Impact of fidget devices on anxiety and physiological responses in adults with ADHD.

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Published in final edited form as: Res Dev Disabil. 2025 Feb 12;158:104944. doi: 10.1016/j.ridd.2025.104944 Impact of fidget devices on anxiety and physiological responses in adults with ADHD Heather Elahi a, b, * , Ha Min Son c , Catrina A Calub a, b , Nahid Nasiri d , Daniel Shapiro e , Katherine Isbister e , Jared Borden a, b , Paul D Hastings f , Xin Liu c , Julie B Schweitzer a, b Author information Article notes Copyright and License information a Department of Psychiatry and Behavioral Sciences, University of California, Davis, 2825 50th Street, Sacramento, CA 95817, United States b MIND Institute, University of California, Davis. 2825 50th Street, Sacramento, CA 95817, United States c Department of Computer Science, University of California, 1 Shields Avenue, Davis, CA 95616, United States d Department of Electrical and Computer Engineering, University of California, 1156 High Street, Santa Cruz, CA 95064, United States e Department of Computational Media, University of California, 1156 High Street, Santa Cruz, CA 95064, United States f Department of Psychology and Center for Mind and Brain, University of California, 1 Shields Avenue, Davis, CA 95616, United States * Correspondence to: UC Davis MIND Institute, Department of Psychiatry and Behavioral Sciences, University of California, Davis, 2825 50th St., Sacramento, CA 95817, United States. helahi@ucdavis.edu (H. Elahi). Issue date 2025 Mar. PMC Copyright notice PMCID: PMC13232519  NIHMSID: NIHMS2175455  PMID: 39946958 The publisher's version of this article is available at Res Dev Disabil Abstract Background: Fidgeting devices are popular for managing ADHD, but their effectiveness remains unclear. This study investigated whether fidgeting modulates self-regulation in adults with ADHD during stress, exploring intrinsic versus extrinsic fidgeting with a fidget ball. We hypothesized that individuals with a fidget ball would demonstrate improved self-regulation under stress, reflected by higher heart rate variability (HRV) and lower anxiety levels. Methods: Adults with ADHD were randomly assigned to a fidget ball (n = 49) or no fidget ball (n = 24) group. Physiological stress was measured via HRV and intrinsic fidgeting, that is, ankle and wrist movements, were measured via actigraphy. Physiological data were collected at baseline and during the Trier Social Stress Test (TSST). Self-rated anxiety was measured using the State Anxiety Inventory. Results: Heart rate and fidgeting increased across TSST stages for both groups, with no significant differences in heart rate, HRV, or intrinsic fidgeting between groups. Notably, the fidget ball group had weaker correlations between fidgeting and HRV compared to the no fidget ball group. At baseline, the no fidget ball group showed a positive correlation between intrinsic fidgeting and HRV, suggesting an association between intrinsic fidgeting and self-regulation, however during stress the no fidget ball group showed a significant negative correlation between intrinsic fidgeting and HRV. The relationship between physiological measures and anxiety differed between groups and TSST stages. Conclusions: These results provide preliminary evidence that the fidget ball may disrupt the expected association between intrinsic fidgeting and HRV, emphasizing the need for further studies to explore its impact on self-regulation. Keywords: ADHD, Fidgeting, Self-regulation, Heart rate variability, Stress, Anxiety 1. Introduction 1.1. General characteristics of ADHD and fidgeting in ADHD Attention Deficit Hyperactivity Disorder (ADHD) is a childhood-onset neurodevelopmental condition that affects 3–7 % of US children aged 4–17 years ( Visser et al., 2014 ), often persisting into adulthood ( Sibley et al., 2016 ), affecting up to 5 % of adults ( Willcutt, 2012 ). Individuals with ADHD often experience a range of negative outcomes that can significantly impact their educational, occupational, and personal lives. Research has consistently linked ADHD to increased rates of substance use disorders, underemployment, higher rates of accidents, and lower educational attainment ( Barkley et al., 2006 ; Barkley, 2015 ; Charach et al., 2011 ; Kuriyan et al., 2013 ; Rohner et al., 2023 ). ADHD is characterized by symptoms of inattention, hyperactivity, and impulsivity. Symptoms of inattention can manifest as a tendency to become off task or difficulty maintaining focus whereas hyperactivity can be identified as excessive motor behaviors such as difficulty sitting in a seat. While overt hyperactivity is primarily a childhood symptom, adults with ADHD commonly display more subtle forms of hyperactivity, such as fidgeting, for example, frequent leg movements, doodling, or object manipulation ( American Psychological Association, 2013 ). Fidgeting is characterized by involuntary or habitual repetitive movements of the body, typically involving the hands, feet, or legs ( Koiler et al., 2022 ). It can manifest in various forms, ranging from subtle, intrinsic movements to more noticeable, extrinsic behaviors involving the use of an object, material, hair, and so forth. We are characterizing intrinsic fidgeting as subtle movements that do not require interaction with external objects, such as bouncing legs up and down, tapping feet, drumming fingers, or twirling hair. These movements are often unconscious and may serve as an unconscious form of self-regulation mechanism for attention, stress, or energy levels ( Sarver et al., 2015 ). In contrast, we are characterizing extrinsic fidgeting as involving movements that includes interaction with external objects, such as clicking a pen repeatedly, playing with a fidget spinner, or doodling. Adults with ADHD frequently exhibit fidgeting, particularly of the intrinsic type, as noted in diagnostic criteria for the disorder ( American Psychological Association, 2013 ) which may reflect underlying neurobiological mechanisms. While the exact mechanisms underlying fidgeting are unknown, ADHD is associated with differences in structure and function in brain regions involved in motor activity and control (e.g., cortico-striatal circuits and the cerebellum) and neurotransmitter systems—particularly dopamine and norepinephrine—which also play a role in motor control and impulse regulation ( Faraone et al., 2021 ; Narukawa et al., 2023 ). While fidgeting is often construed as an outward manifestation of stress or anxiety, for those with ADHD, fidgeting may also serve as a compensatory mechanism to aid in sustaining attention during cognitively demanding tasks ( Hartanto et al., 2016 ; Son et al., 2024 ). Recent research suggests that fidgeting may have some positive effects, such as attention regulation and stress relief ( Carriere et al., 2013 ; Farley et al., 2013 ). For some individuals fidgeting may serve as a behavioral coping mechanism for relieving stress, though, cultural differences may play a role in how fidgeting is perceived; for instance, Japanese individuals tend to view it less favorably compared to Americans ( Watabe et al., 2024 ), and its prevalence varies across cultures, such as between the UK and Hong Kong ( Chan et al., 2024 ). While fidgeting can have some potential benefits, such as increasing physiological arousal and helping individuals feel more alert ( Levine et al., 2000 ), it can also have negative impacts on various aspects of life ( Graziano et al., 2020 ). Excessive fidgeting may distract from learning and affect academic outcomes, while in professional settings, fidgeting behaviors might be perceived negatively and impact job performance or interpersonal relationships ( Graziano et al., 2020 ). Additionally, chronic fidgeting, especially when it interferes with daily activities, can contribute to stress and anxiety. 1.2. ADHD and self-regulation Challenges in self-regulation is increasingly recognized as a hallmark issue of ADHD ( Barkley, 2011 ; Beheshti et al., 2020 ; Reed et al., 2020 ), with potential to contribute to challenges in managing stress and anxiety. Self-regulation is essential not only for improving focus and behavior but also for mitigating stress and anxiety ( Wilson et al., 2014 ). People with ADHD often display a heterogeneity of symptoms of self-regulation that extend to poor impulse control, management of emotions, organization of thoughts and behaviors ( Elahi et al., 2024 ). Impaired self-regulation can exacerbate stress responses, rendering individuals more susceptible to daily stressors and result in heightened, persistent reactions that are challenging to modulate or deactivate. These challenges with self-regulation are likely related to the finding that ADHD is frequently associated with elevated anxiety levels, with higher rates of anxiety disorders compared to the general population ( Katzman et al., 2017 ; Quenneville et al., 2022 ; van der Meer et al., 2018 ). While challenges with self-regulation have most often been assessed with survey and behavioral measures, research has also shed light on the physiological underpinnings of self-regulatory processes. One physiological marker of self-regulation is heart rate variability (HRV) ( Segerstrom & Nes, 2007 ; Williams et al., 2015 ; Zahn et al., 2016 ). In the National Institutes for Mental Health (NIMH) research domain criteria (RDoC) framework ( Pacheco et al., 2022 ) and theoretical literature, high-frequency HRV has been posited as a biomarker of self-regulation ( Beauchaine & Bell, 2020 ; Hastings et al., 2014 ; Porges, 2011 ). HRV measures the variations in cardiac activity that occur in synchrony with the respiratory cycle, and as this is regulated via the vagus nerve, HRV serves as an indicator of the functioning of the parasympathetic branch of the autonomic nervous system ( Eckberg, 1983 ; Quintana & Heathers, 2014 ). Greater tonic parasympathetic nervous system (PNS) activity, as assessed through baseline or resting HRV, reflects greater capacity both to down-regulate or calm arousal states, and to mobilize resources for orientation and engagement ( Porges, 2007 ). HRV decreases in response to stimuli or situations reflect withdrawal of PNS influence, increasing arousal and allowing the sympathetic nervous system (SNS) to drive active coping, with strong HRV decreases indicative of stressful arousal ( Hastings & Kahle, 2019 ). Children and adults with ADHD often exhibit reduced baseline HRV, suggesting autonomic dysfunction characterized by a sympathovagal imbalance ( Geiss et al., 2023 ; Griffiths et al., 2017 ; Rash & Aguirre-Camacho, 2012 ; Rukmani et al., 2016 ). This imbalance is marked by a reduction in overall HRV and a dominance of sympathetic over parasympathetic activity ( Rukmani et al., 2016 ). Such autonomic dysregulation has been linked to difficulties in self-regulation, attention, and emotional stability, which are core challenges in ADHD ( Bellato et al., 2022 ; Rukmani et al., 2016 ). Children with ADHD may have diminished tonic PNS activity, as evidenced by lower baseline HRV and higher resting heart rates compared to their neurotypical peers ( Rash & Aguirre-Camacho, 2012 ). This reduced HRV is associated with impaired executive functions associated with ADHD, such as response inhibition and attention regulation ( Bellato et al., 2022 ). Lackschewitz et al., (2008) compared the stress response via multiple measures, including salivary cortisol, heart rate (HR) as a cardiovascular stress marker, and both time-domain and frequency-domain parameters of HRV to analyze autonomic nervous system function in adults with ADHD compared to healthy controls using the Trier Social Stress Test (TSST). The ADHD group reported significantly higher subjective stress levels compared to healthy controls, but mixed results regarding the physiological responses ( Lackschewitz et al., 2008 ). The ADHD group exhibited a reduced HR response during the stress phase compared to controls. No significant group differences were observed for other autonomic nervous system parameters, though there was a trend toward significance in the low frequency/high frequency [LF/HF] ratio of HRV and lower overall salivary cortisol levels. These findings represent the first evidence of a significant alteration in a specific physiological stress measure (HR) in adults with ADHD. Moreover, the results highlight differences in the psychological aspects of stress response in this population. 1.3. Fidgeting devices associated with ADHD Fidgeting devices, such as fidget spinners, stress balls, or other small handheld objects have gained popularity as tools to help individuals with ADHD manage their symptoms, yet little is known about the characteristics of fidgeting in adulthood or whether it can be harnessed to enhance self-regulation with the use of an external device. Studies suggest fidgeting devices can negatively influence a child with ADHD’s attentional functioning ( Croley et al., 2023 ; Graziano et al., 2020 ), whereas other studies suggest fidget spinners can increase on-task behavior ( Aspiranti & Hulac, 2022 ). Fidgeting without a device in adult populations can provide sensory input that may help the brain filter out distractions ( Rapport et al., 2009 ), thereby improving task concentration ( Son et al., 2024 ). This is particularly important to understand in environments requiring sustained attention, such as classrooms or the work setting – environments where adults with ADHD tend to underperform ( Fuermaier et al., 2021 ). While there is considerably more research on fidgeting in children with ADHD, there is a notable gap in studies focusing on adults with ADHD. 1.4. Current study Our objective in this study was to investigate whether fidgeting modulated and affected self-regulation in response to stress in adults with ADHD. Specifically, we explored both intrinsic and extrinsic fidgeting patterns, focusing on the use of a fidget ball as a self-regulation tool during exposure to a stress inducing experience, the TSST using physiological and self-report measures. We hypothesized that those with a fidget ball during a stress-induced task would have enhanced self-regulation, reflected in increased HRV and reduced self-reported anxiety levels. Ultimately, this study aims to provide valuable insights into the potential benefits of facilitated fidgeting for adults with ADHD, particularly in high-stress situations, contributing to the development of effective non-pharmacological strategies. 2. Methods 2.1. Participants This study draws from a larger study that investigated the effect of an external fidget device on cognitive performance and emotional regulation, as well as the relation between intrinsic fidgeting (i.e., foot and hand movements) in adults with ADHD in a controlled laboratory setting. Participants were randomized to a condition with a computerized fidgeting device in a 2:1 ratio with assignment blocked according to their working memory performance (Digit Span Backward from the Wechsler Adult Intelligence Scale–IV (WAIS-IV) and sex assigned at birth. [A greater number of participants were randomized to the ball condition in order to study broader aims of this project (which are out of the scope of this current report, including machine learning analyses with the fidget ball), that required a higher number or participants interacting with the “intervention” (i.e., the ball).] The current study included 78 participants of whom five were excluded for missing TSST data with a total of 73 participants in the final analyzed sample as depicted in Fig. 1 . The participants were between the ages of 18 and 50 years (mean = 31.4 years) with a diagnosis of ADHD (inattentive, hyperactive/impulsive, or combined). Fig. 1. Open in a new tab Consort diagram. Participants were recruited through a social media campaign (e.g., Facebook, Instagram) from the UC Davis Health Department of Public Affairs and Marketing, local clinics at UC Davis, the institution’ electronic health system, the MIND Institute’s research registry, and from previous studies. Participants were initially screened via a video call to confirm ADHD diagnosis using the Adult Mini International Neuropsychiatric Interview (MINI) (Version 7.0.2) for DSM-5 with supporting information to confirm diagnosis using the Barkley Adult ADHD Rating Scale-IV 4th edition (BAARS) ( Barkley, 2011 ). The BAARS-IV includes three components: the BAARS-IV Other-Report, BAARS-IV Self-Report and BAARS-IV Observer. The BAARS-IV Other-Report is completed by a caregiver or parent to identify ADHD symptoms present in the participant during childhood, specifically between the ages of 5 and 12. The BAARS-IV Self-Report is administered to the participant to capture their current ADHD symptoms over the past six months. The BAARS-IV Observer was completed by someone who evaluates current ADHD symptoms of the participant, typically a parent, spouse, partner, or friend. All versions consist of 18 items rated on a 4-point scale (1 = never or rarely to 4 = very often). The total score, which ranges from 18 to 72, is calculated by summing the scores of all 18 items. The BAARS-IV has shown strong internal consistency and test-retest reliability ( Barkley, 2011 ). It also demonstrates satisfactory convergent and discriminant validity and overall has been established to be a valid and reliable tool for assessing ADHD symptoms in childhood and adulthood ( Barkley, 2011 ). One participant from the fidget ball group and one participant from the no-ball group did not have Observer BAARS-IV data. For those individuals, we used other evidence they provided (e.g., report cards or more in-depth discussion of history) to inform the diagnosis. Participants who met DSM-5 ADHD criteria which was confirmed by a licensed clinical psychologist and endorsed fidgeting with hands or feet or squirming in seat” as occurring “often” or “very often” were invited to proceed to the in-person assessment. Inclusion criteria included: 1) IQ ≥ 80; 2) aged 18–50 years; 3) meeting DSM-5 ADHD criteria based on clinical interview for DSM-5 for adults and informed by BAARS-IV ratings of inattentive and/or hyperactive/impulsive symptoms on current (self and observer) and retrospective (caregiver) ratings using the BAARS-IV informing diagnostic criteria decision-making. Individuals taking prescribed stimulant medications were requested to attend the in-person evaluation during their medication-free periods. Specifically, they were invited to the further assessment and test session on a day they had not taken their medication, usually ensuring at least a 24-hour gap between their last dose and the start of the testing session. Informed written consent was obtained from all participants. The University of California, Davis Institutional Review Board approved the study. Participants were excluded if they met any of the following criteria: currently prescribed psychoactive medication, with the exception of stimulant medication for ADHD or medication that can affect heart rate; presence of significant depression or psychotic disorders based on the Adult MINI DSM-5 and confirmed by study team clinician or clinician trainees; diagnosis of autism spectrum disorder; or visual or hearing impairment or any other disorder that may interfere with task performance. A licensed psychologist with extensive experience diagnosing ADHD evaluated initial phone screening data to determine initial eligibility and final determination for the study inclusion or exclusion. 2.2. Procedure 2.2.1. Public speaking task The public speaking task of the TSST was used to elicit a stress response ( Allen et al., 2016 ; Kirschbaum et al., 1993 ). The procedure consisted of the following steps: participants were told by the research coordinator they had five minutes to prepare a five-minute speech explaining why they were the ideal applicant for their dream job. After the five-minute preparation period, the research coordinator appeared to start a Zoom meeting with two judges, who in reality were from a pre-recording where actors posing as judges appeared to be listening and rating the speeches, unbeknownst to the participants. The participant then engaged in a five-minute speech to the prerecorded judges over the simulated Zoom meeting, followed by a recovery period. 2.3. Measures 2.3.1. State social anxiety questionnaire During the recovery period of the TSST, participants rated their social anxiety by completing a 7-item State Social Anxiety Questionnaire ( Kashdan & Steger, 2006 ). The State Social Anxiety questions assess general tendencies to be fearful and/or avoidant of social situations. Participants used a 5-point scale (0 = very slightly/not at all to 5 = extremely). 2.3.2. Barkley adult ADHD rating scale- 4th edition (BAARS-IV) ADHD symptom severity was assessed using the BAARS-IV ( Barkley, 2011 ). The BAARS-IV assesses current domains of impairment and retrospective childhood symptoms. It includes both self-report and other report forms (i.e., spouse, parent, sibling). Current observer and self-report ratings were analyzed to identify group differences and are presented in Table 1 . Table 1. Demographics and clinical characteristics. Characteristics Fidget Ball (N = 49) No-Fidget Ball (N = 24) p-value Age 30.9 (7.4) 32.4 (6.8) 0.42 Sex at birth 20 M, 29 F 8 M, 16 F 0.72 Ethnicity Non-Hispanic 35 (71.4 %) 17 (70.8 %) 1.0 Hispanic 14 (28.6 %) 7 (29.2 %) Race White 27 (55.1 %) 14 (58.3 %) 0.80 Multi-racial 9 (18.4 %) 3 (12.5 %) Asian 8 (16.3 %) 5 (20.8 %) Other / Unknown 3 (6.1 %) 2 (8.3 %) Native American 2 (4.1 %) 0 (0.0 %) Education level High school diploma/GED or less 2 (4.1 %) 1 (4.2 %) 0.46 Some college 16 (32.7 %) 5 (20.8 %) Associate degree 6 (12.2 %) 2 (8.3 %) Bachelor degree 19 (38.8 %) 9 (37.5 %) Graduate/professional degree 6 (12.2 %) 7 (29.2 %) ADHD severity BAARS Observer (Current) 36.0 (8.7) 34.0 (12.9) 0.44 BAARS Self-rated (Current) 34.1 (9.2) 32.8 (10.4) 0.58 Anxiety State Social Anxiety 21.8 (7.6) 20.7 (7.3) 0.56 Open in a new tab 2.4. Intrinsic fidgeting – actigraph Movement data were collected using Actigraph devices from Mindware Technologies (Gahanna, Ohio) to collect intrinsic fidgeting movements and has been demonstrated to significantly correlate with ADHD severity ( Son et al. 2024 ). A device was attached on the left wrist and another device was attached on the right ankle. The left wrist was chosen because some tasks in the session required the right arm to press a button. While the TSST did not require any button presses, the left wrist was used for all tasks for consistency. The right ankle was chosen to capture movement from both sides of the body. The Actigraph devices were configured to sample movement at 500 Hz in three dimensions, defined by the X, Y, and Z coordinates. The movement data were synchronized with the events of the tasks to allow a detailed analysis. Following the procedure introduced by ( Son et al., 2024 ), fidget information was extracted from the movement data. The Fast Fourier Transform (FFT) was first applied to convert the data from the time domain to the frequency domain. The low frequencies (1–20 Hz) were then selected, which represent the repetitive motion characteristic of fidgeting. These frequencies were then transformed back into the time domain using the inverse FFT. From this low-frequency data, two fidget variables were defined in the time domain. “Number of Fidgets” represents the number of peaks within a specified time frame. A higher value indicates an increased frequency of repetitive movements. “Fidget Intensity” represents the average height of the peaks in the movement data. A higher value suggests more intense movements during fidgeting. Fidgeting data were verified using calibration data. Different styles, intensities, and frequencies of fidgeting were controlled during calibration to ensure consistency of the defined fidgeting variables. 2.5. Extrinsic fidgeting – fidget ball External movement data were collected using a smart fidget device. The fidgeting device is a flexible rubber ball, about 4 in. in diameter, housing an Adafruit Feather M0 microcontroller, a real time clock “RTC” daughterboard, 6 pressure sensors, and an inertial measurement unit for capturing the details of fidgeting behavior. It is tethered for power and data transmission with sensors concealed beneath a soft fabric cover. The design is intentionally neutral to afford a wide variety of tactile affordances, like squeezing and stroking, while supplying tactile feedback. Our design approach was informed by prior research on fidget objects, particularly the work of Karlesky and Isbister (2014) , which identified desirable features such as pliability and softness for objects that support subtle physical interactions. Only the presence/absence of the fidget device is relevant in this study but see ( Nasiri et al., 2024 ) for detailed descriptions of the fidget ball’s design and analyses of the tactile signal produced during the TSST test. During the testing session, participants were randomized into the fidget ball group were instructed to hold the fidget ball with a research coordinator demonstrating different ways the participant could interact with the ball. The participants were mostly alone in the testing room, while a research coordinator observed and gave instructions, when necessary, over a microphone, from an adjoining observation room. 2.6. Heart rate variability (HRV) To measure electrocardiography (ECG) and obtain measures of HRV three disposable, adhesives were applied to the participants, one electrode placed on the left clavicle, one electrode placed on the bottom-most right rib, and a ground electrode placed on the bottom-most left rib. The leads connected to the electrodes were attached to a portable MindWare ambulatory monitor device (MindWare Technologies, Westerville, OH, USA), which was used to collect cardiac data. ECG data were transmitted wirelessly via router to a data acquisition computer running Mindware BioLab. This allowed the tester to detect and correct, if necessary, any interferences affecting signal quality such as movement, electrical interferences, and equipment malfunction including disconnection problems and electrode displacement. A 3-minute baseline period of ECG data was acquired while the participant was in a seated position and watched a neutral film clip that depicted nature scenery, animals, and quiet music. Baseline data were collected before the administration of the TSST instructions. The ECG data were edited in MindWare HRV Version 3.2.1, using a frequency band of 0.12–0.40 Hz which is the standard frequency band of an adult’s natural respiration ( Shader et al., 2018 ) to measure respiratory sinus arrhythmia (RSA), the log-transformed [ln(mŝ2)] mean spectral power values of HRV within the respiratory frequency. A second measure of PNS activity, the root mean square of the successive R-R interval differences (RMSSD), was used as a time domain measure of HRV, as time domain measures have been found to be less sensitive to changes in breathing rate than frequency domain measures ( Penttilä et al., 2001 ). RSA and RMSSD were obtained automatically via spectral analysis. RSA and RMSSD values were computed in 30-second epochs over the course of the baseline and TSST tasks. Baseline and TSST preparation and speech values were computed from the averaged values across epochs for each participant. The final epoch was discarded if it was less than 15 seconds. Each participant’s cardiac data were visually inspected for any artifacts and edited by two trained editors to assess interrater reliability. Edits made to the ECG data in Mindware HRV included inserting R peak markers that were missed, either by manually marking an R peak that was visually identifiable or by using the midbeat function to add an R marker between 2 identifiable peaks, and deleting erroneous R peaks when noisy data was flagged as a heartbeat. 3. Statistical analysis Data normality was checked using Q-Q plots and the Shapiro-Wilk test. The following data were log-transformed to account for non-normal distributions: heart rate (preparation, speech), RSA (speech), RMSSD (baseline, preparation), number of fidgets (baseline, speech), and fidget intensity (baseline, preparation, and speech). Outliers in the HRV, fidgeting, and rating scales data were assessed using boxplots and scatterplots, but no extreme values warranted removal. Group differences in demographic and clinical characteristics were calculated using chi-square tests for categorical variables (sex, race/ethnicity, educational level), and two-tailed t-tests for continuous variables (age, BAARS-IV scores, State Social Anxiety score). A MANOVA was performed to examine the effects of two independent variables and their interaction: fidget ball group randomization and stages of the TSST. The analysis included five dependent variables: HR, RSA, RMSSD, number of fidgets, and fidget intensity. Follow-up analyses were conducted to explore any significant multivariate effects, including overall tests and linear and quadratic contrasts for each dependent variable. Pearson’s correlations were used to assess relationships, and differences in correlations between groups were analyzed using Fisher’s Z-transformation. All analyses were conducted using Python and SPSS. For MANOVA, the multivariate normality of residuals was checked using Q-Q plots. The Box’s M test identified a significant violation of the homogeneity of covariance matrices assumption ( M = 240.725, F = 2.989, df 1 = 75, df 2 = 38344.346, p < 0.001). Further investigation revealed that this violation was primarily due to the fidgeting intensity variables at the ANOVA level. As a result, for the MANOVA results, the Pillai’s Trace metric is presented. For tests between fidgeting and HRV, fidget intensity is excluded. The analytic strategies were aligned with the study’s hypotheses, which predicted changes in heart rate, HRV, and fidgeting across different stages of TSST, verified using a MANOVA. Fidgeting and HRV were also expected to have an association, which was examined using correlational analyses. To further explore the effect of the fidget ball, Fisher’s Z was used to evaluate the significance of the discrepancy. 4. Results HRV and fidgeting data were collected from 78 participants, of whom 73 completed the TSST. Participants were randomly assigned to two groups in a 2-to-1 ratio: 49 received a fidget ball and 24 did not. Demographic and clinical characteristics of the participants are presented in Table 1 . The analysis showed no significant differences in demographic or clinical characteristics between the two groups, indicating effective randomized group assignment. Both groups consisted of participants with a higher number of females compared to males. A majority of participants were White (55.1 % in the fidget ball group and 58.3 % in the non-fidget ball group). There were no Black participants who did not also identify as multi-racial. Additionally, most participants had at least earned an associate degree. Notably, there were no significant differences in self-rated anxiety following the TSST between the two groups, suggesting that having a fidget ball did not affect self-rated anxiety. Although the fidget ball did not influence self-rated anxiety, physiological responses such as HR, HRV, and fidgeting may have been affected. Table 2 presents the data on HR, HRV (RSA and RMSSD), and fidgeting. Physiological responses remained consistent across groups. Similar to self-rated anxiety, the presence of the fidget ball did not significantly change HR, HRV, or fidgeting at the group level. However, there were differences in the measures between stages. Specifically, HR and number of fidgets were seen to increase from baseline to preparation, and from preparation to speech. Table 2. Mean (SD) of HRV and fidgeting variables across stages. Variable Fidget Ball Group (n = 49) No-Fidget Ball Group (n = 24) p-value Baseline Heart Rate 74.92 (11.35) 73.33 (11.68) 0.59 RSA 6.09 (1.23) 6.17 (0.75) 0.76 RMSSD 38.69 (24.07) 42.25 (21.86) 0.55 Num. of Fidgets 34.19 (23.68) 25.98 (21.3) 0.16 Fidget Intensity 0.64 (1.06) 0.52 (0.68) 0.61 Preparation Heart Rate 83.43 (14.0) 81.28 (14.44) 0.55 RSA 6.53 (1.05) 6.51 (0.77) 0.93 RMSSD 39.7 (24.41) 42.23 (24.27) 0.68 Num. of Fidgets 44.58 (24.02) 42.53 (18.31) 0.72 Fidget Intensity 0.54 (0.63) 0.51 (0.32) 0.83 Speech Heart Rate 91.53 (17.52) 90.77 (16.02) 0.86 RSA 6.18 (1.07) 6.22 (0.89) 0.87 RMSSD 30.06 (17.81) 31.19 (20.13) 0.81 Num. of Fidgets 48.91 (20.45) 53.95 (20.47) 0.33 Fidget Intensity 0.44 (0.31) 0.46 (0.28) 0.78 Open in a new tab Further multivariate analyses were conducted to assess for differences between groups as well as the difference between the stages of the TSST. Table 3 shows the results of a MANOVA with two independent variables—Randomization (fidget ball vs. no fidget ball) and Stage (baseline, preparation, or speech)—and five dependent variables: HR, RSA, RMSSD, number of fidgets, and fidget intensity. Consistent with the univariate analysis, the MANOVA indicated no differences in physiological responses between the fidget ball and no-fidget ball groups. Table 3. MANOVA results for HRV and fidgeting: effects of randomization and stage and their interaction (Randomization * Stage). Randomization (Fidget ball/No-fidget ball) Stage (Base / Prep / Speech) Randomization * Stage Pillai’s Trace 0.006 0.405 0.018 F-value F(5,209) = 0.27 F(10,420) = 10.68 F(10,420) = 0. 37 p-value 0.931 <0.001 0.958 Partial Eta Squared 0.006 0.203 0.009 Open in a new tab However, there were significant differences related to the Stage variable. Subsequent analysis of these MANOVA results, shown in Supplemental Table 1 showed significant F-values for HR, RMSSD, and number of fidgets, indicating these variables changed significantly across stages of the TSST. Specifically, contrasts of Stage, as shown in Supplemental Table 2 reveal that HR and the number of fidgets increased significantly across stages, while RMSSD decreased significantly. Although RSA did not demonstrate significance in a linear analysis, RSA was significant in a quadratic analysis, suggesting a non-linear relationship between RSA and Stage. These findings for physiological responses across different stages were not influenced by the presence of the fidget ball, as indicated by the non-significant interaction term in Table 2 . The univariate and multivariate analyses indicated no significant differences between the fidget ball and no-fidget ball groups regarding anxiety measures or physiological metrics such as HR, HRV, and fidgeting. However, notable differences were observed in the correlations between self-rated anxiety and physiological measures for the two groups, as shown in Table 4 . We highlight the correlations that differed between the two groups at p < .01. During the baseline period for the fidget ball group, RMSSD was significantly and positively correlated with self-rated anxiety ( r = 0.29), whereas for the no-fidget ball group, this association was non-significant and negative ( r = −0.07). In contrast, for the no-fidget ball group, the number of fidgets was positively correlated with selfrated anxiety ( r = 0.40), whereas for the fidget ball group, no correlation was found ( r = 0.03). During the preparation stage, for the no-fidget ball group, the number of fidgets was positively correlated with self-rated anxiety ( r = 0.47), whereas for the fidget ball group, this association showed a lower non-significant correlation ( r = 0.12). During the speech stage, the correlation between HR and self-rated anxiety differed between groups, with the fidget ball group showing a positive correlation ( r = 0.16) and no-fidget ball group showing a negative correlation ( r = −0.16). Although only a few variables demonstrated significant differences between groups, several variables approached the threshold of significance. Table 4. Correlations between state social anxiety and fidgeting/HRV. Fidget Ball No-Fidget ball p-value Baseline HR −0.16 0.19 0.10 RSA 0.17 −0.12 0.15 RMSSD 0.29 * −0.07 0.09 Num of Fidgets 0.03 0.40 * 0.08 Fidget Intensity −0.15 0.30 0.05 Preparation HR 0.03 0.19 0.28 RSA −0.14 −0.12 0.47 RMSSD −0.13 −0.33 0.22 Num of Fidgets 0.12 0.47 * 0.08 Fidget Intensity −0.06 0.23 0.14 Speech HR −0.16 0.16 0.12 RSA −0.07 −0.17 0.36 RMSSD −0.19 −0.22 0.46 Num of Fidgets −0.05 0.37 0.06 Fidget Intensity −0.09 0.24 0.11 Open in a new tab * p < 0.05, ** p < 0.01. A clearer difference emerged when comparing the correlations between HRV and fidgeting for the two groups, as shown in Table 5 . During baseline, correlations between the number of fidgets and HRV metrics varied significantly between groups. In the fidget ball group, these correlations were generally weaker and varied in direction. Specifically, RSA had a negative correlation with the number of fidgets ( r = −0.13), whereas in the no-fidget ball group showed a positive correlation ( r = 0.32). In the preparation phase, the nofidget ball group demonstrated a strong negative correlation between RMSSD and the number of fidgets ( r = −0.44), in contrast to the fidget ball group, which showed a positive but weaker correlation ( r = 0.15). During the speech phase, the no-fidget ball group showed significant negative correlations between HRV and fidgeting, with both RSA and RMSSD negatively correlated with the number of fidgets ( r = −0.43 and r = −0.41, respectively). In contrast, no significant correlations were observed in the fidget ball group. Table 5. Correlations between fidgeting and HRV. Fidget ball No-Fidget ball p-value Baseline Number of Fidgets HR 0.11 −0.03 0.31 RSA −0.13 0.32 0.02 RMSSD −0.2 0.33 0.04 Preparation Number of Fidgets HR 0.05 0.38 0.10 RSA −0.02 −0.40 0.07 RMSSD 0.15 −0.44 * 0.01 Speech Number of Fidgets HR 0.01 0.52 * 0.02 RSA −0.13 −0.43 * 0.12 RMSSD 0.05 −0.41 * 0.04 Open in a new tab * p < 0.05, ** p < 0.01 5. Discussion The main objective of this study was to explore whether a fidget ball or intrinsic fidgeting improves self-regulation during anxiety-inducing situations in adults with ADHD, as measured by self-rated anxiety and physiological measures. Given that intrinsic fidgeting—fidgeting with one’s own body—has been described as both a physiological response ( Drew et al., 2019 ) and a symptom of ADHD, it serves as a potential mechanism for anxiety regulation. Our results support this perspective, demonstrating a significant correlation between HRV, a traditional physiological measure, and increased fidgeting during the more anxiety-provoking stages of the TSST, specifically the preparation and speech phases, for the control group. Furthermore, intrinsic fidgeting is a hallmark symptom of ADHD, and previous studies have found that increased intrinsic fidgeting is associated with improved sustained attention in both children and adults with ADHD ( Sarver et al., 2015 ; Hartanto et al., 2016 ; Son et al., 2024 ). Regarding extrinsic fidgeting, our study provides preliminary evidence that the fidget ball may disrupt the typical manner selfregulation manifests via intrinsic fidgeting during anxiety-inducing tasks. However, contrary to our expectations, the fidget ball did not improve self-regulation in the anxiety-provoking conditions, as there were no significant differences between the fidget ball group and group with no fidget ball in either physiological responses or self-rated anxiety. This could be due to various reasons. For instance, the fidget ball employed in this study was relatively large, which might not have aligned with participants’ preferences or comfort levels, potentially influencing its effectiveness as a self-regulation tool. Additionally, anxiety management techniques often vary in efficacy from person to person; what works for one individual may not be as effective for another, highlighting the complex nature of anxiety regulation. The specific anxiety-inducing task used in our study may not have been ideally suited to demonstrate the potential benefits of fidget devices, as different contexts or stressors might yield different results. It is possible that external devices are not as effective in managing anxiety as their reputation suggests. If they do work, their effectiveness appears to be less robust than we initially hypothesized. The experimental design included well-matched groups demonstrated by non-significant differences in demographics and clinical characteristics. The TSST effectively induced anxiety, as evidenced by significant changes in HR, HRV, and amount of fidgeting across the baseline, preparation, and speech stages. Nonetheless, our analysis revealed no significant differences in self-rated anxiety or physiological measures between participants using the fidget ball and those in the control group. Both univariate and multivariate analyses indicated that the fidget ball did not improve self-regulation, as measured by self-anxiety ratings or physiological responses, suggesting the device did not provide the anticipated benefits in reducing anxiety or enhancing physiological markers of stress. These results align with previous research, which found that a fidget cube did not improve sustained attention in 3rd grade students with attentional difficulties ( Croley et al., 2023 ). However, there is limited research on how different fidget devices affect self-regulation in adults with ADHD. While there are more studies on fidget spinners, the results are conflicting. Graziano et al. (2020) reported that fidget spinners negatively impacted sustained attention in children, whereas Aspiranti and Hulac (2022) found both immediate and sustained improvements in attention. Moreover, despite their name, fidget spinners do not encourage fidgeting behavior because their spinning mechanism requires minimal finger movement. They also produce noise and are inherently more distracting than our device which was specifically designed to be quiet and less distracting. This raises questions about how these outcomes relate to actual fidgeting versus the effects of different types of devices. While there were no overall differences between the fidget ball group and the no-fidget ball group at the group level, we observed differences in the expected associations with self-rated anxiety and physiological measures for the fidget ball group. We expected to find an association between anxiety and physiological measures, expecting that increased anxiety would correlate with higher HR, decreased HRV, and more fidgeting. These expectations were based on prior studies that support HRV being a biomarker for selfregulation, where lower HRV indicates diminished ability to regulate emotions and behavior, particularly in stressful situations ( Beauchaine & Bell, 2020 ; Hastings et al., 2014 ; Porges, 2011 ). Interestingly, we found that the number of fidgets was more sensitive to changes in stage and provided more meaningful insights compared to the fidgeting intensity, which aligns with the results of Son et al. (2024) . We found increased HR and fidgeting and decreased HRV as participants moved from baseline to preparation and from preparation to speech. Therefore, we expected to find positive correlations between HR and self-rated anxiety, a negative correlation between HRV and self-rated anxiety, and a positive correlation between fidgeting and self-rated anxiety. These findings reveal that, while there were no significant differences in overall anxiety or physiological responses between the two groups, the use of the fidget ball might have influenced the relationship between fidgeting and physiological markers of stress. Specifically, the device may have altered how fidgeting interacted with anxiety-induced changes in HR and HRV. However, this effect was subtle and did not result in the anticipated improvements in self-regulation at the group level, indicating that the fidget ball may not be an effective tool for improving self-regulation or reducing anxiety in this context Indeed, the control group showed this trend as shown in Table 4 . In contrast, the fidget ball group showed smaller and sometimes opposing correlations, suggesting that the use of a fidget device may have buffered participants from fidgeting being an aspect of anxious arousal. However, there are two caveats worth noting. First, not all correlations with self-rated anxiety were significant. For instance, while the control group showed a significant correlation between fidgeting and self-rated anxiety during the baseline and preparation, other measures did not reach significance. Second, the differences in correlations between the fidget ball group and the control group were mostly not significant. One possible explanation for the near-significant results is that self-rated anxiety is a subjective measure and may be confounded by response bias, which could interfere with higher correlations. Nonetheless, the control group consistently showed expected correlation directions, whereas the fidget ball group showed weaker and sometimes contrary correlations. The difference between the correlation between groups consistently showed values that approached significance. These results imply that the fidget ball may alter some associations. Specifically, the fidget ball may be disrupting the typical relationship between fidgeting behaviors and anxiety levels. In addition to self-rated anxiety, we examined correlations of fidgeting behavior with physiological measures, which are objective and more likely to be consistent across participants. Here, we found more significant differences, reinforcing our observations of the impact of the fidget ball. We expected to find a significant correlation between fidgeting and HR, and between fidgeting and HRV. As Table 5 showed, HR and fidgeting increased while HRV decreases across the stages of the TSST. This indicates that as anxiety is induced, both HR and fidgeting increase. In other words, within the same stage, HR and HRV are associated with fidgeting, and individuals who experience more anxiety may exhibit higher HR, lower HRV, and more fidgeting. Table 5 confirms our hypothesis, particularly during periods of induced anxiety. For the control group, during the baseline stage with no induced anxiety, correlations between fidgeting and HR and HRV are not significant. However, during the preparation stage, when anticipatory anxiety is induced, the correlations strengthen, with RMSSD showing a significant negative correlation with fidgeting. In the speech stage, all correlations are significant, with fidgeting positively correlated with HR and negatively correlated with RSA, and RMSSD. These findings for the control group align with our expectations. In contrast, the fidget ball group consistently displayed lower and sometimes contrary correlations, as evidenced by significant p-values in comparisons between the two groups. These results further suggest that while the fidget ball may not affect anxiety and physiological measures at the group level, some associations alter the dynamics of individual responses, indicating a possible moderating effect on how fidgeting interacts with physiological markers during stress-inducing tasks. During the baseline period, there was a significant difference in HRV correlation between the fidget ball and control groups, as shown in Table 5 . In the control group, fidgeting was positively correlated with HRV, suggesting that increased fidgeting is associated with higher HRV and could aid self-regulation when anxiety is not present. This finding aligns with previous studies ( Hartanto et al., 2016 ; Son et al., 2024 ), which found increased fidgeting improved sustained attention. However, the fidget ball group did not show a positive correlation, which may imply that the fidget ball replaces the role of intrinsic fidgeting when there is no anxiety. Nevertheless, as the correlation between HRV and fidgeting was not significant, alternative interpretations should be considered. 5.1. Limitations We had a relatively small sample size and a design with a 2:1 assignment of fidget ball to the non-fidget ball group lessened our ability to explore some relations between measures. A larger sample size with adequate power is needed to test for more robust and generalizable results, potential subgroups of responders and the effect of possible moderators. Additionally, our sample was femaledominant, and the small group size of male participants, particularly in the no-fidget ball group, may have limited our ability to detect potential sex-related effects. Another notable limitation of this study is the potential influence of the ball’s shape on participants’ interactions with it. The design and texture of the fidget ball may lead to varying levels of engagement among individuals, as some participants may find it more appealing or easier to manipulate than others. Fidget devices that individuals typically find and use on their own in their day-to-day living may be more effective, however, we wanted to use a device that could automatically detect fidgeting and various types of fidgeting in a quantifiable way by use of computerized sensors. Thus, variability in interaction was not individualized, which may have affected the overall effectiveness of the fidget ball as a tool for improving self-regulation. While we employed objective measures to assess engagement, these metrics may not fully capture the nuances of individual interaction styles. Future research should consider exploring how different shapes and designs and use of self-selected fidget tools impact user engagement and effectiveness, allowing for a more comprehensive understanding of their role in promoting concentration. Additionally, it is important to recognize that fidget devices may help some individuals with self-regulation while not providing the same benefits to others. Factors such as personal preferences, sensory sensitivities, and individual learning styles can all play a significant role in how effectively a fidget device supports concentration and self-regulation. 5.2. Future directions While our study overall did not find significant effects of a fidget ball on anxiety reduction during a stressful task, there are several avenues for future research that could provide deeper insights into the potential benefits of fidgeting. One promising direction would be to explore the use of more individualized fidgeting devices. Given that people may have different preferences for tactile stimulation, allowing participants to choose from a range of fidget toys or even bring their own preferred fidgeting objects could yield different results. Testing individuals use of fidgets within a natural setting, such as at home, school or work, might also shed more valid and representative data. Additionally, a longitudinal study could shed light on how helpful fidgeting devices are in improving self-regulation over the long term. It is possible that the benefits of fidgeting may accumulate over time as individuals become more adept at using these tools for stress management. Furthermore, reproducing this study with children and adolescents could reveal whether fidget devices have different effects across age groups or developmental stages. Incorporating measures of impedance cardiography and pre-ejection period data would allow for a more comprehensive exploration of both sympathetic and parasympathetic nervous system responses to fidgeting, potentially uncovering subtle physiological effects that were not captured in the current study. Lastly, fidget devices may also have potential to detect mood and cognitive states, as our recent work has demonstrated ( Nasiri et al., 2024 ) and even contribute to objective assessment methods for ADHD. Future studies with larger samples could use advanced statistical techniques, such as latent profile analysis or factor analysis, to explore how fidgeting relates to other ADHD-specific measures and identify sub-groups with distinct fidgeting patterns. Investigating fidgeting’s role in emotional regulation and cognitive processing may clarify its functional significance and contributions to attention and emotion management. Finally, comparing findings with objective measures, such as Continuous Performance Tests to evaluate how closely fidgeting behaviors align with other standardized assessments could contextualize fidgeting within existing diagnostic frameworks. Supplementary Material Supplementary Material NIHMS2175455-supplement-Supplementary_Material.docx (16KB, docx) Appendix A. Supporting information Supplementary data associated with this article can be found in the online version at doi:10.1016/j.ridd.2025.104944 . Footnotes CRediT authorship contribution statement Shapiro Daniel: Writing – review & editing, Resources, Investigation, Funding acquisition, Conceptualization. Andaya Calub Catrina: Writing – review & editing, Supervision, Conceptualization. Nasiri Nahid: Writing – review & editing, Software. Elahi Heather: Writing – original draft, Supervision, Investigation, Conceptualization. Son Ha Min: Writing – original draft, Methodology, Investigation, Formal analysis, Conceptualization. Schweitzer Julie B: Writing – review & editing, Writing – original draft, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. Hastings Paul D: Writing – review & editing, Supervision, Resources, Formal analysis, Conceptualization. Liu Xin: Supervision, Resources, Conceptualization. Isbister Katherine: Resources, Methodology, Investigation, Funding acquisition, Conceptualization. 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