Predicting the intention to use herbal medicines for ...



Predicting the intention to use herbal medicines for anxiety symptoms: A model of health behaviourAbstractBackground: Anxiety is a prevalent mental health condition in the Western world. Adults experiencing anxiety have been found to use a range of herbal medicines to manage anxiety symptoms. Aim: This study aimed to test a theoretical model based on the theory of planned behaviour that predicted the intention to use herbal medicines for anxiety symptoms, and to identify individual predictors of intention.Methods: An online survey was conducted with Australian adults who experienced anxiety and used herbal medicines (N = 400). A two-step approach to structural equation modelling was used to test a path model predicting the intention to use herbal medicines.Results: The model was found to be well-fitting. Attitude, subjective norms, control beliefs, and severity of anxiety symptoms each significantly positively predicted intention to use herbal medicines for anxiety symptoms explaining 56% of the variance. Conclusions: The results suggest that mental health practitioners and policy makers need to ensure people experiencing anxiety have access to accurate and reliable information about herbal medicines to ensure they can effectively manage anxiety symptoms and safely engage in self-care. Declaration of interest: None.Key words (5): anxiety, herbal medicine, complementary medicine, health behaviour, mental healthIntroductionAnxiety is one of the most prevalent mental health conditions in the Western world. Lifetime prevalence of anxiety disorders is over 26% in Australia (Slade et al., 2009), and 33% in the United States (Kessler et al., 2012). Herbal medicine (HM) use is also prevalent in these countries; 37% of Australian adults used HMs in their lifetime for a variety of health reasons (Thomson et al., 2012). Adults experiencing anxiety have been found to use a range of HMs (McIntyre et al., 2016), and those with more severe anxiety have been found to be more likely than those with less severe symptoms to use HMs for anxiety symptoms (Bystritsky et al., 2012; McIntyre et al., 2016).Having alternative treatment options for anxiety is important, as conventional pharmaceutical and psychological treatments are not always suitable for all individuals. For example, the adverse effects from pharmacotherapies can prevent people from taking them (Olatunji et al., 2010), and some people do not respond to more than one class of drug (Katzman, 2009). Lack of response also occurs for psychological therapies, with non-response to cognitive behavioural therapy approximately 36% for generalised anxiety disorder (Taylor et al., 2012). There are also barriers to accessing psychological treatments related to cost, time commitment needed, availability of services (Katzman, 2009), and concerns about stigma (Corrigan et al., 2016).HMs are used to treat anxiety with varying levels of evidence. For example, Piper methysticum (kava) has demonstrated efficacy in treating generalised anxiety symptoms, and Passiflora incarnata (passion flower) has been shown to reduce acute pre-operative anxiety (Sarris et al., 2013). In addition, clinical trials have shown herbal anxiolytics to have fewer side-effects than pharmacotherapies (Sarris et al., 2013). HMs are easily accessible and widely available as over-the-counter medicines; therefore, are an important treatment option for people with anxiety. There are health risks related to HM use. Herb-drug interactions can potentiate the effects of prescribed psychotropic medications, or reduce the effectiveness of prescribed pharmaceuticals such as the oral contraceptive pill (Posadzki et al., 2013). In addition, there is risk related to poor quality HMs, which are difficult for people to identify due to lack of product standardisation, adulteration, and the complexity of herbal formulas (Zhang et al., 2012). These risks are likely to be increased with certain health behaviours. Research has found adults with anxiety who use HMs frequently self-prescribe them, use them concurrently with prescribed pharmaceuticals, do not disclose HM use to health practitioners, and rely on non-professional information sources about HMs (i.e., Internet and family and friends; Gardiner et al., 2007; McIntyre et al., 2016). These behaviours expose people to health risks, including ineffective treatment, incorrect dosing, use of poor quality medicines, and herb-drug interactions (Wardle & Adams, 2014). To mitigate these risks an understanding of this treatment decision-making behaviour is critical.Specific beliefs such as wanting more control over health, and being dissatisfied with the medical encounter have been found to be important predictors of complementary medicine use (including HMs) in various populations (Bishop et al., 2007; McIntyre et al., 2015a). While many studies have sought to determine the influence of psychosocial factors on complementary medicine use, few have focused specifically on HMs or used a theoretical framework of health behaviour (Lorenc et al., 2009). Theory is important as it facilitates a more comprehensive and systematic understanding of the relations between behavioural determinants and helps to identify how to change an undesirable behaviour (Noar & Zimmerman, 2005). These are critical considerations for understanding treatment decision-making and the influence of risk factors in problematic health behaviours, such as using HMs concurrently with prescribed pharmaceuticals. Theory of planned behaviourThe theory of planned behaviour (TPB) hypothesises that attitudes, subjective norms and control factors (perceived behavioural control) are predictors of the intention to act out a behavior; the strength of a person’s intention predicts whether or not they perform the behaviour (e.g., use HMs for anxiety; Ajzen, 1991). Control factors can also be direct predictors of behaviour; however, the strength of this relation is dependent on the behaviour being performed and actual control over the behaviour (McEachan et al., 2011). Intention is the construct that most effectively predicts behaviour; however, it is estimated that intention results in performing the behaviour only 50% of the time (Sheeran & Webb, 2016). This suggests that other moderating factors are involved in engaging in the behaviour. Despite this gap between intention and behaviour, identifying the predictors of intention is an important step in understanding health behaviours, as intention reflects the motivational factors that influence behaviour (Ajzen, 1991).There is robust debate regarding the usefulness of the TPB when predicting behaviour (see Armitage, 2015; Conner, 2015; Sniehotta et al., 2014). As health behaviour theories have varying amounts of predictive validity (Noar & Zimmerman, 2005), a clear process of decision-making is needed to determine the most suitable theory when conducting research. Recommendations by Glanz and colleagues (2008) were followed to identify the most appropriate theory to understand HM use behaviour with consideration of: the logic of the theory, the extent to which the theory is relevant without compromising the ability to manage the number of concepts used, and whether or not it is a suitable fit with existing theories in a specific field. The decision to use HMs is considered a volitional behaviour that is best explained by a theory with a clear link from intention to behaviour (Noar & Zimmerman, 2005), such as the TPB. The TPB is a logical theory to apply to HM use, as it has been previously used to explain complementary medicine use in cancer patients (Hirai et al., 2008), and HM use in older adults (Gupchup et al., 2006). In addition, the combination of attitudes, subjective norms, and control factors have been found to account for 44.3% of the variance in intention across a range of health behaviours (McEachan et al., 2011). Attitude towards HM use Attitude towards a specific behaviour is determined by salient beliefs about that behaviour (Ajzen, 1991). The more positive the attitude to a specific behaviour, the stronger the intention to perform that behaviour. Attitude has been shown in a meta-analysis to be the most influential predictor of intention across a range of health behaviours, explaining up to 12.59% of the variance (McEachan et al., 2011). In addition, salient beliefs that influence attitudes have been found to predict HM use (McIntyre et al., 2015a), and favourable attitudes to HMs have been found to predict intention to use HMs (Gupchup et al., 2006). Therefore, this study predicts that more favourable attitudes towards using HMs for anxiety symptoms will positively predict the intention to use HMs for anxiety symptoms.Subjective norms Subjective norms reflect the amount of perceived social pressure a person receives towards acting out a behaviour, and influence a person’s intention to act out a behaviour to the degree to which they are motivated by the possibility of social rewards and punishments (White et al., 2009). There is disagreement on which social factors are most influential, as it is dependent on the type of behaviour being studied, and how the construct is measured (Conner & Armitage, 1998). These social factors include beliefs about: what other people think about a behaviour (injunctive norms), how other people perform a behaviour (descriptive norms), and a person’s own moral principles towards a behaviour (personal injunctive norms; Conner & Armitage, 1998). As family and friends are an important source of information about HMs for people experiencing anxiety (McIntyre et al., 2016), injunctive norms are likely to be an important predictor of the intention to use HMs for anxiety symptoms, and will be used to measure subjective norms in this study. Therefore, we predict that more agreeable subjective norms will positively predict the intention to use HMs for anxiety symptoms.Control factors Control factors reflect the extent to which a person perceives they can perform a behaviour (Ajzen, 1991). The amount of influence control has on behaviour depends on the specific behaviour, and the resources available to the person (i.e., actual control) to assist them to perform the behaviour (Ajzen, 1991). The construct of control factors measures two distinct processes: self-efficacy (i.e., autonomy beliefs) and control beliefs (i.e., perceived control of the behaviour); each of these constructs directly affects intention (Manstead, 2011). The extent to which perceived control is likely to reflect actual control needs to be considered relative to the specific behaviour before including it in a TPB model (Ajzen, 1991); therefore, this study will consider the influence of anxiety symptoms on decision-making to determine the most appropriate construct to operationalise control factors.Anxiety symptoms can influence a person’s ability to self-regulate, which can reduce their ability to reason (Gino et al., 2012). For example, people with anxiety may worry about making the right treatment decision and avoid making a decision altogether. Further, people with anxiety are reported to have a greater desire for control over their health than those without anxiety, as they perceive they have less control over both internal and external factors related to health (Shapiro et al., 1996). Therefore, it is likely that ‘autonomy beliefs’ may be a more important type of control factor when predicting intention to use HMs for anxiety symptoms in adults experiencing anxiety. Previous research found that beliefs about personal control over health were important predictors of complementary medicine use (including HMs); specifically, that people wanting more control over their health and who believe in individual responsibility for health were more likely to use complementary medicines (McIntyre et al., 2015). Therefore, this study will not explore perceived behavioural control, but instead use a measure of control factors reflecting the perceived control a person would have over their anxiety if they used HMs, and predicts that stronger beliefs about ability to control anxiety will positively predict the intention to use HMs for anxiety symptoms.Anxiety symptoms and decision-makingState anxiety impairs information processing that consequently affects decision-making. When in an anxious state people have been found to rely on the advice of others to help make decisions, and can find it difficult to discriminate between good and bad advice (Gino et al., 2012). In addition, more severe anxiety symptoms have been shown to make people more risk averse, and choices that are perceived as risky (e.g., pharmaceuticals that have side-effects) are associated with heightened physiological arousal associated with fear (Hartley & Phelps, 2012). Consequently, people who are risk averse prefer to choose safer options when making everyday decisions (Hartley & Phelps, 2012). HMs can be perceived as a safer option than pharmaceuticals as they are natural and have less side-effects (O’Callaghan & Jordan, 2003; Siahpush, 1998, 1999); therefore, it is likely that severity of anxiety symptoms will influence a person’s intention to use HMs to treat anxiety symptoms. Hypothesised modelNo studies have used health behaviour theory to understand HM use for anxiety symptoms, or sought to identify psychosocial predictors of intention to use HMs for anxiety symptoms. Consequently, this study aims to test a hypothesised model based on the TPB that predicts the intention to use HMs for anxiety symptoms, and the following hypotheses:More favourable attitudes towards using HMs for anxiety symptoms will positively predict the intention to use HMs for anxiety symptoms.More agreeable subjective norms will positively predict the intention to use HMs for anxiety symptoms.Stronger control beliefs will positively predict the intention to use HMs for anxiety symptomsMore severe anxiety symptoms will positively predict the intention to use HMs for anxiety symptomsMethodRecruitment and sampleCharles Sturt University Human Ethics Committee (2014/33) approved this study that conforms to the Declaration of Helsinki. Informed consent was obtained when participants continued to complete the survey after being presented with an information page.Adults who experienced anxiety symptoms in their lifetime, and used HMs were recruited for an online survey using purposive criterion sampling in September 2014. Members of a commercial research database representative of the Australian adult population (N = 10,575) were emailed an invitation for the survey. The response rate was 8.47% (n = 896), with 400 people (197 females, 203 males; mean age = 49.1, SD 15.53) meeting the inclusion criteria. Participants received a financial incentive that reflected the time taken to complete the survey. MeasuresThe questionnaire included demographic items for age, gender, postcode, and education level. Before being presented with questions about HMs, the following definition was provided: HMs are medicines made from whole plant parts in the form of tablets, capsules, liquid extracts, teas, decoctions, creams and ointments. Anxiety symptoms. The mean score on the anxiety subscale of the Depression Anxiety and Stress Scale short version (DASS-21; Lovibond & Lovibond, 1996) was used to operationalise the latent variable anxiety symptoms (AS). The DASS-21 is a valid and reliable clinical screening tool for anxiety symptoms (? = .79; Crawford et al., 2011).Theory of planned behaviour variables. Items included reflected the latent constructs attitude (AT: 5 items), subjective norms (SN: 2 items), control factors (CF: 2 items), and intention (IN: 2 items). Development of TPB items was informed by literature review, qualitative interviews (McIntyre et al., 2015b), and guided by Ajzen’s recommendations ADDIN PAPERS2_CITATIONS <citation><uuid>1F7E1567-C8AD-42BB-AA9F-B3B5A8E004C2</uuid><priority>0</priority><publications><publication><publication_date>99200600001200000000200000</publication_date><accepted_date>99201310111200000000222000</accepted_date><title>Constructing a theory of planned behavior questionnaire</title><uuid>AA4BA821-FDCD-4515-A546-519367E6B273</uuid><subtype>403</subtype><type>400</type><citekey>Ajzen:2006tw</citekey><url> Ajzen</title><citekey>Untitled:0we</citekey><type>-300</type><subtype>-300</subtype><uuid>754C34B0-2F5B-4F94-BA95-BFD7CC8A1F15</uuid></publication></bundle><authors><author><firstName>Icek</firstName><lastName>Ajzen</lastName></author></authors></publication></publications><cites></cites></citation>(2006). Face validity of the items was assessed by subject matter experts (health practitioners who prescribed HMs and had experience with treating people with anxiety symptoms) to ensure clarity, comprehension, and length of the items was optimal. Items were scored using 7-point Likert type scales that varied for each item relative to the construct being measured (Table 1). The mean score of the sum of the items for each TPB construct was used to operationalise the latent variables included in the hypothesised model. Prior to analysis items presented in a negative direction were recoded to ensure that higher values represented a positive direction. Data analysis IBM SPSS Statistics Premium Edition Version 22 was used to screen the data, and check statistical assumptions. Jamovi was used to conduct reliability tests for each scale. IBM SPSS Amos Version 22 was used for the confirmatory factor analysis (CFA) and structural equation modeling (SEM) analysis. A two-step approach to SEM was used for testing the latent path model (Anderson & Gerbing, 1988). First, a measurement model was assessed using CFA to determine the relations between the observed variables and corresponding latent constructs, and the validity of the TPB variables used in the path model (Tabachnick & Fidell, 2013). The latent factor AS was not included in the CFA, as the anxiety subscale of the DASS-21 is a well-validated and reliable measure of anxiety symptoms. Composite latent variables for SN, AT, IN, and CF were created from the observed variables for use in the path model. Second, the latent path model was tested, with the direction, size, and significance of each parameter estimated. A model generating approach to SEM was used in which a hypothesised model is proposed, tested, and adjusted as necessary to achieve a model that is both driven by theory and statistically well-fitting (Byrne, 2010). There was adequate power (? = .90) to detect medium effects with a sample size of 400 (Christopher Westland, 2010).Maximum likelihood estimation in SEM assumes the sample population is multivariate normal to allow for reliable interpretation of parameters (Byrne, 2010); however, meeting this assumption is considered an unrealistic expectation for most studies, and maximum likelihood estimation is reasonably robust against moderate violations of nonnormality (Lei & Lomax, 2005). Assessment of Mahalanobis distance detected 64 multivariate outliers (p < .05). As removing this number of cases would reduce the power of the analysis and misrepresent the sample they were retained. Curve estimation was used to test relationships included in the latent model, which were sufficiently linear for inclusion in the SEM.ResultsReliability Following reliability analysis, each of the latent factors showed very good to excellent reliability (Kline, 2005). Table 2 reports the correlation coefficient for the two item constructs, and McDonalds omega and Cronbach alpha for constructs with more than two items. Measurement model fit and validityAll model parameters were successfully estimated. The fit indices showed the 2 value was significant (2 = 62.13, df = 37, p = .006), suggesting inadequate fit. However, this statistic is unreliable when data are non-normal, or samples have over 200 cases. Therefore, other fit indices were used to assess model fit (Lei & Lomax, 2005), which suggest that the model is well-fitting. The CFI value .994 indicates that the model is a is a good fit and adequately describes the sample data (Byrne, 2010). The SRMR value .0234 also indicates a good fit (Byrne, 2010), and that the correlations are explained to within an average error of .02. The RMSEA value .041 suggests good fit and that this is a parsimonious model (Tabachnick & Fidell, 2013). The PCLOSE value (.779) is nonsignificant; therefore, we can be 90% confident that the RMSEA value will fall between .022 and .059 in the population. See Figure 1 for the measurement model and its correlations. As shown in Table 3, the convergent and discriminant validity was adequate for all factors according to established conventions (Hair et al., 2015).Latent path model The model fit indices showed the 2 value was not significant (p = .739), suggesting a good fitting model. The CFI, SRMR, RMSEA and PCLOSE fit indices support the model being a good fit (Table 4). We can be 90% confident that the RMSEA value will fall between .000 and .059 in the population.Attitude, SN, CF, and AS each positively predicted IN to varying degrees (explaining 56% of the variance), as shown by the standardised regression weights (see Figure 2). CF explained the greatest amount of variance in IN, while SN explained the least. The model shows that having a stronger belief that taking HMs helps to control anxiety increased the intention to use HMs for this purpose. In addition, a more positive attitude towards using HMs for anxiety symptoms increased the intention to use HMs, as did a stronger belief that important others supported the use of HMs to treat anxiety symptoms. The more severe the anxiety symptoms the greater the intention to use HMs to relieve them. DiscussionThe aim of this study was to test a model predicting the intention to use HMs for anxiety symptoms, and identify individual predictors of intention. Attitude, subjective norms, control factors, and anxiety symptoms were each significant predictors of intention to use HMs for anxiety symptoms to varying degrees. Control factors were found to be the strongest predictor of intention to use HMs for anxiety symptoms. The stronger the belief that HMs would help control anxiety symptoms the stronger the intention to use HMs, which is consistent with previous research in general population samples reporting people who perceived they had more control over their health were more likely to use complementary medicine (Sirois, 2008), and to try complementary medicine prior to conventional medicine (Thomson et al., 2014). Conversely, in one study control factors did not predict HM use in older outpatients (Gupchup et al., 2006); however, this study used an alternative operational definition for control beliefs making it difficult to draw comparisons. The perception of having greater control over anxiety symptoms when taking HMs may be partially explained by their perceived qualities (e.g., having less side-effects, being natural and therefore safe), and being easily self-prescribed as they are widely available. Barriers to accessing mental health care are reported by people with anxiety disorders (Prins et al., 2008), which may contribute to a perceived lack of control; consequently, being able to self-prescribe HMs may help them regain control of their health. A positive attitude towards using HMs for anxiety symptoms was a significant predictor of intention. Research has repeatedly found that attitude towards a behaviour is the strongest predictor of intention in various health behaviours (McEachan et al., 2011). In this study control factors were a more important predictor of intention, which is consistent with the finding that people experiencing anxiety perceive they have less control over their health, and a greater need to control their health than those without anxiety (Shapiro et al., 1996).Subjective norms were the weakest predictor of intention in the model, which is consistent with other health behaviours in which subjective norms explained the least amount of variance in intention compared to other variables (McEachan et al., 2011). The findings suggest that the perceptions people have about what important others think about their HM use may not strongly influence their treatment decision-making relative to other factors. It is possible that people seek specific types of information about HMs when consulting their social networks, such as which herb to use or herbalist to consult. Anecdotal evidence provided by important others (i.e., descriptive norms) may be a more important predictor, as qualitative research in adults with anxiety found that people also valued anecdotal evidence from friends and family when deciding to use HMs (McIntyre et al., 2015b). Severity of anxiety symptoms predicted the intention to use HMs for these symptoms, which is consistent with studies finding severity of anxiety symptoms to be associated with HM use (Bystritsky et al., 2012; Ravven et al., 2011). In addition, people with an anxiety disorder diagnosis in the previous 12 months have been found to have a greater perceived need for treatment, and severity of common mental health disorders is associated with help seeking (Codony et al., 2009). A possible explanation for this finding is the influence of anxiety on decision-making. People with clinical anxiety have been found to be more risk averse than non-clinical controls (Maner et al., 2007), and risk aversion is related to heightened physiological arousal in response to choices considered to be risky (Hartley & Phelps, 2012). It is possible that people who experience anxiety and use HMs have beliefs that HMs are a safer option than pharmaceuticals, consequently perceiving them to be a less risky form of treatment. Beliefs that HMs are safer and more natural than pharmaceuticals have predicted complementary medicine use (including HMs) in general population samples (O’Callaghan & Jordan, 2003; Siahpush, 1999).This study identified important predictors involved in the intention to use HMs for anxiety symptoms; however, there are limitations. Use of single indicators to reflect complex constructs, although recommended in the TPB, may not adequately explain the variance accounted for in intention. In addition, two items were used to measure the constructs control factors and subjective norms; use of more items may have provided greater explanation of the variance in the model. The sample size of this study did not allow for testing a more complex theoretical model. As this was a cross-sectional study of an intermittent health behaviour, current or past behaviour was not used as a proxy for future behaviour as it is likely to introduce bias (Weinstein, 2007). This study did not include a prospective measure of actual behaviour due to time and financial constraints. Larger studies using prospective measures of behaviour are needed that seek to understand the relation between intention and future HM use and the influence of moderating variables. As 44% of the variance in intention is unexplained in the model, future research could aim to identify additional predictors of the intention to use HMs. Finally, the results may not be generalisable to all people experiencing anxiety, as only HM users participated in the study, and there was a low response rate suggesting a possible response bias. Future research should seek to understand this health behaviour in adults experiencing anxiety irrespective of herbal medicine use.This study is the first to use a theoretical model to explain the relations between variables involved in decision-making in using HMs for anxiety symptoms. Control factors, attitudes to HMs, social norms, and anxiety symptoms were found to be significant predictors of intention to use HMs for anxiety symptoms. These results provide an important contribution to our understanding of this health behaviour that will assist health practitioners, policy makers, and researchers. Health practitioners can help people experiencing anxiety to feel more in control of their health through shared decision making, and providing education about anxiety and evidence-based treatments. This is important as there is generally poor mental health literacy for anxiety disorders (Furnham & Lousley, 2013). Policy makers need to ensure people with anxiety have access to accurate and reliable information about HMs to ensure they can engage in safe and effective self-care. ReferencesAjzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. Ajzen, I., 2006. Constructing a theory of planned behavior questionnaire [online]. Icek Ajzen. Available from: [Accessed 11 Oct 2013].Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. Armitage, C. J. (2015). Time to retire the theory of planned behaviour? 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British Journal of Social Psychology, 48(1), 135-158. doi:10.1348/014466608X295207Zhang, J., Wider, B., Shang, H., Li, X., & Ernst, E. (2012). Quality of herbal medicines: Challenges and solutions. Complementary Therapies in Medicine, 20(1-2), 100-106. doi:10.1016/j.ctim.2011.09.004Figure 1Figure 1. Four-factor measurement model.Figure 2Figure 2. Path model predicting intention to use HMs for anxiety symptoms reporting standardised regression weights for each relation. Note. All paths were significant, p = .00, except subjective norms to intention, p = .016.Table 1. TPB items and response scales reflecting the latent constructs attitude, intention, control beliefs and subjective norms.Latent constructItems (direct measures of latent construct)Recoded response scalesAttitudeMy using HMs to treat anxiety symptoms would be:1 = unfavourable to 7 = favourableMy using HMs to treat anxiety symptoms would be:1 = bad to 7 = goodMy using HMs to treat anxiety symptoms would be:1 = unpleasant to 7 = pleasantMy using HMs to treat anxiety symptoms would be:1 = harmful to 7 = beneficialMy using HMs to treat anxiety symptoms would be:1 = negative to 7 = positiveIntentionDuring the next 3 months I plan to use HMs to treat my anxiety symptoms1 = unlikely to 7 = likelyDuring the next 3 months how likely is it that you will use HMs to help treat your anxiety symptoms?Control factorsI am in control of my health when I take HMs.1 = disagree to 7 = agreeI am in control of my anxiety symptoms when I take HMs.Subjective normsMost people who are important to me approve of me using HMs for treating anxiety symptoms1 = disagree to 7 = agreeMost people whose opinions I value would approve of me using HMs for treating anxiety symptomsTable 2. Mean, standard deviation, skewness, kurtosis, and reliability statistics for the latent constructs in the hypothesised path modelLatent constructNo. of itemsM (SD)SkewnessKurtosisrAttitude525.65 (6.65)-0.750.45.94.95-Intention28.77 (4.20)-0.36-1.180--.97Subjective norms 210.29 (3.16)-0.830.26--.88Control factors29.50 (2.96)-0.52-0.30--.69Anxiety symptoms75.82 (5.14)0.940.19.90.90-Note. = Cronbach alpha; = McDonald’s omega, r = Pearson’s inter-item correlation coefficientTable 3.AVE, CR and factor loadings for latent factorsSubjective NormsAttitudeIntentionControl BeliefsCRAVESubjective Norms0.94???0.930.87Attitude0.720.88??0.940.77Intention0.540.620.99?0.990.97Control factors0.460.550.630.840.830.70Note. CR = composite reliability; AVE = average variance extractedTable 4.Summary of Goodness-of-fit Indices for the path model predicting intention to use HMs for anxiety symptoms2dfCFISRMRRMSEA90% CIPCLOSE1.24831.00.009.000[.000–.059].918 ADDIN ................
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