How Variability Is Addressed in Interventions for Neurodiverse Conditions: Implications for Stuttering

Abstract

Purpose:

This scoping review aimed to investigate strategies from peer-reviewed literature for addressing behavioral and experiential variability in neurodiverse conditions such as autism spectrum disorder, anxiety disorders, attention-deficit/hyperactivity disorder, mood disorders, and Tourette syndrome. Specifically, we explored how approaches used with other conditions could be adapted to better account for variability in the assessment and treatment of stuttering.

Method:

A comprehensive search of Google Scholar, PubMed, PsycINFO, Scopus, and Web of Science was conducted for studies published between 2000 and March 2025, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. Fifty-five studies met inclusion criteria by describing methods for measuring or managing variability across situations, across tasks, or over time. A narrative synthesis was used to analyze and interpret findings.

Results:

Key strategies for addressing variability in assessment included real-time data collection through ecological momentary assessment and contextual analysis using tailored rating scales. Key strategies for managing variability included personalized treatment, adaptive treatment models, cognitive therapy techniques, environmental modifications, and psychoeducation. These methods hold potential for improving the evaluation and management of variability within the population of individuals who stutter.

Conclusions:

Adapting strategies from other neurodiverse conditions to stuttering has the potential to offer benefits such as improved measurement of variability and more personalized interventions. This review emphasizes the value of cross-disciplinary approaches to enhance quality of life for those who stutter.

Understanding changes in the frequency and intensity of symptoms and experiences—whether across different situations, across tasks, or over time—is critical for supporting individuals with various neurological conditions and differences (American Psychiatric Association, 2013). Previous studies have shown that variability can mask or mimic certain symptom patterns, resulting in diagnostic challenges and suboptimal treatment (Shefer et al., 2014). In particular, research involving neurodivergent individuals has highlighted the role of personal, environmental, and task-related factors in shaping symptom expression and daily experiences. For instance, autistic individuals may communicate more easily in familiar or structured settings yet struggle when facing changes in routine or increases in social demands (Chester et al., 2019). People with anxiety disorders may demonstrate situation-specific surges in symptoms (e.g., panic attacks in crowded places) that are less likely to occur in lower stress settings (Walz et al., 2014). Similarly, children with attention-deficit/hyperactivity disorder (ADHD) may exhibit pronounced hyperactivity and inattention in a noisy classroom yet appear to be relatively symptom-free in more focused, one-on-one activities (Imeraj et al., 2013), demonstrating task-based differences. Beyond situational and task-driven patterns, temporal variability is also well documented: Individuals with mood disorders often experience daily or even moment-to-moment fluctuations in symptom severity (Schwartz et al., 2016). These fluctuations suggest that static assessments that measure symptoms in a single environment or at a single point in time may fail to capture the true nature of an individual's characteristics or experiences. Such findings reinforce the importance of accounting for real-world dynamics in order to ensure accurate diagnosis and effective treatment for a range of neurodiverse conditions.

Conceptual Framework: Types of Variability

Symptom variability in neurodiverse conditions is generally considered along three speaker-focused dimensions: across situations, across tasks, and over time, each capturing a distinct way that symptoms may fluctuate (Ebner-Priemer & Trull, 2009; Wright & Woods, 2020). Variability across “situations” refers to changes in symptom expression tied to the demands of different environments or social contexts. For example, individuals with anxiety disorders may experience heightened symptoms when speaking in front of a group of strangers compared to speaking in more familiar, lower pressure settings (Purper-Ouakil et al., 2004). Similarly, for an individual with ADHD, symptom severity often differs between home and school environments, underscoring the importance of cross-situational assessment (Rommelse et al., 2015). Variability across “tasks” refers to changes in symptom expression tied to the characteristics of different activities that an individual may be performing. For example, individuals with ADHD may demonstrate different reaction time patterns when engaging in response inhibition tasks as opposed to working memory exercises; these differences reflect task-specific cognitive demands (Chauvin et al., 2021). Similarly, in individuals with autism spectrum disorder (ASD), reading aloud can result in distinct error patterns and neural activation profiles when compared to silent decoding. This reflects differences associated with processing in different tasks (Peristeri et al., 2024). Variability across “time” describes fluctuations from moment-to-moment, day-to-day, or over longer periods. For example, research on mood dynamics in individuals with depression has identified distinct patterns of average mood, variability, and emotional inertia over time (van Genugten et al., 2022). Defining variability in this way provides a clear, standardized foundation for mapping how assessment and intervention strategies capture and respond to variability across conditions.

Stuttering Variability

Stuttering, a condition involving both overt and covert features, exhibits marked variability associated with factors such as speaking context, linguistic complexity, and emotional state (Bloodstein et al., 2021; Brown et al., 2025; C. D. Constantino et al., 2016; Jokar, Salehi, & Yaruss, 2025; Karimi et al., 2013; Ortiz-Alvarez & Arenas, 2025; Tichenor & Yaruss, 2021). In clinical sessions, a speaker might exhibit fewer disfluencies than they do in everyday conversations, particularly when they are experiencing elevated anxiety or situational demands (Baxter et al., 2015). These seemingly unpredictable fluctuations can interfere with clinicians' ability to accurately assess stuttering, develop meaningful goals, and measure progress in therapy (C. D. Constantino et al., 2016). Beyond posing challenges for clinicians, variability has direct consequences for people who stutter (Jokar, Salehi, & Yaruss, 2025; Tichenor & Yaruss, 2021). Unpredictable shifts in stuttering behaviors and experiences can heighten anticipatory anxiety, erode self-efficacy, restrict communicative participation, and increase avoidance; all of these experiences can reduce quality of life for adults and children who stutter. These fluctuations also affect caregivers, who may struggle with uncertainty about how to best support the speaker across contexts (Jokar, Salehi, & Yaruss, 2025). For example, parents and family members report heightened stress when stuttering severity varies from day to day, as this unpredictability can disrupt family routines, complicate school or workplace advocacy, and amplify feelings of helplessness or worry. In this way, stuttering variability may affect not only the individual but also their broader support network. Such findings underscore the need for assessment and treatment strategies that account for the experiences of both speakers and their caregivers. Although several authors have underscored the importance of accounting for stuttering variability in clinical work and research (e.g., Brundage et al., 2021; Jokar, Bayat, et al., 2025), there is presently no consensus regarding standardized methods for measuring and interpreting the differences that people who stutter experience in both their observable stuttering behaviors and their reactions to stuttering across different contexts and over time.

Current Stuttering Practices in Capturing Variability

Several stuttering-specific tools and clinical practices already attempt to capture variability across situations, across tasks, and, in some cases, over time. For example, the Overall Assessment of the Speaker's Experience of Stuttering (Yaruss & Quesal, 2016) protocol asks respondents about the impact of stuttering across everyday situations, such as at work, at home, or in social situations. The Speech Situation Checklist (part of the Behavior Assessment Battery [BAB]; Brutten & Vanryckeghem, 2007) indexes emotional reactions, speech disruption, avoidance, and attitudes across a variety of speaking situations—for example, talking on the telephone, introducing oneself, or ordering food in a restaurant. The Self-Efficacy Scaling for Adults Who Stutter (SESAS; Ornstein & Manning, 1985) examines confidence across 50 different speaking tasks or situations. Likewise, many therapy programs also incorporate practices designed to address variability across contexts. Hierarchy-based desensitization systematically exposes speakers to increasingly challenging situations (e.g., beginning with reading aloud in a safe context and progressing to spontaneous speech with unfamiliar partners), enabling gradual adjustment to fluctuations in stuttering severity. Transfer and generalization activities extend therapy gains into everyday life, requiring clients to practice communication strategies in varied real-world settings such as the workplace, social gatherings, or academic environments. These approaches are explicitly designed to prepare speakers for the dynamic nature of communication, acknowledging that stuttering severity may change across tasks, partners, and environments (Menzies et al., 2009; Murphy et al., 2007; Yonovitz et al., 1977). In this way, therapeutic activities attempt not only to reduce disfluency but also to build resilience and flexibility, ensuring that gains made in the clinic are sustainable across situations and over time.

At the same time, there are important gaps in our understanding of how variability should be addressed in both research and clinical work related to stuttering. For example, many procedures aggregate or average across items or tasks, thereby obscuring context-specific patterns in the primary score. High-frequency, momentary sampling that links changes to specific antecedents does not appear to be routine (C. D. Constantino et al., 2016). Standardized multi-informant procedures that preserve variability in scoring and reporting are also rare; most studies rely on single-speaker or clinician ratings, with limited integration of multiple perspectives (e.g., Onslow et al., 2003; Riley, 2009). Clinical trials infrequently report variability-sensitive outcomes such as within-person variance, stability indices, or context-conditioned effects. Instead, they often rely on aggregate measures such as pre/post %syllables stuttered (for a review, see Bothe et al., 2006). Although experts have emphasized the importance of incorporating variability measures into clinical practice (Brundage et al., 2021), it does not appear that such practices are yet widespread. These gaps illustrate why simply noting variability is insufficient: Without systematic methods for quantifying and tracking it, variability cannot meaningfully inform assessment or treatment planning. Thus, while the field of stuttering has made progress in acknowledging variability, our review sought to build on prior work by synthesizing variability-focused design features established in other disciplines.

Aims of the Present Study

As noted, variability is not unique to stuttering; researchers and clinicians in other disciplines have long grappled with similar challenges, and their approaches offer valuable examples for how variability can be addressed in both assessment and treatment. The premise of the present work is that the field of stuttering can benefit from examining what has been done for other conditions. Thus, we conducted a scoping review of how variability is recognized and addressed across neurodiverse populations, with the goal of informing stuttering research and clinical practice. Specifically, by synthesizing insights from literatures on other conditions, we aimed to help clinicians and researchers develop more robust, context-aware assessments of stuttering. Our ultimate goal is to support interventions that accommodate the nuances of each individual's personal characteristics and communicative environments, including how these may change across settings and over time. For this review, we focused on conditions that are commonly recognized as reflecting neurodivergence, that is, conditions that reflect neurological development, processing, and functioning that are different from what is conventionally considered typical (Armstrong, 2011; Singer, 1999). This category encompasses conditions such as ASD, ADHD, anxiety disorders, mood disorders, obsessive-compulsive disorder (OCD), Tourette syndrome (TS), oppositional defiant disorder (ODD), and conduct disorder (CD). These conditions were selected because they share two features with one another and with stuttering: (a) They are conceptualized as lifelong neurodevelopmental or neurodivergent profiles, and (b) they demonstrate well-documented patterns of variability across situations and tasks over time. We did not include acquired neurological conditions such as traumatic brain injury, stroke, or Parkinson's disease. Although these acquired conditions also exhibit variability, we did not include them in this review because they are not considered part of neurodiversity.

Accordingly, we sought to answer the following research question: “How is variability addressed in neurodiverse conditions, and how can these strategies be applied to improve the assessment and treatment of stuttering?” By exploring established methodologies across disciplines, we aimed to elucidate practical, evidence-based strategies for embracing—rather than overlooking—variability in stuttering.

Method

We conducted this scoping review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines (Peters et al., 2020) to ensure a rigorous and transparent research process. This approach allowed us to systematically integrate findings from a wide range of designs and outcome measures.

Search Strategy

We conducted a comprehensive search of PubMed, APA PsycINFO (Ovid), Scopus, Web of Science Core Collection, and Google Scholar for records published between January 1, 2000, and March 30, 2025 (final search: March 30, 2025). We began in 2000 to align with the introduction of contemporary frameworks for evidence-based practice. The strategy combined three concept clusters (variability, assessment/management, and target conditions) using database subject headings (e.g., MeSH, APA Thesaurus) and free-text key words with Boolean operators and truncation. Representative variability terms were “variability,” “fluctuation,” “instability,” “lability,” “within-person,” “moment-to-moment,” “temporal,” “situational/contextual,” and methods such as “ecological momentary assessment (EMA)” and “experience sampling method (ESM).” Assessment/management terms were “assessment,” “measurement,” “evaluation,” “monitoring,” “management,” “treatment,” “intervention,” “therapy,” and design terms such as “adaptive,” “personalized,” “dynamic treatment,” and “stepped care.” Target conditions were ASD, ADHD, anxiety disorders, mood disorders, OCD, TS, ODD, and CD. We combined clusters with AND (e.g., variab AND assess* AND (autis* OR ADHD OR anxi* … )) and expanded synonyms with OR. Searches were limited to English, human participants, and peer-reviewed journal articles.

To enhance coverage, we performed backward (reference list) and forward (citation) searches of all included articles. In Google Scholar, we ran paired queries (e.g., “experience sampling” variability autism, EMA variability ADHD, within-person variability anxiety assessment/treatment) and screened the first ~200 results per query by title/abstract. All records were exported to Zotero (Version 6.0) for de-duplication and then screened at title/abstract and full text by the first and second authors; discrepancies were resolved by consensus. Reasons for full-text exclusion were logged, including cases where studies did not address variability in assessment or treatment, did not involve a neurodiverse or stuttering population, lacked sufficient methodological detail, or represented duplicate reports of the same data set. The selection process is summarized in the PRISMA-ScR flow diagram shown in Figure 1.

Figure 1.

Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews diagram.

Inclusion and Exclusion Criteria

Studies were included if they (a) examined ASD, anxiety disorders, ADHD, mood disorders, OCD, TS, ODD, or CD and (b) specifically considered variability, meaning that the study included a clear method for measuring (for assessment) or responding to (for treatment) changes in symptoms, behaviors, or experiences across situations (e.g., home vs. school, familiar vs. unfamiliar partners), across tasks (e.g., reading, conversation, problem solving), and over time (e.g., repeated measures, daily diaries, longitudinal follow-up). Eligible studies presented original empirical data (quantitative, qualitative, or mixed methods), discussed or evaluated approaches to assessing or managing variability, and were published in peer-reviewed journals in English. Studies were excluded if they (a) did not address ASD, anxiety disorder, ADHD, mood disorders, OCD, TS, ODD, or CD; (b) lacked a focus on variability without describing how it was measured or managed were excluded; (c) were theoretical or nonempirical (e.g., reviews, commentaries, or editorials); (d) were not published in English; or (e) were duplicates of previously identified articles. To reduce subjectivity, inclusion and exclusion decisions were made independently by two reviewers, with discrepancies resolved through discussion until full consensus was achieved.

A total of 356 articles were initially identified through database searches and imported into an Excel file. After 10 duplicates were removed, 346 articles proceeded to screening. The first and second authors independently reviewed titles and abstracts to assess initial eligibility, with each study requiring an “include” vote from both reviewers to proceed to full-text review. Discrepancies, or situations in which one reviewer voted “exclude” and the other voted “include,” were resolved through discussion. In total, 23 discrepancies were identified across the 346 reviewed studies, and these discrepancies were all resolved through discussion so that full consensus was ultimately achieved. Overall, 250 articles were excluded for the following reasons: (a) not addressing the targeted conditions (ASD, anxiety disorders, ADHD, mood disorders, OCD, TS, ODD, or CD; n = 112), (b) lacking a focus on symptom variability (n = 93), and (c) not presenting original empirical data (n = 45).

During the full-text review of the remaining 96 articles, an additional 41 articles were excluded for various reasons, including failure to address the targeted conditions, lack of focus on symptom variability, or nonempirical formats (e.g., case studies, commentaries, or editorials). Again, disagreements between reviewers were resolved through discussion to reach consensus, ensuring 100% consistency in study selection. Ultimately, 55 studies meeting all inclusion criteria were included in the scoping review.

Data Extraction

To ensure accuracy and consistency in data collection, a standardized data extraction form was developed. This form allowed for documentation of relevant information from each study, including sample characteristics such as age and gender or participants, methods used to account for variability, and key findings related to variability. The first and second authors completed data extraction for all included articles, and each entry was verified to prevent discrepancies. Twelve discrepancies in the extracted data were resolved through collaborative discussion, ensuring the reliability of the information gathered. The extraction process focused specifically on methods and tools that were used to assess variability, as well as on strategies that were employed for management of variability. To build a comprehensive understanding of how symptom variability was addressed in the reviewed studies, the reviewers recorded a range of details from each study. This included the specific measurement techniques employed, the observed fluctuations in the frequency and intensity of symptoms over time, and the contextual or situational factors that influenced these variations. This structured approach facilitated consistent comparison of results across studies, supporting the thematic synthesis of findings in the review.

Data Synthesis

Given the heterogeneity of study designs and methodologies among the included studies, a qualitative narrative synthesis approach was employed to organize and interpret the data. Narrative synthesis is a method used in scoping reviews to synthesize findings from multiple studies when statistical meta-analysis is not feasible due to the diversity of the evidence base (Popay et al., 2006). Rather than pooling numerical data, this approach uses a textual summary to explore relationships within and between studies, identify recurring patterns, and construct a coherent narrative from the findings. Our data synthesis employed a hybrid approach, combining both deductive and inductive elements. The process began with a deductive framework directly informed by our primary research question, which established two broad, high-level categories to guide the initial coding: (a) approaches to assessing variability and (b) strategies for managing variability in treatment. This top-level structure was theoretically driven by the fundamental clinical need to both measure an issue and intervene upon it. Within these two predefined categories, our process became inductive. The first and second authors independently read the full texts of the included studies to identify specific subthemes as they emerged from the data (e.g., “personalized treatments,” ‘“real-time observations,” “cognitive therapy”). This bottom-up process allowed the thematic structure to be shaped by the evidence within the literature rather than by our preconceptions. We then compared these potential subthemes across studies by creating a tabular summary that highlighted commonalities and distinctions. Through iterative group discussions held biweekly over a 2-month period, the research team refined the list of themes by merging overlapping concepts and structuring the final framework. To enhance evidentiary rigor, we required that each theme be supported by findings from at least four independent studies, consistent with recommendations for robust theme development in narrative and thematic synthesis (Braun & Clarke, 2006; Popay et al., 2006; Thomas & Harden, 2008). This practice aligns with principles of thematic synthesis, where themes must be substantiated by sufficient evidence from the primary studies to be considered credible and meaningful (Thomas & Harden, 2008).

To ensure trustworthiness, coding was cross-checked by at least two team members for each study, with interrater reliability calculated as the percentage of codes on which the raters agreed. The initial agreement rate was approximately 85%. Discrepancies were resolved through consensus discussions in nine cases. A detailed audit trail was maintained, documenting all coding decisions, theme iterations, and resolution outcomes. The final narrative synthesis highlighted overarching patterns and key distinctions in addressing variability, offering a clear and reliable framework for interpreting the data. Subsequently, the third and fourth authors reviewed the themes and results to ensure accuracy and coherence; no further changes were made based on this review.

Results

This scoping review analyzed 55 studies, covering a diverse array of research on assessment tools and strategies for managing variability across various conditions (see the Appendix). Several key themes were identified describing how variability is assessed and managed within these conditions.

Assessment of Variability

The studies examined in this scoping review involved using both structured instruments and dynamic observational methods to systematically capture how symptoms and behaviors fluctuate across different situations, tasks, and time (see Table 1).

Table 1.

Studies addressing variability in assessment of neurodiverse conditions.

Theme	Condition	Approach	References
Standardized rating scales	ASD	Utilization of the Social Responsiveness Scale (SRS) and teacher report tools to assess social behaviors across contexts and time, capturing both situational and temporal variability	Chan et al. (2017), Cholemkery et al. (2014), Schanding et al. (2012)
	AD	Application of the State–Trait Anxiety Inventory (STAI) and HADS-A to distinguish between temporary and long-standing anxiety, validating contextual and temporal fluctuations in clinical samples (e.g., epilepsy, pregnancy)	de Lemos Zingano et al. (2019), Delgado et al. (2016), Lü et al. (2022)
	ADHD	Use of Conners' Rating Scales and ADHD Rating Scale–IV to compare parent vs. teacher ratings across home and school, highlighting contextual differences in symptom perception	François-Sévigny et al. (2022), Izzo et al. (2019), McGoey et al. (2007)
	MD	Use of the Mood Disorder Questionnaire (MDQ) to detect fluctuations in mood states, with sensitivity to temporal variability and differentiation of bipolar vs. unipolar conditions	Gervasoni et al. (2009), Hirschfeld et al. (2000), Yang et al. (2014)
	OCD	Use of the Yale–Brown Obsessive–Compulsive Scale (Y-BOCS I & II) to assess symptom severity, sensitive to week-to-week variability	Alić et al. (2022), Mataix-Cols et al. (2002)
	TS	Use of the Yale Global Tic Severity Scale (YGTSS) to capture tic severity across dimensions (frequency, intensity, interference), validating temporal and situational variability	Haas et al. (2021), Yan et al. (2022)
Clinicians' observations	ASD	Experimental and clinician-led observation of gaze, action prediction, and communication contexts to reveal situational variability in performance	Schilbach et al. (2012), Von Der Lühe et al. (2016)
	AD	Behavioral observation during VR-assisted public-speaking exposure to track situational variability in anxiety responses	Artemi et al. (2025)
	ADHD	Direct classroom observations comparing silent work, whole-class teaching, and noninstructional settings to reveal context-driven differences in attentional control	Lauth et al. (2006)
	TS	Longitudinal clinician assessment linking life events with tic severity, highlighting situational and temporal variability	Hoekstra et al. (2004)
	ODD	Observations of preschoolers across examiner vs. parent contexts, revealing situational variability in disruptive behaviors	De Los Reyes et al. (2009)
Clients' observations	ASD	Ecological momentary assessment (EMA) documenting daily life social interactions, affect, and context (household vs. peers), capturing both situational and temporal variability	Feller et al. (2023)
	ADHD	EMA capturing daily mood, peer interactions, and ADHD symptoms to reveal moment-to-moment fluctuations	Kennedy et al. (2024), Murray et al. (2023)
	AD	EMA measuring affective inertia and moment-to-moment variability, distinguishing current vs. remitted disorders	Schoevers et al. (2021)
	MD (MDD)	Experience sampling method (ESM) capturing daily affect before and after therapy, revealing temporal and situational changes in affective reactivity	Eddington et al. (2017)
	OCD	EMA recording frequency and burden of obsessions/compulsions across situations, sensitive to treatment changes	Rupp et al. (2019)
	Depression	EMA showing affective instability and heightened temporal variability in mood among BPD vs. MDD/dysthymia	Trull et al. (2008)

Note. Conditions represented in the table include autism spectrum disorder (ASD), anxiety disorders (AD), attention-deficit/hyperactivity disorder (ADHD), mood disorders (MD; including major depressive disorder [MDD] and depression), obsessive-compulsive disorder (OCD), Tourette syndrome (TS), and oppositional defiant disorder (ODD). HADS-A = Hospital Anxiety and Depression Scale–Anxiety; VR = virtual reality; BPD = borderline personality disorder.

Standardized Rating Scales That Account for Variability

These scales address variability by measuring frequency and severity across different scenarios. For example, in the assessment of autistic individuals, the Social Responsiveness Scale (SRS; J. N. Constantino, 2013) has been used to gather multi-informant data on social behaviors that differentiate autism-specific issues from co-occurring conditions. This scale also accounts for real-time monitoring to examine variability over time (Chan et al., 2017; Cholemkery et al., 2014; Schanding et al., 2012). Similarly, for individuals with anxiety disorders, the State–Trait Anxiety Inventory (STAI; Spielberger, 1983) has been used to distinguish temporary from long-standing anxiety to assess variability over time (de Lemos Zingano et al., 2019; Delgado et al., 2016; Lü et al., 2022). For those with ADHD, scales such as the Conners' Rating Scales and ADHD Rating Scale–IV (McGoey et al., 2007) employ separate parent and teacher forms to detect behavioral differences observed at home and at school (François-Sévigny et al., 2022; Izzo et al., 2019). For those with mood disorders, the Mood Disorder Questionnaire (MDQ; Hirschfeld et al., 2000) has been used to track fluctuations in mood states across situations (Gervasoni et al., 2009; Yang et al., 2014). In OCD and TS, the Yale–Brown Obsessive–Compulsive Scale (Y-BOCS; Alić et al., 2022) and Yale Global Tic Severity Scale (YGTSS; Yan et al., 2022), respectively, have been used to monitor changes in symptom severity across different contexts (Haas et al., 2021; Mataix-Cols et al., 2002). Each of these scales provides a comprehensive picture of how symptoms vary by collecting data in different situations or from different informants, thereby allowing clinicians and researchers to gather consistent, standardized information about how symptoms and experiences of various conditions vary across contexts. Standardized rating scales primarily capture variability across situations (different contexts and informants) and, when repeatedly administered, across time.

Real-Time Observations Across Contexts

Real-time observational methods have also been used to provide detailed insights into variability by capturing moment-to-moment changes in symptoms, behaviors, and experiences across diverse contexts. Unlike static assessments, these approaches offer nuanced perspectives on how situational factors dynamically influence an individual's presentation. Real-time observations were categorized into two subthemes based on who conducted the observations: clinicians or clients themselves.

Clinician observations. Clinician-based real-time observations reveal contextual influences on moment-to-moment changes in symptoms, behaviors, and experiences that static assessments may overlook. Such results provided a more dynamic view of symptom expression and an enhanced understanding of how situational factors may shape behavior and experiences. For example, in autistic individuals, structured observations during social interactions have been used to reveal context-dependent variations in communication and behavioral responses. Such observations, which are critical for understanding individualized patterns in social engagement and adaptive functioning, have offered insights into how situational factors influence social variability (Schilbach et al., 2012; Von Der Lühe et al., 2016). In autism research, such structured observations are often guided by standardized templates, most notably the Autism Diagnostic Observation Schedule (Lord et al., 2000), which provides a replicable framework for eliciting and rating behaviors across social contexts. Comparable structured observation protocols have also been developed for task-based assessments in anxiety (Craske et al., 2014). Individuals with anxiety disorders have been evaluated through behavioral assessments that are administered during controlled exposure tasks. In a simulated public-speaking challenge, for example, clinicians may observe avoidance behaviors—such as hesitations or stepping away from the microphone—and record physiological responses (e.g., elevated heart rate, sweating), thereby capturing how anxiety intensity varies under stress (Lauth et al., 2006). For individuals with ADHD, situational variability has been assessed through direct observations of inattentiveness and hyperactive behaviors in classrooms and home environments (Lauth et al., 2006). Similarly, individuals with TS have been assessed via video observations across diverse settings; this reflects changes in tic frequency and intensity (Hoekstra et al., 2004). The assessment of individuals with ODD has employed direct observations during interactions with peers and adults to gauge situational variability in defiant behaviors (De Los Reyes et al. 2009), while assessment of individuals with CD has used peer nomination techniques to evaluate conduct issues across social contexts, thereby identifying variability in peer interaction experiences (Cillessen & Mayeux, 2004). Together, these clinician observation strategies can reveal the interplay between individual vulnerabilities and situational factors in order to enhance clinicians' understanding of moment-to-moment variability. Clinician observations primarily capture variability across situations and tasks and, when repeated, can extend to variability over time.

Client observations. A common subtheme that was identified among studies addressing variability is real-time observation by clients. EMA and ESM are examples of such measures, which are designed to collect moment-to-moment data on behaviors, emotions, and experiences as reported by the client or individual living with a condition (Trull et al., 2008). EMA involves the collection of self-reported data throughout the day, often via mobile devices; this practice helps to reduce recall bias and increases the amount of detail captured within specific contexts (Burke et al., 2017). ESM gathers random samples of an individual's current state; this practice improves understanding of moment-to-moment shifts across social interactions (Van Berkel et al., 2018). These methods have been applied across various conditions. In autistic individuals, EMA has been used to monitor real-time, self-reported changes in social behaviors and communication. Doing so helps clinicians and researchers identify specific stressors that affect reactions (Feller et al., 2023). Similarly, for those with anxiety disorders, EMA has been employed to capture shifts in anxiety levels correlated with situational stressors (Schoevers et al., 2021). For individuals with ADHD, EMA has been used to track fluctuations in inattention, hyperactivity, and impulsivity (Kennedy et al., 2024; Murray et al., 2023). For those with mood disorders, ESM has been used to provide frequent updates on mood fluctuations and uncovered stressors such as interpersonal conflicts (Eddington et al., 2017). In individuals with OCD, EMA has been used to log the frequency and intensity of obsessions and compulsions, and in those with TS, EMA has been used to document the occurrence of tics and related contextual factors (Rupp et al., 2019). Together, these real-time observation methods enhance understanding of client behaviors and experiences across conditions, and these data support the development of more targeted interventions that can improve quality of life (Johnson et al., 2022). Client-based EMA and ESM methods primarily capture variability across situations and over time because they document moment-to-moment experiences within natural contexts.

Summary of Strategies for Assessing Variability

In summary, variability has been addressed in assessment of neurodiverse conditions through methods that systematically examine symptom fluctuations across contexts, informants, and time points using multidimensional data collection strategies and contextually sensitive methods. Standardized rating scales (e.g., SRS for ASD, STAI for anxiety disorders, Y-BOCS for OCD) gather multi-informant data to assess symptom frequency and severity in diverse settings, while real-time observational methods such as EMA and ESM, along with clinician observations, track moment-to-moment changes influenced by situational factors. This dual approach ensures a comprehensive understanding of variability.

Management of Variability

The studies examined in this scoping review revealed several strategies for effectively addressing variability in treatment; in particular, analyses highlighted the value of personalized and adaptive treatments that respond dynamically to the fluctuating nature of these various conditions (see Table 2).

Table 2.

Studies addressing variability in management strategies of neurodiverse conditions.

Theme	Condition	Approach	References
Personalized treatments	ASD	Pivotal response treatment (PRT) to improve communication behaviors; individualized sensory integration for sensory/social–emotional regulation; TEACCH program supporting adaptive behavior and daily living skills; family-focused treatments improving engagement and behavior	Hardan et al. (2015), Koegel et al. (2014)
	AD	Training emotion regulation flexibility to select strategies based on situational demands; personalized exposure therapy with real-time arousal feedback; CBT targeting emotion regulation as treatment mediator	Asnaani et al. (2020), Lin et al. (2019), Specker & Nickerson (2024)
	ADHD	Daily behavior report cards linking classroom behavior with home reinforcement; daily report cards (DRCs) tied to IEP goals; individualized classroom-based reinforcement	Fabiano et al. (2025), Jurbergs et al. (2010)
	MD	Interpersonal and social rhythm therapy (IPSRT) to stabilize routines; MBCT to reduce relapse and stabilize residual symptoms; family-focused interventions for adolescents	Miklowitz et al. (2004), Swartz et al. (2011)
Adaptive treatment models	ASD	Patient-centered sensory strategies in EDs (quiet rooms, dim lighting, sensory tools, communication about preferences); sensory room therapy reducing overstimulation and behavioral fluctuations	Awaida et al. (2024), DeGuzman et al. (2024)
	AD	Acceptance-based behavior therapy (ABBT) and applied relaxation for GAD; adaptive CBT protocols addressing situational variability in treatment adherence	Hayes-Skelton et al. (2013)
	ADHD	Virtual classroom cognitive remediation (VCCR) simulating distractors to build sustained attention and reduce impulsivity	Bioulac et al. (2020)
	OCD	Exposure and response prevention (ERP) dynamically adapted to symptom fluctuations; ACT and parent-enhanced CBT for OCD	Twohig et al. (2010), Whittal et al. (2005)
Cognitive therapy techniques	ASD	Adapted CBT (BIACA) reducing anxiety and improving adaptive functioning; mindfulness-based therapy (MBT-AS) lowering depression and anxiety; mindfulness/acceptance approaches addressing variability linked to alexithymia	Maisel et al. (2016), Speck et al. (2013), Wood et al. (2020)
	AD	CBT improving emotion regulation and impulse control; scenario-based CBT mobile apps reducing dysfunctional beliefs; therapist competence/adherence influencing treatment variability in panic disorder	Asnaani et al. (2020)
	MD	MBCT reducing relapse and stabilizing symptom variability; scenario-based CBT mobile interventions improving mood	Hur et al. (2018), Kuyken et al. (2008), Shallcross et al. (2015)
Environmental modifications	ASD	TEACCH program to structure environments; sensory room therapy improving stability of behavior; ED environmental adjustments to reduce sensory load	Awaida et al. (2024)
	ADHD	Virtual classroom cognitive remediation (VCCR) using adaptive distractor levels to improve focus and reduce impulsivity	Bioulac et al. (2020)
Psychoeducation and family interventions	AD	Large-group CBT psychoeducation reducing anxiety severity and improving coping understanding	Houghton & Saxon (2007)
	BD	Online psychoeducation (with/without peer support) improving illness control and adherence; adjunctive family-focused treatment reducing symptoms in adolescents	Proudfoot et al. (2012)

Note. Conditions include autism spectrum disorder (ASD), anxiety disorders (AD), attention-deficit/hyperactivity disorder (ADHD), obsessive-compulsive disorder (OCD), bipolar disorder (BD), mood disorder (MD; including major depressive disorder and depression), and depressive disorders. TEACCH = Treatment and Education of Autistic and Related Communication-Handicapped Children; CBT = cognitive behavioral therapy; IEP = Individualized Education Program; MBCT = mindfulness-based cognitive therapy; ED = emergency department; GAD = generalized anxiety disorder; ACT = acceptance and commitment therapy; BIACA = behavioral–imaginal–affective–cognitive analysis.

Personalized Treatments

Variability has been reported among individuals with autism, anxiety, and depression, and personalized treatments have been developed to address this inherent variability (Thompson et al., 2017). For autistic individuals, treatment approaches such as pivotal response treatment (PRT; Koegel et al., 2014) focus on flexibility and adaptability in order to address behaviors that change across a broad range of social and communication outcomes. PRT is designed to be responsive to moment-to-moment variability in behavior, while allowing for personalized adjustments; this enables the approach to align closely with each individual's needs and behavioral states at any given point in time (Hardan et al., 2015). Individuals with anxiety disorders have also been shown to benefit from individualized strategies that helped them manage fluctuating anxiety symptoms (Specker & Nickerson, 2024). Such strategies can enhance people's ability to cope with variability in anxiety-provoking situations so that they can maintain emotional stability despite changing circumstances. For example, some individuals experienced sudden spikes in anxiety in social settings, while others encountered gradual increases during stressful periods; tailored mindfulness practices were adapted in real time to match these varying patterns. In individuals with ADHD, behavioral treatments have been used to manage fluctuations in attention and impulsivity throughout the day (Jurbergs et al., 2010). For example, strategies used at school, such as frequent breaks and task segmentation, can be used to address periods of heightened inattention, while home-based treatments, such as visual schedules, can be used to manage routines during times of increased distractibility. For those with mood disorders, treatments such as interpersonal and social rhythm therapy (IPSRT) have been used to stabilize daily routines and reduce mood variability through regulation of sleep–wake cycles, mealtimes, and social interactions (Swartz et al., 2011). For example, a person with bipolar disorder who struggled with irregular sleep patterns worked through IPSRT to maintain a regular sleep schedule, which helped prevent extreme mood episodes. Overall, these findings highlight that personalized, flexible treatments can enhance treatment effectiveness across neurodiverse conditions by directly accounting for individual patterns of variability.

Adaptive Treatment Approaches

Adaptive treatment models allow clinicians to dynamically adjust treatments based on ongoing assessments of symptom variability, ensuring that treatments remained aligned with an individual client's current needs. Research has shown that autistic individuals can benefit from sensory integration techniques that seek to address fluctuations in behavior by either amplifying or diminishing sensory input based on a person's state (Pfeiffer et al., 2011). For instance, noise-canceling headphones or dimmed lighting can reduce sensory overload, while swinging, deep-pressure massage, or textured objects can boost sensory input when needed. These techniques have been shown to support regulation, improve adaptive behaviors, enhance daily participation, reduce anxiety in unpredictable environments, and foster neuroplasticity for long-term sensory improvements (DeGuzman et al., 2024). Variations in the experiences of individuals with anxiety disorders have been managed through situation-specific exposure therapy involving situations where anxiety is most pronounced (Craske et al., 2014). Exposure hierarchies can be dynamically adjusted to ensure that therapy remains effective as anxiety stressors changed (Lin et al., 2019). For individuals with ADHD, dynamic monitoring and adjustment involved tools such as daily report cards have been used to monitor symptom changes in real time and allow for immediate adjustments in treatments strategies (Fabiano et al., 2025). Similarly, OCD has been managed through strategies such as exposure and response prevention (ERP), which involves gradual exposure to anxiety-provoking stimuli with the prevention of compulsive responses. ERP can be adjusted based on symptom variability, ensuring that therapeutic treatments remain responsive to changes in OCD symptomatology (Whittal et al., 2005).

Cognitive Therapy

The review of the literature revealed that cognitive therapy techniques are widely used to manage variability across conditions, as they address the ever-changing interaction between thoughts, emotions, and behaviors (Boswell et al., 2013). For example, cognitive behavioral therapy (CBT; Wenzel, 2017) has been used to provide individuals with skills to manage variability by modifying unhelpful thought patterns and behaviors. In autistic individuals, cognitive approaches can help reframe unhelpful thoughts, manage anxiety, and improve cognitive flexibility through structured sessions that teach practical coping strategies (Wood et al., 2020). Mindfulness-based cognitive therapy (MBCT) has also been used with autistic people to boost emotional self-awareness and stabilize social interactions by reducing impulsive responses (Kiep et al., 2015). Acceptance and commitment therapy (ACT; Hayes & Pierson, 2005) has also been used to help autistic individuals to address variability across social situations by enhancing psychological flexibility, encouraging acceptance of difficult emotions, aligning actions with personal values (Maisel et al., 2016). In individuals with anxiety disorders, CBT has been used to reduce unhelpful thought patterns, enabling them to reframe negative thoughts and manage fluctuations in anxiety (Asnaani et al., 2020). ACT has also been applied to help individuals accept anxious thoughts without judgment and focus on meaningful behaviors in order to reduce the variability of anxiety experiences across situations (Hayes-Skelton et al., 2013). Structured CBT treatments have also been used to address unhelpful thought processes and reduce variability in emotional responses for those with mood disorders (Hur et al., 2018). Similar results have been reported for the use of MBCT in individuals with mood disorders: Mindfulness exercises helped individuals observe depressive thoughts nonjudgmentally, and this led to more stable emotional patterns over time (Kuyken et al., 2015). For those with OCD, CBT has been widely used to reduce intrusive thoughts and compulsive behaviors, thereby helping individuals maintain control over symptoms despite varying anxiety levels (Reynolds et al., 2013). ACT has been shown to be effective at promoting acceptance of intrusive thoughts and reducing compulsive rituals, resulting in greater symptom stability through mindfulness and values-based strategies (Twohig et al., 2010). Together, these findings show that individualized, interactive cognitive therapies can help to reduce variations in emotional responses and experiences across diverse conditions.

Environmental Modifications

Identifying environmental stressors has been shown to help clinicians and clients manage symptom variability by adjusting surroundings to promote stability. For example, adapting physical or social settings can support more consistent behaviors and reduced symptom-exacerbating stressors for autistic individuals. Predictable frameworks, such as the Treatment and Education of Autistic and Related Communication-Handicapped Children (Panerai et al., 2002), reduce anxiety and promoted stability (Awaida et al., 2024). Similarly, adaptations to the environment, such as variable lighting or adjustable seating, can accommodate sensory and social variability and thereby improve focus, comfort, and engagement (Barrett et al., 2015). Structuring exposure to environmental stressors, such as social situations or phobias, has been used to help people with anxiety disorders to reduce anxiety (MacDonald et al., 2015). Individuals with ADHD have been shown to experience enhanced focus and reduced hyperactivity through reduced distractions in a classroom (Bioulac et al., 2020). Identifying environmental stressors has also been used to help individuals with depression proactively mitigate the impact of variability. For example, recognizing interpersonal conflicts or work pressures in advance can support the use and development of stronger coping mechanisms that reduce relapse rates by minimizing trigger-induced fluctuations (Couser, 2008). Together, these studies show that modifications that proactively structure an individual's physical and social environment can address symptom variability and foster more stable, adaptive functioning across diverse conditions.

Psychoeducation

Psychoeducation methods involve structured educational treatments that inform clients and their families about the nature of their condition, its symptoms, potential stressors, and effective management strategies (de Souza Tursi et al., 2013). This knowledge has been shown to empower people to recognize that fluctuations in symptoms are common and manageable. Moreover, it can foster a greater sense of control over the changes that people experience as they live with their condition (Proudfoot et al., 2012). For individuals with anxiety disorders, for example, structured psychoeducational treatments have been used to demystify the triggers that lead to increased anxiety, to identify patterns in anxiety experiences, and to build confidence in self-monitoring varying symptoms (Houghton & Saxon, 2007). For those with mood disorders, structured educational treatments have been used to demystify the condition and foster a sense of self-efficacy regarding variability. This encouraged people to adapt more proactively to mood shifts and ultimately reduced the impact of depression on daily life (Proudfoot et al., 2012). Psychoeducation has also been shown to help those with bipolar disorder to recognize patterns of manic and depressive episodes. Understanding stressors and early warning signs of mood shifts can allow for more prompt treatment; in turn, this can reduce the severity and duration of such episodes (Perry et al., 1999) and lead to increased adherence to treatment, early relapse detection, and greater lifestyle regularity (Miklowitz et al., 2004). Together, these studies show that psychoeducational strategies can help to manage symptom variability and improve clinical outcomes and quality of life.

Summary of Strategies for Managing Variability

The reviewed studies reveal that managing symptom variability across conditions often involves treatments that are both responsive and individualized. Personalized approaches and adaptive treatment models adjust client support based on ongoing assessments of symptom changes. Cognitive and mindfulness-based therapies promote emotional and behavioral stability by enhancing flexible thinking and self-regulation. Modifications to environmental contexts minimize stressors and support consistent functioning. Psychoeducation equips clients and families with the knowledge and strategies necessary to more effectively anticipate and manage fluctuations. These approaches emphasized the importance of dynamic, context-sensitive management for reducing the impact of variability across diverse clinical populations.

Discussion

This study involved a scoping review of strategies used to manage symptom variability across a range of neurodivergent conditions, such as ASD and ADHD. The primary goal was to identify and synthesize approaches and translate these insights to the domain of stuttering variability. Analyses revealed a number of strategies that have been used to address variability in assessment and treatment within and across diverse conditions; consideration of these strategies may help speech-language pathologists and researchers to better account for the variability associated with stuttering.

Assessment Strategies for Stuttering Variability

The review of methods for assessing variability in neurodivergent conditions showed that assessment tools are often explicitly designed to capture fluctuations across situations, tasks, and time. For example, standardized rating scales explicitly measure contextual differences (e.g., SRS scores distinguishing home vs. school functioning in people with ASD); in people with mood disorders, self-report questionnaires such as the MDQ track fluctuations across situations; and in the clinical assessment of OCD and TS, repeated administrations of the Y-BOCS and YGTSS monitor symptom changes over time. Real-time methods such as EMA and ESM are also widely used to reduce recall bias and capture moment-to-moment changes in everyday life in these conditions.

Despite the fact that variability is well established in the study of stuttering (C. D. Constantino et al., 2016; Jokar, Salehi, & Yaruss, 2025; Karimi et al., 2013; Tichenor & Yaruss, 2021), established assessments do not routinely account for variability. For example, the Stuttering Severity Instrument–Fourth Edition (Riley, 2009) advises sampling across tasks (e.g., reading vs. spontaneous speech) and settings (e.g., clinic vs. outside), but the resulting scores are aggregated, potentially obscuring situational differences. Similarly, most research studies fail to account for variability across tasks, settings, or time (Landers et al., 2025). Some stuttering-specific measures do capture aspects of variability. For example, the Palin Parent Rating Scales (Millard & Davis, 2016) provides multi-informant perspectives by gathering parents' observations, the SESAS (Ornstein & Manning, 1985) gauges confidence across diverse speaking contexts. Still, these tools typically capture variability only indirectly, and their results are often aggregated in ways that obscure meaningful patterns across different scenarios. These findings suggest that the stuttering field could benefit from adopting cross-disciplinary strategies that explicitly document fluctuations across settings, tasks, and time points. For example, assessments could more consistently incorporate multi-informant perspectives (e.g., parents, teachers, colleagues, and communication partners), self-reports of experiences across the prior week, and ecologically valid speaking tasks with different conversational partners (Ingham & Cordes, 1997).

In the absence of formal tools designed to capture variability systematically, clinicians can adapt existing stuttering assessments to better reflect real-world speaking situations. This might include interviewing people who regularly interact with the speaker (for measuring variability in observable stuttering behavior), documenting perceived differences across environments (home, school, work, social settings), and encouraging clients to provide structured self-reports or logs. EMA and ESM, already established in fields such as anxiety and ADHD (Miguelez-Fernandez et al., 2018; Rupp et al., 2019), could also be applied to stuttering to capture moment-to-moment changes in observable stuttering severity and adverse impact. Combining input from multiple observers, across diverse contexts, and over repeated time points would provide a more comprehensive understanding of how stuttering fluctuates in daily life, beyond the clinic.

Although these suggestions for improving assessment of stuttering variability have merit, they are not without potential challenges. For example, consistently logging events throughout the day can become burdensome for some speakers, and technological barriers—such as limited access to or discomfort with mobile apps—may prevent some individuals from engaging fully in real-time self-monitoring. Still, clinicians in other fields have overcome similar challenges by blending several complementary strategies into their monitoring protocols. They balance detailed sampling with lighter sampling, alternating full EMA entries during some weeks with brief end-of-day check-ins on other weeks, to gather rich data without overwhelming participants (Murray et al., 2023). Multiple reporting options, from one-tap severity or impact sliders to voice memos or simple paper checklists, can help to accommodate different comfort and commitment levels, while ensuring that the level of technical requirements match user confidence (Businelle et al., 2024; Stone & Shiffman, 2002). By adapting these and other cross-disciplinary strategies, speech-language pathologists can design more feasible, engaging, and user-friendly protocols for assessing stuttering variability.

Management Strategies

The review of methods for managing variability highlighted four broad strategies: (a) personalized and adaptive treatments tailored to individual profiles, (b) environmental modifications to reduce or manipulate variability triggers, (c) cognitive and mindfulness-based therapies to reframe the impact of variability, and (d) psychoeducation to normalize fluctuations. Across conditions, these approaches consistently sought to recognize variability as inherent to the conditions and to embed mechanisms that either mitigate its negative consequences or harness it for therapeutic benefit.

First, personalized and adaptive treatments tailored to individual profiles should allow for a more dynamic and responsive approach that accounts for stuttering variability across individuals (Jokar, Salehi, & Yaruss, 2025) and across times, settings, tasks, and emotional states (Tichenor & Yaruss, 2021). A key element of this adaptability involves identifying specific environmental or emotional triggers, such as high-pressure speaking situations or unfamiliar settings, that may exacerbate or ameliorate stuttering behaviors and experiences (Karimi et al., 2024). Clinicians could use this information to personalize stuttering treatments and respond to variability in real time. For example, they could identify situations that exacerbate stuttering behaviors and experiences and then incorporating desensitization techniques. In this way, people who stutter could gradually confront, rather than avoid, these high-demand scenarios (Menzies et al., 2009). To move further, clinicians could simulate personalized high-demand speaking situations and gradually increase communicative pressure, enabling people who stutter to rehearse the strategies learned in therapy. In the same way, clinicians could identify situations that ameliorate stuttering and build upon those to support clients as they face more difficult situations. This adaptive, personalized approach would not only improve communication effectiveness; it would also enhance speakers' self-efficacy and reduce speaking-related anxiety (Kuyken et al., 2008).

Second, cognitive therapy approaches that have been used to mitigate the adverse impact of variability in other conditions can also be applied to stuttering. This has been demonstrated in several studies that have highlighted how cognitive strategies can be directly integrated into stuttering treatment (e.g., Arney et al., 2025; Beilby et al., 2012; Menzies et al., 2019). Variability in stuttering behaviors and experiences generates specific adverse impacts for children, adults, and their caregivers, particularly through distinct emotional reactions tied to fluctuations in stuttering (Jokar, Salehi, & Yaruss, 2025; Tichenor & Yaruss, 2021). By applying cognitive therapy techniques, clinicians can help individuals who stutter reframe negative thoughts associated with stuttering variability into adaptive thought patterns. For example, cognitive restructuring enables the replacement of maladaptive beliefs, including catastrophic predictions about stuttering worsening under stress, with appraisals that emphasize communicative competence despite fluctuations in stuttering. As noted, CBT has been successfully adapted for people who stutter, showing benefits in reducing speech-related anxiety and maladaptive cognitions (Menzies et al., 2009, 2019). Similarly, ACT has been employed to support individuals who stutter in fostering psychological flexibility and reducing avoidance behaviors (Beilby et al., 2012). Likewise, mindfulness treatments can enhance present-moment awareness to mitigate emotional reactivity to variability result in reducing physiological arousal during moments of stuttering (Arney et al., 2025; Boyle, 2011).

Third, present findings suggest that psychoeducational approaches can help speakers view variability as an inherent and normal feature of stuttering; this can lessen its adverse impact, as speakers come to view fluctuations as expected, rather than frustrating aspects of their experience of stuttering. As they learn that stuttering naturally fluctuates over time and across different contexts, clients can learn to focus less on identifying consistent patterns and more on communicating. These and other strategies can directly address the “consequences” of variability and help to reduce the impact of variability on people's lives.

Again, these approaches are not without hurdles: They require specialized expertise to monitor progress and adjust therapy plans “on the fly.” Clinicians with less expertise in stuttering therapy may be less confident in their ability to adapt therapy to account for ongoing variability. Moreover, such modifications may also be particularly difficult for younger children, who may lack the abstract reasoning and self-reflection skills necessary for traditional cognitive techniques. Other fields have addressed these challenges by embedding detailed curriculum checklists and fidelity measures into treatment manuals (Rogers & Dawson, 2010); this helps to support clinicians who are less familiar with therapy procedures. The availability of detailed treatment manuals also helps to extend clinician guidance through parent coaching so that caregivers can deliver and adapt strategies in natural settings (Bearss et al., 2015). For example, concrete, sensory-engaged activities, such as guided “mindful games,” can make mindfulness therapy more accessible even to very young children (Greenland, 2010). By adapting these cross-disciplinary strategies, speech-language pathologists can develop treatment protocols that systematically address stuttering variability by (a) using standardized guides to ensure consistency, (b) leveraging caregiver coaching to extend therapy beyond the clinical setting, and (c) integrating developmentally tailored cognitive and mindfulness techniques to enhance client engagement and efficacy.

Limitations and Future Research Directions

While this study offers a comprehensive synthesis of strategies for managing symptom variability, there are limitations that can be addressed through future research. First, some of the recommendations from other conditions may not be as directly relevant to stuttering. Still, the consistency in measurement and treatment strategies used for other conditions suggests a certain universality to methods for addressing variability in neurodiverse conditions. Additionally, the heterogeneity in study designs and the frequent reliance on self-reported data introduce potential biases that may limit the generalizability of the findings. Longitudinal studies employing multimethod approaches, including objective measures alongside self-reports, are needed to validate the effectiveness of tailored interventions over time.

Conclusions

Stuttering variability poses challenges for intervention that require individualized, multifaceted approach to ensure reliable and valid measurement. Drawing on insights from research on ASD, anxiety disorders, ADHD, mood disorders, OCD, TS, ODD, and CD, this study identified a number of strategies that can be adapted to improve the consideration of variability in stuttering assessment and treatment. Clinicians can adopt flexible, technology-integrated, multiperspective, interdisciplinary approaches that will yield more comprehensive data about stuttering behaviors and experiences across situations and tasks and over time. In this way, they can improve treatment outcomes and quality of life for individuals who stutter.

Data Availability Statement

This study is a scoping review. No new data sets were generated or analyzed. All data supporting the findings are derived from previously published studies, which are cited in the reference list and publicly available through their respective publishers or repositories.

Appendix

Summary of Included Studies

Table A1.

Characteristics from studies included based on assessment for symptom variability.

Study	Neurodiverse condition(s) investigated	Participant characteristics	Type(s) of variability examined		Assessment procedure(s)	Assessment theme
	see note for abbreviations		Across situations	Over time		Real-time observations across contexts	Standardized rating scales that account for variability
Cholemkery et al. (2014)	ASD	N = 165 children/adolescents (ages 6–18 years) Groups: 55 with ASD (no intellectual delay), 55 with ODD/CD, 55 typically developing (TD); outpatient clinical setting in Germany	X		Standardized rating scales (SRS, SCQ, CBCL, FBB-SSV; parent-reported)		X
Chan et al. (2017)	ASD	N = 237 adults with ASD (ages 18–57 years; Mage = 29.5, SD = 8.8). About 70% had intellectual disability; most lived with parents. Sample drawn from a longitudinal study in Wisconsin and Massachusetts	X	X	Standardized rating scales (SRS subscales: Social Communication and Interaction [SCI], Restricted/Repetitive Behavior [RRB]); ADI-R (social vs. behavioral domains); Adult Behavior Checklist (ABCL)		X
Schanding et al. (2012)	ASD	N = 1,663 children with ASD and N = 1,712 unaffected siblings (controls); ages 4–17 years 11 months; recruited through family-based evaluations	X	X	Standardized rating scales (SCQ-Current, SCQ-Lifetime, SRS teacher form); standardized diagnostic instruments (ADI-R, ADOS) administered by research-reliable clinicians		X
Delgado et al. (2016)	AD	N = 150 women in labor (ages 15–45 years); assessed in a hospital setting	X		Standardized rating scale (STAI S-Anxiety; 20 items) administered via interview by a trained physiotherapy resident		X
de Lemos Zingano et al. (2019)	AD	N = 103 adults with drug-resistant mesial temporal lobe epilepsy (57.3% female; Mage = 36.4 years; mean education = 7.3 years)	X		Standardized rating scales (HADS-A, STAI-Trait, STAI-State) alongside psychiatric interviews		X
Lü et al. (2022)	AD	N = 233 undergraduate students (ages 18–20 years; Mage = 18.95, SD = 0.79; 152 female, 81 male)	X		Standardized rating scales (STAI, Social Phobia Scale [SPS], Social Interaction Anxiety Scale [SIAS]); physiological monitoring (continuous ECG and blood pressure) during social and nonsocial stressor tasks		X
Izzo et al. (2019)	ADHD	General (school) sample: 591 children/adolescents (ages 8–18 years; 54% male) self-reports; 631 parent reports (ages 6–18 years; 84% mothers); 325 teacher reports (ages 6–18 years; all female teachers) Clinical ADHD sample: 55 youth with ADHD (ages 8–18; 93% male; ~50% with comorbid ODD, learning disorder, anxiety, mood disorder, or motor coordination disorder); 63 parent reports; 15 teacher reports	X		Standardized rating scales (Conners 3–Short Form: self, parent, teacher versions; 4-point Likert scale). Administration in classrooms, at home (parents), or in ADHD/neuropsychological centers (clinical sample)		X
François-Sévigny et al. (2022)	ADHD and giftedness	N = 92 French-speaking children (ages 6–16 years; M = 9.85, SD = 2.51; ~74% male)	X		Conners 3 Rating Scales (parent, teacher forms)		X
Yang et al. (2014)	MD—bipolar disorder (BD-I, BD-II) and unipolar depression	N = 1,487 patients with mood disorders (ages 16–65); multicenter trial across China		X	Chinese version of the Mood Disorder Questionnaire (MDQ)		X
Gervasoni et al. (2009)	MD—BD subtypes	N = 146 psychiatric outpatients (median age = 40, range: 19–64; 53% female)		X	Mood Disorder Questionnaire (MDQ, French version)		X
Mataix-Cols et al. (2002)	OCD	N = 117 adult outpatients with OCD (55% male; Mage = 35 years)		X	Yale–Brown Obsessive–Compulsive Scale (Y-BOCS)		X
Haas et al. (2021)	TS	N = 706 children and adolescents (541 boys, 165 girls; ages 3–16 years; M = 10.67, SD = 2.81)		X	Yale Global Tic Severity Scale (YGTSS)		X
McGoey et al. (2007)	ADHD	N = 563 preschool children (ages 3–5 years)	X		ADHD Rating Scale–IV		X
Hirschfeld et al. (2000)	MD — Bipolar Spectrum Disorder	N = 451 adult psychiatric outpatients recruited from community and academic centers; all presented with mood symptoms		X	MDQ		X
Rupp et al. (2019)	OCD	N = 39 adults with DSM-5–diagnosed OCD	X	X	Ecological momentary assessment (EMA; smartphone prompts 10 × daily for 4 days, pre- and post-treatment) measuring obsessions, compulsions, avoidance, suppression, monitoring, emotions (e.g., anxiety, shame, guilt, tension), stress, and burden; standardized clinical rating with Y-BOCS (German version)	X
Murray et al. (2023)	ADHD	N = 240 adolescents (ages 11–14 years) 120 with a clinical ADHD diagnosis 120 age- and gender-matched controls without ADHD	X	X	EMA: smartphone-based surveys 5 × daily for 2 weeks measuring ADHD symptoms, medication use, emotions, emotion regulation strategies, peer interactions, sleep, and physical activity. Supplemented with online intake and end surveys (demographics, ADHD strengths, peer/teacher relationships, self-esteem, attachment, rejection sensitivity, autistic traits) and parent surveys (ADHD symptoms, comorbid diagnoses, internalizing problems, peer conflict, friendships)	X
Alić et al. (2022)	OCD	N = 110 adults (52.8% female; Mage = 34.8 years) in Dutch clinical settings		X	Standardized rating scale (Yale–Brown Obsessive–Compulsive Scale–II [Y-BOCS-II], Dutch translation) measuring frequency, intensity, interference, resistance, and control over the past week		X
Yan et al. (2022)	TS with co-occurring obsessive-compulsive symptoms (OCS)	N = 367 children and adolescents with tic disorders (ages 5–16 years; M = 9.2 ± 2.1; 293 boys, 74 girls) in China		X	Children's Yale–Brown Obsessive–Compulsive Scale (CY-BOCS) for OCS Yale Global Tic Severity Scale (YGTSS; motor/vocal tics and impairment, past week)		X
Schilbach et al. (2012)	ASD	N = 57 adults; 29 with ASD (Mage = 32 years; 17 male, 12 female); 28 matched controls (Mage = 30 years; 12 male, 16 female)	X		Structured task-based observation (stimulus–response compatibility [SRC] task) involving button presses to congruent vs. incongruent gaze shifts. Stimuli included: face gaze (social), face-like images (intermediate), object images (nonsocial) Measures: reaction times, correct responses, incongruency costs	X
Von Der Lühe et al. (2016)	ASD	N = 32 adults; 16 with ASD (average/above average IQ); 16 matched healthy controls	X		Structured social observation task using point-light displays of two agents interacting. Context manipulation: communicative vs. noncommunicative gestures. Behavioral observations: clinician-style monitoring of detection accuracy, reaction patterns, and adaptation to context. Gaze behavior monitoring: eye-tracking recorded attentional focus and fixation patterns.	X
Artemi et al. (2025)	AD	N = 39 university students (Mage = 21, SD = 3.05)	X		Structured real-time clinician observation in VR. Participants delivered speeches to progressively challenging virtual audiences (neutral → rude). Behavioral indicators: willingness to speak, observable delivery changes, self-reported distress, and confidence ratings. Physiological responses: heart rate and variability as objective arousal markers	X
Lauth et al. (2006)	ADHD	N = 110 primary school children (ages 7–11 years); 55 students with ADHD problems; 55 matched controls (same age, gender, class, and teacher)	X		Systematic real-time classroom observation using the Munich Observation of Attention Inventory (MAI), across three natural classroom contexts	X
Hoekstra et al. (2004)	TS	N = 57 patients with tic disorders 25 children (ages 7–16 years) 32 adults (ages 18–64 years)	X	X	Weekly self-report diaries: tic severity (0–10) and occurrence of small life events. Instruments: Inventory of Small Life Events (ISLE) for adults (46 undesirable events); Small Events Inventory–Child Reports for children (41 items: desirable + undesirable). Baseline clinician assessment: tic severity with Yale Global Tic Severity Scale (YGTSS). Observation logic: repeated weekly measures provided clinician-style, dynamic tracking of tic fluctuation in daily life	X
De Los Reyes et al. (2009)	ODD—disruptive behavior problems in preschoolers	N = 327 low-income preschoolers (ages 3–5 years)	X		Disruptive Behavior Diagnostic Observation Schedule (DB-DOS): 50-min structured lab observation including parent–child interaction and two examiner–child interactions (engaged vs. busy). Ratings distinguished normative misbehavior vs. clinically concerning behavior. Additional reports: parent report: Kiddie Disruptive Behavior Disorders Schedule (K-DBDS); teacher report: Early Childhood Symptom Inventory (ECI)	X
Trull et al. (2008)	MD—borderline personality disorder (BPD)	N = 60 psychiatric outpatients (Mage = 35 years; 88% women; majority low-income and on psychotropic medication) 34 with BPD and affective instability 26 with current major depressive disorder (MDD) or dysthymia (DYS), but not BPD Recruited from four psychiatric outpatient clinics in Missouri		X	EMA: Participants carried Palm electronic diaries for 28 days. Random prompts: 6 times/day. Mood items: Positive and Negative Affect Schedule (PANAS-X), including Hostility, Fear, and Sadness subscales. Real-time, clinician-style monitoring captured in situ affective states and reduced recall bias	X
Feller et al. (2023)	ASD and 22q11.2 deletion syndrome (22q11DS)	N = 102 adolescents and young adults (ages 12–30 years) 26 with ASD (diagnosis confirmed with ADOS-2 and ADI-R/SCQ) 32 with 22q11DS (confirmed genetic diagnosis) 44 typically developing (TD) controls	X	X	EMA: prompts 8 times/day for 6 days. Context coding: reported whether they were alone, with household members, familiar persons, or unfamiliar persons. Experience measures: aloneness (ExpA): isolation, exclusion, desire for company; social interactions (ExpSI): enjoyment, feeling judged, nervousness, desire to be alone; positive affect; negative affect	X
Schoevers et al. (2021)	MD—AD	N = 365 adults (Mage = 49 years; 62%–70% female depending on subgroup) Current disorder group: 95 participants (depression and/or anxiety in past 6 months) Remitted disorder group: 178 participants (lifetime history but no episode in past 6 months) Control group: 92 participants (no lifetime history of depression or anxiety)		X	EMA: smartphone prompts 5 × daily for 14 days. Participants reported current affect states in real time, capturing fluctuations in both positive and negative emotions.	X
Kennedy et al. (2024)	ADHD	N = 90 adolescents (ages 13–18 years; M = 14.7 years; 66% boys)		X	EMA: smartphone-based surveys 4 × daily for 17 consecutive days. Self-reports of ADHD symptoms were recorded in real time.	X
Eddington et al. (2017)	MD	N = 55 adults (Mage = 37; 80% female)		X	Experience sampling methodology (ESM): interactive voice response system prompted participants 8 × daily for 7 days at baseline and again after psychotherapy treatment. Participants reported affect and daily functioning repeatedly across contexts.	X

Note. All studies cited in this table are included in the reference list. ASD = autism spectrum disorder; ODD/CD = oppositional defiant disorder/conduct disorder; SRS = Social Responsiveness Scale; SCQ = Social Communication Questionnaire; CBCL = Child Behavior Checklist; FBB-SSV = Fragebogen zur Beurteilung des Sozialverhaltens (German Social Behavior Questionnaire); ADI-R = Autism Diagnostic Interview–Revised; ABCL = Adult Behavior Checklist; AD = anxiety disorder; STAI = State–Trait Anxiety Inventory; HADS-A = Hospital Anxiety and Depression Scale–Anxiety Subscale; ECG = electrocardiography; ADHD = attention-deficit/hyperactivity disorder; MD = mood disorder; BD-I = bipolar disorder type I; BD-II = bipolar disorder type II; MDQ = Mood Disorder Questionnaire; OCD = obsessive–compulsive disorder; TS = Tourette syndrome; VR = virtual reality.

Table A2.

Main findings from studies included based on assessment for symptom variability.

Study	Main findings
Cholemkery et al. (2014)	The study demonstrates that ASD symptoms show situational variability—the SRS differentiated social interaction difficulties across diagnostic groups and contexts.
Chan et al. (2017)	The study demonstrates that ASD symptoms show situational and temporal variability—SCI scores predicted adaptive functioning, while RRB scores predicted psychopathology, highlighting context-specific differences in outcomes. Longitudinally, SCI at Time 7 predicted Vineland scores at Time 8, and RRB predicted later ABCL scores, showing variability in predictive associations over time.
Schanding et al. (2012)	The study demonstrates that ASD symptoms show situational and temporal variability—teacher reports on the SCQ-Current and SRS aligned more strongly with current clinician observations (ADOS) than parent reports, reflecting contextual differences. Temporal variability was also observed: Discrepancies emerged between historical behaviors (SCQ-Lifetime, ADI-R) and current behaviors (SCQ-Current, SRS, ADOS), highlighting meaningful differences across time.
Delgado et al. (2016)	The study demonstrates that anxiety symptoms show situational variability—the STAI-S revealed two distinct but inversely related factors: absence of anxiety (feelings of well-being/safety) and presence of anxiety (feelings of tension/worry).
de Lemos Zingano et al. (2019)	The study demonstrates that anxiety symptoms show situational variability—symptom presentation and detection accuracy differed depending on the assessment context, with variation across HADS-A, STAI-T, and STAI-S scores.
Lü et al. (2022)	The study demonstrates that anxiety symptoms show situational variability—general trait anxiety predicted broad cardiovascular dysregulation across stressors, while trait social anxiety effects were context-specific, emerging only in social stressor conditions.
Izzo et al. (2019)	The study demonstrates that ADHD symptoms show situational variability—parents, teachers, and youth perceived and reported different aspects of the child's difficulties, and the short Conners forms captured these context-specific patterns rather than yielding redundant results
François-Sévigny et al. (2022)	The study demonstrates that ADHD and giftedness symptoms show situational variability—parents and teachers perceived and reported different aspects of the children's difficulties. Parents tended to report more inattention, learning, and executive functioning problems, whereas teachers highlighted hyperactivity more strongly in gifted/ADHD youth.
Yang et al. (2014)	The study demonstrates that mood disorder symptoms show temporal variability—patients with BD-I, BD-II, and unipolar depression reported different lifetime patterns of mood episodes, reflecting fluctuations between mania/hypomania and depression across time.
Gervasoni et al. (2009)	The study demonstrates that mood disorder symptoms show variability over time—when patients completed the MDQ at two different points, responses were largely stable across 4–6 weeks, even as depressive or hypomanic symptom severity shifted. Patients tended to endorse slightly more symptoms during acute phases, but overall the MDQ captured consistent patterns of lifetime mood changes.
Mataix-Cols et al. (2002)	The study demonstrates that OCD symptoms show variability over time—while most patients retained their core symptom dimensions, certain obsessions and compulsions waxed and waned within those dimensions across 2 years.
Haas et al. (2021)	The study demonstrates that tic symptoms show variability over time—the YGTSS captured waxing and waning patterns of motor and phonic tics within week-long recall periods, while maintaining acceptable reliability across sites and ages.
McGoey et al. (2007)	The study demonstrates that ADHD symptoms show situational variability—parent and teacher ratings correlated moderately, but parents consistently reported higher mean ADHD symptom scores than teachers, highlighting context-dependent differences across home and school environments.
Hirschfeld et al. (2000)	The study demonstrates that bipolar disorder symptoms show temporal variability—the MDQ captured lifetime episodes of mania and hypomania, with patients endorsing different symptom patterns across the course of their illness
Rupp et al. (2019)	The study demonstrates that OCD symptoms show temporal and situational variability—EMA revealed daily-life fluctuations in obsessions, compulsions, and avoidance. Following treatment, obsessions and avoidance decreased, and obsessions were experienced as less burdensome even when present, highlighting EMA's sensitivity to real-time changes beyond static assessments.
Murray et al. (2023)	The study demonstrates that ADHD symptoms show temporal and situational variability—EMA is expected to capture daily-life fluctuations, with adolescents with ADHD anticipated to display heightened emotional lability, stronger reactivity to peer conflict, and shifting symptom expression across time and contexts.
Alić et al. (2022)	The study demonstrates that OCD symptoms show temporal variability—the Y-BOCS-II effectively captured short-term fluctuations in symptom severity, reflecting weekly changes across multiple symptom dimensions while maintaining reliability and responsiveness in clinical populations.
Yan et al. (2022)	The study demonstrates that obsessive-compulsive symptoms in tic disorders show temporal variability.
Schilbach et al. (2012)	The study demonstrates that social gaze cues show situational variability in action control—in the control group, gaze from a virtual face reduced reaction-time costs for incongruent responses, whereas individuals with high-functioning autism did not benefit from the social gaze context, highlighting differences in context-dependent processing of social information.
Von Der Lühe et al. (2016)	The study demonstrates that clinician-style, real-time observations reveal situational variability in the predictive use of social cues—neurotypical adults adapted their responses when a communicative gesture was present, improving detection of another person's action. By contrast, individuals with high-functioning autism did not adjust their responses to this contextual cue, showing reduced sensitivity to the social context in action prediction.
Artemi et al. (2025)	The study demonstrates that clinician-based real-time observations reveal situational variability in public speaking anxiety—during VR exposure, clinicians directly observed spikes in distress and physiological arousal when participants faced aversive audiences. Across repeated exposures, reductions in observed anxiety and greater willingness to speak reflected habituation, offering a dynamic view of context-dependent symptom expression.
Lauth et al. (2006)	The study demonstrates that clinician-style real-time observations reveal situational variability in ADHD symptoms—students with ADHD problems showed more disruption and inattention than peers but also engaged in meaningful on-task behavior depending on the classroom context. Disruptive behavior peaked during silent desk work, whereas inattentiveness was more frequent during whole-class teaching. Teachers' global ratings emphasized lack of conformity to expected on-task behavior, overlooking context-dependent instances of self-initiated and prompted task engagement.
Hoekstra et al. (2004)	The study demonstrates that tic symptoms show variability over time—weekly clinician-style tracking revealed waxing and waning tic severity in both children and adults. Although small stressful life events were weakly associated with tic severity at the group level, only a minority of patients showed consistent links, and the direction of these effects differed across individuals, highlighting meaningful individual-level variability.
De Los Reyes et al. (2009)	The study demonstrates that clinician-based real-time observations reveal situational variability in disruptive behavior—preschoolers showed distinct behavior patterns depending on whether they interacted with a parent or an examiner. These context-specific behaviors aligned with discrepancies in parent vs. teacher reports, supporting the idea that such discrepancies reflect meaningful cross-situational variability rather than error. For example, children disruptive only with parents were identified by parents but not teachers, while those disruptive with examiners were identified by teachers only. Pervasive disruptive patterns were consistently observed by both informants
Trull et al. (2008)	The study demonstrates that clinician-style real-time monitoring reveals temporal variability in BPD—patients with BPD showed greater fluctuations and instability in affect compared to those with depression, with frequent and unpredictable shifts in hostility, fear, and sadness across the day. Extreme spikes in hostility were especially characteristic of BPD, offering a more dynamic view of affective expression than static self-report questionnaires.
Feller et al. (2023)	The study demonstrates that clinician-style real-time observations reveal contextual variability in social functioning—although adolescents and young adults with ASD and 22q11DS spent a similar amount of time with others as TD peers, EMA showed that ASD participants experienced social interactions more negatively, reporting greater feelings of judgment, nervousness, and exclusion. In contrast, 22q11DS participants rated social interactions more positively, showing experiences closer to those of TD peers.
Schoevers et al. (2021)	The study demonstrates that clinician-style real-time monitoring reveals temporal variability in depression and anxiety—patients in a current episode showed the greatest instability of both positive and negative affect, with frequent, unpredictable shifts and slower recovery from negative moods. Remitted patients showed intermediate levels of instability, suggesting that heightened affect variability persists as a vulnerability marker even outside of acute episodes.
Kennedy et al. (2024)	The study demonstrates that clinician-style real-time monitoring reveals temporal variability in ADHD—adolescents' self-rated symptoms decreased across the 17-day period, with acute reductions often occurring in the hours immediately following EMA prompts.
Eddington et al. (2017)	The study demonstrates that clinician-style real-time monitoring reveals temporal variability in depression—psychotherapy reduced fluctuations in negative affect and improved resilience to stressful events. Patients reported fewer experiences of sadness, guilt, and self-critical thoughts when confronted with stress after treatment.

Note. All studies cited in this table are included in the reference list. ASD = autism spectrum disorder; SRS = Social Responsiveness Scale; SCI = Social Communication and Interaction; RRB = Restricted/Repetitive Behavior; ABCL = Adult Behavior Checklist; SCQ = Social Communication Questionnaire; ADOS = Autism Diagnostic Observation Schedule; ADI-R = Autism Diagnostic Interview–Revised; STAI-S = State–Trait Anxiety Inventory–State Form; HADS-A = Hospital Anxiety and Depression Scale–Anxiety Subscale; STAI-T = State–Trait Anxiety Inventory–Trait Form; ADHD = attention-deficit/hyperactivity disorder; BD-I = bipolar disorder type I; BD-II = bipolar disorder type II; MDQ = Mood Disorder Questionnaire; OCD = obsessive–compulsive disorder; YGTSS = Yale Global Tic Severity Scale; EMA = ecological momentary assessment; Y-BOCS-II = Yale–Brown Obsessive–Compulsive Scale–II; BPD = borderline personality disorder; 22q11DS = 22q11.2 deletion syndrome; TD = typically developing.

Table A3.

Characteristics from studies included based on treatment for symptom variability.

Study	Neurodiverse condition(s) investigated	Participant characteristics	Type(s) of variability examined		Treatment approach(es)	Treatment theme
	see note for abbreviations		Across situations	Over time
Hardan et al. (2015)	ASD	N = 53 children with ASD and significant language delay (ages 2–6 years; Mage = 4.1 years) Randomized to PRT parent training group (N = 27) or psychoeducation group (N = 26). Clinical settings: Stanford University and collaborating sites	X		Pivotal Response Treatment Group (PRTG): 12-week parent group training in PRT strategies. Weekly 90-min sessions, plus 1:1 parent–child practice with clinician coaching. Parents taught to elicit communication using motivational and naturalistic ABA strategies. Control: Psychoeducation group (general ASD info, no PRT techniques)	Personalized treatments
Specker & Nickerson (2024)	AD	N = 109 adults recruited via MTurk (Mage ≈ 37; 58% male). Participants were grouped by anxiety severity: low (normal–mild, n = 42) vs. high (moderate–severe, n = 67)	X		Participants were randomly assigned to flexible vs. inflexible emotion regulation (ER) conditions. Flexible (DHRL): instructed to use distraction for high-intensity images, reappraisal for low-intensity images. Flexible (RHDL): inverse pattern (reappraisal for high, distraction for low). Inflexible: only reappraisal or only distraction regardless of context.	Personalized treatments
Jurbergs et al. (2010)	ADHD	43 low-income African American elementary-school children (ages 6–9 years; Mage = 7.4). All had ADHD diagnoses; 25% were medicated.	X	X	Daily Behavior Report Cards (DBRCs) implemented with two conditions: (a) teacher feedback only (NPC), and (b) teacher feedback plus home-based parent consequences (PC). Teachers gave immediate daily feedback on behavior; parents in PC group provided rewards contingent on report card performance.	Personalized treatments
Swartz et al. (2011)	MD (primarily Bipolar I and II, also major depressive disorder and related conditions)	N = 718 adults with MD, including: outpatients (n = 48, Mage = 43.8 years); inpatients (n = 602, Mage = 39.3 years); intensive outpatients (n = 68, M = 41.2 years). Participants were treated across multiple settings within an academic medical center.		X	Interpersonal and social rhythm therapy (IPSRT), adapted for group, inpatient, intensive outpatient, and outpatient care. Focused on stabilizing daily routines (sleep/wake, mealtimes, social activity) and regulating interpersonal patterns to address mood fluctuations.	Personalized treatments
Pfeiffer et al. (2011)	ASD	N = 37 children (ages 6–12 years; 32 boys, 5 girls) diagnosed with ASD or PDD-NOS, all identified with sensory processing difficulties. Participants were enrolled in a therapeutic summer program in Philadelphia.		X	Randomized controlled trial comparing: Sensory Integration (SI) group: 18 individualized sessions over 6 weeks, therapist-engineered “just-right challenges” with sensory opportunities, adaptive responses, and child-directed activities. Fine Motor (FM) control group: equivalent dosage of structured fine motor activities.	Adaptive treatment approaches
DeGuzman et al. (2024)	ASD	N = 44 total participants: 14 adults with ASD; 30 caregivers of children with ASD. All participants had visited an emergency department (ED) within the past 2 years.	X		Qualitative descriptive design with patient-centered recommendations for “sensory-friendly care” in the ED. Strategies included dimmable lighting, quieter/private rooms, use of unscented cleaning products, weighted blankets, sensory/fidget toys, headphones/earplugs, and communication with patients/caregivers about preferences before interventions.	Adaptive treatment approaches
Lin et al. (2019)	AD	N = 50 young adults (Mage = 25.6 years; 74% female) in Singapore. Participants reported at least minimal social anxiety symptoms and were recruited from the community.	X	X	Arousal feedback–based exposure therapy integrating EEG and heart rate monitoring to dynamically adjust virtual audience reactions. Weekly 60-min sessions for 4 weeks, with real-time personalized calibration of anxiety-provoking contexts.	Adaptive treatment approaches
Fabiano et al. (2025)	ADHD	N = 213 children (ages 5–13 years, Grades K–7) with ADHD and an IEP. Sample drawn from Western New York and Southern Florida schools. 71.8% combined type ADHD, 26.8% inattentive type; 43.2% had comorbid ODD.	X	X	Daily report card (DRC) linked to IEP goals, completed multiple times daily by teachers and reinforced at home with parent-mediated rewards. Intervention adapted dynamically (goals modified based on child's performance).	Adaptive treatment approaches
Whittal et al. (2005)	OCD	N = 83 adults with a primary DSM-IV diagnosis of OCD (18–65 years). 59 completed treatment (22 men, 37 women; Mage ≈ 35).	X		CBT condition: Focused on challenging maladaptive appraisals of intrusive thoughts through cognitive restructuring and behavioral experiments (without formal exposure). ERP condition: Traditional exposure and response prevention, emphasizing habituation to triggering stimuli without cognitive strategies. Both treatments delivered individually across 12 weekly sessions (50–60 min).	Adaptive treatment approaches
Boswell et al. (2013)	AD	N = 379 adults with a principal diagnosis of panic disorder; subset of 256 completed treatment. Participants recruited across multiple clinical trial sites.		X	Manualized cognitive behavioral therapy (CBT) for panic disorder was designed to reduce variability in patient outcomes by standardizing key components (psychoeducation, cognitive restructuring, interoceptive exposures, and situational exposures). To directly monitor variability in treatment delivery, each session was rated for therapist adherence and competence. This allowed the researchers to capture fluctuations in fidelity across sessions and therapists, ensuring that variability in how the intervention was implemented could be identified and addressed through supervision and training.	Cognitive therapy
Wood et al. (2020)	ASD	N = 167 children with ASD (ages 7–13 years; Mage = 9.9 years; 20.5% female), recruited from 3 U.S. university sites.	X		Standard CBT (Coping Cat): 16 × 60-min sessions, targeting anxiety recognition, reappraisal, exposure, and reinforcement. Adapted CBT for ASD (BIACA): 16 × 90-min sessions, modular and personalized to ASD-related variability. Adjustments included: parent involvement, addressing disruptive behavior, embedding special interests, teaching social engagement skills, and flexible modular tailoring to match moment-to-moment behavioral needs.	Cognitive therapy
Speck et al. (2013)	ASD	N = 42 high-functioning adults with ASD (ages 18–65 years); randomized to intervention (n = 21) or wait-list control (n = 21); Netherlands Autism clinic setting		X	Modified Mindfulness-Based Therapy for Autism Spectrum Disorders (MBT-AS), adapted from MBCT but simplified to address ASD-specific processing styles (e.g., no metaphors, slower pacing, extended practice). Intervention aimed at addressing moment-to-moment fluctuations in mood and ruminative thinking by fostering present-moment awareness and acceptance.	Cognitive therapy
Pahnke et al. (2019)	ASD	N = 39 autistic adults (21–72 years; M IQ ≈ 108; 21 males, 18 females). All had average or above-average intellectual ability and were outpatients in a Swedish psychiatric setting.	X	X	Adapted acceptance and commitment therapy (NeuroACT)—14 weekly group sessions targeting flexibility in handling stress, cognitive fusion, avoidance, and autistic mannerisms. The treatment included mindfulness, cognitive defusion, values work, and acceptance exercises designed to help participants adapt moment-to-moment to fluctuating internal states and social demands.	Cognitive therapy
Asnaani et al. (2020)	AD	N = 274 treatment-seeking adults (ages 18–71 years, M = 32.5 years) from an outpatient specialty anxiety clinic.		X	Evidence-based CBT, primarily exposure-based protocols (e.g., ERP, PE), delivered in a naturalistic outpatient setting.	Cognitive therapy
Hayes-Skelton et al. (2013)	AD	N = 81 adults with a principal diagnosis of GAD (65.4% female, Mage ≈ 33 years). Participants were randomized to Acceptance-Based Behavior Therapy (ABBT; n = 40) or Applied Relaxation (AR; n = 41).	X	X	ABBT: Targeted experiential avoidance and reactivity to emotions by cultivating mindfulness, acceptance, and valued living. Designed to respond flexibly to moment-to-moment fluctuations in worry and anxiety. AR: Focused on relaxation strategies (breathing, progressive muscle relaxation) applied when early anxiety cues were detected.	Cognitive therapy
Hur et al. (2018)	MD	N = 34 adults (30 females; ages 18–35 years, M = 23.7) with DSM-5 diagnosis of other specified depressive disorder, recruited via clinics and advertisements in South Korea.	X		Scenario-based CBT mobile app (Todac Todac) that trained participants across three steps: identifying dysfunctional beliefs in scenarios; decatastrophizing to reduce extreme predictions; and distancing (giving advice to a hypothetical friend). This format targeted variability in dysfunctional thoughts and emotional responses across different scenarios.	Cognitive therapy
Kuyken et al. (2008)	MD	N = 123 adults (61 MBCT + antidepressant taper, 62 maintenance antidepressant medication); all with ≥ 3 previous depressive episodes; primary care settings in the United Kingdom.		X	MBCT: 8-week, group-based program combining mindfulness practices and CBT skills, with follow-ups and support for antidepressant tapering. Focus on increasing awareness of early warning signs of relapse and teaching flexible, in-the-moment responses to shifting symptoms. Compared against maintenance antidepressant medication (m-ADM), the standard approach to stabilize variability in relapse over time.	Cognitive therapy
Kuyken et al. (2015)	MD	N = 424 adults with recurrent major depressive disorder (≥ 3 previous depressive episodes), recruited from U.K. primary care practices; ages 20–79 years.		X	Mindfulness-based cognitive therapy with support to taper/discontinue antidepressants (MBCT-TS) vs. maintenance antidepressant medication (m-ADM). MBCT was designed to increase adaptive responses to fluctuating thoughts, feelings, and bodily sensations, thereby targeting dynamic variability in relapse risk.	Cognitive therapy
Reynolds et al. (2013)	OCD	N = 50 youth (ages 12–17 years; Mage = 14.5 years) diagnosed with OCD, recruited from NHS services in the United Kingdom. All had moderate–high OCD severity; many had comorbid anxiety disorders.	X		Manualized CBT (14 sessions) including exposure and response prevention and cognitive restructuring. Two formats: Parent-enhanced CBT: parents attended every session, supported exposures, tracked progress, and reduced accommodation. Individual CBT: parents attended only 3 sessions (psychoeducation, mid-treatment, final).	Cognitive therapy
Twohig et al. (2010)	OCD	N = 79 adults (66% female, Mage = 37 years, range: 18–67 years). All met DSM-IV criteria for OCD; 51% had ≥ 1 comorbid disorder. Average OCD duration ≈ 20 years	X		8 weekly sessions of acceptance and commitment therapy (ACT) (focusing on acceptance, defusion, psychological flexibility, and values-based action) compared to progressive relaxation training (PRT). No formal ERP (exposure and ritual prevention) was used.	Cognitive therapy
Panerai et al. (2002)	ASD	N = 16 children (all male, ~9 years old on average), diagnosed with autism and severe intellectual disability (DSM-IV; CARS).	X		The TEACCH program used structured teaching, individualized educational plans, environmental adaptation, and alternative communication strategies. Variability was addressed by making the environment clear and predictable with visual aids (clarifying where–how–when–how long), adapting tasks to individual profiles, and continuously monitoring progress across daily contexts. This approach explicitly targeted the day-to-day variability in autistic children's learning and behavior, minimizing confusion and reducing maladaptive behaviors that typically increased in unstructured contexts.	Environmental modifications
Awaida et al. (2024)	ASD	N = 548 children with ASD (ages 4–12 years at therapy initiation) across five health and educational centers in Lebanon.	X		Combined sensory room therapy with conventional therapy (ABA, speech, psychomotor, occupational, psychotherapy). Sensory rooms provided demand-free, multisensory stimulation tailored to children's sensory profiles (e.g., adaptive lighting, tactile equipment, projections). This approach addressed variability by offering flexible, individualized sensory input in real-time, targeting hyper- and hypo-responsiveness across sensory modalities. Variability in symptoms was monitored using the Parental Concerns Questionnaire Inferring Alterations (PCQIA), capturing changes across contexts (home, therapy, social).	Environmental modifications
Bioulac et al. (2020)	ADHD	N = 51 children with ADHD (ages 7–11 years; Mage = 8.9 years). Groups: Virtual remediation (n = 16), methylphenidate (n = 16), psychotherapy control (n = 19).	X		A virtual classroom–based cognitive remediation program trained children to maintain focus in the presence of escalating distractors (auditory, visual, mixed). This directly targeted variability in attention and impulse control by systematically exposing children to fluctuating distractibility contexts. Methylphenidate acted as a pharmacological comparison, while psychotherapy controlled for nonspecific support effects.	Environmental modifications
Houghton & Saxon (2007)	AD	N = 191 patients referred to a psychotherapy department in the U.K. NHS; 140 attended at least one session, 120 completed follow-up; outpatient clinic setting.	X		Large-group CBT psychoeducation course (four 90-min weekly sessions). Content covered anxiety physiology, avoidance/exposure, cognitive restructuring, and problem-solving. Delivered by CBT-trained nurses. The stepped-care design explicitly addressed variability by offering a low-intensity, flexible group intervention as a first step, acknowledging differences in patients' symptom severity, treatment needs, and capacity to benefit from psychoeducation versus requiring individual CBT.	Psychoeducation
Proudfoot et al. (2012)	MD	N = 407 adults (diagnosed within past 12 months; ages 18–75 years). Randomized to online psychoeducation only (n = 139), psychoeducation + peer support (n = 134), or attentional control (n = 134).		X	The Online Bipolar Education Program (BEP) consisted of 8 weekly audiovisual modules with behavioral tasks, supplemented by daily mood charting to capture fluctuations in symptoms and functioning. In one arm, BEP was combined with an informed supporter (peer coach via e-mail) who provided individualized feedback and encouragement. This adjunctive support was designed to address variability by stabilizing adherence and enhancing participants' sense of control and understanding during periods of fluctuating mood states.	Psychoeducation
Miklowitz et al. (2004)	MD	N = 20 adolescents (11 boys, 9 girls; Mage = 14.8 years) with Bipolar I, II, or NOS; most had recent mood episodes (manic, depressive, mixed). Many had comorbid ADHD (40%), ODD (55%), and anxiety disorders (70%)		X	Family-Focused Treatment for Adolescents (FFT-A): 21 outpatient sessions (psychoeducation, communication enhancement, problem-solving skills). Mood charting and relapse prevention plans tailored to daily fluctuations. Integration of family context, stressors, and protective factors to stabilize daily rhythms and reduce mood variability. Delivered alongside pharmacotherapy.	Psychoeducation

Note. All studies cited in this table are included in the reference list. ASD = autism spectrum disorder; ABA = applied behavior analysis; AD = anxiety disorder; DHRL = distraction–high/reappraisal–low condition; RHDL = reappraisal–high/distraction–low condition; ADHD = attention-deficit/hyperactivity disorder; NPC = non–parent-contingent; PC = parent-contingent; MD = mood disorder; PDD-NOS = pervasive developmental disorder—not otherwise specified; SI = sensory integration; EEG = electroencephalography; IEP = Individualized Education Program; ODD = oppositional defiant disorder; OCD = obsessive–compulsive disorder; DSM = Diagnostic and Statistical Manual of Mental Disorders; CBT = cognitive behavioral therapy; ERP = exposure and response prevention; PE = prolonged exposure; BIACA = Behavioral Intervention for Anxiety in Children with Autism; MBCT = Mindfulness-Based Cognitive Therapy; MBCT-TS = Mindfulness-based cognitive therapy with support to taper/discontinue antidepressant; ACT = Acceptance and Commitment Therapy; CARS = Childhood Autism Rating Scale; NOS = not otherwise specified.

Table A4.

Main findings from studies included based on treatment for symptom variability.

Study	Main findings
Hardan et al. (2015)	The study demonstrates that pivotal response treatment (PRT) directly addressed variability in children's communication by targeting functional utterances across different contexts and prompts. Children in the PRT group showed greater increases in variable language use, particularly in imitative and nonverbally prompted speech, compared to controls. By training parents to deliver PRT with high fidelity (84% met fidelity criteria), the intervention ensured that children were exposed to responsive, flexible input across daily situations, rather than relying only on therapist-led sessions. Gains generalized to the Vineland-II adaptive communication domain, with improvements in expressive and receptive language that reflected dynamic, real-world variability in language use. This suggests that parent-mediated PRT enhanced children's ability to flexibly adapt their communication across changing social and environmental contexts.
Specker & Nickerson (2024)	The study demonstrates that instructed ER flexibility directly addressed variability in anxious individuals' responses across contexts. Among participants with high anxiety, those in the Flexible–DHRL condition reported lower negative affect compared to the inflexible Reappraisal-only condition. This shows that tailoring strategies to moment-to-moment contextual demands (e.g., matching strategy to stimulus intensity) reduced distress more effectively than a rigid single-strategy approach. No significant benefit was found for low-anxiety participants, highlighting that variability-based intervention may be especially critical for individuals with heightened anxiety.
Jurbergs et al. (2010)	The study demonstrates that personalized, feedback-based interventions addressed situational variability in ADHD by increasing on-task classroom behavior. Both conditions improved attentiveness, but adding parent-mediated consequences amplified gains (PC: +51% on-task vs. NPC: +42%). Work accuracy also improved significantly in the NPC group, showing that even teacher-only feedback can stabilize performance. These results highlight that variability in ADHD symptoms across classroom contexts can be managed through dynamic teacher feedback, with additional parent involvement further enhancing consistency in behavior and academic outcomes.
Swartz et al. (2011)	IPSRT was feasible across levels of care, with adaptations ensuring fit to patient acuity. Outpatients and intensive outpatients showed significant reductions in depressive symptoms (QIDS-SR scores decreased over time). Inpatients had significantly higher group attendance after IPSRT introduction. Variability in mood symptoms was addressed through structured regulation of daily rhythms, flexible tailoring to care setting, and patient-specific rhythm goals. These strategies helped stabilize temporal fluctuations in mood and reduce vulnerability to relapse.
Pfeiffer et al. (2011)	The study demonstrates that personalized SI treatment addressed variability in ASD by improving children's ability to regulate and adapt across multiple domains. Children in the SI group showed significantly greater progress toward individualized goals (Goal Attainment Scaling), particularly in sensory processing, social–emotional skills, and motor function, compared to the FM group. SI also reduced autistic mannerisms (repetitive behaviors) more than FM interventions. These findings suggest that tailoring interventions to individual sensory and behavioral variability enables more adaptive responses, with children demonstrating more consistent attention and social engagement across time.
DeGuzman et al. (2024)	The study demonstrates that patient- and caregiver-identified strategies addressed situational variability in sensory experiences across six domains (visual, auditory, tactile, smell, taste, proprioception). Sensory triggers (e.g., harsh lighting, strong odors, loud noises, rough textures) varied across contexts, and recommendations emphasized tailoring the ED environment and procedures to individual sensory profiles. Importantly, variability was best addressed when clinicians asked about each patient's specific preferences prior to care, highlighting the need for personalized, flexible adaptations to reduce overstimulation and prevent escalation.
Lin et al. (2019)	The study demonstrates that personalized, clinician-style real-time monitoring revealed variability in public speaking anxiety symptoms. By dynamically adjusting virtual audience reactions based on arousal levels, the system captured moment-to-moment fluctuations in anxiety. Participants in the intervention group showed greater reductions in public-speaking anxiety, fear of negative evaluation, and negative self-statements compared to controls, though changes in overall social anxiety (LSAS) were small. These results suggest that variability in physiological arousal and situational demands was directly addressed through individualized feedback, enhancing treatment responsiveness.
Fabiano et al. (2025)	The study demonstrates that ADHD symptoms show variability across classroom contexts and over time. The DRC intervention reduced classroom rule violations and teacher-rated ADHD symptoms compared to school-as-usual. Teacher-rated impairment decreased significantly in the DRC group, but not in controls. Importantly, children with low-quality IEPs benefited the most, suggesting that DRCs helped compensate for variability in the quality of educational planning. Academic outcomes were unchanged, but behavioral variability was effectively managed through personalized, real-time monitoring and feedback.
Whittal et al. (2005)	The study demonstrates that symptom variability in OCD was directly addressed through structured therapeutic approaches. Both CBT and ERP reduced fluctuations in obsessional distress and compulsive behavior over time, with effects maintained at follow-up. CBT targeted variability in maladaptive appraisals by teaching flexible cognitive strategies, while ERP targeted variability in symptom triggers by reducing avoidance through systematic exposure. Homework compliance allowed ongoing adaptation to daily symptom changes, showing that variability could be managed dynamically through consistent engagement outside therapy sessions.
Boswell et al. (2013)	The study demonstrates that variability in the delivery of CBT for panic disorder was itself a critical factor. Adherence and competence ratings varied both between therapists and across sessions within the same therapist, revealing that treatment fidelity is not stable but context-dependent. Importantly, therapists tended to drift in adherence and competence over time, and patient characteristics such as higher interpersonal aggression contributed to further variability. By monitoring adherence session by session, the trial highlighted how supervision and personalized therapist training can directly address this variability, ensuring that CBT remains effective across different contexts and patient presentations.
Wood et al. (2020)	The study demonstrates that CBT adapted for ASD directly addressed variability in both anxiety and social functioning. Compared to standard CBT and treatment as usual, the adapted CBT showed: Greater reductions in anxiety severity over time (Pediatric Anxiety Rating Scale). Improvements in ASD-related social-communication difficulties and adaptive social functioning, highlighting sensitivity to contextual variability. Higher positive response rates (92.4% vs. 81.0% for standard CBT and 11.1% for τ). Gains that reflected dynamic responsiveness—by tailoring strategies to fluctuating anxiety levels, behavioral states, and social contexts, the adapted CBT promoted more consistent improvements across daily environments.
Speck et al. (2013)	The study demonstrates that MBT-AS directly addressed variability by reducing temporal fluctuations in depression, anxiety, and rumination, while increasing positive affect. Improvements reflected patients' enhanced ability to manage dynamic shifts in mood and intrusive thoughts across daily life. Importantly, reductions in rumination mediated decreases in anxiety, showing that MBT-AS provided a mechanism to stabilize emotional variability and improve resilience in adults with ASD.
Pahnke et al. (2019)	The study demonstrates that autistic adults experience significant variability in stress, avoidance, and psychological flexibility across time, and that ACT-based treatment can directly address this variability. Participants in the NeuroACT group reported greater reductions in perceived stress, cognitive/behavioral avoidance, and cognitive fusion than treatment-as-usual controls, alongside improvements in quality of life. Importantly, reductions in autistic mannerisms (reflecting behavioral inflexibility) emerged after treatment, suggesting that enhancing psychological flexibility helped participants manage fluctuations in stress and internal experiences more effectively over time.
Asnaani et al. (2020)	The study demonstrates that CBT outcomes for anxiety-related disorders reflect temporal variability in underlying mechanisms. Reductions in ER difficulties and anxiety sensitivity (AS) mediated decreases in anxiety symptoms over time. Among ER subscales, improvement in impulse control difficulties was the strongest mediator of symptom change, suggesting that CBT addressed moment-to-moment variability in managing strong emotional impulses. This indicates that symptom reduction was linked not only to overall CBT exposure but to personalized improvements in regulating fluctuating emotional states.
Hayes-Skelton et al. (2013)	The study demonstrates that treatments addressing variability in anxiety showed significant temporal improvements. Both ABBT and AR led to large decreases in GAD severity, worry, stress, and anxiety symptoms from pre- to posttreatment, with gains maintained at 6-month follow-up. ABBT specifically emphasized responding flexibly to moment-to-moment fluctuations in internal experiences, while AR emphasized relaxation in response to early variability in anxiety cues. Both approaches successfully reduced symptom variability over time, though no significant differences were found between them.
Hur et al. (2018)	The study demonstrates that a scenario-based CBT mobile app addressed variability in dysfunctional beliefs and mood symptoms by offering repeated, context-specific exercises. Participants in the intervention group showed significant reductions in dysfunctional attitudes (DAS scores), depression (BDI-II), and anxiety (STAI-X2) compared to controls. These changes reflected reduced variability in maladaptive thinking patterns and greater stability in emotional responses, highlighting that real-time, scenario-driven interventions can dynamically target fluctuations in depressive cognition and anxiety.
Kuyken et al. (2008)	MBCT reduced relapse/recurrence rates (47%) compared to m-ADM (60%) over 15 months, though difference was borderline significant. MBCT participants showed greater reductions in residual depressive symptoms and psychiatric comorbidity than m-ADM, highlighting that variability in symptom persistence was better managed. Quality of life improved more in MBCT (physical and psychological domains), showing positive effects on fluctuating daily functioning. Importantly, MBCT helped patients taper/discontinue antidepressants (75% discontinued), offering a personalized approach to managing long-term variability in treatment needs.
Kuyken et al. (2015)	The study demonstrates that MBCT addressed temporal variability in depression by training participants to recognize and adapt to moment-to-moment changes in internal states. Over 24 months, relapse rates were similar between MBCT-TS and m-ADM, showing both approaches effectively managed recurrent variability in symptoms. Importantly, MBCT conferred greater protection among participants with high variability risk (i.e., those reporting severe childhood abuse), suggesting that personalized, skills-based approaches may be especially beneficial for subgroups facing elevated relapse vulnerability.
Reynolds et al. (2013)	The study demonstrates that CBT for OCD addressed variability in symptom severity over time, with both parent-enhanced and individual CBT showing large reductions in OCD symptoms that were maintained at 6-month follow-up. Although no significant differences emerged in primary OCD outcomes between conditions, parent-enhanced CBT was associated with greater reductions in comorbid anxiety and depression in per-protocol analyses. This suggests that involving parents may enhance the generalization of strategies across variable daily contexts, helping youth regulate fluctuating symptoms beyond core OCD features.
Twohig et al. (2010)	ACT led to larger and more sustained reductions in OCD severity (Y-BOCS) compared to PRT, with improvements maintained at 3-month follow-up. ACT participants showed greater reductions in depression (for those with mild+ depression), larger increases in psychological flexibility, and better tolerance of obsessional variability. Clinical response rates were substantially higher in ACT (46%–66% across outcomes) compared to PRT (13%–18%). Results demonstrate that ACT addresses variability in OCD symptoms over time by helping patients flexibly adapt to moment-to-moment changes rather than relying on symptom suppression.
Panerai et al. (2002)	TEACCH children showed statistically significant improvements in imitation, perception, motor coordination, cognitive performance, and daily living skills compared to controls. Adaptive skills improved especially in structured contexts (play/leisure, personal routines), but less so in interpersonal communication, reflecting the persistent variability of social challenges. Clinician observations highlighted that maladaptive behaviors (stereotypy, self-injury) decreased during structured activities but increased in unstructured times—underscoring the program's role in addressing situational variability.
Awaida et al. (2024)	No significant change in variability scores from conventional therapy alone, but significant improvements when sensory room therapy was added (PCQIA mean increased from 34.1 to 41.7, p < .001). Improvements observed in sensory regulation, motor skills, communication, and social engagement, with reduced compulsions and repetitive behaviors. Parents reported variability-dependent improvements: 62% noted better behavior, 80% observed increased extracurricular participation, and 98% would recommend the combined approach. Variability in outcomes was influenced by family income, highlighting the role of socioeconomic context.
Bioulac et al. (2020)	The study demonstrates that ADHD symptoms show contextual variability, and virtual remediation addressed this by systematically training children to resist distractors in a dynamic, ecologically valid environment. Children in the virtual remediation group showed significant improvements in correct hits and reduced impulsive errors on the virtual classroom and CPT tasks, reaching performance levels comparable to methylphenidate. This indicates that real-time, context-sensitive interventions can reduce variability in attention and impulsivity in ADHD, suggesting an effective nonpharmacological alternative to stimulant medication.
Houghton & Saxon (2007)	The study demonstrates that outcomes following large-group CBT psychoeducation for anxiety disorders showed substantial variability. While some patients experienced clinically reliable improvement and were discharged after group treatment, nearly half required further individual CBT, highlighting heterogeneous treatment responsiveness. This variability reflected differences in symptom severity, coping resources, and capacity to benefit from low-intensity interventions. Importantly, the stepped-care model addressed this variability by providing an initial group-based option that successfully supported a subset of patients, while also serving as a screening mechanism to identify those needing more intensive, individualized therapy.
Proudfoot et al. (2012)	The study demonstrates that bipolar disorder symptoms and illness perceptions show variability over time. Across all groups, participants reported increased perceptions of control, decreased stigma, and reduced depression/anxiety from pre- to posttreatment. Variability in adherence was partly addressed by peer support: participants receiving adjunctive support showed significantly higher adherence rates than unsupported participants, though symptom outcomes did not differ substantially. This highlights that while online psychoeducation can improve perceived control during periods of instability, personalized support is critical for maintaining engagement in the face of fluctuating symptoms.
Miklowitz et al. (2004)	The study demonstrates that personalized, family-focused treatment directly addressed variability in bipolar adolescents by helping families monitor and manage daily mood swings, communication breakdowns, and relapse triggers. FFT-A improved both depressive and manic symptoms (38% and 46% reduction, respectively) and reduced behavior problems across 1 year. Parents learned to adapt responses to “bad days” and anticipate fluctuations, while adolescents improved in recognizing and self-monitoring mood variability. This flexible, contextualized approach provided more stability than pharmacotherapy alone.

Note. All studies cited in this table are included in the reference list. ASD = autism spectrum disorder; ER = emotion regulation; DHRL = distraction–high/reappraisal–low condition; ADHD = attention-deficit/hyperactivity disorder; PC = parent-contingent; NPC = non–parent-contingent; IPSRT = Interpersonal and Social Rhythm Therapy; QIDS-SR = Quick Inventory of Depressive Symptomatology—Self-Report; SI = sensory integration; FM = fine motor; ED = emergency department; LSAS = Liebowitz Social Anxiety Scale; DRC = daily report card; IEP = Individualized Education Program; OCD = obsessive–compulsive disorder; CBT = cognitive behavioral therapy; ERP = exposure and response prevention; MBT-AS = Mindfulness-Based Therapy for Autism Spectrum Disorders; ABBT = Acceptance-Based Behavior Therapy; AR = Applied Relaxation; GAD = generalized anxiety disorder; DAS = Dysfunctional Attitude Scale; MBCT = Mindfulness-Based Cognitive Therapy; m-ADM = maintenance antidepressant medication; MBCT-TS = Mindfulness-based cognitive therapy with support to taper/discontinue antidepressant; ACT = acceptance and commitment therapy; Y-BOCS = Yale–Brown Obsessive Compulsive Scale; TEACCH = Treatment and Education of Autistic and Related Communication-Handicapped Children; PCQIA = Parental Concerns Questionnaire Inferring Alterations; FFT-A = Family-Focused Treatment for Adolescents.

Funding Statement

Research reported in this publication was supported by National Institute on Deafness and Other Communication Disorders Award R01DC018795 (awarded to J. Scott Yaruss). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.