> [!] My Summary > - People with more severe anxiety initially showed more improvement in their anxiety from the mindfulness app, but not necessarily in problem-solving skills. > - Those who had trouble controlling repetitive thoughts, speaking fluently, or who were older, generally got better results from the mindfulness app. Women and Caucasians showed more improvement in mindfulness and problem-solving skills compared to others. # Which Client with Generalized Anxiety Disorder Benefits from a Mindfulness Ecological Momentary Intervention Versus a Self-Monitoring App Developing A Multivariable Machine Learning Predictive Model - ScienceDirect ## Introduction Generalized anxiety disorder (GAD) is a mental disorder linked to compromised socio-occupational functioning, physical health, and quality of life (Newman et al., 2017, Zainal and Newman, 2022b, Zhou et al., 2017). Fortunately, effective interventions exist. These include face-to-face cognitive-behavioral therapies (CBT; Newman et al., 2020), mindfulness-based interventions (MBIs; Zainal & Newman, in press), and guided and self-guided digital mental health interventions (DMHIs) that can treat GAD and comorbid symptoms (e.g., panic; Apolinário-Hagen et al., 2020). However, providing high-intensity face-to-face psychotherapy to all clients with GAD is infeasible in many resource-limited treatment settings where demand outstrips supply (Rebello et al., 2014). Self-guided DMHIs can aid dissemination when logistical barriers prevent seeking face-to-face psychotherapies, such as shame, stigma, and financial constraints (Goetter et al., 2020). Persons with GAD also reported high levels of acceptability toward DMHIs, such as mindfulness ecological momentary interventions (MEMIs) and self-monitoring apps (SMs) to manage symptoms alone (Newman et al., 2021). EMI represents a growing field of keen interest, holding substantial promise in the context of GAD and related mental health problems. A recent review evidenced that users generally found EMIs beneficial as long as disturbances, such as measurement burden, did not interfere with participants' everyday routines (Dao et al., 2021). Thus, determining which clients with GAD differentially benefit from unique scalable self-guided DMHIs is essential. Heterogeneity of treatment effects (i.e., the fact that more efficacious interventions at the group level may not be the most effective for a specific patient) is intrinsic to all psychotherapies (Kaiser et al., 2022), including low-intensity self-guided DMHIs. For example, evidence suggested that for a particular subgroup, an SM alone helped to alleviate anxiety, depression, and related symptoms (Gruszka et al., 2019). A meta-analysis also indicated that MEMIs, more than other DMHIs, were more suitable for depression and anxiety during pregnancy in patients with specific symptoms and demographic profiles (Silang et al., 2022). However, most DMHI randomized controlled trials (RCTs) thus far solely examined aggregate treatment effects for GAD and related disorders (Carl et al., 2020b). However, doing so precludes understanding heterogeneity of treatment effects (Goldberg et al., 2022). Thus, from a resource-management perspective, evaluating which DMHI yields the most optimal treatment effect for which specific client with GAD would be profitable for patients, clinicians, and public health administrators. Additionally, there remains room for improvement when determining what specific DMHIs work best for which clients with GAD. Previous studies tended to use approaches based on classical ordinary least squares regression (OLS; Faaland et al., 2022). Traditional OLS is suboptimal as it necessitates manual specification of non-linearity and interactions and can lead to underfitting of the data (Wallert et al., 2022). Machine learning (ML) may facilitate the building of precise predictive models. ML comprises data-driven methods that empower computer algorithms to determine and iteratively improve the best parameters to fit complex predictor patterns (Jordan & Mitchell, 2015). Despite compromising model interpretability to some extent, fitting flexible nonlinear and higher-order interaction ML algorithms instead can enhance model derivation and performance and optimize model prediction (Pearson et al., 2019). Further, methods exist to determine significant treatment predictors and delineate or characterize how each predictor correlates with the outcome (Archer & Kimes, 2008). By managing collinearity and carefully regulating overfitting, the accuracy of ML algorithms can be evaluated (Christodoulou et al., 2019). Harnessing ML for prediction using DMHI datasets is a nascent yet rapidly growing endeavor in clinical science (Jacobson & Nemesure, 2021). Combining ML with DMHIs could be valuable since self-guided DMHIs are easily implementable and have promising preliminary results. However, previous DMHI studies that used ML to predict intervention outcomes for adults with anxiety, depression, and obsessive-compulsive and related disorders (e.g., Hornstein et al., 2021; Lenhard et al., 2018; Pearson et al., 2019) had the shortcoming of testing a restricted set of possible ML models. Also, the only digitally delivered MBI that used ML to date (Lekkas et al., 2021) focused narrowly on compliance features as predictors of post-treatment stress reduction. Compliance features are not optimal because they rely on information collected during treatment as opposed to using information collected at baseline that may inform immediate prescriptive treatment assignment. Testing a broader predictor set comprising baseline symptom severity, demographic variables, and theory-based predictors with ML models (Canby et al., 2021, Elhai and Montag, 2020) can help to optimally determine which DMHI app works best for which person with GAD. Drawing from the compensation model (Cheavens et al., 2012), treatment efficacy is contingent upon effectively addressing the specific deficiencies within patients' disorder-pertinent vulnerabilities. Individuals with elevated perseverative cognitions might experience more gains in utilizing MEMIs than SMs, given deficits in understanding and applying mindfulness techniques. Higher trait perseverative cognition could afford individuals with GAD an expanded opportunity to cultivate mindfulness skills over time. Consequently, this could increase their chances of experiencing symptom relief through MEMIs more than SMs (Spinhoven et al., 2018). Such distinct effects would probably arise since individuals grappling with excessive worry and self-focused repetitive thinking tend to derive the greatest advantage from breaking free from those unproductive habits (Hallion et al., 2022). They stand to gain by redirecting their attention toward the present task and fostering a sense of personal agency (Gallagher et al., 2014) via mindfulness exercises. These patterns may manifest as individuals with GAD possess greater potential to nurture present-focused awareness and positive emotions through mindfulness practices (Perestelo-Perez et al., 2017). Mindfulness theories propose that although sustaining mindfulness can be challenging, staying mindful by optimally using cognitive resources can foster more robust insight, cognitive flexibility, and self-regulation abilities (Hofmann and Gomez, 2017, Spinhoven et al., 2022). Lower levels of trait mindfulness could prevent participants from reaping optimal benefits from a MEMI vs. an SM. More robust levels of mindfulness traits likely promote a better response to a MEMI than an SM because they help to encourage prefrontal cortex-mediated regulation and reduce the inclination to experience or avoid negative emotions (Mizera et al., 2015, Spinhoven et al., 2017). Collectively, heightened trait mindfulness could facilitate being optimized by a MEMI vs. SM to improve global trait mindfulness (cf. _capitalization model_; Murphy et al., 2021). In addition, the _self-regulatory executive function_ (S-REF; Matthews & Wells, 2004) model and _cognitive model of pathological worry_ (Hirsch & Mathews, 2012) can be extended to suggest that weaker executive function (EF) skills may hamper assistance provided by a MEMI vs. an SM. EF refers to higher-order cognitive control skills that govern thought and action repertoires (Keller et al., 2023). Empirical data showed that EF deficits correlated consistently with attentional dyscontrol, metacognitive beliefs (e.g., thoughts of the "helpfulness" of rumination), and maladaptive coping across time (Hoffart et al., 2022). Therefore, _inhibition_ (abstaining from autopilot responses) and _working memory_ (WM; mentally attending to and altering information in real-time) problems might contribute to worse responses to a MEMI than an SM. Issues with _set-shifting_ (fluently switching between distinct cognitive modes) and _verbal fluency_ (producing apt thematic words under a time limit; Renna et al., 2018; Zainal & Newman, 2022a) could likewise prevent individuals from strengthening psychological flexibility and related processes that could enhance emotion regulation during treatment. Accordingly, the goal of the present study was to use various ML algorithms to evaluate which DMHI app (i.e., MEMI vs. SM) would work best for which person with GAD and which theory-based predictors predicted a better outcome with the MEMI vs. SM. Our study offers novel contributions in several ways. First, inquiring into which GAD sufferers benefitted from a specific DMHI has been a neglected research question (McDevitt-Murphy et al., 2018). Relatedly, by determining _prescriptive_ predictors that informed optimal intervention assignment across different DMHIs, we extended the precision psychiatry literature that primarily examined prognostic predictors within and between specific face-to-face psychotherapies (Aafjes-van Doorn et al., 2021). Prescriptive predictors refer to variables that contribute to heterogeneity of treatment effects (Wester et al., 2022). Second, our mostly self-report pretreatment predictor set could be practical to implement in routine care settings. Our method is an alternative approach to most prior ML-based treatment predictor studies intended to build clinical decision-making support tools with biomarker or neuroimaging data (e.g., Tymofiyeva et al., 2019). Although the potential of biomarker and neuroimaging techniques is undeniable, their practical application in outpatient and hospital environments is constrained by limited availability and high expenses. Our study was a secondary analysis of a DMHI RCT for GAD that primarily evaluated efficacy using an intention-to-treat approach (Zainal & Newman, 2023). The primary RCT findings most relevant for the current study were that the MEMI significantly outperformed the SM in reducing pretreatment to one-month follow-up (pre-1MFU) GAD severity, perseverative cognitions (Cohen's |_d_| = 0.393–0.394), trait mindfulness (_d_ = 0.303), and EF (|_d|_ = 0.280) measured dimensionally. Herein, we evaluated the following pretreatment variables as potential prescriptive predictors: EF, GAD severity, trait mindfulness, perseverative cognitions, and sociodemographic variables. Building on the primary RCT that determined differential treatment efficacy on continuous outcomes, we examined how these potential predictors would function as prescriptive predictors of pre-1MFU clinically reliable improvement (a categorical outcome; Blampied, 2022) in GAD severity, trait perseverative cognitions, mindfulness, and EF. First, we expected that the MEMI but not SM would significantly yield pre-1MFU clinically reliable improvement in GAD severity, global perseverative cognitions, trait mindfulness, and EF. Second, we hypothesized that ML models would produce high-accuracy predictive models of prescriptive predictors (Hypothesis 2). Last, we tested the prediction that higher pretreatment GAD severity, trait perseverative cognitions, mindfulness, and EF would predict more reliable change in all of these pre-1MFU outcomes for the MEMI than SM (Hypothesis 3). ## Section snippets ## Participants The present RCT was preregistered (NCT04846777 on ClinicalTrials.gov). Table 1 summarizes sociodemographic and clinical descriptive statistics. Participants (_n =_ 110) averaged 20.80 years of age (_SD =_ 5.41, range = 18–52). Further, 86.67% were women, 13.63% were men, and one person declined to disclose their gender identity. Also, 64.55% identified as White, and the rest as African American (5.45%), American/Pacific Islander (1.82%), Asian or Asian American (13.63%), Hispanic (7.27%), Native ## Differential treatment effects on reliable improvement in pre-1MFU outcomes The MEMI yielded substantially higher reliable improvement in GAD severity (_n_ = 49, 72.06% vs. _n_ = 24, 58.54%; χ²(_df =_ 1) = 9.30, _p_ =.002) and trait perseverative cognitions (_n =_ 39, 57.4% vs. _n =_ 11, 26.2%; χ²(_df =_ 1) = 10.17, _p =_.001) from pretreatment to 1MFU than the SM. However, the MEMI (_n_ = 24, 35.5%) vs. SM (_n_ = 10, 23.8%) yielded no significant differential treatment efficacy on reliable improvement in trait mindfulness (χ²(_df =_ 1) = 1.60, _p =_.205). Likewise, MEMI (_n_ = 25, 59.5%) vs. ## Discussion Overall, the MEMI outperformed the SM to generate pre-1MFU reliable improvement in GAD severity and perseverative cognitions, but not trait mindfulness and global EF for participants with GAD. The more remarkable pre-1MFU reliable improvement rate in GAD severity and perseverative cognitions in the MEMI vs. SM was comparable to other DMHIs for GAD, such as a CBT app provided across six weeks (Miller et al., 2021). Plausibly, the non-significant differential treatment efficacy on clinically ## CRediT authorship contribution statement **Newman Michelle G.:** Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Supervision, Validation, Writing – original draft, Writing – review & editing. **Zainal Nur Hani:** Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Visualization, Writing – original draft, Writing – review & editing. ## Declaration of Competing Interest My research team and I have no conflicts of interest to declare. ## Acknowledgements An anonymous donation supported the current study. ## Statement of Ethics This study was conducted in compliance with the American Psychological Association (APA) ethical standards in the treatment of human participants and approved by the institutional review board (IRB). Further, this research was conducted was conducted ethically in accordance with the World Medical Association Declaration of Helsinki. Informed consent was obtained from participants as per IRB requirements at the Massachusetts General Hospital and © 2024 Elsevier Ltd. All rights reserved.