. D., Morgan, T. F., Rejeski, W. J., Sevick, M. A., … & 
Williamson, J. D. (2004). Exercise and dietary weight loss in overweight and obese older 
adults with knee osteoarthritis: The Arthritis, Diet, and Activity Promotion Trial. Arthritis 
and Rheumatism, 50(5), 1501-1510. 
Mischkowski, D., Crocker, J., & Way, B. M. (2016). From painkiller to empathy killer: 
Acetaminophen (paracetamol) reduces empathy for pain. Social and Cognitive Affective 
Neuroscience, 11(9), 1345-1353. 
Mork, P. J., Holtermann, A., & Nilsen, T. I. (2013). Physical exercise, body mass index and risk 
of chronic arm pain: Longitudinal data on an adult population in Norway. European 
Journal of Pain, 17(8), 1252-1258. 
Myers, A., & Rosen, J. C. (1999). Obesity stigmatization and coping: Relation to mental health 
symptoms, body image, and self-esteem. International Journal of Obesity, 23, 221-230. 
Nahin, R. L. (2015) Estimates of pain prevalence and severity in adults: United States, 2012. 
Journal of Pain, 16(8), 769-780. 

51 
 
Narouze, S., & Souzdalnitski, D. (2015). Obesity and chronic pain. Systematic review of 
prevalence and implications for pain practice. Regional Anesthesia and Pain Medicine, 
40(2), 91-111. 
Novack, V., Fuchs, L., Lantsberg, L., Kama, S., Lahoud, U., Horev, A., … & Ifergane, G. 
(2011). Changes in headache frequency in premenopausal obese women with migraine 
after bariatric surgery: A case series. Cephalalgia, 31(13), 1336-1342. 
O’Connor, M. F., Wellisch, D. K., Stanton, A. L., Eisenberger, N. I., Irwin, M. R., & Lieberman, 
M. D. (2008). Craving love? Enduring grief activates brain’s reward center. NeuroImage, 
42, 969– 972. 
Ogden, C. L., Carroll, M. D., Kit, B. K., & Flegal, K. M. (2014). Prevalence of childhood and 
adult obesity in the United States, 2011-2012. Journal of the American Medical 
Association, 311(8), 806-814. 
Okifuji, A., Bradshaw, D. H., & Olson, C., (2009). Evaluating obesity in fibromyalgia: 
Neuroendocrine biomarkers, symptoms, and functions. Clinical Rheumotology, 28(4), 
475-478. 
Okifuji, A., Donaldson, G. W., Barck, L., & Fine, P. G., (2010). Relationship between 
fibromyalgia and obesity in pain, function, mood, and sleep. Journal of Pain, 11(12), 
1329-1337. 
Okifuji, A., & Hare, B. D. (2015). The association between chronic pain and obesity. Journal of 
Pain Research, 8, 399-408. 
Olson, K. L., Landers, J. D., Thaxton, T. T., & Emery, C. F. (2018). The pain of weight-related 
stigma among women with overweight or obesity. Stigma and Health, advance online 
publication. 
Pearl, R. L., Hopkins, C. H., Berkowitz, R. I., & Wadden, T. A. (2016). Group cognitive 
behavioral treatment for internalized weight stigma: a pilot study. Eating and Weight 
Disorders, 23(3), 357-362. 
Pearl, R. L., & Puhl, R. M. (2016). The distinct effects of internalizing weight bias: An 
experimental study. Body Image, 17, 38-42. 
Pearl, R. L., Wadden, T. A., Chao, A. M., Walsh, O., Alamuddin, N., Berkowitz, R. I., & 
Tronieri, J. S. (2018). Weight bias internalization and long-term weight loss in patients 

52 
 
with obesity. Annals of Behavioral Medicine, epub ahead of print. doi: 
10.1093/abm/kay084. 
Penninx, B. W., Abbas, H., Ambrosius, W., Nicklas, B. J., Davis, C., Messier, S. P., & Pahor, M. 
(2004). Inflammatory markers and physical function among older adults with knee 
osteoarthritis. Journal of Rheumatology, 31(10), 2027-2031. 
Planton, J., & Edlund, B. J. (2010). Regulatory components for treating persistent pain in long-
term care. Journal of Gerontological Nursing, 36, 49–56. 
Puhl, R. M., Himmelstein, M. S., & Quinn, D. M. (2018). Internalizing Weight Stigma: 
Prevalence and Sociodemographic Considerations in US Adults. Obesity, 26(1), 167-175. 
Puhl, R. M., & Heuer, C. A. (2009). The stigma of obesity: A review and update. Obesity, 17(5), 
941-964. 
Randles, R. J., Heine, S. J., & Santos, N. (2013). The common pain of surrealism and death: 
Acetaminophen reduces compensatory affirmation following meaning threats. 
Psychological Science, 24, 966-973. 
Ricciotti, E., & FitzGerald, G. A. (2011). Prostaglandins and inflammation. Arteriosclerosis, 
Thrombosis, and Vascular Biology, 31(5), 986-1000. 
Roberts, I. D., Krajbich, I., Cheavens, J. S., Campo, J. V., & Way, B. M. (2017). Acetaminophen 
reduces distrust in individuals with borderline personality disorder features. Clinical 
Psychological Science, 6(1), 145-154. 
Ronti, T., Lupattelli, G., & Mannarino, E. (2006). The endocrine function of adipose tissue: An 
update. Clinical Endocrinology, 64(4), 355-365. 
Rosenberg, M. (1965). Society and the adolescent self-image. Princeton, Princeton University 
Press, 1965. 
Ryff, C. D. (1989). Happiness is everything, or is it? Explorations on the meaning of 
psychological well-being. Journal of Personality and Social Psychology, 57, 1069-1081. 
Schvey, N. A., Puhl, R. M., & Brownell, K. D. (2011). The impact of weight stigma on caloric 
consumption. Obesity, 19(10), 1957-1962. 
Schvey, N. A., Puhl, R. M., & Brownell, K. D. (2014). The stress of stigma. Exploring the effect 
of weight stigma on cortisol reactivity. Psychosomatic Medicine, 76(2), 156-162. 

53 
 
Sellinger, J. J., Clark, E. A., Shulman, M., Rosenberger, P. H., Heapy, A. A., & Kerns, R. D. 
(2010). The moderating effect of obesity on cognitive–behavioral pain treatment 
outcomes. Pain Medicine, 11(9), 1381-1390. 
Senna, M. K., Sallam, R. A., Ashour, H. S., & Elarman, M. (2012). Effect of weight reduction on 
the quality of life in obese patients with fibromyalgia syndrome: A randomized controlled 
trial. Clinical Rheumatology, 31, 1591-1597. 
Singh, D., Park, W., Hwang, D., & Levy, M. S. (2014). Severe obesity effect on low back 
biomechanical stress of manual load lifting. Work, 51(2), 337-348. 
Takai, Y., Yamamoto-Mitani, N., Abe, Y., & Suzuki, M. (2015). Literature review of pain 
management for people with chronic pain. Japan Journal of Nursing Science, 12, 167-
183. 
Tomiyama, A. J. (2014). Weight stigma is stressful. A review of evidence for the Cyclic 
Obesity/Weight-Based Stigma model. Appetite, 82, 8-15. 
Tomiyama, A. J., Epel, E. S., McClatchey, T. M., Poelke, G., Kemeny, M. E., McCoy, S. K., & 
Daubenmier, J. (2014). Associations of weight stigma with cortisol and oxidative stress 
independent of adiposity. Healthy Psychology, 33(8), 862-867. 
Tompkins, D. A., Hobelmann, J. G., & Compton, P. (2017). Providing chronic pain management 
in the “Fifth Vital Sign” Era: Historical and treatment perspectives on a modern-day 
medical dilemma. Drug and alcohol dependence, 173, S11-S21. 
Treede, R., Rief, W., Barke, A., Aziz, Q., Bennet, M. I., Benoliel, R., … & Wang, S. (2019). 
Chronic pain as a symptom or a disease: The IASP Classification of Chronic Pain for the 
International Classification of Diseases (ICD-11). Pain, 160(1), 19-27. 
United States National Library of Medicine (2015). Health risks of obesity. Retrieved January 
24, 2019 from 
https://www.nlm.nih.gov/medlineplus/ency/patientinstructions/000348.htm. 
Valenti, G., Libby, L. K., & Eibach, R. P. (2011). Looking back with regret: Visual perspective 
in memory images differentially affects regret for actions and inactions. Journal of 
Experimental Social Psychology, 47, 730-737. 
van Beest, I., & Williams, K. D. (2006). When inclusion costs and ostracism pays, ostracism still 
hurts. Journal of Personality and Social Psychology, 91(5), 918-928. 

54 
 
Vartanian, L. R. (2015). Development and validation of a brief version of the Stigmatizing 
Situations Inventory. Obesity Science & Practice, 1(2), 119-125. 
Ventafridda, V., Saita, L., Ripamonti, C., & De Conno, F. (1985). WHO guidelines for the use of 
analgesics in cancer pain. International Journal of Tissue Reactions, 7, 93-96. 
Vieira, P. N., Palmeira, A. L., Mata, J., Kolotkin, R. L., Silva, M. N., Sardinha, L. B., & 
Teixeira, P. J. (2012). Usefulness of standard BMI cut-offs for quality of life and 
psychological well-being in women. Obesity Facts, 5, 795-805. 
Vuolteenaho, K., Koskinen, A., & Moilanen, E. (2014). Leptin – A link between obesity and 
osteoarthritis. Applications for prevention and treatment. Basic & Clinical Pharmacology 
& Toxicology, 114(1), 103-108. 
Vyvey, M. (2010). Steroids as pain relief adjuvants. Canadian Family Physician, 56(12), 1295-
1297. 
Ware, J. E. (2000). SF-36 health survey update. Spine, 25(24), 3130-3139. 
Watson, D., Clark, L., & Tellegen, A. (1988). Development and validation of brief measures of 
positive and negative affect: The PANAS scales. Journal of Personality and Social 
Psychology, 54, 1063-1070.  
Wellen, K. E., & Hotamisligil, G. S. (2003). Obesity-induced inflammatory changes in adipose 
tissue. The Journal of Clinical Investigation, 112(12), 1785-1788. 
Williamson, D. A., Davis, C. J., Bennett, S. M., Goreczny, A. J., & Gleaves, D. H. (1989). 
Development of a simple procedure for assessing body image disturbances. Behavior 
Assessment, 11(4), 433-446. 
World Health Organization (2018). Obesity and overweight. Retrieved January 23, 2019 from 
https://www.who.int/en/news-room/fact-sheets/detail/obesity-and-overweight#. 
World Health Organization (2011). Waist circumference and waist-hip ratio: Report of a WHO 
expert consultation. Geneva: World Health Organization. 39 p. 
World Health Organization (2015). WHO’s cancer pain ladder for adults. Retrieved February 2, 
2019 from http://www. who.int/cancer/palliative/painladder/en/. 
Wright, L. J., Schur, E., Noonan, C., Ahumada, S., Buchwald, D., & Afari, N. (2010). Chronic 
pain, overweight, and obesity: Findings from a community-based twin registry. Journal 
of Pain, 11(7), 628-635. 

55 
 
Yoo, J. J., Cho, N. H., Lim, S. H., & Kim, H. A. (2014). Relationships between body mass index, 
fat mass, muscle mass, and musculoskeletal pain in community residents. Arthritis & 
Rheumotology, 66(12), 3511-3520 
Yunus, M. B., Arslan, S., & Aldag, J. C. (2002). Relationship between body mass index and 
fibromyalgia features. Scandinavian Journal of Rheumotology, 31(1), 27-31. 
Zigmond, A. S., & Snaith, R. P. (1983). The Hospital Anxiety and Depression Scale. Acta 
Psychiatrica Scandinavica, 67(6), 361-370. 
 
 

56 
 
 
 
 
 
 
 
 
 
 
 
 
 
Appendix A: Tables and Figures 
 
 

57 
 
Table 1: Experimental conditions and participants in study 1 (N = 160) 
 
Participants with Overweight or Obesity 
Acetaminophen + Control (n = 40) 
Acetaminophen + Stigma (n = 40) 
Placebo + Control (n = 40) 
Placebo + Stigma (n = 40) 
 
 
 

58 
 
Table 2: Experimental conditions and participants in study 2 (N = 120) 
 
Participants with Overweight or Obesity 
Participants with Normal Weight 
Acetaminophen + Stigma (n = 40) 
Placebo + Stigma (n = 40) 
Placebo + Stigma (n = 40) 
 
 
 
Data from these two groups from study 1 will be utilized for study 2. 

59 
 
Figure 1: Hypothesized model for experienced weight stigma as a mediator in the relationship 
between BMI and baseline bodily pain.  
 
BMI 
Experienced Weight 
Stigma 
Baseline Pain 

60 
 
 
Figure 2: Hypothesized model for perceived weight stigma as a mediator in the relationship 
between BMI and baseline bodily pain.  
 
BMI 
Perceived Weight 
Stigma 
Baseline Pain 

61 
 
Figure 3: Hypothesized model for internalized weight stigma as a mediator in the relationship 
between BMI and baseline bodily pain.  
 
BMI 
Internalized Weight 
Stigma 
Baseline Pain 

62 
 
Figure 4: Hypothesized model for internalized weight stigma as a mediator in the relationship 
between BMI and post-induction pain. 
 
 
 
 
 
 
 
 
BMI 
Internalized Weight 
Stigma 
Post-Induction Pain 
Controls: Baseline pain and rating 
of employee’s deservedness of the 
promotion 

63 
 
Figure 5: Hypothesized model for drug condition (acetaminophen or placebo) moderating the 
effect of internalized weight stigma as a mediator between BMI and post-induction pain. 
 
 
 
 
 
 
BMI 
Internalized Weight 
Stigma 
Post-Induction Pain 
Controls: Baseline pain and rating 
of employee’s deservedness of the 
promotion 
Drug Condition 

64 
 
Figure 6: Flow diagram of experimental sessions (note: all participants with normal weight will 
be placed in the placebo and weight stigma condition) 
 
Anthropometric measurements 
& consent form 
Blood and saliva sample 
collection; Randomization 
Acetaminophen 
administration 
Placebo 
administration 
Self-report questionnaires 
Self-report questionnaires 
Weight stigma 
induction 
Weight stigma 
induction 
Control 
induction 
Control 
induction 
Self-report questionnaires 
Self-report questionnaires 
Saliva sample 
Saliva sample 
Debriefing and study payment 
Debriefing and study payment