Abstract:Multiple sclerosis (MS) is a neurodegenerative disorder that causes a wide range of symptoms, which, if left untreated, worsen over time. Despite the availability of effective medications, however, many MS patients fail to take their medications. One possibility is that these patients fail to follow through on treatment recommendations because they do not value these treatments-despite their effectiveness. Prior studies have used principles of microeconomic demand to quantify subjects' valuation of commodities… Show more
“…During the medical decision‐making task (Jarmolowicz, Reed, Bruce, et al, 2018; Jarmolowicz et al, 2020), patients indicated their likelihood of taking a hypothetical DMT on an 11‐point decile scale (i.e., 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100%). On the opening screen of the task, patients were told that they were making decisions about a hypothetical DMT that has mild side effects (adapted from the World Health Organizations adverse effects reporting guidelines; World Health Organization, 2005):Mild side effects are intermittent and cause mild discomfort.…”
Section: Methodsmentioning
confidence: 99%
“…Patients completed a medication purchase task based on prior studies on MS patients (Jarmolowicz et al, 2020). Patients were asked to report their likelihood of purchasing a year's supply of effective medication across a range of prices (20 values ranging from free to $500,000/year).…”
Section: Methodsmentioning
confidence: 99%
“…Over the past several decades, substantial evidence has accumulated that links core behavioral economic processes and health behaviors (Bickel et al, 2016; Jarmolowicz et al, 2020). These developments have yielded actionable insights into the processes that drive negative health decision making in substance use disorder and obesity (Amlung et al, 2016; Epstein et al, 2010; Stein et al, 2017).…”
mentioning
confidence: 99%
“…Demand analyses functionally separate consumption of a good into behavioral measures of unconstrained cost/effort (i.e., Q 0 ; demand intensity) and sensitivity of consumption to changes in price (i.e., α). These distinct demand elements are derived from demand curve models (Hursh & Silberberg, 2008; Koffarnus et al, 2015), where the propensity of consumption—defined as either quantity of a good consumed, or in the case of one‐off consumables (e.g., single‐purchase goods, subscriptions, yearly supply; Brown et al, 2021; Harsin et al, 2021; Jarmolowicz et al, 2020; Reed et al, 2016; Roma et al, 2016; Schwartz & Hursh, 2022; Strickland et al, 2022), the likelihood of consumption ( Q )—is assessed within‐subjects across increasing prices ( C ):(with k representing the range of consumption in log units). Higher demand intensity and lower α values are associated with and predictive of substance use quantity‐frequency, severity, and treatment outcomes for both licit (González‐Roz et al, 2019; Zvorsky et al, 2019) and illicit drugs (Strickland et al, 2020a).…”
The reinforcer pathology model posits that core behavioral economic mechanisms, including delay discounting and behavioral economic demand, underlie adverse health decisions and related clinical disorders. Extensions beyond substance use disorder and obesity, however, are limited. Using a reinforcer pathology framework, this study evaluates medical adherence decisions in patients with multiple sclerosis. Participants completed behavioral economic measures, including delay discounting, probability discounting, and a medication purchase task. A medical decision-making task was also used to evaluate how sensitivity to mild side effect risk and efficacy contributed to the likelihood of taking a hypothetical diseasemodifying therapy. Less steep delay discounting and more intense (greater) medication demand were independently associated with greater adherence to the medication decision-making procedure. More generally, the pattern of interrelations between the medication-specific and general behavioral economic metrics was consistent with and contributes to the reinforcer pathology model. Additional research is warranted to expand these models to different populations and health behaviors, including those of a positive health orientation (i.e., medication adherence).
“…During the medical decision‐making task (Jarmolowicz, Reed, Bruce, et al, 2018; Jarmolowicz et al, 2020), patients indicated their likelihood of taking a hypothetical DMT on an 11‐point decile scale (i.e., 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100%). On the opening screen of the task, patients were told that they were making decisions about a hypothetical DMT that has mild side effects (adapted from the World Health Organizations adverse effects reporting guidelines; World Health Organization, 2005):Mild side effects are intermittent and cause mild discomfort.…”
Section: Methodsmentioning
confidence: 99%
“…Patients completed a medication purchase task based on prior studies on MS patients (Jarmolowicz et al, 2020). Patients were asked to report their likelihood of purchasing a year's supply of effective medication across a range of prices (20 values ranging from free to $500,000/year).…”
Section: Methodsmentioning
confidence: 99%
“…Over the past several decades, substantial evidence has accumulated that links core behavioral economic processes and health behaviors (Bickel et al, 2016; Jarmolowicz et al, 2020). These developments have yielded actionable insights into the processes that drive negative health decision making in substance use disorder and obesity (Amlung et al, 2016; Epstein et al, 2010; Stein et al, 2017).…”
mentioning
confidence: 99%
“…Demand analyses functionally separate consumption of a good into behavioral measures of unconstrained cost/effort (i.e., Q 0 ; demand intensity) and sensitivity of consumption to changes in price (i.e., α). These distinct demand elements are derived from demand curve models (Hursh & Silberberg, 2008; Koffarnus et al, 2015), where the propensity of consumption—defined as either quantity of a good consumed, or in the case of one‐off consumables (e.g., single‐purchase goods, subscriptions, yearly supply; Brown et al, 2021; Harsin et al, 2021; Jarmolowicz et al, 2020; Reed et al, 2016; Roma et al, 2016; Schwartz & Hursh, 2022; Strickland et al, 2022), the likelihood of consumption ( Q )—is assessed within‐subjects across increasing prices ( C ):(with k representing the range of consumption in log units). Higher demand intensity and lower α values are associated with and predictive of substance use quantity‐frequency, severity, and treatment outcomes for both licit (González‐Roz et al, 2019; Zvorsky et al, 2019) and illicit drugs (Strickland et al, 2020a).…”
The reinforcer pathology model posits that core behavioral economic mechanisms, including delay discounting and behavioral economic demand, underlie adverse health decisions and related clinical disorders. Extensions beyond substance use disorder and obesity, however, are limited. Using a reinforcer pathology framework, this study evaluates medical adherence decisions in patients with multiple sclerosis. Participants completed behavioral economic measures, including delay discounting, probability discounting, and a medication purchase task. A medical decision-making task was also used to evaluate how sensitivity to mild side effect risk and efficacy contributed to the likelihood of taking a hypothetical diseasemodifying therapy. Less steep delay discounting and more intense (greater) medication demand were independently associated with greater adherence to the medication decision-making procedure. More generally, the pattern of interrelations between the medication-specific and general behavioral economic metrics was consistent with and contributes to the reinforcer pathology model. Additional research is warranted to expand these models to different populations and health behaviors, including those of a positive health orientation (i.e., medication adherence).
“…Demand is often analyzed in a demand curve, which displays the functional relation between the amount of a commodity/activity consumed and its price. Demand analysis has been used to quantify the likelihood of initiating and continuing other health‐related behaviors, such as treatment selection (Gilroy & Feck, 2022; Gilroy et al, 2022), drug use (e.g., Jacobs & Bickel, 1999; MacKillop et al, 2008; Murphy & MacKillop, 2006), food intake (e.g., Epstein et al, 2010, 2018), problematic technology use (e.g., Hayashi & Blessington, 2021; Hayashi et al, 2019a, 2019b), risky sex (e.g., Dolan et al, 2020; Harsin et al, 2021; Jarmolowicz et al, 2016; Strickland et al, 2020), medication adherence (e.g., Jarmolowicz et al, 2019, 2020), and exercising (e.g., Anokye et al, 2012). In a typical demand task, consumption is quantified by the amount of a commodity/activity consumed at a given price, but it can also be quantified by the probability of a commodity/activity consumed (Roma et al, 2016, 2017).…”
Section: Breastfeeding and Behavioral Economic Demand Analysismentioning
The present study determined whether behavioral economic demand analysis could characterize mothers' decision to exclusively breastfeed in the workplace. Females, aged between 18 and 50 who have given birth in the past three years, completed a novel demand task with hypothetical scenarios, in which they returned to work with a 2-month-old baby. Participants rated their likelihood of breastfeeding their baby at a workplace lactation room versus formula-feeding their baby at their desk. The distance to the lactation room ranged from 10 s to 60 min. This assessment was conducted with and without hypothetical financial incentives for 6-month exclusive breastfeeding. Primary dependent measures were demand intensity and change in demand elasticity, which could conceptually represent initiation and continuation of breastfeeding, respectively. Demand for breastfeeding was more intense and less elastic (i.e., more likely to initiate and continue breastfeeding) among mothers with an experience of 6-month exclusive breastfeeding and under the condition with the financial incentives. The novel demand task can potentially provide a useful behavioral marker for quantifying mothers' decision to initiate and continue exclusive breastfeeding in the workplace, informing workplace policy regarding lactation rooms, identifying risk for early cessation, and developing and individualizing an intervention to assist mothers to exclusively breastfeed in the workplace.
The operant demand framework has achieved high levels of adoption as an approach to quantify how various ecological factors influence choice. A central goal of the framework proposed by Hursh and Silberburg (2008) was to isolate the “essential value” of reinforcers—namely, their effects on behavior given various contextual factors. The effect of reinforcers on behavior is a phenomenon that is expected to vary as a function of reinforcer magnitude/dosage (i.e., units of reinforcement), price (i.e., schedule requirements), the intensity of demand (i.e., consumption in free operant conditions), the availability of reinforcers (i.e., supply, presence of alternatives), and the individual's current and historical context. This technical report provides a historical summary of the concept, describes the quantitative basis for essential value in the framework of Hursh and Silberburg (2008), reviews prior attempts to extract a generalizable index of essential value, and presents a newer formulation using exact solution that provides a more succinct and durable index. Proofs and solutions are provided to clarify the bases for novel and existing representations of essential value. Recommendations are provided to improve the precision and accuracy of behavioral economic metrics as well as support consensus regarding their interpretation in the operant demand framework.
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