Mindfulness is generally defined as a non-elaborative, non-judgmental, present-centered awareness where different thoughts, feelings, and sensations that arise in one’s attentional field are acknowledged and accepted as they are (Kabat-Zinn, 1990). It has been described as a trait-like kind of awareness, though mindfulness can also be viewed as a mode, or state-like quality, that is maintained only when attention to experience is intentionally set with an open, non-judgmental orientation to experience (Bishop et al., 2004). In fact, the rationale of mindfulness-based interventions (MBI) is that evoking the state of mindfulness regularly across meditation practice increases the propensity of an individual toward mindfulness in everyday life (Davidson, 2010). In this regard, Kiken et al. (2015) showed that repeated mindfulness meditation practice within an MBI increased individuals’ state mindfulness over time, which in turn, predicted changes in trait mindfulness and psychological distress.

As repeated measures of key state variables during MBIs contribute to a better understanding of the trajectory of mindfulness training (Navarrete et al., 2022), the need for researchers and clinicians to have access to reliable measures is growing. Nevertheless, when attempting to measure mindfulness, the difficulty arises depending on whether trait or state mindfulness is the target. Most self-report measures of mindfulness assess the construct as a trait-like behavior (i.e., a general tendency to be mindful in daily life) as opposed to a state-like construct (Siegling and Petrides, 2014; Baer, 2019). An example of the most popular trait mindfulness measure is the Five-Facet Mindfulness Questionnaire (FFMQ; Baer et al., 2006), which is used worldwide (Lecuona et al., 2020). In contrast, there are very few self-report measures focused on state mindfulness, which consequently means that their psychometric properties have been less studied (Baer, 2019).

Currently, the two most important self-report measures of state mindfulness are (Baer, 2019): the Toronto Mindfulness Scale (TMS; Lau et al., 2006) and the State Mindfulness Scale (SMS; Tanay and Bernstein, 2013). The TMS is a reliable and valid measure of state mindfulness, derived from Bishop et al.’s (2004) operational definition of mindfulness which comprised the components of ‘Orientation to Experience’ -characterized by openness and curiosity- and ‘Self-Regulation of Attention’ – being aware of current thoughts, feelings, and sensations without getting caught up in ruminative or elaborative thinking-. In contrast, the original SMS framework was based on an integration of the traditional Buddhist concept of mindfulness and also Bishop et al.’s (2004) construct definition.

The TMS is a 13-item scale that encompasses two separate factors: curiosity and decentering. Curiosity refers to the quality with which one becomes aware in the present moment, while decentering refers to the ability to be aware of one’s experience without being drawn in by the different stimuli (Lau et al., 2006). The TMS has been adapted and validated to Korean (Lee et al., 2010) and Chinese (Yu et al., 2021) populations, though only the last performed factor analysis. In addition, Ireland et al. (2019) replicated and expanded Lau et al. (2006) findings regarding the original –English- version of the TMS. Regarding dimensionality, Ireland et al. (2019) and Yu et al. (2021) found support for the two-factor structure after implementing some modifications that mainly involved the curiosity factor. Overall, the TMS showed a good internal consistency and adequate convergent/discriminant validity, correlating positively with trait mindfulness (only decentering factor), decentering, frequency of meditation practice, wellbeing, self-awareness, inner peace, positive affect, and negatively with stress, depression, and anxiety with a small-to-medium strength for all the associations (r range from 0.02 to 0.54; Lee et al., 2010; Ireland et al., 2019; Yu et al., 2021).

The SMS is a 21-item scale that assesses two state mindfulness aspects: one reflecting state mindfulness of bodily sensations and the other reflecting state mindfulness of mind. Dimensionality analyses from the original validation study showed that the SMS entailed a hierarchical two-factor structure including a higher-order state mindfulness factor (Tanay and Bernstein, 2013). As the original authors noted (Ruimi et al., 2022), this scale has been translated in to various languages, including Spanish, and has been used in many research settings (e.g., Navarrete et al., 2021a). However, except for the original work by Tanay and Bernstein (2013), only one study has assessed its psychometric properties in another culture (Andrade et al., 2019). In a Portuguese convenience sample, Andrade et al. (2019) showed that the original hierarchical two-factor structure plus four pairs of correlated error terms were not supported. In addition, they tested a modified hierarchical two-factor structure with six pairs of correlated error terms, for which they found an adequate goodness of fit. In both Tanay and Bernstein (2013) and Andrade et al. (2019) studies, internal consistency was adequate and convergent/discriminant validity analyses were in the expected direction, showing that state mindfulness was positively correlated with curiosity, decentering, some facets of trait mindfulness, and positive affect, and negatively with suppression emotion regulation strategy with a small-to-medium strength for all the associations (r range from 0.05 to 0.56; Tanay and Bernstein, 2013; Andrade et al., 2019).

As mindfulness research grows among Spanish-speaking countries, the need for reliable and valid Spanish adaptations of these scales arises (Hervás et al., 2016). Moreover, further psychometric studies of the TMS and SMS are warranted in order to clarify some aspects about their factor structure (Ireland et al., 2019; Ruimi et al., 2022). Finally, previous studies used maximum likelihood or maximum likelihood robust estimation methods when confirmatory factor analyses were conducted, which are less optimal methods for Likert-type data than diagonally weighted least squares (Li, 2016).

Against this background, the present work assesses the psychometric properties of the Spanish versions of the TMS and the SMS in pooled samples from the Spanish general population. More specifically, the objectives of this study were to analyze the factor structure of both scales (i.e., the TMS and the SMS), their reliability, convergent/discriminant validity, and sensitivity to change. Regarding the first objective, we expected that a correlated two-factor model and a hierarchical two-factor model for the TMS and the SMS, respectively, would yield the best fit in our dataset (Hypothesis 1). Moreover, we expected adequate internal reliability (Hypothesis 2) and convergent/discriminant validity for these measures. In this sense, we hypothesized that the TMS scores would be positively associated with mindfulness facets, decentering, and non-attachment, as well as negatively associated with depression, anxiety, and stress with a small-to-medium strength for all the associations (Hypothesis 3). Similarly, we hypothesized that SMS scores would be positively associated with TMS scores (strong association expected), trait mindfulness facets (i.e., observing, describe, non-judging, and non-reactivity), positive affect, and self-reassurance, as well as negatively associated with self-criticism with a small-to-medium strength for all the associations (Hypothesis 4). Finally, it was anticipated that both state mindfulness measures would be sensitive to change after meditation practice, showing statistically significant higher TMS and SMS scores in participants after common meditation practices (Hypothesis 5).

In this study, we evaluated the psychometric properties of the Spanish versions of the TMS and SMS, which assess state mindfulness, in pooled non-clinical samples from the Spanish general population. Regarding Hypothesis 1, the TMS showed a correlated two-factor structure (curiosity and decentering), consistent with the original model (Lau et al., 2006), though with three pairs of correlated error terms proposed in previous psychometric studies (Ireland et al., 2019; Yu et al., 2021). So far, the correlated two-factor structure has been considered the best factorial model and previous studies has not shown evidence in favor of the presence of a general factor (Lau et al., 2006; Ireland et al., 2019; Yu et al., 2021). Regarding the SMS, a bifactor model was confirmed, instead of the hierarchical two-factor model proposed by Tanay and Bernstein (2013). Even so, both factor structures theoretically allow the scoring of the SMS subscales (mindfulness of body and mind) and a general factor of the whole scale (Brunner et al., 2012). In fact, this result might be in line with recent unpublished reports about dimensionality of the SMS by the original authors, who found support for a “similar factor structure as originally reported” (Ruimi et al., 2022, p. 13).

Regarding reliability (Hypotheses 2), the TMS scores demonstrated adequate internal consistency as expected, with similar values to those obtained in previous studies (Lau et al., 2006; Yu et al., 2021). In that sense, it should be noted that we have reported McDonald’s ω for the first time as an estimator of internal consistency of this scale. With respect to the SMS, although internal consistency analysis suggested that the total score was a reliable measure of state mindfulness, in contrast with Andrade et al. (2019), a small portion of reliably measured variance could be attributed to body and mind factors. According to our results, despite the multidimensionality of the items, these specific factors showed low reliability because they seem to be tapped primarily by the general factor of state mindfulness. The discrepancy between the present findings and those of Andrade et al. (2019) regarding the SMS might be due to the use of different approaches to model the structure of the latent variables, a hierarchical model in Andrade et al. (2019) vs. a bifactor model here. In a hierarchical model, the reliability estimates of subfactors are typically expected to be higher compared to a bifactor model because the subfactors share a substantial amount of common variance due to their direct dependence on the higher-level factor. On the contrary, the specific factors in bifactor models are intentionally designed to capture specific and unique dimensions of the construct, and they may not share as much common variance or exhibit high internal consistency (Reise et al., 2013). The presence of a strong general construct that explains much more variance than the specific constructs is a common circumstance in many self-report measures (Brunner et al., 2012; Luciano et al., 2014). Therefore, it is not recommended to compute state mindfulness of bodily sensations and state mindfulness of mind scores separately because an interpretation of a person’s level in any of both specific domains involves great uncertainty. Nevertheless, it might be useful to compute the subscales scores when complex measurement models are required (Reise et al., 2013), for instance when testing Tanay and Bernstein’s (2013) definition of state mindfulness.

With respect to convergent/discriminant validity of the TMS (Hypothesis 3), these results showed that the TMS scores were not significantly related to the facets of trait mindfulness, except for a weak association between decentering scores and non-reactivity to inner experience. Similarly, Ireland et al. (2019) found that only decentering was significantly associated to trait mindfulness (measured with the Mindful Attention Awareness Scale; Brown and Ryan, 2003). Moreover, Yu et al. (2021) reported a significant correlation between both TMS subscales scores and only observing and non-reacting. In addition, TMS scores were positively associated to decentering and non-attachment, except for curiosity with non-attachment. Similarly, Lee et al. (2010) found a significant correlation of TMS scores with decentering assessed with the EQ. However, no previous studies included the non-attachment construct, but similar ones with which curiosity and decentering (TMS) were significantly related, that is, psychological mindedness and private self-awareness (Lau et al., 2006; Yu et al., 2021). In addition, TMS scores have not shown a significant association with psychopathology symptoms in this study nor in previous ones (Lau et al., 2006; Lee et al., 2010; Yu et al., 2021), except for Ireland et al. (2019), who found a significant association between decentering and DASS-21 scores.

In this line (Hypothesis 4), SMS scores were significantly related to positive affect, but not to negative affect, similar to what Andrade et al. (2019) reported. Also, our results showed for the first time a negative association between state mindfulness and self-criticism. Meanwhile compassion research has shown interest in the influence of self-criticism on compassion states or compassion practice quality (Gilbert et al., 2006; Naismith et al., 2018; Navarrete et al., 2021b), further research is needed to study the influence of self-criticism in the process of generating mindfulness states and mindfulness meditation practice, specifically to clarify its directionality.

Our results showed a significant association between SMS scores and the observing facet of mindfulness, which Tanay and Bernstein (2013) and Andrade et al. (2019) also reported. However, there were no significant associations between SMS scores and describing non-reactivity, or non-judging meanwhile those authors did find them. It should be noted that assessing construct validity by correlating both trait and state measures might lead to counter-intuitive results (e.g., lack of or inconsistent correlations between state mindfulness and facets of trait mindfulness). However, the fact that TMS and SMS scores do not correlate very well with more stable measures (e.g., FFMQ or MAAS) probably also indicates that they are actually capturing “state” constructs. That is, state measures should show a higher correlation with other state measures than with trait measures on a given occasion (Zuckerman, 1983). In that regard, there was a significant association between the TMS and SMS scores. As previously reported, the magnitude was moderate indicating the differences in conceptual aspects of both scales (Tanay and Bernstein, 2013).

Regarding sensitivity to change (Hypothesis 5), participants who participated in the 1-month Vipassana meditation retreat, a compassion-based meditation or a loving-kindness meditation showed a significant increase in state mindfulness levels, as expected. Along this line, the TMS and SMS have been able to detect state mindfulness changes in a variety of mindfulness psychoeducation and practices (Lau et al., 2006; Tanay and Bernstein, 2013; Ruimi et al., 2022).

The main implication of this study is that the Spanish version of the TMS and SMS can be used for state mindfulness assessment in intervention research among Spanish participants from the general population. Overall, both have shown to be reliable measures with expected patterns of convergent validity and good responsiveness. However, these findings must be interpreted understanding the following limitations. First, the best-fitting CFA models achieved the standards of good model fit by the narrowest of margins. Indeed, the higher RMSEA values in the 90% confidence interval suggest that there is room for model improvements in both scales. Moreover, the variety of samples included in this study might partially bias the results because the recruitment and assessment were independent and different for each one (Podsakoff et al., 2003). In this regard, the TMS and SMS were administered either online or in person and along with different questionnaires. In addition, the study samples were recruited by a non-probability (convenience) sampling process. Therefore, it is difficult to determine how well the Spanish population is represented by them, which limits the generalizability of the findings. For instance, the samples were not representative of the Spanish general population in terms of their gender distribution (more than 70% in each sample were women) and level of education (high proportion of participants with university studies). Also, the extent to which our findings can be generalized to other Spanish-speaking countries might be limited. Moreover, all the measures used were self-report measures. Furthermore, the sample sizes were modest, especially for the TMS. Although the present samples were enough for CFA analyses according to commonly cited rules of thumb (ratio of 5:10 or a minimum sample size of 100–200; Brown, 2015), larger samples guarantee higher statistical power and robustness of the models. Regarding sensitivity to change, the studies interventions had not a control group to determine whether the changes were due to the intervention or not. Thus, the changes captured by the paired-samples t-tests could be partially (not totally) related to the meditations. Furthermore, participants were generally female, young, university educated, and without previous experience in meditation practice, which might limit the generalizability of the results to the wider population. In this line, future cross-sectional validation studies should recruit a large sample. Then, the factor structure of the TMS and the SMS Spanish versions should be tested to explore potential model modifications or alternative explanations to enhance the CFA models fit. Additionally, it is recommended to conduct replication studies using Spanish samples from various levels of education, age ranges, and in accordance with the population’s gender ratio. Finally, future research should consider studying the reliability of the SMS in detail to inform about the possibility of computing the subscale scores.

The data analyzed in this study is subject to the following licenses/restrictions: The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to a privacy issue. Requests to access these datasets should be directed to AF-S, albert.feliu@uab.cat.

The studies involving human participants were reviewed and approved by the Ethics Committee at the Sant Joan de Déu Foundation (PIC-141-19). The patients/participants provided their written informed consent to participate in this study.

AC-C, JS-M, DP, A-JS-L, JG-C, MD, JS, AC, AF-S, and JL designed and executed the study. JN and MF-M analyzed the data and wrote the manuscript. All authors reviewed and approved the final version of the manuscript for submission.

We are grateful to the CIBER of Epidemiology and Public Health (CIBERESP CB22/02/00052; ISCIII), CIBER of Mental Health (CIBERSAM), and CIBER of Obesity and Nutrition (CIBEROBN) for their support. JN has a research contract from the Institute of Health Carlos III (ISCIII; ICI20/00080). JS-M has a PFIS predoctoral contract from the ISCIII (FI20/00034). AC-C has a FI predoctoral contract from AGAUR (FI_B/00216). AF-S acknowledges the funding from the Serra Húnter program (UAB-LE-8015). The ISCIII did not have any role in the analysis and interpretation of data, in the writing of the manuscript, or in the decision to submit the paper for publication.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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