Neuroscience and Biobehavioral Reviews 143 (2022) 104957 Available online 9 November 2022 0149-7634/© 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by- nc-nd/4.0/). Efficacy of mindfulness to regulate induced emotions in the laboratory: A systematic review and meta-analysis of self-report and biobehavioral measures Rosaria María Zangri a,1, Catherine I. Andreu b,1, Inés Nieto a, Ana María González-Garzón c, Carmelo Vázquez a,* a School of Psychology, Complutense University of Madrid, Spain b Polibienestar Institute, University of Valencia, Spain c Department of Psychology, University of Massachusetts Boston, USA A R T I C L E I N F O Keywords: Mindfulness Emotion regulation Mood induction Mindfulness-based intervention Meta-analysis A B S T R A C T A substantial part of the research on the efficacy of mindfulness-based interventions on mood regulation is conducted in the laboratory. Nevertheless, a systematic review of the results is lacking. This meta-analysis aimed to investigate the effects of mindfulness as an emotion regulation (ER) strategy when using mood induction procedures. A systematic search of databases was conducted and a total of 43 studies were included in the meta- analysis. We found a small significant overall effect size of mindfulness [g= − 0.15 (95% CI [− 0.30, − 0.01], p = 0.04)], which became non-significant after removing outliers (g=− 0.15, p = 0.06). We also found high levels of heterogeneity which was not explained by the moderating variables analyzed. Thus, there is limited meta- analytic evidence of the efficacy of mindfulness strategies in down-regulating or preventing heightened or chronic effects of induced mood states in well-controlled laboratory settings. We propose that this could be partially due to some limitations in laboratory methodologies and suggest some guidelines to overcome them in future primary research. 1. Introduction Emotion regulation (ER) refers to the process by which individuals implement strategies (conscious and nonconscious) to modulate their emotional experiences, expression, and physiology, to adaptively respond to environmental demands (Gross et al., 1998; Aldao, 2013). ER is an important mediator between emotions, and mental and physical health (Nyklicek, Vingerhoets, & Zeelenberg, 2011). Also, numerous studies have not only demonstrated individual differences in the use and efficacy of emotion regulation strategies (Gross and John, 2003) but that these differences are related to different psychopatho- logical conditions (Aldao et al., 2010; Campbell-Sills & Barlow, 2007; Sloan et al., 2017), emotional health, well-being, and interpersonal functioning (Gross and John, 2003). Modern conceptions of ER emphasize that the adaptiveness of a given ER strategy depends on the context (Aldao et al., 2015) and the individual’s intentions in using the strategy (Ford et al., 2019). However, there is evidence that, in general, strategies like reappraisal, acceptance, or problem-solving enhance psychological flexibility, emotional health, and wellbeing (Gross & Thompson 2007), whereas other strategies (e.g., suppression, rumination, worry, avoidance) are generally associated with psychological symptoms and are deemed as less adaptive (Aldao et al., 2010). 1.1. Emotion regulation and mindfulness In recent years, there has been a growing research field on the effects of mindfulness on well-being and mental health. Although there are different meanings and uses of the concept of mindfulness (Nash et al., 2013; Nilsson and Kazemi, 2016), a commonly accepted idea is the non-elaborative, non-judgmental awareness that emerges because of intentionally paying attention to the present experience (Kabat-Zinn, 2005). Two main components are underlined in this definition: (1) the monitoring of attention to and awareness of present-moment * Correspondence to: School of Psychology, Universidad Complutense de Madrid, Madrid 28223, Spain. E-mail address: cvazquez@ucm.es (C. Vázquez). 1 Equal contribution Contents lists available at ScienceDirect Neuroscience and Biobehavioral Reviews journal homepage: www.elsevier.com/locate/neubiorev https://doi.org/10.1016/j.neubiorev.2022.104957 mailto:cvazquez@ucm.es www.sciencedirect.com/science/journal/01497634 https://www.elsevier.com/locate/neubiorev https://doi.org/10.1016/j.neubiorev.2022.104957 https://doi.org/10.1016/j.neubiorev.2022.104957 https://doi.org/10.1016/j.neubiorev.2022.104957 http://crossmark.crossref.org/dialog/?doi=10.1016/j.neubiorev.2022.104957&domain=pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ http://creativecommons.org/licenses/by-nc-nd/4.0/ Neuroscience and Biobehavioral Reviews 143 (2022) 104957 2 experiences, and (2) a mental attitude of acceptance and non-judgment toward momentary experiences (Bishop et al. (2004); Quaglia, et al., 2014; (Lindsay and Creswell, 2017; Malinowski, 2013). These two components are the central skills developed across different well-validated Mindfulness-based Interventions (MBIs) (Creswell, 2017; Wielgosz et al., 2019). Meta-analytic studies have found that MBIs are significantly associ- ated with reductions of stress and other negative emotions both in non- clinical (Galante et al., 2021) and clinical populations (Goldberg et al., (2018, 2021); (Hedman-Lagerlöf et al., 2018; Wielgosz et al., 2019), which is consistent with the view that mindfulness leads to improve- ments in mental health by enhancing our ability to regulate emotions (Alsubaie et al., 2017; Hölzel et al., 2011; Chambers, Gullone, & Allen, 2009; Gratz & Tull, 2010; Erisman and Roemer, 2010; Wielgosz et al., 2019) and prevent amplification of negative affect. Specifically, corre- lational, experimental, and treatment studies converge to suggest that the practice of mindfulness is associated with healthy emotion regula- tion by modulating the level of emotional response (intensity or dura- tion) or receptiveness, and vulnerability to negative emotions (Chiesa et al., 2013; Fogarty et al., 2015). Influential theoretical models of mindfulness have suggested that the mediators of outcomes (e.g., in the reduction of stress) are related both to attentional changes (Malinowski, 2013; Bishop et al., 2004; Tang et al., 2015) and improvements in emotion regulation (Lutz et al., 2008; Ostafin et al., 2015). This tenet is supported by empirical studies showing that reductions in distress and increases in well-being, after MBIs, are associated with attentional changes towards emotional stimuli (e.g., Roca & Vázquez, 2020) and increased use of certain ER strategies like positive reappraisal or acceptance (Garland et al., 2017; Lindsay et al., 2018). However, most of these conclusions are based on obser- vational or correlational studies in which clinical or psychological im- provements after MBIs are associated with higher use of those presumably adaptive ER strategies (Chambers et al., 2008). Experi- mental research is particularly needed to clarify the mechanisms through which MBIs operate (Lindsay and Creswell, 2017; Kober et al., 2019; Guendelman et al., 2017). 1.2. Mindfulness in controlled laboratory settings Going beyond correlational studies, a more rigorous approach to the study of the role of mindfulness in ER is by using experimental designs in which participants are subjected to the same contingencies aimed to elicit negative emotions (i.e., Mood Induction Procedures, MIPs). MIPs have been extensively used in the field of experimental psychology and psychopathology (Westermann et al., 1996) and favor the possibility of analyzing the efficacy of certain strategies to amplify or reduce the induced emotional responses. MIPs have used a diversity of approaches such as listening to music, watching emotional images or film clips, recalling autobiographical events, performing mathematical exercises, or using specific paradigms such as the Trier Social Stress Test (TSST), reading emotional sentences (i.e., Velten’s procedure), Cold Pressor Task, or the Maastricht Acute Stress Test (MAST). There is meta-analytic evidence showing that MIPs are effective procedures to induce emotions and, in particular, negative ones, for which the average effect size is twice as large than for positive induced moods (Joseph et al., 2020). Although there are variations in the procedures, we show in Fig. 1a schematic outline of typical MIP studies. These studies usually elicit the targeted mood (e.g., specific emotions such as shame or general nega- tive/positive affect) by, for example, recalling autobiographical events (typically thinking or writing about it) associated with that specific af- fective state. To check the effectiveness of the induction, ideally par- ticipants’ mood should be assessed with self-report measures before and after the task (e.g., using the Positive and Negative Affect Schedule, PANAS; Watson et al., 1988). For instance, to induce psychological stress, specific paradigms have been designed and validated in the literature, such as the Trier Social Stress Test (TSST; Kirschbaum et al., 1993). In the original protocol, for which there is robust evidence of its effectiveness (Allen et al., 2017), participants undergo a 10-minute Fig. 1. Typical study designs included in this meta-analysis test the effects of mindfulness on affect regulation in the context of laboratory mood induction procedures (MIP). Studies analyze the effects of A) standardized Mindfulness-Based Interventions (MBI), B) guided mindfulness instructions, or C) participants’ preexisting characteristics (e.g., level of trait mindfulness or experience with meditation). Regardless of the study design, all down-regulating effects in these studies could be understood as indicators of an overall benefit that allows people to regulate emotions in a way that prevents the unfolding of heightened or chronic states of negative affect. R.M. Zangri et al. Neuroscience and Biobehavioral Reviews 143 (2022) 104957 3 anticipation period, and then 10 min of delivering a speech and per- forming a mental arithmetic test in front of an audience, inducing psy- chobiological changes as a result (Kirschbaum et al., 1993). There is already abundant literature on the use of well-controlled MIPs to assess the efficacy of mindfulness as an ER strategy. Following the above-described procedure, a negative mood is induced in partici- pants, such as stress or sadness, and they are asked to down-regulate it by either using their mindfulness expertise or, to increase the experi- mental control of the procedure, by following guided instructions. These instructions can also vary in their focus, being the most frequently used those focusing on redirecting attention (Sanders and Lam, 2010) or emphasizing compassion (Cȃndea & Szentágotai-Tătar, 2018). Even though MIPs have become a standard paradigm in the field of ER research, the evidence shows mixed results in the context of the regu- lation of induced affect. While self-report measures suggest favorable effects of MBIs in the reduction of stress, physiological measures such as cortisol, show more heterogeneous results (Morton et al., 2020). Some authors suggest a decrease in the acute cortisol response following mindfulness training (Lindsay and Creswell, 2019), whereas others report no significant effect (Nyklíček et al., 2013). Mixed results from studies using self-report and physiological mea- sures of emotion could be explained by the lack of strong convergence and intrinsic divergences between these measures (Mauss and Robinson, 2009). However, it is also possible that the overall disparity in results of studies addressing the efficacy of mindfulness strategies to regulate induced negative moods, relates to the existence of mediators that may intervene in the ER process and have not been systematically examined. For example, it has been found that people with clinical or subclinical symptoms regulate their mood differently compared to healthy samples (e.g., depression and anxiety related to rumination and worry; Aldao et al., 2010). Thus, it is possible that the use of mindfulness as a strategy to down-regulate negative emotions has a different impact on people with different psychopathologies and different levels of symptoms. There are also significant variations regarding the design of the MIP and the experimental conditions. Many different MPIs have been used in mindfulness studies aimed at studying ER. Some studies have used music (Karl et al., 2018), and others have used images (Wu et al., 2019), or a structured procedure like the TSST (Manigault et al., 2019). Second, there have been a variety of induced emotions for which mindfulness strategies are required to intervene. For instance, some studies have induced stress (Nyklíček et al., 2013), whereas others have induced sadness (Sanders and Lam, 2010), or rather nonspecific negative emotional states (Rosenberg et al., 2015). Third, the types of in- struments to measure the impact of the intervention also vary. Although self-reports are the most common measure in these studies (Cȃndea & Szentágotai-Tătar, 2018; Keng and Tan, 2017; Wilson et al., 2014), physiological measures have also been frequently used (Engert et al., 2017; Koerten et al., 2020). Regarding psychophysiological measures, MPI studies have used stress reactivity measures like cortisol (Basso et al., 2019; Creswell et al., 2014; Engert et al., 2017; Hoge et al., 2018; Lindsay et al., 2018; Nyklíček et al., 2013), cardiovascular measures including heart rate and heart-rate variability-HRV (Engert et al., 2017), respiratory sinus arrhythmia-RSA (Kemeny et al., 2012) and blood pressure (Nyklíček et al., 2013), electrodermal activity (Erisman and Roemer, 2010; Scavone et al., 2020; Wilson et al., 2014) and inflam- matory biomarkers (Hoge et al., 2018). A careful examination of the literature reveals that, regarding the experimental condition, there is remarkable variation in the instructions given to participants to regulate their emotions and the type of control group implemented to compare its efficacy (active control group or wait- list controls). Some laboratory studies present the mood induction pro- cedure before the instruction to use an emotion regulation strategy (Keng & Tan, 2018; Lindsay et al., 2018; Wu et al., 2019), while others ask to down-regulate mood during or after the MIP (Azam et al., 2016; Remmers et al., 2016). Other studies do not even present an instruction to use a mindfulness-based strategy but rely on the score of well-known questionnaires that measure different aspects of the ‘mindfulness trait’ (Watford et al., 2020). Moreover, those studies based on long-term meditators may use the MIP before and after the intervention to mea- sure a potential mood/stress reactivity change, but may not present participants with an explicit instruction to regulate their mood (Rosenberg et al., 2015). Thus, as studies have been heterogeneous in their methodology (e.g., population characteristics, control conditions, MBI protocols, intervention dosage, and type of MIP), these differences have also been considered as potential moderators of stress reactivity results from studies that implement cortisol, cardiovascular, and self-report measures. In sum, due to the high heterogeneity in the type of designs of mindfulness studies using MIPs and their mixed results, a systematic review and meta-analyses of MIPs and mindfulness can help to shed light on this research field. Several reviews have already been carried out to summarize and analyze together the effects of mindfulness on emotional regulation (reactivity and/or recovery) and to examine the variability across studies in the field. Again, results are highly variable, with some reviews showing that meditation practice leads to decreased physio- logical markers of non-induced stress (Pascoe et al., 2017), whereas others conclude that stress-buffering effects of mindfulness in- terventions are robust when measured by self-report but less clear for physiological measures (Morton et al., 2020). In the same line of nega- tive findings, a recent meta-analysis found that MBIs were not effica- cious in increasing heart rate variability relative to control conditions (Brown et al., 2021). Furthermore, in regard to the regulation of nega- tive affect, two meta-analyses (Schumer et al., 2018; Leyland et al., 2019) have found that brief mindfulness training reduces negative affect, with a small but significant effect size (g=.21 and d=− .28, respectively). The quality of the studies in the literature should be carefully examined to determine the effectiveness of the results. There are currently no reviews synthesizing and critically analyzing if mindfulness strategies are efficacious to down-regulate affect after a MIP. Studies using MIPs are important because they provide better explanatory indicators of the role of the relationships between emotion, behavior, and cognition. Although their ecological validity is often questionable, they allow to control for the source and intensity of mood induction in controlled conditions and thus shed light on how a given intervention (e.g., mindfulness-based) contributes to down-regulating disruptive emotions. The latter is of extreme importance in preventing the unfolding of heightened or chronic states of negative affect that can lead to mental health problems. Our systematic review and meta- analysis aim to provide a comprehensive quantitative synthesis of findings, which is lacking. We aim to (1) analyze the evidence of mindfulness efficacy for emotion regulation after a controlled MIP, (2) evaluate whether this efficacy is moderated by selected variables from the literature (i.e., type of sample, type of induced affect, self-report vs biobehavioral measurement methods, time of induction, and time of measurement), (3) examine the methodological quality of the studies and whether it affects the magnitude of the results and (4) use these findings to provide recommendations for improving future mindfulness research on mood regulation in the context of induced mood. 2. Method 2.1. Systematic search and eligibility A systematic search was conducted on the databases of PsycINFO, PubMed, and Scopus for all years covered through March 2021. The search terms were related to stress tasks and mood induction procedures (mood induc* OR stress induc* OR emotion induc* OR stress task OR stress test) and mindfulness practice (meditation OR mindful*). As a secondary search strategy, the reference lists from retrieved studies and reviews and meta-analyses in the field were revised to identify studies. The inclusion criteria were 1) studies with stress or negative mood induction procedures, 2) including at least one measure of emotional R.M. Zangri et al. Neuroscience and Biobehavioral Reviews 143 (2022) 104957 4 reactivity (e.g., emotional responses to an event detected by self-report questionnaires, heart rate responses, etc.) after the induction, and 3) looking at the relationship between emotional reactivity and mindfulness-based emotion regulation. The only restriction regarding the mood induction procedure (e.g., TSST, Cold Pressor task) was to be a standardized controlled procedure. There were no restrictions on the type of instrument to measure emotional reactivity, giving the possi- bility to include self-report, neurological measures, galvanic response, cardiac indices, or any other. Different study designs were included if they looked at the relationship specified in criterion 3, i.e., studies with a mindfulness-based intervention comparing emotional reactivity be- tween the experimental and a control group, experimental studies in which participants are asked to use this type of regulation during or after the induction procedure, and studies comparing reactivity between groups of participants with different levels of mindfulness trait or practice. Studies without a comparison group were not included. Exclusion criteria included non-empirical studies, children, or adolescent samples (<18 y/o), studies testing practices such as yoga, tai chi, or other contemplative practices without explicit mention of mindfulness, not peer-reviewed studies, and studies not published in English. The study was pre-registered in PROSPERO2 on May 7th, 2021 (CRD42021242081). 2.2. Data collection process The selection and codification processes were independently made by two of the authors (CA and SZ). Disagreements were resolved by a third author (AMG) to reach a consensus. This process was completed in the Covidence systematic review software (Veritas Health Innovation, Melbourne). Records were first screened based on their abstract, fol- lowed by the full-text review of those that were eligible. The details of the selection process are shown in Fig. 2. The potential variables relevant for the analyses that were coded (see Table 1), were the type of sample (clinical, subclinical, or healthy), type of mindfulness-based strategy used to regulate (attention, compassion, other), type of induction procedure (picture/clips, stress task, autobio- graphical memories, other), type emotion induced (negative mood, stress or other), and type of measure of emotion reactivity (HR, HRV, BP, skin conductance, cortisol, self-report). Also, although not included in the pre-register, the following vari- ables were codified and analyzed post-hoc as potential moderators given the high levels of variability found in the literature: mean age of the total sample, control of the effectiveness of the induction procedure, and the control that participants effectively used the emotion regulation strategy (yes, no), type of control condition (distract, endurance, monitor, neutral, wait-list, trait, other), time of induction (before, after the intervention or other). 2.3. Analytic plan 2.3.1. Effect size and multi-level modeling A random-effects multilevel meta-analysis was conducted using the standardized mean difference (d = Xe − Xc Spool ) with Hedge’s correction (g) as the effect size (ES). The mean and standard deviation values of the emotional reactivity measure after the mood induction procedure and after the use of the emotion regulation strategy were used (this did not apply to those studies comparing groups based on the trait of mindfulness or mindfulness practice). Hedge’s g values can be catego- rized as small (0.2–0.5), medium (0.5–0.8), or large (>0.8) (Cohen, 1988). Negative ES values reflect a lower level of emotional reactivity in the experimental (mindfulness) group compared to the control group. To keep congruence with this interpretation, the sign of the difference in emotional reactivity indicators showing a better adjustment (i.e., heart rate variability) was inverted. When means and standard deviations were not provided in the article, alternative parameters were used if available (e.g., F-values from univariate ANOVAs). This was only the case in five articles. Authors were contacted in any other case. Also, the Web Plot Digitizer (Rohatgi, 2021) was used when it was possible to extract data from the figures of primary studies. We used a multilevel approach to consider the dependency between effect sizes. Specifically, we introduced third and fourth levels to model effect sizes based on the different measurement methods nested within the different comparison groups nested within studies. In short, in our study, this model allows effect sizes to vary between participants (level 1), outcome measures (level 2), comparison groups (level 3), and studies (level 4). For those studies using cardiovascular measures, only the most common indices were included to increase homogeneity (i.e., heart rate, RMSSD, HF heart rate variability, and diastolic and systolic blood pressure). Fig. 3 shows a graphical representation of this model. The method of estimation was restricted maximum-likelihood (REML). The analysis was repeated after removing outliers. These were defined as those effect sizes whose 95% confidence interval did not overlap with the 95% confidence interval of the pooled effect size. 2.3.2. Heterogeneity and moderation analyses The Q statistic was used to test the hypothesis of effect size homo- geneity, and the I2 index was used to determine the degree of hetero- geneity for the different levels of the meta-analytic model. The following formula was used (Cheung, 2014): I2 (i) = τ̂2 (I) τ̂2 (2) + τ̂2 (3) + τ̂2 (4) + ṽ When the Q-value has a significance level lower than 0.05 the null hypothesis of homogeneity is rejected, leading to the conclusion that all the studies are not estimating the same parameter. Regarding the I2 values, they can be interpreted as the proportions of the total variation of the effect size explained by the different levels. It is considered that values lower than 25% reflect that the variability is within the normal range of variance expected by chance, 25–50% values reflect low het- erogeneity, 50–75% values reflect moderate heterogeneity, and values higher than 75% are considered as high levels of heterogeneity (Bor- enstein et al., 2009). To explain possible heterogeneity levels, moderation analyses were conducted with a multivariate linear model fitted via the restricted maximum-likelihood method. Table 1 shows the moderator variables that were analyzed. 2.3.3. Quality of studies The quality of individual studies was assessed using the Checklist for assessing the quality of quantitative studies from the Standard Quality Assessment Criteria for Evaluating Primary Research Papers (Kmet et al., 2004). Two items were added to the scale to evaluate more novel criteria framed within the Open Science approach: whether studies had been pre-registered and whether they had open access to their data. The quality of the meta-analysis itself was also assessed following PRISMA recommendations (McKenzie et al., 2021). The checklist can be found in supplementary materials. 2.3.4. Risk of bias Publication bias was analyzed using different procedures. First, the funnel plot was inspected. This shows each effect size against their precision (standard error) so that all values should be randomly 2 Adjustments from pre-register: 1) for inclusion, studies needed to measure emotional reactivity after, but not necessarily before, the induction procedure given the ES (see Analytic plan), 2) dimensional studies looking at the corre- lation between emotional reactivity and mindfulness-based emotion regulation were not included, 3) post-hoc moderation analyses were conducted (see the Results section). R.M. Zangri et al. Neuroscience and Biobehavioral Reviews 143 (2022) 104957 5 distributed in the graph, represented by a symmetric shape and density. Second, the multilevel Egger’s regression test for funnel plot asymmetry (Sterne et al., 2006) tests the null hypothesis that there is perfect sym- metry in the plot (starting point of the regression line/intercept equal to 0). Finally, the trim-and-fill procedure (Duval and Tweedie, 2000) was used to calculate the suppressed effect sizes due to publication bias using the L0 indicator. It is considered that values L0 + >3 (Fernández Castilla et al., 2019) reflect the existence of publication bias. All the analyses were conducted in R 4.1.2. The ‘esc’ package (an R implementation of Lipsey and Wilson, 2001) was used to compute the individual effect sizes and the ‘metafor’ package (Viechtbauer, 2010) was used to fit the meta-analytic multilevel random-effects models with and without moderators. The code from Fernández-Castilla et al., (2019) was used to generate the forest and funnel plots. 3. Results 3.1. Study selection The process of selection and inclusion of studies is shown in Fig. 1. A total of 5011 records were obtained from the database search (1710 duplicates), and 36 additional records were identified from the reference lists of retrieved studies and reviews and meta-analyses in the field. First, 3161 records were excluded based on the screening of their title or abstract. Then, 133 were excluded after full-text reading. The main reason for exclusion (k = 48) was that studies did not use an adequate mood induction procedure, defined as the use of standardized controlled experimental procedures performed in the laboratory and not in un- controlled settings. Nineteen studies were excluded because they did not report enough data to calculate the effect size. A total of 23 authors were contacted for this reason, but only 5 authors replied and 3 shared the data. There was only one study in which data was extracted from a plot. Also, fifteen studies were excluded because they did use contemplative practices such as yoga or tai-chi, rather than standardized mindfulness practices from the attentional or constructive meditation family (focused attention, open-monitoring, compassion, or loving-kindness practices) or validated MBIs (MBSR, MBCT, MBRP). Finally, a total of 43 studies were included in the quantitative analysis. 3.2. Study characteristics Studies included in the meta-analysis were published from 2006 to 2021, being only two records from the first decade of the century. The total sample size ranged from 26 to 176 participants with a mean age of 29.46 years old (nine records did not report these data). Twenty-five studies included healthy participants (only five of them used a Fig. 2. PRISMA 2020 flow diagram. R.M. Zangri et al. NeuroscienceandBiobehavioralReviews143(2022)104957 6 Table 1 Characteristics of the studies included in the meta-analysis. The table includes all the moderators used in the meta-analysis. Authors Year Type of sample Mean age total sample Type of mindfulness- based strategy Manipulation check experimental condition Type of control condition Emotion induction procedure Emotion induced Time of induction Manipulation check induction Measurement method Time of measurement (minutes since end of stressor) Wu et al. 2019 Healthy 21.6 Attention No Other Pictures/clips Negative mood After intervention/ instruction No Self-report (state anxious and negative emotion intensity) 0 Rosenberg et al. 2015 Healthy 48 Attention Yes Wait-list Pictures/clips Negative mood After intervention/ instruction Yes Self-report (anger, confusion, disgust, distress, embarrassment, fear, and sadness) 0 Nyklicek et al. 2013 Healthy 46.2 Attention No Wait-list Stress ( mental arithmetic and speech task) Stress After intervention/ instruction Yes Cardiovascular parameters (DBP, SBP, RMSSD, HF) and Biomarkers (cortisol) 10 Manigault et al. 2019 Healthy 26.6 Attention Yes Wait-list Stress (TSST) Stress After intervention/ instruction No Biomarkers (cortisol) 10, 20, and 45 Lindsay et al. 2018 Healthy 32.3 Attention Yes Other and Monitor Stress (TSST) Stress After intervention/ instruction Yes Biomarkers (cortisol) 7, 17, 42 Hoge et al. 2018 Clinical 39 Attention No Neutral Stress (TSST) Stress After intervention/ instruction No Biomarkers (ACTH, IL-6, TNF-alpha, cortisol) 0 Faucher et al. 2016 Clinical 38 Attention No Other Stress ( speech task) Stress After intervention/ instruction No Self-report (VAS anxiety scale), Cardiovascular parameters (HF), and Biomarkers (AUC and reactivity score cortisol) 30 Engert et al. 2017 Healthy 40.4 Attention Yes Wait-list Stress (TSST) Stress After intervention/ instruction Yes Self-report (state anxiety) and Biomarkers (cortisol) 12.5 and 15 Crosswell et al. 2017 N/A N/A Attention No Wait-list Autobiographical exercise Negative mood After intervention/ instruction No Self-report (anger, anxiety, sadness) and Cardiovascular parameters (SBP, DBP, HR) 0 and 12 Cȃndea & Szentágotai- Tătar 2018 N/A N/A Compassion No Wait-list and Endurance Autobiographical exercise Other (shame) After intervention/ instruction No Self-report (shame, and negative affect) 0 Britton et al. (2012) 2012 Clinical 46.7 Attention Yes Wait-list Stress (TSST) Stress After intervention/ instruction Yes Self-report (state anxiety) 0, 40, and 90 Basso et al. 2019 Healthy N/A Attention Yes Neutral Stress (TSST) Stress After intervention/ instruction Yes Self-report (state anxiety) and Cortisol 0 Wilson et al 2014 Healthy 27.1 Attention No Neutral and Endurance and Other Stress (verbal analogy trials) Stress After intervention/ instruction No Self-report (anxiety) 0 Watford & Stafford 2015 Healthy 19.3 Attention Yes Neutral Pictures/clips Negative mood After intervention/ instruction Yes Self-report (negative affect) 0 Vinci et al. (2014) 2014 Healthy 20 Attention Yes Neutral and Monitor Pictures/clips Negative mood After intervention/ instruction Yes Self-report (negative affect) 0 (continued on next page) R.M . Zangri et al. NeuroscienceandBiobehavioralReviews143(2022)104957 7 Table 1 (continued ) Authors Year Type of sample Mean age total sample Type of mindfulness- based strategy Manipulation check experimental condition Type of control condition Emotion induction procedure Emotion induced Time of induction Manipulation check induction Measurement method Time of measurement (minutes since end of stressor) Steffen, Larson (2015) 2015 Healthy 20.3 Attention No Neutral Stress (math stressor) Stress After intervention/ instruction No Self-report (state anxiety) and Cardiovascular parameters (SBP, DBP) 0, 9 and 19 Sanders & Lam 2010 Healthy 36.5 Attention No Endurance Autobiographical exercise Other (Negative mood and sadness) Before intervention/ instruction Yes Self-report (sadness) 0 Remmers et al. 2016 Healthy 22.3 Attention No Endurance and Distract Other Other (Sadness) Before intervention/ instruction No Self-report (negative affect) 0 Ramos Díaz et al. (2014) 2014 Healthy 23.1 Attention No Endurance and Other Autobiographical exercise Stress Before intervention/ instruction Yes Self-report (negative affect) 0 Keng & Tan 2017 Subclinical 20.9 Attention Yes Wait-list Autobiographical exercise Other (Shame) Before intervention/ instruction Yes Self-report (state shame) 10, 11, 12, and 13 Ortner & Zelazo 2014 Healthy N/A Attention No Wait-list and Distract Autobiographical exercise Other (Anger) Before intervention/ instruction No Self-report (anger and negative affect) 10 Mc Clintock & Anderson 2015 Subclinical 19.1 Attention No Distract Other Negative mood Before intervention/ instruction Yes Self-report (state anxiety and negative affect) 20 Kuehner et al. (2009) 2009 Healthy 22.3 Attention No Endurance and Distract Other Negative mood Before intervention/ instruction Yes Self-report (negative affect) 8 Koerten et al. 2020 Subclinical 19.4 Attention No Wait-list and Monitor Stress (failure task) Stress Before intervention/ instruction Yes Cardiovascular parameters (HF and HR) 5 Keng & Tan 2018 Clinical 21.7 Attention and compassion Yes Wait-list Other Other (Social rejection) After intervention/ instruction Yes Self-report (negative affect) 0, 1, 2, and 3 Karl et al. 2018 Subclinical 30 Compassion No Endurance Other Negative mood Before intervention/ instruction Yes Self-report (sadness) 4 Hsu, Forestell (2021) 2021 Healthy 20.9 Attention No Neutral Stress ( unsolvable anagrams) Negative mood Before intervention/ instruction Yes Self-report (negative affect) 15 Hooper et al. (2011) 2011 Subclinical 25 Attention No Other Other Other (Fear) After intervention/ instruction No Self-report (state anxiety) 0 Fitzpatrick et al. (2019) 2019 Clinical 27 Attention No Other and Distract Autobiographical exercise Other (distress related to body image) Before intervention/ instruction Yes Self-report (state anxiety) 10 Fergus & Wheless 2018 N/A N/A Attention No Other and Distract Other Other (Worry) Before intervention/ instruction No Self-report (state cognitive and somatic anxiety) 12 Erisman & Roemer 2010 Healthy N/A Attention Yes Neutral Pictures/clips Negative mood After intervention/ instruction Yes Self-report (negative affect), Cardiovascular parameters (HR) and Skin conductance 0 and 3 (continued on next page) R.M . Zangri et al. NeuroscienceandBiobehavioralReviews143(2022)104957 8 Table 1 (continued ) Authors Year Type of sample Mean age total sample Type of mindfulness- based strategy Manipulation check experimental condition Type of control condition Emotion induction procedure Emotion induced Time of induction Manipulation check induction Measurement method Time of measurement (minutes since end of stressor) Cassin, Rector (2011) 2011 N/A N/A Attention No Neutral and Distract Autobiographical exercise Other (Social anxiety) After intervention/ instruction Yes Self-report (net affect= positive PANAS- negative PANAS) 5 Borchardt, Zoccola (2018) 2018 Healthy N/A Attention Yes Neutral and Wait-list Stress ( mental arithmetics) Stress Before intervention/ instruction Yes Self-report (negative affect), Cardiovascular parameters (SBP, DBP, HR, RMSSD), and Skin conductance 1 and 20 Azam et al. (2015) 2016 Clinical and healthy 19.7 Attention No Neutral Stress ( cognitive stress-induction) Stress Before intervention/ instruction No Cardiovascular parameters (HF) 10 Azam et al. (2016) 2015 Subclinical and healthy 21.3 Attention No Neutral Stress ( cognitive stress-induction) Stress Before intervention/ instruction No Cardiovascular parameters (HF) 3 and 8 Arch & Craske (2006) 2006 N/A N/A Attention Yes Other Pictures/clips Negative mood After intervention/ instruction No Self-report (negative affect) 0 Watford, Stafford (2015) 2020 Healthy 19.9 Attention NA Trait/ Experience Other Stress NA No Cardiovascular parameters (HF and HR) 2.5 and 5 Rosenkranz et al. (2016) 2016 Healthy 49.4 Other NA Trait/ Experience Stress (TSST) Stress NA No Self-report (stress) and Biomarkers (alpha amylase and cortisol) 0 Luo et al. (2018) 2018 Healthy 19.7 Compassion NA Trait/ Experience Stress (TSST) Stress NA No Self-report (negative affect), Cardiovascular parameters (HR and RMSSD) 0 and 5 Greenberg, Meiran (2014) 2014 Healthy 30.2 Other NA Trait/ Experience Pictures/clips Other (Sadness) NA Yes Self-report (sadness and general mood) 0 Gamaiunova et al. 2019 Healthy 49.3 Other NA Trait/ Experience Stress (TSST) Stress NA Yes Self-report (stress), Cardiovascular parameters (HR, RMSSD) and Biomarkers (cortisol) 0 and 10 Fennell et al. (2016) 2016 Healthy 35.4 Other NA Trait/ Experience Autobiographical exercise Other (Anger) NA No Self-report (anger), Cardiovascular parameters (SBP, DBP, HF) 0 Carroll, Lustyk (2018) 2018 Clinical 43.4 Attention No Other Stress ( cognitive stress-induction) Stress After intervention/ instruction No Cardiovascular parameters (HF) 15 Note: N/A = Not Available; NA = Not Applicable; ACTH = adrenocorticotropic hormone; AUC = area under curve; DBP = diastolic blood pressure; HF = heart frequency; HR = heart rate; IL-6 = interleukin 6; PANAS = Positive and Negative Affect Schedule; RMSSD = root mean square of the successive differences (as a measure of heart rate variability); SBP = systolic blood pressure; TNF = tumor necrosis factor; VAS = visual analogue scale R.M . Zangri et al. Neuroscience and Biobehavioral Reviews 143 (2022) 104957 9 screening procedure to prove it), five studies included participants with subclinical symptoms, six studies used a clinical sample, and five studies did not mention their type of sample. Two studies included and compared groups with different conditions (i.e., participants with and without headaches, and participants with and without high levels of perfectionism). The most common comparison group was the waitlist/do nothing control group (k = 13). Fourteen studies used two different control groups, three studies compared two different types of mindfulness manipulation with one type of control group, one study compared two different types of mindfulness manipulation with two different control groups, and one study compared two different types of mindfulness manipulation with three different control groups. Attention was the most common type of mindfulness-based strategy used to regulate mood (k = 37). Regarding the assessment of the effectiveness of the MPI, only thirteen studies reported some type of manipulation check to see if the mindfulness-based intervention had an effect or if the instruction to use the emotion regulation strategy was actually implemented. Regarding the induction procedure, eighteen studies used a stress task (nine the TSST), nine used an autobiographical memory task, seven used pictures or clips to induce the emotion and nine used other types of tasks (e.g., a mixed combination of the previous ones, social rejection manipulation, worry provocation task, etc.). The induction procedure took place after the intervention or the instruction implementation in most of the studies (k = 22), while some of them induced the emotion first (k = 15). Six studies did not provide intervention or instructions to regulate affect given that their focus was on the comparison of groups based on their experience or trait mindfulness, as measured by a ques- tionnaire. Only twenty-three studies (53.5% of the studies) included a manipulation check for the emotion induction procedure and 20 studies did not report the effectiveness of the MIP (46.5% of the studies). Twelve studies included more than one method to assess emotional reactivity, mostly a self-report and a physiological measure (cardiovas- cular, skin conductance, or cortisol). Self-reports included the mea- surement of general negative affect as well as the measurement of specific emotions, such as anger or anxiety. The time of measurement varied from 0 min after the stressor ended to 90 min after the stressor. The mean quality of primary studies (M=0.72, SD= 0.10) was within the common range when using the Standard Quality Assessment Criteria for Evaluating Primary Research Papers (from 0.65 to 0.80; Kmet et al., 2004). 3.3. Multi-level modeling and heterogeneity The results comparing the fit of the one-, two-, three-, and four-level models are shown in Table 2. One-sided likelihood-ratio tests showed that all models significantly improved the fit of their corresponding reduced model. Thus, the four-level model accounting for the de- pendency of the data (outcomes measures clustered within pairs of comparison groups clustered within individual studies) was chosen. A total of 186 individual effect sizes were evaluated, being the level of heterogeneity between all of them Q(185)= 850.65 (p < .0001). I2 indices showed that 16.3% of variance corresponded to the vari- ance due to sampling methods, 27.2% to the second level (outcome measures), 0.00% to the third level (comparison groups), and 56.5% to the fourth level (individual studies). 3.4. Overall effect size and moderator analyses The overall effect size was g= − 0.15 (95%CI [− 0.30; − 0.01], p = 0.04), showing a significant but small difference in emotional reactivity between mindfulness and comparison groups. The negative value expresses that the levels of reactivity were higher for the com- parison groups. The forest plot can be found in Fig. 4. This main effect became non-significant after removing 32 outliers (17% of all effect sizes) whose 95% CI limits fell either below the lower 95%CI limit of the pooled effect size or above the higher 95%CI limit of the pooled effect size (g= − 0.15, 95%CI[− 0.30; − 0.01]). The level of variance for the second level (i.e., outcome measures) became 0 in this new model. Moderator analyses revealed that none of the codified variables significantly explained the high levels of variance found in effect sizes Fig. 3. Illustration of the four-level meta-analytic data structure of our study. Adapted from Fernández-Castilla et al. (2020). Table 2 Model Fit Indices, Model Comparison Statistics, and Variance Components. Model Levels Added Higher Level Model Fit Indices Model comparison Variance Components AIC LogLik Models LRT (p) σ2 1 σ2 2 σ2 3 One 726.75 -362.37 Two Measure 394.82 -195.41 1 vs. 2 333.93 (<.001) 0.35 Three Comparison group 394.03 -194.01 2 vs. 3 2.79 (.045) 0.06 0.29 Four Study 390.46 -191.23 3 vs. 4 5.56 (0.01) 0.12 0.00 0.26 Note: AIC = Akaike Information Criterion; LogLik = Log-Likelihood; LRT = Likelihood-Ratio Test. The Likelihood-ratio test statistic is tested against a chi-square distribution with 1 degree of freedom. The restricted maximum-likelihood (REML) estimation was used. R.M. Zangri et al. Neuroscience and Biobehavioral Reviews 143 (2022) 104957 10 (see Table 3). As a further analysis to explore the nature of the relatively high proportion of outliers in our study (17%), we conducted chi-square tests between outliers and non-outliers for each of the moderators shown in Table 3. Our results showed significant differences regarding type of task (X2 (3, 186) = 9.35, p = .02), type of measure (X2 (5, 186) = 24.5, p = .0001), and type of design (X2 (2, 186) = 7.82, p = .02). More specifically, the percentage of outliers was higher in studies using stress tasks, designs with participants of MBI programs (vs. mindfulness in- structions or trait/experience studies), and studies using HRV and cortisol measures. 3.5. Risk of bias The multilevel Egger’s regression test for funnel plot asymmetry (shown in Fig. 5) showed the intercept was not equal to 0 (coef- ficient=− 4.87, p < .0001) leading to the rejection of perfect symmetry. Moreover, the L0 indicator of the trim-and-fill procedure estimated there were 18.8 suppressed effect sizes, which reflected a high level of pub- lication bias. 4. Discussion This is the first meta-analysis to examine the effects of mindfulness- based strategies on the regulation of moods that have been induced in controlled laboratory settings. The primary results of our study showed an overall significant, but small effect size (g=− 0.15) which revealed that relative to control conditions, mindfulness strategies were more effective in down-regulating negative moods. However, when outliers Fig. 4. Forest plot (J = number of effect sizes extracted from each study). R.M. Zangri et al. Neuroscience and Biobehavioral Reviews 143 (2022) 104957 11 were excluded from the analyses, this effect size became nonsignificant. The small size effects found in our study are convergent with other recent meta-analyses in the field that have found small or null effect sizes. For instance, Brown et al. (2021) found that resting HRV, a car- diovascular marker associated with both physical and mental health, is not significantly improved in RCTs of MBIs programs (g=0.19 after removing outliers). In general, the results inquiring about the mecha- nisms of MBIs do not allow clear conclusions. For instance, Yakobi et al. (2021), in their meta-analysis of RCTs on the MBIs effects on cognition, found significant but small effects for attention (g=0.18) and working memory (g=0.18), and no effects for executive control, which has been proposed as the main cognitive function involved the practice of mindfulness (Tang et al., 2015) In a similar meta-analysis, in this case including healthy individuals but also participants with physical or psychiatric problems, Im et al. (2021) found small but significant effects of MBIs on executive functioning (SMD=0.29) but not for attention, working memory, and long-term memory. These small or null results contrast with the efficacy of MBI interventions to significantly reduce psychological distress both in healthy and unhealthy individuals (e.g., Galante et al., 2021; Goldberg et al., 2018, 2021). However, these sig- nificant results typically come from full intervention programs that, like the MBSR, last several weeks and include different components (e.g., body scan, mindful movement, breath-awareness meditation, among others) besides specific attentional techniques. It is also important to keep in mind that the mood induction pro- cedures typically used in the psychological literature have some limi- tations. As noted by Oishi et al. (2022), in general, the effects are very transient (probably due to the use of weak inductions) and even if there is a significant emotional change after induction, the overall affective tone of the participants is positive. These overarching limitations may be relevant to understand the relatively small effect sizes found in our meta-analysis. A general finding of the study, which has also been reported in other meta-analyses on MBIs (e.g., Sumantry and Stewart, 2021; Im et al., 2021) is the high level of heterogeneity of the studies. However, none of the moderators selected in this study (i.e., type of mindfulness-based strategy, control condition, type of emotion induced, among others) had a significant effect on the small effect size. To improve the inter- pretability of results, it would be helpful to unify protocols of assessment and interventions in this emerging field. The most salient source of variability in our results is the methodological design of the studies, varying from interventions with several weeks of mindfulness training to studies using a single practice, brief instructions in the laboratory to use mindfulness strategies, and research comparing participants with different levels of self-reported experience or trait mindfulness measured by a questionnaire. It is difficult to draw conclusions from such different designs and it is necessary to either unify methodologies within the field or to compare only a particular type of study in future reviews. The type of control group was another important source of vari- ability. Waiting lists and active control (Manigault et al., 2019) were the most common type of comparison groups, but the instructions to the active control group also varied between studies’ designs. For example, in lab-based studies with single instructions to regulate an induced emotion, the active control group was generally instructed to use alternative strategies, such as distraction (McClintock and Anderson, 2015). But, if the study used standardized MBI, such as the MBSR, active control groups were often allocated to another training of the same duration (e.g., CBT) even though the use of waiting-list controls was the most common practice in this case. The choice of control groups is a particularly important point in the design of mindfulness studies, and it has been a constantly criticized methodological issue in the field (Davidson and Kaszniak, 2015; Van Dam et al., 2018). Only five of the studies included in this analysis comprised a clinical sample. Hoge et al. (2018), found a greater reduction in the stress-related ACTH hormone following MBSR training in adults with Table 3 Results of the moderator analyses. Moderator Number of ESs Test of moderators F dfs p Type of sample Healthy 162 111 0.57 2, 159 .56 Subclinical 23 Clinical 28 Type of strategy Attention 186 157 0.71 2, 183 .49 Compassion or other 29 Control instruction No 162 88 0.07 1, 160 .80 Yes 74 Control group Distract 186 12 1.11 6, 179 .36 Endurance 10 Monitor 8 Neutral 40 Other 23 Trait 24 Wait-list 69 Induction procedure Autobiographical 186 44 1.94 3, 182 .13 Other 25 Pictures/clips 22 Stress task 95 Emotion induced Negative mood 186 38 1.70 2, 183 .19 Other 48 Stress 100 Time of induction After 166 92 0.28 2, 163 .76 Before 70 Other 4 Control induction No 186 76 0.86 1, 184 .36 Yes 110 Measurement method Blood pressure 186 20 0.92 5, 180 .47 Conductance 6 Cortisol 23 Heart rate 17 Heart rate variability 25 Self-report 95 Age 128 0.76 1, 126 .39 Time to measurement 186 0.06 1, 184 .81 Fig. 5. Funnel plot. R.M. Zangri et al. Neuroscience and Biobehavioral Reviews 143 (2022) 104957 12 Generalized Anxiety Disorder (GAD), compared to an active control group, following a TSST laboratory procedure. Also, Faucher et al. (2016) found that MBSR training reduced self-reported symptoms (but not physiological indicators) in social anxiety disorder (SAD) in com- parison to a CBT group. In the context of mindfulness programs, the MIP was applied before and following the training protocol, following the recommended timings for physiological measures, and including base- line assessments before the stressor (Hoge et al., 2018; Faucher et al., 2016), which is a recommended study design (see Fig. 1). Although the number of studies using MIPs in clinical samples is still small, these favorable results suggest the benefits of MBSR in enhancing resilience to stress in a clinical population. Also, different results may be obtained from the use of different mindfulness-based strategies. In this meta-analysis, the main mindful- ness strategy applied was focused attention. The instructions given to participants were adaptations of classic meditation trainings of directed attention to the breath and attention to the present moment (Fergus and Wheless, 2018; Ortner and Zelazo, 2014). The main idea is that partic- ipants are asked to direct their attention to the breath, noticing and accepting mind-wandering and non-judgmentally returning their attention to the breath. Other studies included focused attention to bodily sensations and breath, more like a mindfulness practice of “body scan” (Koerten et al., 2020). Some other studies included specific training programs based on focused attention to the breath or the pre- sent moment such as Brief Meditation Training (Wu et al., 2019) and Journey Meditation (Basso et al., 2019). Distinct types of mindfulness can have differential effects on mood following MIP. Keng and Tan (2018), found participants showed significantly quicker recovery following MIP in a mindful breathing group but not in a loving-kindness meditation group, pointing out the difference among meditation types in buffering the effects of social rejection in a clinical population. There- fore, further studies are needed to investigate different MBIs as regula- tory strategies in the context of clinical samples and with different levels of symptom severity. Furthermore, MIPs might be more efficacious in inducing the intended mood in clinical populations compared to healthy participants, which could be explored by further research. Around half the studies examined in this analysis applied a stress task as a MIP, divided between two procedures highly investigated in pre- vious literature: the TSST and autobiographical memory recall. Meta- analytic reviews of the TSST support this well-established procedure to induce stress (Allen et al., 2017) but also warn about discrepancies in protocol modifications and high variability in testing procedures, which may lead to misinterpretation of results and pose challenges when comparing results across studies (Linares et al., 2020). In the case of autobiographical recall tests, authors suggest its effectiveness in inducing discrete emotions such as anxiety and anger, although they note it may not be the most effective MIP (Joseph et al., 2020). On the other hand, only 7 studies reported using picture clips which, when used as pictures of facial expressions, are most effective for inducing negative affect and discrete emotions such as happiness or sadness (Joseph et al., 2020). Overall, the high variability in MIP and the discrepancies in protocols used might contribute to the inconsistencies in the literature. Future studies should carefully select the mood induction procedure based on the emotion to be elicited and follow validated paradigms when possible (Joseph et al., 2020; Diener et al., 2022). Stress was the main emotion induced in the studies examined, fol- lowed by general negative mood and few studies looking at specific emotions such as anger, sadness, shame, fear, worry, social anxiety and rejection. Regarding negative mood, Schumer (2018) found small but significant effects of brief mindfulness training in reducing general negative affect (g=.21). However, the type of emotion induced was not a significant moderator in the present analyses. Furthermore, specific emotions might be best regulated by different MBIs, which cannot be fully determined in this analysis due to the few studies investigating distinct emotions. A more striking methodological limitation of the reviewed studies is that many of them did not check for, or did not report, the effectiveness of the MIP itself. This is highly problematic as it does not allow us to verify whether the intended emotion was actually evoked and regulated. A way to overcome this limitation is to use self-report questionnaires that can specifically target the emotion or state intended, before and immediately after the MIP, as well as after the MBI strategy when applicable (see Fig. 1). Previous reviews have examined MBIs in mediating the physiological effects of stress showing favorable results (Pascoe, 2017). However, the overall risk of bias was reportedly high and other authors show out- comes measured as physiological variables to be ambiguous, which could be attributed to differences in timing and procedures of measures collected or to the fact that different physiological systems are differ- entially affected by type and dose of mindfulness training (Morton, 2020). When using validated MIPs such as the TSST, other meta-analyses recommend specific timings to be used in the collection of physiological measures such as cortisol and resting periods before the stressor that should be applied (Goodman et al., 2017). We found high variability in the measurement methods used to report reactivity or recovery to induced moods, making it unclear if all physiological mea- sures reflect reactivity equally well or if some stand out to induce spe- cific emotions. Mainly, self-report and cardiovascular measures were used, but even within each category, a high diversity of instruments and indices was found. Future studies are needed to clarify better ways to analyze different types of measurements to assess the impact of mind- fulness on emotional regulation. There is no doubt that the validity of research on MBIs would benefit, in general, from using stricter methodological criteria (Van Dam et al., 2018). An important caution about the high heterogeneity in the find- ings is the presence of low-quality studies, which may be attributed to a lack of clarity in reporting study design characteristics, and publication bias, in which null results might not have been published or studies with negative effects may have been conducted but not published (Schumer et al., 2018), highlighting the need of mindfulness studies using rigorous methodologies and adherence to guidelines and quality checklists. The present research controlled for the quality of the studies using a standard checklist of methodological criteria for experimental studies (Standard Quality Assessment Criteria for Evaluating Primary Research Papers from a Variety of Fields). Although our results indicated that the average quality was within the common range (Kmet et al., 2004), there are important aspects of experimental designs using MIPs procedures that could be improved. For instance, given that MIP aims to manipulate mood, it should be a standard practice to evaluate and report whether the procedure significantly altered that specific mood in the expected direction (Crosswell et al., 2017). This lack of information is unfortunate given that the intensity of the emotion which must be down-regulated could explain part of the effectiveness of the interventions. It would be helpful to explore the moderating role of the magnitude of the mood induction (Diener et al., 2022), which was not explored in the studies included in this analysis due to a relative lack of information in primary studies. Considering the small effect of MBI in regulating induced mood states, there is indication that further moderators should be explored to see the true potential of these interventions. Our results must be considered considering the mentioned method- ological limitations of current literature on mindfulness and mood regulation in laboratory settings. The present meta-analysis may help researchers to identify some methodological areas that require improvement and to expand the focus of the research to samples (e.g., clinical samples) in which significant mood regulation effects are more likely to be found. Investigating the mechanisms underlying emotional regulation is a scientifically relevant task. In this sense, MIPs are a very promising way to control the emotional states to which individuals are exposed. However, it would be desirable to follow best methodological practices (e.g., measures of mood immediately before and after the in- duction), and to select induction methods appropriate for target desig- nated moods (e.g., the Trier Social Stress Test is relevant to increase R.M. Zangri et al. Neuroscience and Biobehavioral Reviews 143 (2022) 104957 13 stress but not other types of moods). These MIP methods are comple- mentary to other research that attempts to explore the relationships between emotional regulation strategies and subsequent mood. For example, experience sampling methodology studies are an important source of insight into relationships between ER strategies and mood states in natural contexts (see a review in Boemo et al., 2022) and these methodologies could also be relevant in the context of MPI studies (Diener et al., 2022). Given that the advantage of experimental pro- cedures using MIPs is that they allow controlling for exposure factors (e. g., sad images) that elicit a given mood state, it seems important to improve MPI study designs following the lines of the current meta-analysis. Regarding our meta-analysis, several strengths deserve to be mentioned. First, the quality of individual studies has been assessed using the Standard Quality Assessment Criteria for Evaluating Primary Research Papers from a Variety of Fields Checklist. Also, multilevel models have been employed, which nowadays is recommended to ac- count for the dependency of effect sizes (Borenstein et al., 2009; Van den Noortgate et al., 2013) and both the number of primary studies and the number of individual outcomes are within the normal range of multi- level meta-analyses in the area (Fernández-Castilla et al., 2020). Some recommendations arise from the results of this meta-analysis to improve the quality of research in the emerging field of the effectiveness of mindfulness strategies to regulate affect. Future studies should care- fully select the best available protocols to appropriately induce the targeted emotion. They should also check for the effectiveness of the MIP with evaluations before and following the induction procedure. Furthermore, physiological measures and reactivity data should be collected within the recommended time windows established by previ- ous literature on specific measures but also keeping in mind that most MIPs elicit transient mood changes. Of note, we found a high percentage of outlier effect sizes in studies using cortisol and HRV measures. Finally, participants should be given specific instructions to apply the intended regulation strategies, which might be checked with self-report measures when applicable. It is possible that following these indications may lead to a more robust generation of studies with more consistent results that can shed light on how individuals can implement effective mindfulness strategies to down-regulate specific mood states and thus help prevent the unfolding and maintenance of emotional disorders and emotional suffering. Funding This work was partially supported by a Spanish Ministry of Science grant (PID2019–108711GB-I00) to Carmelo Vazquez, a Complutense University predoctoral fellowship (CT17/17-CT18/17) to Ines Nieto, a Spanish Ministry of Science predoctoral fellowship (Formacion de Per- sonal Investigador, PRE2020–092011) to Rosaria M. Zangri, and a María Zambrano postdoctoral grant from the Ministry of Universities of the Spanish Government to Catherine Andreu (Reference number: ZA21–056). Trial registration This systematic review and meta-analysis was pre-registered in PROSPERO number CRD42021242081. Declaration of interest The authors report no declarations of interest. Data availability Data will be made available on request. References Aldao, A., 2013. The future of emotion regulation research: Capturing context. Perspect. Psychol. Sci. 8 (2), 155–172. https://doi.org/10.1177/1745691612459518. Aldao, A., Nolen-Hoeksema, S., Schweizer, S., 2010. 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