Anabolic-androgenic steroids (AAS), primarily used by weightlifters and bodybuilders, are also used by recreational gym goers (Kanayama et al., 2009b). Misuse¹ of supraphysiological doses² of AAS has been linked with a myriad of undesired health effects (Goldman and Basaria, 2018) including a dependency syndrome, currently estimated to effect up to 30% of people who use AAS (Pope et al., 2014). Although development of substance dependency is thought to be facilitated by drug craving (Donny et al., 2008), craving remains poorly understood (Flannery et al., 2001) and AAS craving has not yet been explored. To explore AAS dependence and craving, valid and reliable assessment instruments are required. The overarching aim of the current research was to develop two such instruments and to validate their scores.

AAS research has demonstrated evidence of a dependency syndrome since late 1980 (Brower et al., 1989). Subsequently, research into this area attempted to diagnose AAS dependence by using diagnostic criteria previously used to identify substance misuse and dependence. The two most accredited criteria for assessing the presence of dependence to substances of misuse are the Diagnostic and Statistical Manual of Mental Disorders criteria (DSM; American Psychiatric Association DSM-5, Task Force, 2013), and the International Classification of Diseases (ICD; World Health Organization, 1992). However, the DSM and ICD disagree on how AAS are viewed. While the ICD volume 10 (ICD-10) does not consider AAS as substances that can cause dependence or symptoms of withdrawal (Midgley et al., 1999), the DSM criteria classifies AAS in the ‘other’ category of substance use disorders. For this reason, research on AAS dependence has primarily utilized the DSM criteria (Kanayama et al., 2009a; Ip et al., 2011; Scarth et al., 2022).

Despite the DSM criteria being primarily used to diagnose dependence toward intoxicating substances of misuse, research identified the presence and prevalence of AAS dependence³ among populations of male weightlifters using the DSM criteria (Brower et al., 1991; Kanayama et al., 2009a; Scarth et al., 2022). Unlike commonly misused substances, AAS are not immediately intoxicating (Kanayama et al., 2009a). Instead, AAS are consumed over prolonged periods of time to obtain a delayed reward of increased musculature and strength, rather than administered to obtain the sensation of an instantaneous ‘high’ (Kanayama et al., 2009b). To account for this, researchers have provided recommendations to make the DSM criteria more specific to the use of AAS (Kanayama et al., 2009b).⁴ Amendments included items identifying unsuccessful attempts to stop AAS use due to anxiety over loss of muscle size in drug-free periods, avoidance of important activities in favor of maintaining supraphysiological muscle mass, and excessive time spent training, attending to diet, associating with other AAS users (see Kanayama et al., 2009b). However, these amendments may actually represent risk factors for AAS use, such as muscle dysmorphia, exercise addiction and eating disorders (Cole et al., 2003; Griffiths et al., 2018; Gunnarsson et al., 2022), or even different typologies of AAS users (i.e., ‘Expert’; see Christiansen et al., 2017) rather than dependence alone.

Another method of assessing AAS dependence is the Severity of Dependence Scale (SDS; see Gossop et al., 1995). Initially developed to assess dependence on drugs of abuse (e.g., alcohol, heroin, cocaine and amphetamine), the SDS is a concise and internally consistent dependence measure for such substances (Gossop et al., 1995; Martin et al., 2006). More recently, two studies have reported using an adapted version of the SDS to measure AAS dependence (Cole et al., 2003; Griffiths et al., 2018). Despite this, there are concerns of the use of the adapted SDS to measure AAS dependence. First, like the DSM criteria, the SDS was originally established to identify dependence on intoxicating drugs of abuse (e.g., alcohol, heroin, cocaine, and amphetamine; see Gossop et al., 1995). Second, there is extremely limited evidence for the psychometric properties of the adapted version of the SDS (see Griffiths et al., 2018), as the validity and reliability of scores obtained through its use are largely unknown.

The limitations of the DSM and SDS approaches are further highlighted when one considers current models of AAS dependence. Several models have been presented over the last three decades to try to explain AAS dependence (Brower, 2002; Kanayama et al., 2010; Hildebrandt et al., 2011). In an early model, Brower (1992) indicates AAS may cause dependence via four possible mechanisms: (1) primary reinforcement through neurological reward pathways (e.g., opioid pathways), (2) secondary reinforcement from increased musculature (including increased self-esteem, outside admiration and winning competitions), (3) avoidance of biologically mediated withdrawal symptoms, or (4) avoidance of psychosocial withdrawal symptoms (e.g., depression due to decreased athletic performance). Limitations with this proposed model include difficulties in differentiating between each of the reinforcing factors within survey and case report research (Kean, 2003), as many of these factors often present themselves simultaneously (Yesalis et al., 1990). There also remains a disagreement over the psychoactive nature of AAS. Midgley et al. (1999) indicated AAS dependence is likely to be caused by secondary reinforcing effects due to the personal and socially rewarding nature they exert, rather than via psychoactive stimulation. With the model demonstrating difficulties in practical use within research and the absence of agreement over the psychoactive and reinforcing nature of AAS, use of this model in the development of measures to assess and understand AAS dependence could be problematic.

Bahrke and Yesalis (1994) presented a model of AAS dependence whereby development of dependence originates from socio-cultural contexts, subsequently motivating individuals, primarily males, to engage in an intense and frequent rigmarole of training sessions to build highly muscular physiques. Within this model, it is the training sessions that produce improvements in mood, self-esteem and are associated with controlled dietary programs. Therefore, the reinforcing effects of these anabolic compounds can be attributed to their muscle-building properties (Midgley et al., 1999), and the regimented routines of AAS administration facilitate compulsive training and dietary protocols. Thus, the positively reinforcing effects of this model may relate more to exercise dependence and a desire to boost body capital (Kotzé and Antonopoulos, 2021; Gunnarsson et al., 2022), rather than AAS dependence alone, potentially causing issues for measures of AAS dependence created from this model.

In a later model, Brower (2002) proposed a two-stage process of AAS dependence. The first stage sees AAS being used in high doses to build supraphysiological muscle mass when combined with a strict diet and training regime, reinforced by the development of increased muscle mass this behavior is maintained despite encountering any adverse effects. The second stage is characterized by individuals administering high dosages of AAS activating neurological mediated reward pathways preventing the individual from halting use of AAS (Brower, 2002). The latter stage is characterized by the experience of psychoactive effects (i.e., mood changes, increased aggressive behaviors), and associated with polysubstance use of compounds such as opioids, and can be a target for addiction treatments (Arvary and Pope, 2000; Mhillaj et al., 2015). However, Brower (2002) stipulates there is a dearth of evidence of AAS dependence without associated weight training or ergogenic effects on musculature, therefore the positively reinforcing effects of AAS could be attributed more toward an underlying dependence on improving muscular strength, esthetics, and physical performance.

Kanayama et al. (2010) indicated that AAS dependence may present via three distinct mechanisms: the anabolic effect, the androgenic effect, and the hedonic effect. The anabolic mechanism is modulated by the presence of muscle dysmorphic disorder, whereby an individual will maintain the use of AAS due to a ‘fear’ of losing their musculature when abstaining from AAS administration. The androgenic mechanism points to the effects of hypothalamic pituitary gonadal axis suppression, facilitating the development of AAS induced hypogonadism (ASIH) and associated symptoms (see Tan and Scally, 2009). Therefore, AAS are administered to alleviate the symptoms of ASIH experienced during substance free periods (Brower, 2002). Lastly, the hedonic mechanism demonstrates AAS dependence sharing similarities with dependence to other substances of abuse (e.g., opioids), further demonstrated via animal models (Wood, 2008).

More traditional models based upon the allostatic framework of addiction (Hildebrandt et al., 2011) have been presented, whereby the development of AAS dependence is believed to be both a psychological and a physical construct. Hildebrandt et al. (2011) describes how an individual can improve their hedonic state by implementing protocols of exercise and AAS use simultaneously, thereby improving their hedonic tone and bringing about positive reinforcement through social benefit. Combined with chronic AAS use, psychological dependence is established. Physical dependence is achieved once the individual administers ancillary compounds to negate undesired effects (Hildebrandt et al., 2011). However, evidence in the current literature suggests many individuals who use AAS combine their anabolic compounds with ancillary substances to counteract undesired effects (Kanayama et al., 2010; Pope et al., 2014), therefore this may not be indicative of AAS dependence.

The presence of multiple pathways within models of AAS dependence (Brower, 1992, 2002; Bahrke and Yesalis, 1994; Kanayama et al., 2010; Hildebrandt et al., 2011), demonstrates that AAS dependence is not unidimensional but contains many underlying dimensions, as such it is important that measures are able to identify this by containing multiple factors. Presently, measures used to assess AAS dependence capture it as a single factor (Gossop et al., 1995; Gillespie et al., 2007; Ray et al., 2008) and therefore are limited in representing the likely multidimensional nature of AAS dependence. Research in this area could benefit from a multidimensional measure to discriminate between the underlying dimensions of AAS dependence allowing researchers to identify if specific dimensions are of a greater importance to AAS dependence than others.

To accurately identify, diagnose and further understand AAS dependence, the multidimensional nature identified within existing theories needs to be addressed. Sub-dimensions have been identified within the extant literature, with researchers categorizing AAS dependence as almost unbroken use sustained over time, despite incurring undesired (physical, psychological, and social) effects, and experience of withdrawal symptoms in periods of abstinence (Kanayama et al., 2010). Another sub-dimension identified within models of AAS dependence is the belief that administering chronic supraphysiological doses of AAS improves the effectiveness of an AAS regime in enhancing muscular and strength gains (Brower, 2002). By utilizing Kanayama et al.’s (2010) theory of AAS dependence and multiple sub-dimensions identified within the literature, a more accurate understanding of AAS dependence may be established.

A contributing factor in the development of drug dependency is the presence of drug craving (Drummond, 2001; American Psychiatric Association DSM-5, Task Force, 2013). Believed to manifest itself in a myriad of ways, craving presents as an intrusive and dominating sensation causing an individual substantial distress (Tiffany and Wray, 2012). Craving is recognized as a conscious desire for substance use (Sayette et al., 2000), characterized by a want, urge or compulsion to engage in satiating behavior (Kozlowski and Wilkinson, 1987). With little consensus over the definition of craving, it remains poorly understood (Flannery et al., 2001). Craving is believed to present itself during periods of drug abstinence elicited when experiencing drug-related cues (e.g., environmental cues and drug exposure; Drummond et al., 1995), incentive salience (Berridge and Robinson, 2016), and due to drug expectancy (see Drummond, 2001). Thereby increasing the propensity for drug seeking behavior in individuals with compromised self-efficacy (Marlatt and Gordon, 1985), only satiated by drug use.

Craving research has demonstrated its presence within substance use disorders, including alcohol, tobacco, opioid, cocaine, cannabis, and other psychoactive substances (Serre et al., 2018; Kakko et al., 2019). As such, craving has been included within the ICD-10 diagnostic criteria for substance dependence (World Health Organization, 1992), and more recently in DSM-V (American Psychiatric Association DSM-5, Task Force, 2013) for substance use disorder. However, despite consensus that drug craving has a role in drug dependency (Tiffany and Wray, 2012), there remains a dispute among researchers on the presence of a relationship between craving and relapse, indicative of a substance dependency syndrome (Wray et al., 2013). Furthermore, craving has been established as a state-like construct due to daily fluctuations in response to internal and external stimuli (Sayette et al., 2000; Serre et al., 2015), whereas dependence is considered a more stable construct (Geiser et al., 2017; Flannery et al., 2019). Therefore, to identify the presence of craving alongside dependence, it may be more beneficial to retain craving as an independent measure rather than consider it a symptom of dependence and incorporate it into measures of substance dependence.

Research on craving has predominantly focused on alcohol and smoking, subsequently seeing the development of many measures suited to these substances (Sayette et al., 2000), and their adaptation to suit research of other substances of misuse (Tiffany et al., 1993). With no widely accepted or drug specific standardized instruments available, the assessment of drug craving has been diverse (Tiffany and Wray, 2012). This leaves researchers in a predicament where they must pick the most suited measure available, leading to inconsistencies when attempting to identify an appropriate measure (Sayette et al., 2000). Despite the development and validation of multi-item craving measures (Raabe et al., 2005) identifying the varied nature of drug craving, single-item scales remain the most commonly used within craving research (Tiffany and Wray, 2012). This somewhat limits our understanding of drug craving as unidimensional single-item measures are unable to reflect the different multifaceted theories of drug craving (Tiffany, 1990; Tiffany et al., 2000).

Extant research looking at the mechanisms of AAS dependence has identified behaviors associated with craving through animal models. Conditioned place preference and drug seeking behaviors, synonymous with drug craving, have been identified through environmental cues associated with AAS (Wood, 2008). Furthermore, medications to alleviate the symptoms of craving (e.g., Naltrexone) have been seen to inhibit behaviors of self-administration of AAS among animal models (Wood, 2008), supporting the notion AAS can induce a craving-like response. However, some researchers believe animal models are limited in explaining the nature of craving (Mezinkis et al., 2001) due to the inability to communicate sensations and perceptions associated with drug use (Drummond, 2001). Thus, with literature suggesting the presence of AAS craving it is important to further explore AAS craving to better understand whether it associates with AAS dependence.

To accurately identify and diagnose AAS craving and any existing sub-dimensions, a new measure should aim to represent the multi-dimensional nature of the construct (Tiffany and Wray, 2012). Researchers have characterized craving as eliciting several experiences upon an individual, these include cue-elicited craving (Drummond et al., 1995), outcome expectancy (Marlatt and Gordon, 1985), and associated positive and negative mood states (Tiffany et al., 2000; Shiffman and Waters, 2004). Cue-elicited craving has been identified within alcohol research, whereby an individual associates their use of a substance (e.g., alcohol) with an environment (e.g., a bar), and elicits a desire or urge to administer the substance if immediate substance use does not take place (Anton, 1999). Outcome expectancy is explained by the motivation and desire to administer a substance due to the positive outcome of its use, increasing the likelihood of drug-seeking behaviors (Marlatt and Gordon, 1985). The effects of mood on craving have been identified within smoking research, whereby negative affect will increase craving and increase the risk of substance use in periods of abstinence (Shiffman and Waters, 2004). While for positive affect craving is facilitated by pleasurable and positively reinforcing effects increasing the risk for drug seeking behavior (Baker et al., 1986). By utilizing Niaura’s (2000) theory of drug craving and multiple sub-dimensions identified within the literature exploration of AAS craving may be conducted.

Therefore, the overall aim of this study was twofold. First, we aimed to develop measures of AAS dependence and AAS craving and validate their scores. As part of this, we aimed to determine the number of sub-dimensions within each construct to understand the components of AAS dependence and craving and the level of these sub-dimensions within each measure. Based on the theories of dependence and craving previously discussed, we hypothesized: H1a) AAS dependence would have a five-factor structure covering major aspects of AAS dependence (e.g., Increase use of AAS due to dissatisfaction with the effectiveness of current AAS regime, AAS use to self-medicate withdrawal-like symptoms, and continued use despite the experience of adverse physical, psychological and socio-occupational effects attributed to AAS use) and H2a) AAS craving would present with a 4-factor structure, reflecting the various dimensions of AAS-associated craving (e.g., drug expectancy, environmental cues, and positive and negative mood states). Second, we aimed to determine the presence of a higher order factor for both scales, which would support the existence of general dimensions of AAS dependence and AAS craving. Based upon our previous arguments, we hypothesized: H1b) the AAS dependence measure would demonstrate a higher order factor, supporting the existence of an overarching concept of AAS dependence and H2b) the AAS craving measure would demonstrate a higher order factor, supporting the existence of an overarching concept of AAS craving. To summarize, this research sought to develop two psychometric instruments: an AAS dependence scale and an AAS craving scale. Throughout the study we followed the guidelines and procedures for instrument development and validation present within the research literature (i.e., Messick, 1995; Fabrigar et al., 1999; Clark and Watson, 2019).

Throughout this study we considered five of the six aspects of construct validity proposed by Messick (1995); content, structural, substantive, generalizability, and external. Expert opinion was used for the content aspect via identifying the representativeness and quality of the items for each of the newly developed measures. The structural aspect identifies if the scoring structure is in alignment with the structure of the domains being assessed, this was achieved through factor analysis. The substantive aspect of construct validity was addressed within the study, by examining the association of the scores from our new measures and those from theoretically associated variables. To identify the extent of score properties and interpretations generalizing to and across groups and settings, multisample analysis was conducted. The presence of convergent and discriminant validity was addressed though the external aspect, this was achieved through association with theoretically relevant instrument scores. The final component of construct validity, consequential, is identified through positive and negative consequences occurring from the use of the new measures. As such, this was beyond the scope of an instrument development and validation study, and more applicable to future applications of the measures created within the study.

Research has highlighted the prevalence of AAS dependence among those who use AAS (Brower et al., 1991; Kanayama et al., 2009; Pope et al., 2014; Hauger et al., 2020; Scarth et al., 2022). It is also possible – but to date not examined – that craving plays a role in AAS dependence. However, existing measures of AAS dependence (see Kanayama et al., 2009; Griffiths et al., 2018) have significant limitations (Gossop et al., 1995; Gillespie et al., 2007; Ray et al., 2008; Kanayama et al., 2009b), and there are no validated measures to assess AAS craving. Therefore, the overarching aim of this study was to develop and validate psychometrically robust scales to assess dependence and craving within populations who use AAS.

Previous research suggested several dimensions may underlie and contribute to AAS dependence (Brower, 1992; Bahrke and Yesalis, 1994; Brower, 2002; Kanayama et al., 2010; Hildebrandt et al., 2011). Consequently, we developed a multidimensional scale and hypothesized that we would see evidence of five lower-order factors and one higher-order factor with scores obtained using the final instrument. Factor analysis of the data from both samples supported these hypotheses, providing evidence for the multifaceted nature of AAS dependence and supporting H1a and H1b. These findings are important in furthering our understanding of AAS dependence as present measures capture only a single factor (Gossop et al., 1995; Gillespie et al., 2007; Ray et al., 2008), whereas the AASDS can capture and aid our understanding of the complex multidimensional nature of AAS dependence.

The development of the AASDS as a valid and reliable multidimensional scale will further our understanding of AAS dependence via assessment of novel research questions that could not be answered with existing measures. For example, previous research has identified those dependent on AAS administer higher doses of AAS per week, and higher numbers of anabolic compounds than non-dependent individuals (see Ip et al., 2011). Now, with the AASDS, researchers may answer in-depth questions such as, ‘Which dimension of AAS dependence is linked most strongly with higher dosages of AAS?’, or ‘Which dimension of AAS dependence is associated most strongly with use of more anabolic compounds?’. In time, such research could enable harm reduction practices to tailor their approach to the specific needs of individuals with AAS dependence to deliver more effective harm reduction interventions (Bates et al., 2021).

Furthermore, the current literature indicates AAS are at times administered to self-medicate against withdrawal-like symptoms when off-cycle (Kanayama et al., 2020). Therefore, researchers could use the AASDS to address research questions such as ‘Is the subdimension of withdrawal linked with a greater propensity to shorten off-cycle periods, or progress to a blast and cruise protocol?’. Evidence also suggests a link between AAS dependence, and a greater number of self-reported undesired effects associated with use of AAS (Ip et al., 2011). The AASDS now allows researchers to investigate possible links between the three subdimensions linked to undesired effects (physiological, psychological, and social) and an increased number and severity of undesired effects. Answering questions such as these could help practitioners to identify primary areas to be targeted within therapeutic interventions and develop more effective harm reduction services for people who use AAS (Bates et al., 2019).

With no existing measure for AAS craving and evidence in the existing literature identifying the presence of craving-like behaviors in AAS research (Wood, 2008) we also sought to develop a measure of AAS craving. As existing literature indicates potential for drug craving to be multifaceted (Raabe et al., 2005; Tiffany and Wray, 2012), we aimed to develop a multidimensional scale and hypothesized the presence of four lower-order and one higher-order factor in the final instrument. Factor analysis with both samples provided support for these suppositions, therefore supporting hypotheses H2a and H2b. The development of a multidimensional measure of AAS craving is important as it will allow researchers to examine the potential role of the different dimensions of AAS craving in the development of AAS dependency specifically and use of AAS more generally.

With previous literature indicating the multi-faceted and state-like nature of craving (Shiffman et al., 1996; Sayette et al., 2000; Raabe et al., 2005), it is important to have an independent multidimensional scale to examine craving within AAS users. The four dimensions identified within this study (i.e., expectation, environment, positive and negative mood) indicate AAS craving may be experienced in a multitude of ways. Therefore, existing measures such as the DSM-V, containing a single item of craving, may not be sufficient to capture the different dimensions craving experienced within AAS users; in a reliable and valid manner (see Kline, 2000). Furthermore, as craving is considered a state-like construct (Flannery et al., 2019), it is important to separate it from trait-like phenomena such as dependence. By doing so, the AASCS will allow future research to explore the temporal patterns in craving potentially experienced by those that administer AAS.

The AASCS will allow researchers to address a wide range of important research questions that until now could not be addressed. For instance, researchers could seek to determine which dimension/s of drug craving predict increases in AAS dependence over time, whether environmentally influenced craving is associated with the amount of time athletes spend in the gym environment, or whether manipulation of athletes’ mood moderates any relationship between mood-related craving and AAS use. Addressing research questions such as these will not only benefit our current understanding of how AAS craving may contribute to AAS dependence, but it will also enable practitioners to target specific subdimensions that have stronger associations with AAS dependence and can therefore be the target of therapeutic intervention by harm reduction practitioners.

An essential component of scale development is the provision of evidence for convergent validity (Messick, 1995). Such evidence provides support that the new measure can capture the construct it was designed to assess. In terms of the study, evidence for the convergent validity of AASDS scores was demonstrated through strong positive correlations with the AAS adapted DSM criteria (Kanayama et al., 2009b) and of AASCS scores through associations with the AAS adapted WSWS (Welsch et al., 1999). These associations are consistent with recommendations for establishing convergent validity (Kline, 2005; Byrne, 2012). This is important as it provides evidence that the AASDS and AASCS assess their respective psychological constructs of dependence and craving. In terms of the AASDS, this is particularly important given evidence for the convergent validity of the DSM-V is presently absent (Welch et al., 2013).

Another crucial element of establishing construct validity is providing evidence for concurrent validity (Kline, 2005). Here, evidence should be provided showing the new scale can predict theoretically related constructs when data on the two constructs are collected at the same time (Kline, 2005). Presently, we provided evidence for the concurrent validity of AASDS scores by exhibiting strong positive correlations between AASDS scores and self-reported undesired effects from AAS use, whereas for AASCS scores we demonstrated a moderate positive correlation with doping MD and a moderate negative association with doping SRE (Boardley et al., 2018). Support for the concurrent validity of AASDS and AASCS scores is important, as it indicates scores with both measures can predict theoretically related constructs.

Correlations examining concurrent validity showed differences across the subscales of the AASDS and AASCS. For the AASDS, the “use of AAS despite experiencing associated undesired physical effects” subscale had the strongest association with self-reported experience of undesired effects, and “use of AAS despite experiencing associated undesired social effects” the weakest. This is consistent the extant literature on AAS dependence, whereby AAS dependent users reported experiencing more undesired physical effects while using AAS (Ip et al., 2011), and reported a higher frequency of undesired physical effects compared to undesired psychological and social effects (Pope et al., 2010; Ip et al., 2011). For the AASCS, the “environmental cues” and “AAS expectancy” subscales had the strongest associations with doping MD and doping SRE, and “negative mood” indicated the weakest association with these constructs. These findings are consistent with non-AAS research on craving, where SRE has shown a negative association with craving (Shadel and Cervone, 2006) and a positive association with MD (Ahmadi et al., 2019). The differential associations with theoretically related constructs for the AASDS and AASCS subscales are important as they provide evidence the subscales are capturing different elements of dependency and craving.

Evidence for discriminant validity is a key consideration when validating new scales (Messick, 1995). Moderate evidence of discriminant validity was established for the AASDS as subscale scores were moderately to strongly intercorrelated, indicating while they are all underpinned by a higher-order dependency construct, they are distinct from one another. For AASCS scores, there was evidence of discriminant validity through strong intercorrelations between subscales, evidencing each subscale was underpinned by a higher-order craving construct while being distinct from each other. By providing evidence of discriminant validity, we have demonstrated the items within the respective subscales are related but distinct from one another (Clark and Watson, 2019).

Finally, new measures should also provide evidence supporting the internal consistency of scale and subscale scores as this provides evidence for the homogeneity of items (Clark and Watson, 2019). Assessment of the internal consistency values for both the AASDS and AASCS and their respective subscales exceeded the minimum criterion levels recommended when developing novel scales (i.e., 0.80; Clark and Watson, 2019) in both samples. This provides strong and consistent evidence for the internal consistency of scale scores for the AASDS and AASCS, as well as their respective subscales.

The development and validation of the AASDS and AASCS provides several advantages to researchers. Previous scales used to measure AAS dependence, such as the DSM-IV criteria, required administration by trained clinicians (Pope et al., 2010). The development of the AASDS now means researchers can assess AAS dependence without the need for a trained clinician, therefore making its assessment easier and more accessible. Also, to date research on AAS dependence has largely assessed it as a binary construct (i.e., it is present or it is not; see Kanayama et al., 2009a). However, researchers have recently contradicted this and have argued that AAS dependence is a continuous construct (see Scarth et al., 2022). The development of the AASDS is therefore timely, as it represents the first validated measure of AAS dependence that assesses it as a continuous construct. Finally, the latest edition of the DSM criteria includes an item on craving in its classification of substance use disorder. While there is support for craving as a criterion for diagnosis of substance dependence, currently there is no consensus on whether craving merely cooccurs with dependence or is actually a sub-component of it (Tiffany and Wray, 2012). Our development of separate measures of AAS dependence and AAS craving provides researchers with distinct measures of the two constructs that will facilitate research to help elucidate our understanding on this.

Through a rigorous set of procedures, we developed psychometric scales to assess AAS dependence and AAS craving, providing convincing evidence for the validity of scores obtained with both measures. Specifically, evidence for several aspects of construct validity (i.e., convergent, concurrent and discriminant validity) and internal consistency was provided for the AASDS and AASCS. During item development we also provided evidence for high levels of content and face validity of items through feedback from relevant experts. The AASDS makes important contribution to the literature as it is the first AAS dependency measure specifically designed for those who use AAS rather than being adapted from existing measures. In turn, the development of the AASCS is important because it represents the first AAS craving measure. Further, the multidimensional nature of both measures provides exciting possibilities for future research. We look forward to seeing further evaluation and use of these measures in future research.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving humans were approved by University of Birmingham Ethics Committee (ERN_19-1955). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

BZ: Writing – original draft, Writing – review & editing. IB: Writing – original draft, Writing – review & editing.