Latent Profile Analysis of the Five Facet Mindfulness Questionnaire (FFMQ) Subscales in a Naturalistic Sample of Patients with Past or Present Major Depression: A Replication and Extension Study

The sample consisted of a naturalistic uncontrolled cohort of patients with past or present MDD who received MBCT at the Radboud University Medical Centre for Mindfulness in Nijmegen, the Netherlands, between July 2012 and April 2018. Demographic and clinical variables were derived from the electronic patient health records. Those patients with at least one episode of MDD were selected for the latent profile analysis. The current sample thus consisted of patients with different recurrence (recurrent versus single) and remission (current versus remitted) status of MDD; see Table 1.

Patients were referred to the center for mindfulness by their general practitioner or attending psychologist or psychiatrist. All referred patients were clinically assessed before taking part in the MBCT by means of a semi-structured psychiatric interview (see measures). The multi-disciplinary team consisting of the attending psychiatrist, psychologists, and mindfulness trainers evaluated whether MBCT was suitable or whether other evidence-based treatments were preferable for the patient. This was usually the case if patients met one or more of the following criteria: current substance dependency, acute suicidality, acute psychotic symptoms. In addition, motivation and practical barriers for participation in the MBCT were discussed. Patients were offered MBCT if they were willing to participate in a group setting, adhere to homework assignments, and able to attend at least six out of eight sessions and the silent day. Before the start of the MBCT and after the final session of the MBCT, patients were asked to complete a set of self-report questionnaires (see measures) as part of routine outcome monitoring (ROM). All patients who completed the baseline questionnaires were included in the current study. This same dataset (n = 765) was previously used in a study investigating the effectiveness of MBCT for MDD in routine clinical practice (Geurts et al., 2020). In the current study, patients who had missing data on all subscales of the FFMQ at baseline were excluded from analyses (n = 11), resulting in 754 patients for the LPA. All procedures were in accordance with the 1964 Helsinki Declaration and later amendments. The medical ethical committee waived the need for approval (CMO dossier: 2020–7226) per the Dutch Medical Research Involving Human Subjects Act, since participants were not subject to additional procedures or required to follow rules of behavior.

MBCT was provided in accordance with the original protocol (Segal et al., 2002) and consisted of eight weekly sessions of 2.5 hr, a 6-hr silent day in between Sessions 6 and 7, and daily home practice (30–45 min). The MBCT sessions were given in groups of 8–12 participants and consisted of psychoeducation and meditation exercises as well as dialogue and inquiry about those exercises. MBCT was taught by teachers meeting the advanced criteria of the Association of Mindfulness-Based Teachers in the Netherlands and Flanders, which are in concordance with the Good Practice guidelines of the UK Network of Mindfulness-Based Teacher Trainers (Crane et al., 2012).

The Mini International Neuropsychiatric Interview Plus (MINI-Plus; Sheehan et al., 1998) was used to determine psychiatric disorders. The MINI-Plus is a semi-structured psychiatric diagnostic interview developed to assess patients for psychiatric diagnosis according to DSM-IV-TR criteria and has good psychometric properties. It was conducted by trained psychiatrists (in training) or psychologists. Apart from MDD, several other comorbid psychiatric disorders were assessed, e.g., dysthymia, bipolar, anxiety, somatization, ADHD, addiction, and eating disorders (Geurts et al., 2021). Personality disorders and autism spectrum disorders were classified, either when diagnosed in the patient history and the psychiatric review was in accordance with these classifications or when suspected, additional diagnostic interviews were conducted, i.e., the structured clinical interview for DSM-IV personality disorders (SCID-II, First et al., 1997) and the Dutch Interview for diagnosing autism spectrum disorders (NIDA, Vuijk, 2016), respectively.

Mindfulness skills were measured with the Dutch version of the Five Facet Mindfulness Questionnaire-Short Form (FFMQ-SF; Bohlmeijer et al., 2011). This questionnaire consists of 24 items that measure levels of mindfulness on a 5-point Likert scale across the five facets “observing,” “describing,” “acting with awareness,” “non-judgment of inner experience,” and “non-reactivity to inner experience” (Baer et al., 2006). The facets’ scores were calculated by determining the mean of corresponding item scores, which is in accordance with relevant previous literature (Bravo et al., 2016; Bravo et al., 2018; Gu et al., 2020; Pearson et al., 2015). Negatively phrased items were reversed scored before computing the mean scores of the facets. Cronbach’s alpha/McDonald’s omega for the individual facets were 0.78/0.78 (observing), 0.87/0.88 (describing), 0.83/0.84 (acting with awareness), 0.78/0.78 (non-judgment of inner experience), and 0.76/0.77 (non-reactivity of inner experience).

Severity of depressive symptoms was assessed with the Beck Depression Inventory (BDI-II: Beck et al., 1996, Dutch version: Van der Does, 2002). The questionnaire contains 21 items which are scored on a 4-point Likert scale ranging from 0 to 3. Total scores of 0–13 indicate minimal, 14–19 mild, 20–28 moderate, and 29–63 severe depression. Individual items were summed to calculate a total score. Cronbach’s alpha/McDonald’s omega in the current sample was 0.91/0.91.

The Penn State Worry Questionnaire (PSWQ) contains 16 items and was used to assess worry propensity on a 5-point Likert scale ranging from 1 to 5 (Meyer et al., 1990, Dutch version: van Rijsoort et al., 1999). Cronbach’s alpha/McDonald’s omega in the current sample was 0.92/0.92. Total scores of 16–28 indicate very low, 29–38 low, 39–48 average, 49–59 high, and 59–80 very high (clinical range) level of worrying (van der Heiden et al., 2009).

The Outcome Questionnaire-45 (OQ45; Lambert et al., 1996) is a measure of psychological and general functioning consisting of 45 items which are scored on a 5-point Likert scale (0–4). The total score of the Dutch OQ-45 (ranging from 0 to 180) was used as a measure of overall functional impairment (de Jong & Spinhoven, 2008). Higher scores indicate poorer overall functioning. For the Dutch OQ45, the normative (cutoff) score is 55, meaning that a person scoring ≥ 55 belongs to the dysfunctional (clinical) range (de Jong & Spinhoven, 2008). Cronbach’s alpha/McDonald’s omega in the current sample was 0.92/0.92.

The Self-Compassion Scale (SCS) is a measure of self-compassion, consisting of six subscales: self-kindness, self-judgment, common humanity, isolation, mindfulness, and overidentification (Neff, 2003). The Dutch version of the SCS was used, which is highly similar to the original but uses a 7-point response scale (instead of 5) and includes only 24 instead of 26 items (Neff & Vonk, 2009). Subscale scores were computed by calculating the mean of the relevant item scores (range 0–7). The total score was calculated by summing the subscale (mean) scores. There are no clinical norms or scores which indicate that an individual is high or low in self-compassion. Negatively phrased items were reverse-scored prior to calculation of the subscales and total score. Cronbach’s alpha/McDonald’s omega for the total score was 0.91/0.92, and for the individual subscales 0.76/0.76 (self-kindness), 0.81/0.82 (self-judgment), 0.68/0.68 (common humanity), 0.79/0.79 (isolation), 0.76/0.76 (mindfulness), and 0.71/0.71 (overidentification).

From the 754 patients who completed the ROM at baseline, a post-MBCT measurement was available for 500 patients (Table 1). Patients without a post-MBCT measurement were slightly younger, had more comorbid developmental and personality disorders, and attended less MBCT sessions than patients with a full data set. In addition, they reported slightly lower levels of worry and slightly higher levels of some of the subscales of both the FFMQ and SCS at baseline (Table 1). Those with a post-MBCT measurement improved on all outcomes from pre- to post-MBCT (Table 1); also see Geurts et al. (2020).

The BIC, BLRT, and entropy for the latent profile models containing 2 to 8 profiles are presented in Table 2. The BIC value is lowest for the 4-profile-solution while the SABIC is lowest for the 7-profile solution. The BLRT is significant up to the 7-profile solution. In addition, the posterior classification probabilities (Supplementary Table S3) were highest in the 4-profile solution (4-profile; ≥ 0.71, 5-profile; ≥ 0.68, 6-profile; ≥ 0.65). Thus, based upon the BIC and posterior classification probabilities, a model consisting of 4 latent profiles fits the data best, whereas based on the SABIC and BLRT, a 7-profile solution would fit best. However, the 7-profile solution contains one profile with only 14 patients, which is considerably lower than the minimum of 25 as proposed by Lubke and Neale (2006) and was therefore rejected. In addition, when visually inspecting the 5-profile and 6-profile solution, it could be concluded that no “new” distinguished profile emerged (when compared to the other 4 profiles). Thus, when taking into account the profile solution found by Gu et al. (2020), other previous studies in non-clinical samples (Bravo et al., 2016; Bravo et al., 2018; Calvete et al., 2020; Ford et al., 2020; Kimmes et al., 2017; Lecuona et al., 2022; Pearson et al., 2015; Sahdra et al., 2017; Stanmyre et al., 2022; Zhang et al., 2019) and our fit indices, we settled for the 4-profile solution.

Standardized and unstandardized mean scores on the mindfulness subscales across the latent profiles based on the estimated four-profile model are presented in Table 3 and visualized in Fig. 1. Profile 1 contains 13.3% of the sample (n = 100) and had relatively high standardized mean scores on the “non-judging” subscale (z = 0.88 and low scores on the “observing” subscale (z = −0.97), which is comparable to both test and validation sample of the “non-judgmentally aware” subgroup identified by Gu et al. (2020). In addition, standardized scores on the “acting with awareness” subscale were slightly above the mean (z = 0.013), which is comparable to the validation sample in Gu et al. (2020). Therefore, Profile 1 was labeled as “non-judgmentally aware.” Profile 2 contains 24.4% of the sample (n = 184) and was labeled “very low mindfulness” because patients had relatively low standardized scores on all FFMQ subscales (all z-scores ranging from −0.50 to −0.93), comparable to those reported by Gu et al. (2020). Profile 3 contains the majority of the sample (48.5%, n = 366) and was most difficult to label. When examining the standardized scores, it can be appreciated that on all subscales standardized scores of patients are close to the mean (z-scores between −0.24 and 0.43), with the highest mean score for “observing” and the lowest mean score for “non-judging.” In addition, Fig. 1 B shows that patients in Profile 3 have high unstandardized scores on the “observing” (3.76) and “describing” (3.48) subscale and low scores on “non-reactivity” (2.52) and “non-judging” (2.63). This profile structure comes closest to the “judgmentally observing” profile previously described in extant literature (Bravo et al., 2016; Bravo et al., 2018; Kimmes et al., 2017; Pearson et al., 2015), and we therefore decided to label Profile 3 as such. Profile 4 contains 13.8% of the sample (n = 104). We labeled this profile “high mindfulness” because patients had relatively high standardized scores on all FFMQ subscales (z-scores ranging from 0.49 to 1.22).

Mean scores for depressive symptoms (BDI-II), worry (PSWQ), overall functional impairment (OQ-45), and (subscales of) self-compassion (SCS) across the different profiles were evaluated and compared with one another. Profile-specific unstandardized means for the mental health measures are presented in Table 4. A significant overall chi-square test indicates that the means of the four profiles were significantly different for that mental health measure (Table 4). Subsequently, pair-wise comparisons were made to compare the individual profiles with one another. Chi-square test-statistics for pairwise comparison between profiles are presented in Supplementary Table S4.

The “very low mindfulness” profile had higher scores for depressive symptoms (BDI-II) compared to all other profiles, whereas the “high mindfulness” profile had lower scores compared to all other profiles. Depressive symptoms in the “non-judgmentally aware” and “judgmentally observing” profiles did not differ from another but were higher compared to the “high mindfulness” profile and lower compared to the “very low mindfulness” profile.

Scores for worry (PSWQ) were different between all profiles, with highest levels in the “very low mindfulness” profile, and gradually diminishing levels in “judgmentally observing,” “non-judgmentally aware,” and “high mindfulness.”

For overall, functional impairment (OQ-45) scores were highest in the “very low mindfulness” profile and lowest in the “high mindfulness” profile and intermediate scores were found for the “non-judgmentally aware” and “judgmentally observing” profiles. This pattern across the different profiles was the same as compared to depressive symptoms.

For self-compassion, the “high mindfulness” profile had higher total scores on the SCS compared to other profiles, whereas the “very low mindfulness” profile had lower scores. The “non-judgmentally aware” and “judgmentally observing” profiles had intermediate scores. A similar pattern emerged for the subscales of the SCS, though per subscale slight differences were found.

First, we assessed whether the mindfulness profiles were predictive for post-treatment levels of the mental health outcomes. Post-treatment scores on the measures of mental health for the different profiles are presented in Table 5, together with the overall chi-square test statistics. Specific pair-wise comparisons between profiles can be found in Supplementary Table S5.

In line with relations found with the pre-treatment measures, post-treatment mean scores for depressive symptoms, worry, and overall functional impairment were lowest in the “high mindfulness” profile and highest in the “very low mindfulness” profile whereas scores for (subscales of) self-compassion were highest in the “high mindfulness” profile and lowest in the “very low mindfulness” profile. More specifically, while all the mental health measures at baseline significantly differed between the “high mindfulness profile” and all other profiles and between the “very low mindfulness” profile and all other profiles (except for “common humanity”), this distinction became a little bit less pronounced for post-treatment scores. These results show that mindfulness profiles at baseline are related to severity of depression and secondary outcomes measured before MBCT, but also after participation in MBCT.

However, this does not answer the question whether the rate of improvement is comparable over the profiles after accounting for the effect of baseline severity of the dependent variables. Therefore, the means of residualized change scores for depressive symptoms (BDI-II), worry (PSWQ), overall functional impairment (OQ-45), and (subscales of) self-compassion (SCS) were evaluated across the different profiles (Table 6). The overall chi-square tests were non-significant for both the depressive symptoms and all other measures. This suggests that based on the Wald chi-square test, the profiles do not differ in relation to treatment response in terms of residualized change scores on those outcomes. Because those analyses are explorative in nature, we chose to report the chi-square test statistics for pair-wise comparisons between the different profiles in Supplementary Table S6.

Out of 754 participants, only 5 had one or more missing indicator variables (FFMQ subscales) in the original dataset. As a result, the fit criteria for latent profile analyses containing 2 to 8 profiles, which were based on the 10 imputed datasets (Supplementary Table S7), were very similar to those reported in Table 2. In addition, mean scores of mindfulness facets across the latent profiles were very much alike (Table 3, Supplementary Table S8).

The (average) means of baseline, post-treatment, and residualized change scores of mental health measures across the profiles were very similar within the 10 imputed datasets compared to the original dataset that contained missing values at those distal outcomes (Tables 4–6, Supplementary Table S9-S11). In other words, when considering those 10 imputed datasets, profile membership was (still) related to baseline and post-treatment measures of mental health and not related to residualized change in those measures. Moreover, when examining the pair-wise comparisons, results were almost identical (Tables 4–6, Supplementary Table S9-S11).

Major strengths of this study are the large naturalistic sample in routine clinical practice, inclusion being based on clinical interview of both current and remitted patients, and the use of additional measures (OQ-45, PSWQ) to validate the identified profiles. This study also has several limitations. First, mindfulness profiles could have been more accurately estimated using latent transition analysis (LTA). LTA, in addition to between person variability, also accounts for within-person variability by incorporating post-treatment FFMQ scores. However, with respect to previous research, we aimed to replicate Gu et al. (2020) and to answer the clinical question whether profiles established before training are predictive of outcome; we constrained our profile analysis for this report to baseline FFMQ. LTA results will be reported elsewhere. Second, the fit indices did not consistently point to a four-profile solution. While the BIC and posterior classification probabilities indeed favored the 4-profile solution, the BLRT and SABIC favored a 7-profile solution. Because the 7-profile solution contained one profile with only 14 individuals and because a 5- or 6-profile solution would not result in new clearly “distinguishable” profiles, we decided to select the more parsimonious 4-profile solution. Future studies with larger samples may benefit from exploring the fit of >4 profiles (such as a 7-profile solution), but, at the same time, should be cautious for overfitting. Third, the entropy value of 0.62 is relatively low. Entropy values range from 0 to 1, with higher entropy values indicating a more accurate identification of the latent profiles. While a good value of entropy is considered to be 0.80 or higher, a bad or cutoff value for entropy is hard to specify (Weller et al., 2020). In addition, entropy values can be a poor tool for model selection (Collins & Lanza, 2009) and we therefore followed (Weller et al., 2020) to report but not rely on the entropy for selecting the number of profiles. Fourth, we used the 24-item FFMQ-SF to measure the facets of mindfulness, whereas previous studies used the 39-item FFMQ (Gu et al., 2020). Although the 5-factor structure of the FFMQ-SF has now been validated in the current sample and by Bohlmeijer et al. (2011) who additionally showed that the 5-factor structure was highly similar to the long version (high correlations between SF and long version; similar internal consistency in terms of Cronbach’s alpha (Bohlmeijer et al., 2011)), slight differences between the instruments could have resulted in differences in the observed latent profiles. Fifth, a considerable number of patients (34%) did not complete the questionnaires post-MBCT. Within the main analyses, the identification and evaluation of mindfulness profiles before MBCT was based on all available baseline data whereas the residualized change scores could only be calculated for patients with a full data set. Consequently, evaluating the means of post-MBCT and residualized change scores over those profiles could only be done for patients with a full data set. Although patients with a full data set hardly differed from patients without a post-treatment measurement, those differences may have slightly influenced the analyses and reduce generalizability to the whole (n = 754) sample. In addition, for a considerable number of patients, due to change in the clinical questionnaire set, a pre (112 out of 754) and post score (86 out of 500) on the OQ-45 was missing, which may have influenced reliability of estimation of OQ-45 scores across the different profiles. Therefore, we performed sensitivity analyses with ten imputed datasets and included variables that may have been related to missingness within the imputation process. The results of these analyses were almost identical to the original results, which strengthens the robustness of the findings. Of note, the number of imputed datasets as well as the number and selection of predictors within the sensitivity imputation models were chosen arbitrary, which introduces risk for systemic bias. However, despite this limitation, the results obtained from both the main analysis and the sensitivity analysis were nearly identical, suggesting that this issue may have minimal impact on the overall findings.

In conclusion, LPA is a person-centered method that in addition to traditionally used variable centered methods could help to better understand the overall construct of mindfulness and the relations between its individual facets. For future research, it is important to continue to assess whether mindfulness profiles are consistently identified in non-clinical and clinical samples, how they relate to measures of mental health, and whether mindfulness profiles in clinical samples differ from those in non-clinical samples. Furthermore, future research will benefit from studies that use a longitudinal design in which the predictive value of baseline profiles for treatment outcome can be further evaluated. In addition, studies aimed to assess whether MBCT specifically adapted to the needs of individuals (e.g., patients with a self-critical judgmental attitude) improve effectiveness of MBCT may be of particular interest. Moreover, the current study and the study of Gu et al. (2020) only identified the latent profile structure at a specific point in time. This prevents conclusions on whether a patient’s mindfulness profile changes in time (e.g., after a MBI), and whether those hypothesized changes in profile are related to changes in outcome. Latent transition analysis (LTA) could fill this gap. By means of LTA, it could be elucidated whether hypothesized improvement in mindfulness skills after an MBI (e.g., change of a “very low mindfulness” profile to a profile considered to be related to good scores on mental health measures) would go hand in hand with improvement in measures of mental health.