Sensory Processing Sensitivity and Overstimulation in Daily Life: An Experience Sampling Method Study

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Greven, Stefanie J. Schmidt, Céline R. Gillebert This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7030961/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Individuals differ in how they perceive and process social and sensory information in their environment, a personality trait known as Sensory Processing Sensitivity (SPS). Approximately 30% of the general population scores high on this trait making them more responsive to both negative and positive environmental influences than individuals lower in SPS. Overstimulation is one of the biggest challenges associated with SPS. Associations between SPS, triggers, and fluctuations of overstimulation in everyday life were examined using an experience sampling method study in 139 healthy adults. Results showed that overstimulation peaked in the early evening and in the presence of others. Furthermore, more sensitive individuals reported higher levels of overstimulation when auditory and visual stimuli were rated as unpleasant, when fatigued, or in a negative mood. Yet, more sensitive individuals reported lower levels of overstimulation with momentary pleasant auditory and visual stimuli, when not fatigued, and in a positive mood at the current moment. Everyone, but especially individuals high in SPS, might benefit from interventions preventing high levels of fatigue, increasing positive mood and the pleasantness of sensory stimuli in their daily life to reduce feelings of overstimulation. Health sciences/Health care Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology Sensory Processing Sensitivity Overstimulation Experience Sampling Methodology Triggers Fluctuations Figures Figure 1 Figure 2 Figure 3 Introduction Approximately 30% of the general population scores high on the personality trait Sensory Processing Sensitivity (SPS), also referred to as Highly Sensitive Persons (HSPs) 1 . SPS is characterized by lower sensory thresholds, susceptibility to overstimulation, deeper processing of environmental information, heightened emotional and physiological reactivity, and increased awareness of subtle stimuli 2 , 3 . Genetic and neuroimaging studies indicate that differences in SPS are moderately heritable (47%) 4 and driven by a more sensitive nervous system e.g., 2,5–7 . While distinct from the Big Five personality traits, SPS is associated with openness and neuroticism 8 , 9 . SPS is a dimensional trait 10 , 11 , with research suggesting that 20–30% of the individuals fall into a highly sensitive, 40–50% into a medium sensitive, and 20–30% into a low sensitive group 10 , 12 . Individuals high on SPS are more affected by negative and positive environments than individuals lower on SPS 1 , 3 . Evidence showed worse outcomes (e.g., a lower quality of life, physical health complaints, and burnout) in adverse environments 13 – 15 , but better outcomes in supportive environments (e.g., psychological interventions and positive affect) 10 , 13 , 16 , 17 . This dual sensitivity algins with the differential susceptibility model 18 , 19 , incorporating both Diathesis Stress (‘for worse’) 20 and Vantage Sensitivity (‘for better’) 17 , 21 . Evidence supports the coexistence of these models, with findings varying by the studied population, the sensitivity marker, environmental context, outcomes, and methodology 3 , 22 – 24 . Overstimulation in the general population Overstimulation or sensory overload refers to excessive or atypical stimulation that exceeds an individuals’ usual thresholds 25 . It can arise from both objective (e.g., noise produced by large crowds) and subjective (e.g., someone’s expectations about a current situation) factors 25 , 26 . When sensory input (e.g., sounds) surpasses one’s capacity to process it, whether due to volume, intensity or diversity of stimuli, stimuli become aversive and can result in overstimulation 25 , 27 . Overstimulation can result from stimulation in one or multiple sensory modalities 26 , 25 , 28 , 29 , which can be categorized into high (i.e., vision and audition) and low senses (i.e., smell, taste and touch) 30 . Research shows that primarily high sense stimuli with a high intensity (e.g., bright lights) compared to low sense stimuli contribute to overstimulation 30 , 31 . An individual's susceptibility to overstimulation also depends on their psychobiological resources, shaped by genetics, personality traits (e.g., SPS) and the environmental context 25 , 26 , 28 . Personal states like fatigue 26 , 32 and negative mood can increase an individual’s proneness to overstimulation, as they hinder the suppression of irrelevant information 26 . Overstimulation has been mostly examined in relation to clinical populations such, such as Autism Spectrum Disorder (ASD) 33 , Attention Deficit Hyperactivity Disorder (ADHD) 34 , and in patients with acquired brain injury 26 . Yet, also in non-clinical populations overstimulation can reach comparable severity levels 25 , 28 , 33 . In both populations, overstimulation may negatively impact physical (e.g., immunity), mental (e.g., depression and anxiety), cognitive (e.g., decision making), and social (e.g., isolation) wellbeing 13 , 25 , 27 , 31 , 35 . Despite its impact, overstimulation in non-clinical groups is under-researched. In today’s sensory-rich environments (e.g., new technologies, civilization), understanding its triggers and the psychobiological resources to adequately deal with the demands of sensory environments is increasingly important 25 , 27 , 28 . Overstimulation in relation to sensory processing sensitivity Overstimulation is one of the most frequently reported challenge among individuals high on SPS 36 , 37 . Due to lower sensory thresholds, heightened emotional reactivity, and deeper processing, individuals high on SPS may have lower psychobiological resources and respond more strongly to the same sensory input (e.g., background noise, artificial lights, other’s emotions) than individuals lower on SPS 1 , 3 . This means that even low levels of sensory input (e.g., background noise) can lead to overstimulation in individuals high on SPS 2 , 38 . The SPS framework suggests that mainly negatively perceived stimulation can trigger overstimulation, whereas positively perceived stimulation (e.g., music) can enhance wellbeing 3 , 38 . In a qualitative study 36 , individuals high on SPS reported strong emotional and cognitive responses to both positive and negative situations, sensitivity to others’ emotions, attention to detail, and frequent fatigue and stress. Feelings of overstimulation may fluctuate more in individuals high on SPS than in less sensitive ones, depending on their exposure to daily positive (e.g., pleasant scents) and negative environments (e.g., background noise in large offices). Retrospective studies partially supported this idea. Iimura 39 found that individuals high on SPS reported greater fluctuations in well-being linked to weekly positive and negative events. However, Damatac 14 found that SPS was related to more daily negative but not to more daily positive events and Van Reyn 40 found that individuals high on SPS showed stronger reactions to daily negative but not to positive events. The latter 40 was the first study examining the association between SPS and the reactivity to daily positive and negative events by using a daily diary study. In the above studies, positive and negative events were measured retrospectively only once 14 , once per day 40 , or once per week 39 . In contrast, the present study examines reactivity, in terms of stimulation, to momentary environments and personal states in daily life using Experience Sampling Methodology (ESM) 41 , 42 . ESM is a structured moment-to-moment diary method in which individuals respond to brief questions about their current environment (i.e., external triggers) and momentary personal states (i.e., internal triggers) multiple times a day during a specific time frame. This approach minimizes the susceptibility to negativity and recall biases (i.e., the tendency to remember negative events more than positive ones), which are common in traditional questionnaires and retrospective studies 42 , 43 . The present study The aim of the current study was to examine triggers of and fluctuations in overstimulation in healthy individuals high on SPS (hereafter referred to as Highly Sensitive Persons ( HSPs )) compared to individuals low or average on SPS (hereafter referred to as non-HSPs ). Ease of overstimulation is conceptualized as a key characteristic of SPS 1 . However, the current study was the first to empirically examine this relation in greater depth using ESM. The first objective of the present study was to examine whether levels of overstimulation fluctuate more throughout the day and the week in HSPs compared to non-HSPs. We expected higher fluctuations in overstimulation depending on the environments HSPs encounter. The second objective was to explore which positive and negative external and internal triggers are associated with overstimulation in HSPs compared to non-HSPs. Identifying these triggers may provide insights into the underlying mechanisms of overstimulation and thereby on the development of evidence-based interventions targeted to a specific group of individuals high on SPS. Based on key characteristics of SPS, we expected that HSPs, compared to non-HSPs, would be more easily overstimulated in response to negatively perceived stimulation, but less overstimulated in response to positively perceived stimuli. However, due to the lack of evidence, we did not have specific hypotheses regarding particular triggers of overstimulation. Results Preliminary and descriptive analyses The within-person and between-person means, standard deviations, the response rates, and Pearson correlations are reported in Tables A1 and B1 (Supplementary Material A andB). Fluctuations in overstimulation during the day and the week Results showed a quadratic effect of beep (or the time of the day) with levels of overstimulation increasing during the day (between 5 and 7pm), followed by a decrease during the later evening (between 8 and 10pm; Fig. 1). No associations between weekday and overstimulation were found or were there any significant interactions with SPS (Table 1). The fluctuations of overstimulation during the day nested in weekday are visualized in Figure 2. Table 1 Multilevel Random Intercept and Slope Model Results Predicting Fluctuations in Overstimulation (Model 1) Model 1 Β (SE) p -value Intercept 4.07*** (0.10) 95% CI [3.87; 4.07] <.001 Beep 0.45*** (0.08) 95% CI [0.29; 0.45] <.001 Beep² -0.07*** (0.01) 95% CI [-0.09; -0.07] <.001 Weekday -0.07 (0.05) 95% CI [-0.03; 0.03] 0.17 SPS 0.23 (0.14) 95% CI [-0.03; 0.50] 0.09 Beep*SPS 0.04 (0.12) 95% CI [-0.19; 0.27] 0.72 Beep²*SPS -0.01 (0.02) 95% CI [-0.05; 0.03] 0.64 Weekday *SPS -0.01 (0.08) 95% CI [-0.05;0.03] 0.78 ICC .22 R ² (fixed effects) .02 R ² (total) .23 Note. SPS = Sensory Processing Sensitivity, categorized as HSP versus non-HSP; Beep ² is the quadratic term of beep. ICC= Intraclass correlation coefficient. R² = proportion of variance explained by the fixed effects and/or fixed and random effects. CI = confidence interval *** p < .001 (two-sided). Fluctuations in overstimulation during the day. The full lines represent the raw data and the dashed lines represent the estimated data. The estimated confidence intervals are plotted around the estimated lines. Fluctuations in overstimulation during the day across the different days of the week. The full lines represent the raw data and the dashed lines represent the estimated data. The prediction of overstimulation by sensory processing sensitivity, the pleasantness of stimuli in the current environment, the type of environment, mood, fatigue, and its interactions Regarding momentary observations, higher levels of overstimulation were associated with higher SPS, lower pleasantness of both high and low sense stimuli (Table 2), being in the presence of others (Table 3), and higher levels of fatigue (Table 4). Regarding the interactions with SPS (Fig. 3) we found that HSPs were more affected (steeper slopes) by the pleasantness of high sense stimuli (Table 3), fatigue, and mood (Table 4) than non-HSPs. HSPs showed higher levels of overstimulation than non-HSPs when they rated the pleasantness of high sense stimuli as low, fatigue as high, and mood as negative. In contrast, HSPs showed lower levels of overstimulation than non-HSPs when they rated the pleasantness of high sense stimuli as high, fatigue as low, and mood as positive. The proportion of interaction (PoI) of these models indicated that 29, 22 and 19 percent (pleasantness high sense stimuli, fatigue, and mood, respectively) of the interaction occurred at the ‘for better’ side and the remaining percentages (i.e., 71, 78, and 81 percent respectively) at the ‘for worse’ side. Regarding the examined associations with lagged predictors at the previous beep, results were similar but weaker (Tables 2-4; Supplementary Material C, Fig. C1). Looking at interactions between the lagged predictors and SPS, HSPs showed higher levels of overstimulation than non-HSPs when rating the pleasantness of high sense stimuli at the previous beep as low (Table 2) and when reporting a negative mood and high levels of fatigue at the previous beep (Table 4). As an exploratory analysis, we controlled for age and sex differences. Across all models results remained similar when including main effects of age and sex (Supplementary Material D, Table D1-2). Table 2 Multilevel Random Intercept and Slope Model Results Predicting Overstimulation by SPS, the Pleasantness of Stimuli in the Environment, and Its Interactions, Including Momentary and Lagged Variables (Model 2) Within-person centered predictors (momentary observations) Time-lagged predictors (previous observation) Β (SE) p -value Β (SE) p -value Intercept 4.24*** (0.06) 95% CI [4.12; 4.36] <.001 4.09*** (0.20) 95% CI [3.69;4.49] <.001 Pleasantness high senses -0.11* (0.05) 95% CI [-0.20; -0.02] 0.021 0.04 (0.03) 95% CI [-0.03;0.10] 0.232 Pleasantness low senses -0.10** (0.04) 95% CI [-0.17; -0.03] 0.006 0.01 (0.04) 95% CI [-0.07;0.08] 0.873 SPS 0.20* (0.09) 95% CI [0.03; 0.37] 0.025 0.90** (0.27) 95% CI [0.35;1.44] 0.001 Pleasantness high senses * SPS -0.23*** (0.07) 95% CI [-0.36; -0.11] <.001 -0.16** (0.05) 95% CI [-0.26;-0.07] 0.001 Pleasantness low senses * SPS 0.02 (0.05) 95% CI [-0.08; 0.02] 0.679 -0.01 (0.06) 95% CI [-0.12;0.10] 0.882 ICC .25 .12 R ² (fixed effects) .06 .02 R ² (total) .30 .21 Note. SPS = Sensory Processing Sensitivity, categorized as HSP versus non-HSP; ICC= Intraclass correlation coefficient. R² = proportion of variance explained by the fixed effects and/or fixed and random effects. CI = confidence interval. * p < .05; ** p < .01; *** p < .001 (two-sided). Table 3 Multilevel Random Intercept and Slope Model Results Predicting Overstimulation by SPS, the Type of Environment, and Its Interactions, Including Momentary and Lagged Variables (Model 3) Within-person centered predictors (momentary observations) Time-lagged predictors (previous observation) Β (SE) p -value Β (SE) p -value Intercept 3.89*** (0.07) 95% CI [3.69;4.23] <.001 4.03*** (0.14) 95% CI [3.74;4.32] <.001 Alone versus with others 0.43** (0.14) 95% CI [0.15;0.70] 0.002 0.15*(0.07) 95% CI [0.01;0.30] 0.021 Private versus public 0.42*^ (0.20) 95% CI [0.04;0.82] 0.030 0.04 (0.07) 95% CI [-0.09;0.19] 0.495 SPS 0.21* (0.09) 95% CI [0.04;0.38] 0.017 0.13 (0.21) 95% CI [-0.28;0.54] 0.530 Alone versus with others * SPS -0.17 (0.09) 95% CI [-0.35;0.01] 0.060 -0.11 (0.10) 95% CI [-0.33;0.11] 0.230 Private versus public * SPS 0.04 (0.13) 95% CI [-0.21;0.30] 0.730 0.14 (0.11) 95% CI [-0.07;0.35] 0.163 ICC .23 .39 R ² (fixed effects) .06 .01 R ² (total) .27 .39 Note. SPS = Sensory Processing Sensitivity, categorized as HSP versus non-HSP; Alone was coded as 0 and with others as 1. Private was coded as 0 and in public places (including the work or school environment) as 1. ICC= Intraclass correlation coefficient. R² = proportion of variance explained by the fixed effects and/or fixed and random effects. CI = confidence interval. * p < .05; ** p < .01; *** p < .001 (two-sided), ^ p was not significant anymore after controlling for multiple testing. Table 4 Multilevel Random Intercept and Slope Model Results Predicting Overstimulation by SPS, Fatigue, Mood, and the Interactions Between Predictors and SPS, Including Momentary and Lagged Variables (Model 4 and 5) Within-person centered predictors (momentary observations) Time-lagged predictors (previous observation) Β (SE) p -value Β (SE) p -value Fatigue (Model 4) Intercept 4.24*** (0.06) 95% CI [4.11; 4.36] <.001 4.51*** (0.10) 95% CI [4.31;4.72] <.001 Fatigue 0.02 (0.03) 95% CI [-0.04;0.08] 0.508 -0.06* (0.03) 95% CI [-0.11;-0.01] 0.024 SPS 0.19*^ (0.08) 95% CI [0.02;0.37] 0.031 -0.09 (0.16) 95% CI [-0.40;0.22] 0.550 Fatigue *SPS 0.14** (0.05) 95% CI [0.05;0.22] 0.003 0.08* (0.04) 95% CI [0.01;0.15] 0.030 ICC .23 .20 R ² (fixed effects) .02 .01 R ² (total) .25 .21 Mood (Model 5) Intercept 4.24*** (0.06) 95% CI [4.11;4.36] <.001 3.98*** (0.26) 95% CI [3.46;4.49] <.001 Mood -0.12 (0.07) 95% CI [-0.25;0.00] 0.058 0.06 (0.04) 95% CI [-0.03;0.15] 0.206 SPS 0.19*^(0.08) 95% CI [0.02;0.37] 0.03 1.02** (0.35) 95% CI [0.32;1.69] 0.004 Mood*SPS -0.27** (0.09) 95% CI [-0.44;-0.10] 0.002 -0.16** (0.06) 95% CI [-0.28;-0.04] 0.009 ICC .26 .21 R ² (fixed effects) .05 .02 R ² (total) .30 .22 Note. SPS = Sensory Processing Sensitivity, categorized as HSP versus non-HSP. ICC= Intraclass correlation coefficient. R² = proportion of variance explained by the fixed effects and/or fixed and random effects. CI = confidence interval. * p < .05; ** p < .01; *** p < .001 (two-sided). ^ p was not significant anymore after controlling for multiple testing. Overstimulation as predicted by the interactions of SPS and pleasantness of high sense stimuli (a), fatigue (b), and mood (c) as reported momentarily . The grey areas represent the Regions of Significance with respect to X (i.e., Pleasantness high sense stimuli, Fatigue, or Mood). The predictors (X-axis) are within-person mean centered. Only the observed range of values with respect to X were plotted. The estimated confidence intervals are plotted around the estimated lines. Discussion The present study examined fluctuations in overstimulation and its triggers in healthy adults with varying levels on the personality trait SPS. Overstimulation is a frequently reported challenge associated with SPS but was not yet empirically studied in depth. The first objective was to examine whether feelings of overstimulation fluctuate more in individuals with high levels of SPS (HSPs) than in individuals with low or average levels of SPS (non-HSPs) throughout the day and week. The second objective was to identify daily external and internal triggers of overstimulation in both groups. Regarding the first objective, results showed that levels of overstimulation fluctuated throughout the day, but not the week. The highest levels of overstimulation were reported in the early evening (i.e., around 5pm, coinciding with the end of the workday) and then decreased again in the evening (i.e., around 9pm, likely as participants began winding down before bedtime). Contrary to our hypothesis, HSPs did not show greater fluctuations in overstimulation. Possible explanations include: (1) five daily ESM measures may not capture subtle changes, (2) HSPs may have reached ceiling levels, as seen by their higher average levels of overstimulation, and (3) HSPs may have developed strategies to minimize fluctuations in overstimulation, such as avoiding stimulus rich environments or maintaining a structured daily routine. Our results showed that HSPs spent more time in private than in public environments compared to non-HSPs, which may be a strategy to avoid stimulation. Regarding the second objective, overstimulation was higher for both HSPs and non-HSPs in the presence of others, in the presence of unpleasant high and low sense stimuli, when fatigued, and when reporting a negative mood. Notably, overstimulation did not increase in public places. When examining differences between HSPs and non-HSPs, we found a cross-over effect. HSPs reported higher levels of overstimulation than non-HSPs when they reported higher pleasantness of high sense stimuli (visuals and sounds), high levels of fatigue, and a negative mood. Furthermore, HSPs reported lower levels of overstimulation than non-HSPs when they rated high sense stimuli as pleasant and when they reported low levels of fatigue and a positive mood. Non-HSPs their levels of overstimulation were less affected by external and internal triggers. Additionally, we examined associations between overstimulation and triggers reported at the previous beep (between 1 and 4 hours earlier, depending on the timing of beeps). Results remained similar but were weaker. This could be related to methodological constraints leading to a substantial reduction in the number of observations for lagged analyses compared to momentary analyses. Specifically, the first observation of the day could not be lagged to the last observation of the previous day, and any missing observations at the previous beep led to additional missing data. Our findings built further on a previous daily diary study 40 , showing that HSPs react more to daily negative, but not positive events. However, our momentary approach likely captured positive experiences better than retrospective studies, which are often influenced by recall biases toward negative events and memory-experience gaps 44 . In summary, HSPs experienced more overstimulation from negative triggers but also greater relief from momentary positive factors compared to non-HSPs. These results align with previous studies suggesting that more sensitive individuals are more affected by both negative and positive experiences, often described as a ‘for better and for worse’ pattern 19 . The ‘for better’ effects were present in responses to momentary pleasant high sense stimuli, momentary low levels of fatigue, and momentary positive mood. In contrast, the ‘for worse’ effects appeared particularly in relation to high levels of fatigue, unpleasant high sense stimuli, and negative mood. Across models, the proportion of interaction on the ‘for worse’ side was higher than the proportion of interaction on the ‘for better’ side. The results of the current study have important implications for reducing feelings of overstimulation in daily life. Raising awareness (e.g., through psychoeducation) about daily fluctuations and triggers of overstimulation may improve predictability and control, reducing the mismatch between environmental demands and personal resources, and thus lowering overstimulation 26,28 . For example, recognizing patterns of overstimulation, enables proactive coping such as scheduling relaxing activities or alone time after a busy day. Identifying contributors to overstimulation can help individuals to develop strategies to improve the person-environment fit 26 . Qualitative studies suggest that combining avoidance, approach, and acceptance-based coping strategies can mitigate overstimulation 26,36 . To illustrate, when fatigued or in a negative mood, individuals may avoid sensory input by being alone or using noise-canceling headphones. While avoidance can offer short-term relief, it may worsen overstimulation at the long-term, aligning with the fear-avoidance model 45,46 . Graded exposure offers an alternative by gradually increasing sensory tolerance (e.g., listening to relaxing music instead of silence, or engaging in brief social interactions rather than full isolation), promoting habituation and reducing distress. Acceptance involves adjusting expectations and focusing on functionality rather than avoiding stimuli. It is central in therapies like Acceptance and Commitment Therapy and Mindfulness Based Interventions, which have been used to manage overstimulation in individuals with acquired brain injury 47 and autism 48 . However, systematic research on coping strategies in the general population remains limited. In the light of our results, HSPs may benefit even more than non-HSPs from enhancing the pleasantness of visual and auditory stimuli (e.g., ambient lights, relaxing music) and from fostering positive mood (e.g., nature walks). Research showed that HSPs have greater connections to nature 49,50 and benefit more from virtual nature videos in terms of positive affect 51 . Additionally, improving sleep quality may be particularly beneficial for HSPs, as we found that they were more affected by high levels of fatigue than non-HSPs. An important strength of this study is its novel focus on overstimulation in daily life within a general population sample, as prior research has mainly examined overstimulation in clinical groups (e.g., autism, stroke populations). Another strength is the robust ESM-design with high response rates, enabling real-time assessment of momentary experiences. Previous studies have often relied on retrospective self-reports (e.g., recalling childhood experiences) 14,52 or reporting the most positive and negative event of the day 40 or week 39 . Additionally, earlier studies have primarily focused on negative experiences or the absence of negative events rather than the presence of positive ones 52 . Unlike experimental studies inducing positive experiences 10,16,17 , we assessed natural sensitivity to positive stimuli as they occurred in daily life. Our study had also several limitations. First, the sample was skewed towards females, particularly in the HSP group, and included a higher proportion of older participants in the HSP group compared to the non-HSP group. Future studies should include more diverse samples to disentangle SPS effects from potential sex (e.g., hormonal cycles, social acceptance of sensitivity in women) or age-related influences. Second, although standard in personality research, the mere use of a self-report questionnaire to quantify SPS is a limitation. Additionally, predictors were analyzed separately due to high intercorrelations (e.g., mood and fatigue). Future studies should use larger samples and continuous SPS scores to detect more nuanced effects. Third, our analysis focused on momentary and one-directional associations with overstimulation as the outcome variable. Therefore, we cannot make any conclusions on the direction of effects. For example, it is likely that higher levels of overstimulation also influence fatigue, mood or sensory perceptions over time. Future research should explore bidirectional relationships between overstimulation and its predictors. Lastly, although we categorized high and low sense stimuli, we did not differentiate between specific stimuli within these categories (e.g., artificial light vs. sunlight) or considered participant’s control over these stimuli. Future studies should incorporate more detailed sensory assessments and examine perceived control as a moderating factor. To conclude, this study was the first to examine daily fluctuations in overstimulation and its associated factors in a general population sample with varying levels of SPS. Overstimulation peaked in the early evening, but did not fluctuate throughout the week. Higher levels of overstimulation occurred in the presentence of others, with unpleasant sensory stimuli, and during periods of fatigue and negative mood. HSPs were not only more affected by negative internal and external triggers (i.e., fatigue, negative mood, unpleasant sensory stimuli) but also benefited more from momentary positive auditory and visual stimulations, low levels of fatigue, and a positive mood compared to non-HSPs. Raising awareness of these patterns may support individuals to develop coping strategies managing overstimulation, with potentially greater benefits for individuals high on SPS. Future research should investigate coping, sensory control, and bidirectional relationships between overstimulation and its triggers. Methods Participants and procedure The study was approved by the university ethics committee of KU Leuven [G-2022-5629-R2(MAR)]. Healthy participants were drawn from a larger online questionnaire study ( N = 848), recruited via social media channels targeting the general population and via the course participation in research for first year psychology students. Exclusion criteria were non-fluency in Dutch and self-reported neurological, neurodevelopmental, or mental disorders. The larger questionnaire study assessed SPS using the Sensory Processing Sensitivity Questionnaire 53 . Based on total SPSQ scores, participants were grouped into low (30% lowest scores on SPS), medium (40% middle SPS scores), and high sensitivity (30% highest SPS scores). This classification aligns with identified sensitivity levels using latent profile analyses across different samples and including different measures of SPS 10,54,55 . All participants from the larger study who left their contact details to be invited for follow-up studies were invited for the current study, independently of their SPS score. In total 160 adults accepted the invitation and signed a second informed consent form before the start of the current study. Since the sample was not equally distributed regarding participants’ SPS score (Supplementary Material E; Fig. E1), the sample was divided into a HSP and a non-HSP group. The latter was formed by combining the previously identified low and medium sensitivity groups. Participants completing less than 30% of ESM questionnaires ( n = 21) were excluded 56 . A MCAR test 57 suggested that data were missing completely at random (χ 2 = 26412, df = 28297, p = 1). The final sample included 139 adults ( M age = 35.2; SD age = 12.6; range age = 17- 63; 84.2% female) with 51.8% ( n = 72) classified into the HSP group, and 48.2% ( n = 67) in the non-HSP group. The demographics of the total sample, as well as the HSP and non-HSP groups, are presented in Table 5. Participants completed the ESM-questionnaires via the m-Path app 58 on their smartphone five times a day for seven consecutive days. Beeps were sent at semi-random times within two-hour blocks between 7am and 9pm or between 8am and 10pm, based on participants' self-identified morning or evening rhythms, to ensure they were likely awake. Questionnaires could be completed until the next beep. As an incentive, participants had the opportunity to win a noise-cancelling headphone through lottery. Table 5 Demographics of the Total Sample, the HSP Group, and the Non-HSP Group Total Sample HSP group Non-HSP group t χ² N 139 72 67 Age Mean 35.20 39.17 31.63 -3.61*** # Standard deviation 12.60 11.10 13.30 Range 17-63 18-63 17-63 Sex % female 84.20% 90.30% 77.60% 4.18*^ Nationality Belgium 92.10% 91.70% 92.50% 0.10^ The Netherlands 3.60% 7.00% 4.50% 0.40^ German 0.07% 1.40% / 0.94^ Syria 1.40% / 3.00% 1.08^ Highest educational level Primary school 1.50% 1.50% 1.60% <.01^ Secondary school 34.60% 26.90% 42.90% 3.71^ Higher education (minimum a bachelor’s degree) 63.80% 71.60% 55.60% 3.00^ Note. Sex was coded as 1 = female, 2 = male; # = Independent sample t-test (equal variances are not assumed); ^= Chi-Square test. * p <.05 ; ** p < .01; *** p < .001 Measures Sensory Processing Sensitivity SPS was measured once, prior to starting on the ESM, with the Sensory Processing Sensitivity Questionnaire (SPSQ) 53 . All 43 items were rated on a 7-point Likert scale (1 = not at all , 4 = moderately , 7 = extremely ). The total mean score of the SPSQ, with an excellent internal consistency (α = .95), was used. Experience Sampling Methodology The ESM questionnaire (Supplementary Material F) included: one item assessing current levels of overstimulation, 15 items evaluating the pleasantness of stimuli in their current environment, two items assessing current levels of fatigue, 19 items measuring current mood, and five items assessing the type of current environment. Descriptive statistics and response rates are presented in Table A1 (Supplementary Material A). External triggers. (a) Sensory stimuli. Participants rated the pleasantness of various stimuli (e.g., lights, sounds, smells, tastes), if applicable, on a scale from 1 ( not at all ) to 7 ( extremely ). In addition, participants rated their level of stimulation on a scale from -3 ( very understimulated ) to +3 ( very overstimulated ), later converted to a 1 to 7 scale with higher scores indicating greater overstimulation. Based on theory 30 , exploratory and confirmatory factor analyses (Supplementary Material G , Table G1), stimuli were grouped into high (i.e., lights, sounds, bright colors, moving scenes, music, multiple stimuli simultaneously; α = .86) and low (i.e., touches, smells, tastes, temperature; α = .72) senses. Items were developed by the authors of the current study based on a questionnaire study assessing multisensory sensitivity 29 as no prior ESM study has examined this construct. (b) Type of environment. Participants selected their current location (e.g., at home, with family or friends, at work). They reported the presence of others, specifying whether they were alone or with others and with whom (e.g., friends, colleague, stranger, partner). Two dummy variables for the type of environment were created: location (private (0) versus public (including at work or school; 1) and social context (alone (0) versus with others (1)). Mood. Participants rated their current mood on a scale from 1 ( not at all ) to 7 ( extremely ), assessing both positive (happy, satisfied, relaxed, at ease, relieved, energetic, and good) and negative moods (irritated, anxious, uncertain, lonely, guilty, suspicious, sad, restless, gloomy, lethargic, agitated, and stressed). These items are selected from the ESM item repository (https://esmitemrepositoryinfo.com/) and are based on the Positive and Negative Affect Scale (PANAS) 59 . Due to high intercorrelations, a composite mood score (α = .96) was calculated by reversing negative mood items and averaging with positive mood items; higher scores indicate a more positive mood. Fatigue. Fatigue was measured using two items from the ESM item repository: ‘How physically tired are you?’ and ‘How mentally tired are you?’. Both items were answered on a scale from 1 ( not at all ) to 7 ( extremely ). The mean of both items was calculated as a measure of fatigue (α = .87). Data analysis First, we examined descriptive statistics, including within-person means, between-person means, standard deviations, Pearson bivariate correlations, frequencies, and response rates. Second, multilevel models were estimated using restricted maximum likelihood (REML). Minimum sample size estimations were based on guidelines for multilevel modeling 60 recommending at least 50 clusters (participants) per group with at least 30 observations each, since we did not have any estimates on effect sizes for the studied variables. Momentary observations as predictor variables were nested within individuals, random intercepts and random slopes were estimated. Overstimulation was the outcome variable. In each model the main effect of SPS (HSP versus non-HSP; between-person) as well as the interaction effects of SPS with the predictors were included. Per type of additional time-varying momentary (within-person) predictor, a separate model was estimated: (1) time of the day (i.e., beep) and day of the week (i.e., weekday ranging from Monday to Sunday), (2) type of the environment (i.e., location and social context), (3) pleasantness of stimuli in the current environment, (4) current levels of fatigue, and (5) current levels of mood. In models 2 to 5, the predictors were within-person mean centered by distracting the subject-level means from the original variable. Based on visual exploration of the raw data (Fig. 1), beep was included as normal and squared coefficient. Third, we reran the same multilevel models (models 2-5) with type of the environment, pleasantness of stimuli, fatigue, and mood as lagged to the previous measurement moment (t-1). For all multilevel models, we corrected for multiple testing to avoid false positive findings by using False Discovery Rate 61 . Significant interactions using simple slopes, including Regions of Significance (RoS) with respect to X (i.e., at which values of the predictor the effect of regression is statistically significant) and the Proportion of the Interaction 23 (PoI; the proportion of the total interaction that is represented by the left and the right sides of the crossover point) were plotted. As more older and female participants were in the HSP compared to the non-HSP group (Supplementary Material H, Fig. H1), we controlled for age and sex effects as an exploratory analysis (Supplementary Material D). Transparency and openness We report how we determined our sample size, all data exclusions, manipulations, measures, procedures, and results transparently, and we follow JARS 62 . All analyses were run in SPSS Version 28 63 and R version 4.3.2. 64 using packages esmpack 65 , nlme 66 , lavaan 67 , and ggplot2 68 . This study was not preregistered on OSF before data collection started due to tight project timelines. However, all hypotheses, study design, and planned analyses were outlined in detail in a grant proposal and approved before data collection started. Declarations Ethical Approval The study was approved by the university ethics committee of KU Leuven [G-2022-5629-R2(MAR)]. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed Consent Informed consent was obtained from all individual participants included in the study. Acknowledgements and funding We would like to express our gratitude to all the participants. Sofie Weyn was supported by a postdoctoral fellowship by KU Leuven (PDMT2/22/017). Author contributions Author S.W. is the corresponding author and was responsible for the funding acquisition, conceptualization, methodology, data collection, analyses, project administration, and writing the original draft. Author C.R.G. was responsible for supervision, conceptualization, project administration, and reviewing and editing the written manuscript. Authors C.U.G. and S.J.S. were responsible for supervision, reviewing and editing the written manuscript. Conflict of Interest declaration All authors declare that there are no conflicts of interest. Data Availability A link to the anonymized data and analysis code is made available on Open Science Framework (OSF): https://osf.io/598fh/?view_only=00c6acc7361249fbbf24f042985752a7. References Aron, E. N. & Aron, A. Sensory-processing sensitivity and its relation to introversion and emotionality. J. Pers. Soc. Psychol. 73 , 345–368 (1997). Aron, E. N., Aron, A. & Jagiellowicz, J. Sensory processing sensitivity: A review in the light of the evolution of biological responsivity. Personal. Soc. Psychol. Rev. 16 , 262–282 (2012). Greven, C. U. et al. Sensory Processing Sensitivity in the context of Environmental Sensitivity: A critical review and development of research agenda. Neurosci. Biobehav. Rev. 98 , 287–305 (2019). Assary, E., Zavos, H. M. S., Krapohl, E., Keers, R. & Pluess, M. 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The Open Handbook of Experience Sampling Methodology: A Step-by-Step Guide to Designing, Conducting, and Analyzing ESM Studies (2nd Ed.). (Center for Research on Experience Sampling and Ambulatory Methods Leuven., Leuven, 2022). Little, R. J. A. A test of missing completely at random for multivariate data with missing values. J. Am. Stat. Assoc. 83 , 1198–1202 (1988). Mestdagh, M. et al. m-Path: an easy-to-use and highly tailorable platform for ecological momentary assessment and intervention in behavioral research and clinical practice. Front. Digit. Health 5 , (2023). Watson, D., Clark, L. A. & Tellegen, A. Development and validation of brief measures of positive and negative affect: The PANAS scales. J. Pers. Soc. Psychol. 54 , 1063–70 (1988). Maas, C. J. M. & Hox, J. J. Sufficient Sample Sizes for Multilevel Modeling. Methodol. Eur. J. Res. Methods Behav. Soc. Sci. 1 , 86–92 (2005). Benjamini, Y. & Hochberg, Y. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7030961","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":491595035,"identity":"e3358622-8f34-495a-b9af-347c71965a95","order_by":0,"name":"Sofie Weyn","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIie2PMQuCQBSAXwi6PGoVBO0nvHDszyhCk5VjW0GQiz/A/kVT88Wtth+cQy3OTdEgVJaz5xh033Lf8D7eOwCN5icZXNhXjPdLjaMqMahNzKB/0go20iPx0u2A3YC7JLO7kySlC9aZdSZUcDjlwH0qz0cnp8oHXATdib1kHEGGBzE/Okg83NhInYmXR8BrkOuDiKsmWSsTEO8EQAYkYrNJAlAln79k9JzsxcyfIlWTHcaKw9KtcXusZt5QRFeJdemNrEJx2HcXwJi1bvaYb9dteo9qNBrNv/ECAmpHvm1bYz4AAAAASUVORK5CYII=","orcid":"","institution":"University of Bern","correspondingAuthor":true,"prefix":"","firstName":"Sofie","middleName":"","lastName":"Weyn","suffix":""},{"id":491595036,"identity":"da883dfd-9ac4-47fd-889b-74ce153034f2","order_by":1,"name":"Corina U. 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Gillebert","email":"","orcid":"","institution":"KU Leuven","correspondingAuthor":false,"prefix":"","firstName":"Céline","middleName":"R.","lastName":"Gillebert","suffix":""}],"badges":[],"createdAt":"2025-07-02 15:38:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7030961/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7030961/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-31629-3","type":"published","date":"2025-12-18T15:57:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87742896,"identity":"003f3f80-cb8d-4411-b041-47022b7c1cfa","added_by":"auto","created_at":"2025-07-28 13:51:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":90525,"visible":true,"origin":"","legend":"\u003cp\u003eFluctuations in Overstimulation During the Day.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7030961/v1/df17c4b1cfa9cadb7064b994.png"},{"id":87742898,"identity":"992cb68f-2dbf-4d9b-82e0-78a1cd8eceb1","added_by":"auto","created_at":"2025-07-28 13:51:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":209747,"visible":true,"origin":"","legend":"\u003cp\u003eFluctuations in Overstimulation During the Day Across the Different Days of the Week.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7030961/v1/626d75894ce1216b898c867c.png"},{"id":87742897,"identity":"61e3f370-e71b-45b1-890c-44100668a071","added_by":"auto","created_at":"2025-07-28 13:51:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":142844,"visible":true,"origin":"","legend":"\u003cp\u003eOverstimulation as Predicted by the Interactions of SPS and Pleasantness of High Sense Stimuli (a), Fatigue (b), and Mood (c) as Reported Momentarily\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7030961/v1/9211270aac468be825c54d96.png"},{"id":98814112,"identity":"d86dba45-def0-4192-b3ac-ca283e3abea9","added_by":"auto","created_at":"2025-12-22 16:11:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1655189,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7030961/v1/6c704688-816d-401a-a060-8be186bb0d04.pdf"},{"id":87744376,"identity":"599e9075-4fb7-43da-ae7c-d0b03d1c0fe1","added_by":"auto","created_at":"2025-07-28 13:59:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":446847,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialsSR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7030961/v1/e1068ebabcd3e2d752a294f6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sensory Processing Sensitivity and Overstimulation in Daily Life: An Experience Sampling Method Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eApproximately 30% of the general population scores high on the personality trait Sensory Processing Sensitivity (SPS), also referred to as Highly Sensitive Persons (HSPs) \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. SPS is characterized by lower sensory thresholds, susceptibility to overstimulation, deeper processing of environmental information, heightened emotional and physiological reactivity, and increased awareness of subtle stimuli \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Genetic and neuroimaging studies indicate that differences in SPS are moderately heritable (47%) \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e and driven by a more sensitive nervous system \u003csup\u003ee.g., 2,5–7\u003c/sup\u003e. While distinct from the Big Five personality traits, SPS is associated with \u003cem\u003eopenness\u003c/em\u003e and \u003cem\u003eneuroticism\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. SPS is a dimensional trait \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, with research suggesting that 20–30% of the individuals fall into a highly sensitive, 40–50% into a medium sensitive, and 20–30% into a low sensitive group \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Individuals high on SPS are more affected by negative and positive environments than individuals lower on SPS \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Evidence showed worse outcomes (e.g., a lower quality of life, physical health complaints, and burnout) in adverse environments \u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e–\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, but better outcomes in supportive environments (e.g., psychological interventions and positive affect) \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. This dual sensitivity algins with the differential susceptibility model \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, incorporating both Diathesis Stress (‘for worse’) \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e and Vantage Sensitivity (‘for better’) \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Evidence supports the coexistence of these models, with findings varying by the studied population, the sensitivity marker, environmental context, outcomes, and methodology \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e–\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eOverstimulation in the general population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOverstimulation or sensory overload refers to excessive or atypical stimulation that exceeds an individuals’ usual thresholds \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. It can arise from both objective (e.g., noise produced by large crowds) and subjective (e.g., someone’s expectations about a current situation) factors \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. When sensory input (e.g., sounds) surpasses one’s capacity to process it, whether due to volume, intensity or diversity of stimuli, stimuli become aversive and can result in overstimulation \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Overstimulation can result from stimulation in one or multiple sensory modalities \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, which can be categorized into high (i.e., vision and audition) and low senses (i.e., smell, taste and touch) \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Research shows that primarily high sense stimuli with a high intensity (e.g., bright lights) compared to low sense stimuli contribute to overstimulation \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. An individual's susceptibility to overstimulation also depends on their psychobiological resources, shaped by genetics, personality traits (e.g., SPS) and the environmental context \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Personal states like fatigue \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e and negative mood can increase an individual’s proneness to overstimulation, as they hinder the suppression of irrelevant information \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Overstimulation has been mostly examined in relation to clinical populations such, such as Autism Spectrum Disorder (ASD) \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, Attention Deficit Hyperactivity Disorder (ADHD) \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, and in patients with acquired brain injury \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Yet, also in non-clinical populations overstimulation can reach comparable severity levels \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. In both populations, overstimulation may negatively impact physical (e.g., immunity), mental (e.g., depression and anxiety), cognitive (e.g., decision making), and social (e.g., isolation) wellbeing \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Despite its impact, overstimulation in non-clinical groups is under-researched. In today’s sensory-rich environments (e.g., new technologies, civilization), understanding its triggers and the psychobiological resources to adequately deal with the demands of sensory environments is increasingly important \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eOverstimulation in relation to sensory processing sensitivity\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOverstimulation is one of the most frequently reported challenge among individuals high on SPS \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Due to lower sensory thresholds, heightened emotional reactivity, and deeper processing, individuals high on SPS may have lower psychobiological resources and respond more strongly to the same sensory input (e.g., background noise, artificial lights, other’s emotions) than individuals lower on SPS \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. This means that even low levels of sensory input (e.g., background noise) can lead to overstimulation in individuals high on SPS \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. The SPS framework suggests that mainly negatively perceived stimulation can trigger overstimulation, whereas positively perceived stimulation (e.g., music) can enhance wellbeing \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. In a qualitative study \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, individuals high on SPS reported strong emotional and cognitive responses to both positive and negative situations, sensitivity to others’ emotions, attention to detail, and frequent fatigue and stress. Feelings of overstimulation may fluctuate more in individuals high on SPS than in less sensitive ones, depending on their exposure to daily positive (e.g., pleasant scents) and negative environments (e.g., background noise in large offices). Retrospective studies partially supported this idea. Iimura \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e found that individuals high on SPS reported greater fluctuations in well-being linked to weekly positive and negative events. However, Damatac \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e found that SPS was related to more daily negative but not to more daily positive events and Van Reyn \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e found that individuals high on SPS showed stronger reactions to daily negative but not to positive events. The latter \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e was the first study examining the association between SPS and the reactivity to daily positive and negative events by using a daily diary study. In the above studies, positive and negative events were measured retrospectively only once \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, once per day \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, or once per week \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. In contrast, the present study examines reactivity, in terms of stimulation, to momentary environments and personal states in daily life using Experience Sampling Methodology (ESM) \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. ESM is a structured moment-to-moment diary method in which individuals respond to brief questions about their current environment (i.e., external triggers) and momentary personal states (i.e., internal triggers) multiple times a day during a specific time frame. This approach minimizes the susceptibility to negativity and recall biases (i.e., the tendency to remember negative events more than positive ones), which are common in traditional questionnaires and retrospective studies \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eThe present study\u003c/h3\u003e\n\u003cp\u003eThe aim of the current study was to examine triggers of and fluctuations in overstimulation in healthy individuals high on SPS (hereafter referred to as Highly Sensitive Persons (\u003cem\u003eHSPs\u003c/em\u003e)) compared to individuals low or average on SPS (hereafter referred to as \u003cem\u003enon-HSPs\u003c/em\u003e). Ease of overstimulation is conceptualized as a key characteristic of SPS \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. However, the current study was the first to empirically examine this relation in greater depth using ESM. The first objective of the present study was to examine whether levels of overstimulation fluctuate more throughout the day and the week in HSPs compared to non-HSPs. We expected higher fluctuations in overstimulation depending on the environments HSPs encounter. The second objective was to explore which positive and negative external and internal triggers are associated with overstimulation in HSPs compared to non-HSPs. Identifying these triggers may provide insights into the underlying mechanisms of overstimulation and thereby on the development of evidence-based interventions targeted to a specific group of individuals high on SPS. Based on key characteristics of SPS, we expected that HSPs, compared to non-HSPs, would be more easily overstimulated in response to negatively perceived stimulation, but less overstimulated in response to positively perceived stimuli. However, due to the lack of evidence, we did not have specific hypotheses regarding particular triggers of overstimulation.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003ePreliminary and descriptive analyses\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe within-person and between-person means, standard deviations, the response rates, and Pearson correlations are reported in Tables A1 and B1 (Supplementary Material A andB).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eFluctuations in overstimulation during the day and the week\u003c/h2\u003e\n\u003cp\u003eResults showed a quadratic effect of beep (or the time of the day) with levels of overstimulation increasing during the day (between 5 and 7pm), followed by a decrease during the later evening (between 8 and 10pm; Fig. 1). No associations between weekday and overstimulation were found or were there any significant interactions with SPS (Table 1). The fluctuations of overstimulation during the day nested in weekday are visualized in Figure 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003e\u003cem\u003eMultilevel Random Intercept and Slope Model Results Predicting Fluctuations in Overstimulation (Model 1)\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Beta;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e4.07*** (0.10)\u003c/p\u003e\n \u003cp\u003e95% CI [3.87; 4.07]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eBeep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.45*** (0.08)\u003c/p\u003e\n \u003cp\u003e95% CI [0.29; 0.45]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eBeep\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e-0.07*** (0.01)\u003c/p\u003e\n \u003cp\u003e95% CI [-0.09; -0.07]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eWeekday\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e-0.07 (0.05)\u003c/p\u003e\n \u003cp\u003e95% CI [-0.03; 0.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eSPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.23 (0.14)\u003c/p\u003e\n \u003cp\u003e95% CI [-0.03; 0.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eBeep*SPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.04 (0.12)\u003c/p\u003e\n \u003cp\u003e95% CI [-0.19; 0.27]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eBeep\u0026sup2;*SPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e-0.01 (0.02)\u003c/p\u003e\n \u003cp\u003e95% CI [-0.05; 0.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eWeekday *SPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e-0.01 (0.08)\u003c/p\u003e\n \u003cp\u003e95% CI [-0.05;0.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eICC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u0026sup2; (fixed effects)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u0026sup2; (total)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u0026nbsp;\u003c/em\u003eSPS = Sensory Processing Sensitivity, categorized as HSP versus non-HSP; \u003cem\u003eBeep\u003c/em\u003e\u0026sup2; is the quadratic term of beep. ICC= Intraclass correlation coefficient. \u003cem\u003eR\u0026sup2;\u0026nbsp;\u003c/em\u003e= proportion of variance explained by the fixed effects and/or fixed and random effects. CI = confidence interval *** \u0026nbsp;\u003cem\u003ep\u003c/em\u003e \u0026lt; .001 (two-sided).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFluctuations in overstimulation during the day. The full lines represent the raw data and the dashed lines represent the estimated data. The estimated confidence intervals are plotted around the estimated lines.\u003c/p\u003e\n\u003cp\u003eFluctuations in overstimulation during the day across the different days of the week. The full lines represent the raw data and the dashed lines represent the estimated data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe prediction of overstimulation by sensory processing sensitivity, the pleasantness of stimuli in the current environment, the type of environment, mood, fatigue, and its interactions\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegarding momentary observations, higher levels of overstimulation were associated with higher SPS, lower pleasantness of both high and low sense stimuli (Table 2), being in the presence of others (Table 3), and higher levels of fatigue (Table 4). Regarding the interactions with SPS (Fig. 3) we found that HSPs were more affected (steeper slopes) by the pleasantness of high sense stimuli (Table 3), fatigue, and mood (Table 4) than non-HSPs. HSPs showed \u003cem\u003ehigher\u0026nbsp;\u003c/em\u003elevels of overstimulation than non-HSPs when they rated the pleasantness of high sense stimuli as low, fatigue as high, and mood as negative. In contrast, HSPs showed \u003cem\u003elower\u0026nbsp;\u003c/em\u003elevels of overstimulation than non-HSPs when they rated the pleasantness of high sense stimuli as high, fatigue as low, and mood as positive. The proportion of interaction (PoI) of these models indicated that 29, 22 and 19 percent (pleasantness high sense stimuli, fatigue, and mood, respectively) of the interaction occurred at the \u0026lsquo;for better\u0026rsquo; side and the remaining percentages (i.e., 71, 78, and 81 percent respectively) at the \u0026lsquo;for worse\u0026rsquo; side. Regarding the examined associations with lagged predictors at the previous beep, results were similar but weaker (Tables 2-4; Supplementary Material C, Fig. C1). Looking at interactions between the lagged predictors and SPS, HSPs showed \u003cem\u003ehigher\u0026nbsp;\u003c/em\u003elevels of overstimulation than non-HSPs when rating the pleasantness of high sense stimuli at the previous beep as low (Table 2) and when reporting a negative mood and high levels of fatigue at the previous beep (Table 4). As an exploratory analysis, we controlled for age and sex differences. Across all models results remained similar when including main effects of age and sex (Supplementary Material D, Table D1-2). \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003e\u003cem\u003eMultilevel Random Intercept and Slope Model Results Predicting Overstimulation by SPS, the Pleasantness of Stimuli in the Environment, and Its Interactions, Including Momentary and Lagged Variables (Model 2)\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"618\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWithin-person centered predictors (momentary observations)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime-lagged predictors (previous observation)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Beta;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Beta;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.24*** (0.06)\u003c/p\u003e\n \u003cp\u003e95% CI [4.12; 4.36]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.09*** (0.20)\u003c/p\u003e\n \u003cp\u003e95% CI [3.69;4.49]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePleasantness high senses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.11* (0.05)\u003c/p\u003e\n \u003cp\u003e95% CI [-0.20; -0.02]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04 (0.03)\u003c/p\u003e\n \u003cp\u003e95% CI [-0.03;0.10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePleasantness low senses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.10** (0.04)\u003c/p\u003e\n \u003cp\u003e95% CI [-0.17; -0.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01 (0.04)\u003c/p\u003e\n \u003cp\u003e95% CI [-0.07;0.08]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.873\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSPS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.20* (0.09)\u003c/p\u003e\n \u003cp\u003e95% CI [0.03; 0.37]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.90** (0.27)\u003c/p\u003e\n \u003cp\u003e95% CI [0.35;1.44]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePleasantness high senses * SPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.23*** (0.07)\u003c/p\u003e\n \u003cp\u003e95% CI [-0.36; -0.11]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.16** (0.05)\u003c/p\u003e\n \u003cp\u003e95% CI [-0.26;-0.07]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePleasantness low senses * SPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02 (0.05)\u003c/p\u003e\n \u003cp\u003e95% CI [-0.08; 0.02]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.01 (0.06)\u003c/p\u003e\n \u003cp\u003e95% CI [-0.12;0.10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eICC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u0026sup2; (fixed effects)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u0026sup2; (total)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u0026nbsp;\u003c/em\u003eSPS = Sensory Processing Sensitivity, categorized as HSP versus non-HSP; \u003cem\u003e\u0026nbsp;\u003c/em\u003eICC= Intraclass correlation coefficient. \u003cem\u003eR\u0026sup2;\u0026nbsp;\u003c/em\u003e= proportion of variance explained by the fixed effects and/or fixed and random effects. CI = confidence interval. \u0026nbsp;*\u003cem\u003ep\u003c/em\u003e \u0026lt; .05; **\u003cem\u003ep\u003c/em\u003e \u0026lt; .01; *** p \u0026lt; .001 (two-sided).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003e\u003cem\u003eMultilevel Random Intercept and Slope Model Results Predicting Overstimulation by SPS, the Type of Environment, and Its Interactions, Including Momentary and Lagged Variables (Model 3)\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"618\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWithin-person centered predictors (momentary observations)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime-lagged predictors (previous observation)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Beta;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Beta;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.89*** (0.07)\u003c/p\u003e\n \u003cp\u003e95% CI [3.69;4.23]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.03*** (0.14)\u003c/p\u003e\n \u003cp\u003e95% CI [3.74;4.32]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAlone versus with others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.43** (0.14)\u003c/p\u003e\n \u003cp\u003e95% CI [0.15;0.70]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.15*(0.07)\u003c/p\u003e\n \u003cp\u003e95% CI [0.01;0.30]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrivate versus public\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.42*^ (0.20)\u003c/p\u003e\n \u003cp\u003e95% CI [0.04;0.82]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04 (0.07)\u003c/p\u003e\n \u003cp\u003e95% CI [-0.09;0.19]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSPS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.21* (0.09)\u003c/p\u003e\n \u003cp\u003e95% CI [0.04;0.38]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.13 (0.21)\u003c/p\u003e\n \u003cp\u003e95% CI [-0.28;0.54]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.530\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAlone versus with others * SPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.17 (0.09)\u003c/p\u003e\n \u003cp\u003e95% CI [-0.35;0.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.11 (0.10)\u003c/p\u003e\n \u003cp\u003e95% CI [-0.33;0.11]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrivate versus public * SPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04 (0.13)\u003c/p\u003e\n \u003cp\u003e95% CI [-0.21;0.30]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.14 (0.11)\u003c/p\u003e\n \u003cp\u003e95% CI [-0.07;0.35]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eICC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u0026sup2; (fixed effects)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u0026sup2; (total)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u0026nbsp;\u003c/em\u003eSPS = Sensory Processing Sensitivity, categorized as HSP versus non-HSP; \u003cem\u003e\u0026nbsp;\u003c/em\u003eAlone was coded as 0 and with others as 1. Private was coded as 0 and in public places (including the work or school environment) as 1. \u0026nbsp;ICC= Intraclass correlation coefficient. \u003cem\u003eR\u0026sup2;\u0026nbsp;\u003c/em\u003e= proportion of variance explained by the fixed effects and/or fixed and random effects. CI = confidence interval. \u0026nbsp; *\u003cem\u003ep\u003c/em\u003e \u0026lt; .05; **\u003cem\u003ep\u003c/em\u003e \u0026lt; .01; *** p \u0026lt; .001 (two-sided), ^\u003cem\u003ep was\u0026nbsp;\u003c/em\u003enot significant anymore after controlling for multiple testing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003e\u003cem\u003eMultilevel Random Intercept and Slope Model Results Predicting Overstimulation by SPS, Fatigue, Mood, and the Interactions Between Predictors and SPS, Including Momentary and Lagged Variables (Model 4 and 5)\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWithin-person centered predictors (momentary observations)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime-lagged predictors (previous observation)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Beta;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Beta;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFatigue (Model 4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eIntercept\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e4.24*** (0.06)\u003c/p\u003e\n \u003cp\u003e95% CI [4.11; 4.36]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e4.51*** (0.10)\u003c/p\u003e\n \u003cp\u003e95% CI [4.31;4.72]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eFatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e0.02 (0.03)\u003c/p\u003e\n \u003cp\u003e95% CI [-0.04;0.08]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e-0.06* (0.03)\u003c/p\u003e\n \u003cp\u003e95% CI [-0.11;-0.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eSPS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e0.19*^ (0.08)\u003c/p\u003e\n \u003cp\u003e95% CI [0.02;0.37]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e-0.09 (0.16)\u003c/p\u003e\n \u003cp\u003e95% CI [-0.40;0.22]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.550\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eFatigue *SPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e0.14** (0.05)\u003c/p\u003e\n \u003cp\u003e95% CI [0.05;0.22]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.08* (0.04)\u003c/p\u003e\n \u003cp\u003e95% CI [0.01;0.15]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eICC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u0026sup2; (fixed effects)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u0026sup2; (total)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMood (Model 5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e4.24*** (0.06)\u003c/p\u003e\n \u003cp\u003e95% CI [4.11;4.36]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e3.98*** (0.26)\u003c/p\u003e\n \u003cp\u003e95% CI [3.46;4.49]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eMood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e-0.12 (0.07)\u003c/p\u003e\n \u003cp\u003e95% CI [-0.25;0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.06 (0.04)\u003c/p\u003e\n \u003cp\u003e95% CI [-0.03;0.15]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eSPS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e0.19*^(0.08)\u003c/p\u003e\n \u003cp\u003e95% CI [0.02;0.37]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1.02** (0.35)\u003c/p\u003e\n \u003cp\u003e95% CI [0.32;1.69]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eMood*SPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e-0.27** (0.09)\u003c/p\u003e\n \u003cp\u003e95% CI [-0.44;-0.10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e-0.16** (0.06)\u003c/p\u003e\n \u003cp\u003e95% CI [-0.28;-0.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eICC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u0026sup2; (fixed effects)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u0026sup2; (total)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u0026nbsp;\u003c/em\u003eSPS = Sensory Processing Sensitivity, categorized as HSP versus non-HSP. \u0026nbsp;ICC= Intraclass correlation coefficient. \u003cem\u003eR\u0026sup2;\u0026nbsp;\u003c/em\u003e= proportion of variance explained by the fixed effects and/or fixed and random effects. CI = confidence interval. \u0026nbsp;*\u003cem\u003ep\u003c/em\u003e \u0026lt; .05; **\u003cem\u003ep\u003c/em\u003e \u0026lt; .01; *** p \u0026lt; .001 (two-sided). \u0026nbsp; ^\u003cem\u003ep was\u0026nbsp;\u003c/em\u003enot significant anymore after controlling for multiple testing.\u003c/p\u003e\n\u003cp\u003eOverstimulation as predicted by the interactions of SPS and pleasantness of high sense stimuli (a), fatigue (b), and mood (c) as reported momentarily\u003cem\u003e.\u0026nbsp;\u003c/em\u003eThe grey areas represent the Regions of Significance with respect to X (i.e., Pleasantness high sense stimuli, Fatigue, or Mood). The predictors (X-axis) are within-person mean centered. Only the observed range of values with respect to X were plotted. The estimated confidence intervals are plotted around the estimated lines.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study examined fluctuations in overstimulation and its triggers in healthy adults with varying levels on the personality trait SPS. Overstimulation is a frequently reported challenge associated with SPS but was not yet empirically studied in depth. The first objective was to examine whether feelings of overstimulation fluctuate more in individuals with high levels of SPS (HSPs) than in individuals with low or average levels of SPS (non-HSPs) throughout the day and week. The second objective was to identify daily external and internal triggers of overstimulation in both groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegarding the first objective, results showed that levels of overstimulation fluctuated throughout the day, but not the week. The highest levels of overstimulation were reported in the early evening (i.e., around 5pm, coinciding with the end of the workday) and then decreased again in the evening (i.e., around 9pm, likely as participants began winding down before bedtime). Contrary to our hypothesis, HSPs did not show greater fluctuations in overstimulation. Possible explanations include: (1) five daily ESM measures may not capture subtle changes, (2) HSPs may have reached ceiling levels, as seen by their higher average levels of overstimulation, and (3) HSPs may have developed strategies to minimize fluctuations in overstimulation, such as avoiding stimulus rich environments or maintaining a structured daily routine. Our results showed that HSPs spent more time in private than in public environments compared to non-HSPs, which may be a strategy to avoid stimulation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegarding the second objective, overstimulation was higher for both HSPs and non-HSPs in the presence of others, in the presence of unpleasant high and low sense stimuli, when fatigued, and when reporting a negative mood. Notably, overstimulation did not increase in public places. When examining differences between HSPs and non-HSPs, we found a cross-over effect. HSPs reported \u003cem\u003ehigher\u003c/em\u003e levels of overstimulation than non-HSPs when they reported higher pleasantness of high sense stimuli (visuals and sounds), high levels of fatigue, and a negative mood. Furthermore, HSPs reported \u003cem\u003elower\u003c/em\u003e levels of overstimulation than non-HSPs when they rated high sense stimuli as pleasant and when they reported low levels of fatigue and a positive mood. Non-HSPs their levels of overstimulation were less affected by external and internal triggers. \u0026nbsp;Additionally, we examined associations between overstimulation and triggers reported at the previous beep (between 1 and 4 hours earlier, depending on the timing of beeps). Results remained similar but were weaker. This could be related to methodological constraints leading to a substantial reduction in the number of observations for lagged analyses compared to momentary analyses. Specifically, the first observation of the day could not be lagged to the last observation of the previous day, and any missing observations at the previous beep led to additional missing data. Our findings built further on a previous daily diary study\u0026nbsp;\u003csup\u003e40\u003c/sup\u003e, showing that HSPs react more to daily negative, but not positive events. However, our momentary approach likely captured positive experiences better than retrospective studies, which are often influenced by recall biases toward negative events and memory-experience gaps\u0026nbsp;\u003csup\u003e44\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn summary, HSPs experienced more overstimulation from negative triggers but also greater relief from momentary positive factors compared to non-HSPs. These results align with previous studies suggesting that more sensitive individuals are more affected by both negative and positive experiences, often described as a \u0026lsquo;for better and for worse\u0026rsquo; pattern\u0026nbsp;\u003csup\u003e19\u003c/sup\u003e. The \u0026lsquo;for better\u0026rsquo; effects were present in responses to momentary pleasant high sense stimuli, momentary low levels of fatigue, and momentary positive mood. In contrast, the \u0026lsquo;for worse\u0026rsquo; effects appeared particularly in relation to high levels of fatigue, unpleasant high sense stimuli, and negative mood. Across models, the proportion of interaction on the \u0026lsquo;for worse\u0026rsquo; side was higher than the proportion of interaction on the \u0026lsquo;for better\u0026rsquo; side.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results of the current study have important implications for reducing feelings of overstimulation in daily life. Raising awareness (e.g., through psychoeducation) about daily fluctuations and triggers of overstimulation may improve predictability and control, reducing the mismatch between environmental demands and personal resources, and thus lowering overstimulation\u0026nbsp;\u003csup\u003e26,28\u003c/sup\u003e. For example, recognizing patterns of overstimulation, enables proactive coping such as scheduling relaxing activities or alone time after a busy day. Identifying contributors to overstimulation can help individuals to develop strategies to improve the person-environment fit\u0026nbsp;\u003csup\u003e26\u003c/sup\u003e. Qualitative studies suggest that combining avoidance, approach, and acceptance-based coping strategies can mitigate overstimulation \u0026nbsp;\u003csup\u003e26,36\u003c/sup\u003e. To illustrate, when fatigued or in a negative mood, individuals may avoid sensory input by being alone or using noise-canceling headphones. While avoidance can offer short-term relief, it may worsen overstimulation at the long-term, aligning with the fear-avoidance model\u0026nbsp;\u003csup\u003e45,46\u003c/sup\u003e. Graded exposure offers an alternative by gradually increasing sensory tolerance (e.g., listening to relaxing music instead of silence, or engaging in brief social interactions rather than full isolation), promoting habituation and reducing distress. Acceptance involves adjusting expectations and focusing on functionality rather than avoiding stimuli. It is central in therapies like Acceptance and Commitment Therapy and Mindfulness Based Interventions, which have been used to manage overstimulation in individuals with acquired brain injury\u0026nbsp;\u003csup\u003e47\u003c/sup\u003e and autism\u0026nbsp;\u003csup\u003e48\u003c/sup\u003e. However, systematic research on coping strategies in the general population remains limited.\u003c/p\u003e\n\u003cp\u003eIn the light of our results, HSPs may benefit even more than non-HSPs from enhancing the pleasantness of visual and auditory stimuli (e.g., ambient lights, relaxing music) and from fostering positive mood (e.g., nature walks). Research showed that HSPs have greater connections to nature\u0026nbsp;\u003csup\u003e49,50\u003c/sup\u003e and benefit more from virtual nature videos in terms of positive affect\u0026nbsp;\u003csup\u003e51\u003c/sup\u003e. Additionally, improving sleep quality may be particularly beneficial for HSPs, as we found that they were more affected by high levels of fatigue than non-HSPs. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAn important strength of this study is its novel focus on overstimulation in daily life within a general population sample, as prior research has mainly examined overstimulation in clinical groups (e.g., autism, stroke populations). Another strength is the robust ESM-design with high response rates, enabling real-time assessment of momentary experiences. Previous studies have often relied on retrospective self-reports (e.g., recalling childhood experiences)\u0026nbsp;\u003csup\u003e14,52\u003c/sup\u003e or reporting the most positive and negative event of the day\u0026nbsp;\u003csup\u003e40\u003c/sup\u003e or week\u0026nbsp;\u003csup\u003e39\u003c/sup\u003e. Additionally, earlier studies have primarily focused on negative experiences or the absence of negative events rather than the presence of positive ones\u0026nbsp;\u003csup\u003e52\u003c/sup\u003e. Unlike experimental studies inducing positive experiences\u0026nbsp;\u003csup\u003e10,16,17\u003c/sup\u003e, we assessed natural sensitivity to positive stimuli as they occurred in daily life.\u003c/p\u003e\n\u003cp\u003eOur study had also several limitations. First, the sample was skewed towards females, particularly in the HSP group, and included a higher proportion of older participants in the HSP group compared to the non-HSP group. Future studies should include more diverse samples to disentangle SPS effects from potential sex (e.g., hormonal cycles, social acceptance of sensitivity in women) or age-related influences. Second, although standard in personality research, the mere use of a self-report questionnaire to quantify SPS is a limitation. Additionally, predictors were analyzed separately due to high intercorrelations (e.g., mood and fatigue). Future studies should use larger samples and continuous SPS scores to detect more nuanced effects. Third, our analysis focused on momentary and one-directional associations with overstimulation as the outcome variable. Therefore, we cannot make any conclusions on the direction of effects. \u0026nbsp;For example, it is likely that higher levels of overstimulation also influence fatigue, mood or sensory perceptions over time. Future research should explore bidirectional relationships between overstimulation and its predictors. \u0026nbsp;Lastly, although we categorized high and low sense stimuli, we did not differentiate between specific stimuli within these categories (e.g., artificial light vs. sunlight) or considered participant\u0026rsquo;s control over these stimuli. Future studies should incorporate more detailed sensory assessments and examine perceived control as a moderating factor.\u003c/p\u003e\n\u003cp\u003eTo conclude, this study was the first to examine daily fluctuations in overstimulation and its associated factors in a general population sample with varying levels of SPS. Overstimulation peaked in the early evening, but did not fluctuate throughout the week. Higher levels of overstimulation occurred in the presentence of others, with unpleasant sensory stimuli, and during periods of fatigue and negative mood. HSPs were not only more affected by negative internal and external triggers (i.e., fatigue, negative mood, unpleasant sensory stimuli) but also benefited more from momentary positive auditory and visual stimulations, low levels of fatigue, and a positive mood compared to non-HSPs. Raising awareness of these patterns may support individuals to develop coping strategies managing overstimulation, with potentially greater benefits for individuals high on SPS. Future research should investigate coping, sensory control, and bidirectional relationships between overstimulation and its triggers.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eParticipants and procedure\u003c/h2\u003e\n\u003cp\u003eThe study was approved by the university ethics committee of KU Leuven [G-2022-5629-R2(MAR)]. \u0026nbsp;Healthy participants were drawn from a larger online questionnaire study (\u003cem\u003eN\u0026nbsp;\u003c/em\u003e= 848), recruited via social media channels targeting the general population and via the course participation in research for first year psychology students. Exclusion criteria were non-fluency in Dutch and self-reported neurological, neurodevelopmental, or mental disorders. The larger questionnaire study assessed SPS using the Sensory Processing Sensitivity Questionnaire\u0026nbsp;\u003csup\u003e53\u003c/sup\u003e. Based on total SPSQ scores, participants were grouped into low (30% lowest scores on SPS), medium (40% middle SPS scores), and high sensitivity (30% highest SPS scores). This classification aligns with identified sensitivity levels using latent profile analyses across different samples and including different measures of SPS\u0026nbsp;\u003csup\u003e10,54,55\u003c/sup\u003e. All participants from the larger study who left their contact details to be invited for follow-up studies were invited for the current study, independently of their SPS score. In total 160 adults accepted the invitation and signed a second informed consent form before the start of the current study. Since the sample was not equally distributed regarding participants\u0026rsquo; SPS score (Supplementary Material E; Fig. E1), the sample was divided into a HSP and a non-HSP group. The latter was formed by combining the previously identified low and medium sensitivity groups. Participants completing less than 30% of ESM questionnaires (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 21) were excluded\u0026nbsp;\u003csup\u003e56\u003c/sup\u003e. A MCAR test\u0026nbsp;\u003csup\u003e57\u003c/sup\u003e suggested that data were missing completely at random (\u0026chi;\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e= 26412, \u003cem\u003edf\u003c/em\u003e = 28297, \u003cem\u003ep \u0026nbsp;\u003c/em\u003e= 1). The final sample included 139 adults (\u003cem\u003eM\u003c/em\u003e\u003csub\u003eage\u0026nbsp;\u003c/sub\u003e= 35.2; \u003cem\u003eSD\u003c/em\u003e\u003csub\u003eage\u003c/sub\u003e= 12.6; range\u003csub\u003eage\u003c/sub\u003e = 17- 63; 84.2% female) with 51.8% (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 72) classified into the HSP group, and 48.2% (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 67) in the non-HSP group. The demographics of the total sample, as well as the HSP and non-HSP groups, are presented in Table 5.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eParticipants completed the ESM-questionnaires via the m-Path app \u003csup\u003e58\u003c/sup\u003e on their smartphone five times a day for seven consecutive days. Beeps were sent at semi-random times within two-hour blocks between 7am and 9pm or between 8am and 10pm, based on participants\u0026apos; self-identified morning or evening rhythms, to ensure they were likely awake. Questionnaires could be completed until the next beep. As an incentive, participants had the opportunity to win a noise-cancelling headphone through lottery.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u0026nbsp;\u003c/strong\u003e\u003cem\u003eDemographics of the Total Sample, the HSP Group, and the Non-HSP Group\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Sample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHSP group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-HSP group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003et \u0026chi;\u0026sup2;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.61***\u003cem\u003e#\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStandard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17-63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18-63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17-63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e% female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e84.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e90.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e77.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.18*^\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNationality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBelgium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e92.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e91.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e92.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.10^\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThe Netherlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.40^\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGerman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.07%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.94^\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSyria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.40%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.08^\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHighest educational level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrimary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.01^\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSecondary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.71^\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigher education (minimum a bachelor\u0026rsquo;s degree)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.00^\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u0026nbsp;\u003c/em\u003eSex was coded as 1 = female, 2 = male; \u003cem\u003e# = Independent\u003c/em\u003e sample t-test (equal variances are not assumed); ^= Chi-Square test.\u003c/p\u003e\n\u003cp\u003e* \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt;.05\u003cem\u003e; ** p\u0026nbsp;\u003c/em\u003e\u0026lt; .01; *** \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; .001\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eMeasures\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSensory Processing Sensitivity\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSPS was measured once, prior to starting on the ESM, with the Sensory Processing Sensitivity Questionnaire (SPSQ)\u0026nbsp;\u003csup\u003e53\u003c/sup\u003e. All 43 items were rated on a 7-point Likert scale (1 = \u003cem\u003enot at all\u003c/em\u003e, 4 = \u003cem\u003emoderately\u003c/em\u003e, 7 = \u003cem\u003eextremely\u003c/em\u003e). The total mean score of the SPSQ, with an excellent internal consistency (\u0026alpha; = .95), was used.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eExperience Sampling Methodology\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ESM questionnaire (Supplementary Material F) included: one item assessing current levels of overstimulation, 15 items evaluating the pleasantness of stimuli in their current environment, two items assessing current levels of fatigue, 19 items measuring current mood, and five items assessing the type of current environment. Descriptive statistics and response rates are presented in Table A1 (Supplementary Material A).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExternal triggers. (a) Sensory stimuli.\u003c/strong\u003e Participants rated the pleasantness of various stimuli (e.g., lights, sounds, smells, tastes), if applicable, on a scale from 1 (\u003cem\u003enot at all\u003c/em\u003e) to 7 (\u003cem\u003eextremely\u003c/em\u003e). In addition, participants rated their level of stimulation on a scale from -3 (\u003cem\u003every understimulated\u003c/em\u003e) to +3 (\u003cem\u003every overstimulated\u003c/em\u003e), later converted to a 1 to 7 scale with higher scores indicating greater overstimulation. Based on theory\u0026nbsp;\u003csup\u003e30\u003c/sup\u003e, exploratory and confirmatory factor analyses (Supplementary Material G , Table G1), stimuli were grouped into high (i.e., lights, sounds, bright colors, moving scenes, music, multiple stimuli simultaneously; \u0026alpha; = .86)\u0026nbsp;and low\u0026nbsp;(i.e., touches, smells, tastes, temperature; \u0026alpha; = .72)\u0026nbsp;senses. Items were developed by the authors of the current study based on a questionnaire study assessing multisensory sensitivity\u0026nbsp;\u003csup\u003e29\u003c/sup\u003e as no prior ESM study has examined this construct. \u003cstrong\u003e(b) Type of environment.\u0026nbsp;\u003c/strong\u003eParticipants selected their current location (e.g., at home, with family or friends, at work). They reported the presence of others, specifying whether they were alone or with others and with whom (e.g., friends, colleague, stranger, partner). Two dummy variables for the type of environment were created: location (private (0) versus public (including at work or school; 1) and social context (alone (0) versus with others (1)).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMood.\u0026nbsp;\u003c/strong\u003eParticipants rated their current mood on a scale from 1 (\u003cem\u003enot at all\u003c/em\u003e) to 7 (\u003cem\u003eextremely\u003c/em\u003e), assessing both positive (happy, satisfied, relaxed, at ease, relieved, energetic, and good) and negative moods (irritated, anxious, uncertain, lonely, guilty, suspicious, sad, restless, gloomy, lethargic, agitated, and stressed). These items are selected from the ESM item repository (https://esmitemrepositoryinfo.com/) and are based on the Positive and Negative Affect Scale (PANAS) \u003csup\u003e59\u003c/sup\u003e. Due to high intercorrelations, a composite mood score (\u0026alpha;\u0026nbsp;= .96) was calculated by reversing negative mood items and averaging with positive mood items; higher scores indicate a more positive mood.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFatigue.\u0026nbsp;\u003c/strong\u003eFatigue was measured using two items from the ESM item repository: \u0026lsquo;How physically tired are you?\u0026rsquo; and \u0026lsquo;How mentally tired are you?\u0026rsquo;. Both items were answered on a scale from 1 (\u003cem\u003enot at all\u003c/em\u003e) to 7 (\u003cem\u003eextremely\u003c/em\u003e). The mean of both items was calculated as a measure of fatigue\u0026nbsp;(\u0026alpha; = .87).\u0026nbsp;\u003c/p\u003e\n\u003ch2 id=\"_Toc106195209\"\u003eData analysis\u003c/h2\u003e\n\u003cp\u003eFirst, we examined descriptive statistics, including within-person means, between-person means, standard deviations, Pearson bivariate correlations, frequencies, and response rates. Second, multilevel models were estimated using restricted maximum likelihood (REML). Minimum sample size estimations were based on guidelines for multilevel modeling\u0026nbsp;\u003csup\u003e60\u003c/sup\u003e recommending at least 50 clusters (participants) per group with at least 30 observations each, since we did not have any estimates on effect sizes for the studied variables. Momentary observations as predictor variables were nested within individuals, random intercepts and random slopes were estimated. Overstimulation was the outcome variable. In each model the main effect of SPS (HSP versus non-HSP; between-person) as well as the interaction effects of SPS with the predictors were included. Per type of additional time-varying momentary (within-person) predictor, a separate model was estimated: (1) time of the day (i.e., beep) and day of the week (i.e., weekday ranging from Monday to Sunday), (2) type of the environment (i.e., location and social context), (3) pleasantness of stimuli in the current environment, (4) current levels of fatigue, and (5) current levels of mood. In models 2 to 5, the predictors were within-person mean centered by distracting the subject-level means from the original variable. Based on visual exploration of the raw data (Fig. 1), beep was included as normal and squared coefficient. Third, we reran the same multilevel models (models 2-5) with type of the environment, pleasantness of stimuli, fatigue, and mood as lagged to the previous measurement moment (t-1). For all multilevel models,\u0026nbsp;we corrected for multiple testing to avoid false positive findings by using False Discovery Rate\u0026nbsp;\u003csup\u003e61\u003c/sup\u003e. Significant interactions using simple slopes, including Regions of Significance (RoS) with respect to X (i.e., at which values of the predictor the effect of regression is statistically significant) and the Proportion of the Interaction\u003csup\u003e23\u003c/sup\u003e (PoI; the proportion of the total interaction that is represented by the left and the right sides of the crossover point) were plotted.\u0026nbsp;As more older and female participants were in the HSP compared to the non-HSP group (Supplementary Material H, Fig. H1), we controlled for age and sex effects as an exploratory analysis (Supplementary Material D).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eTransparency and openness\u003c/h2\u003e\n\u003cp\u003eWe report how we determined our sample size, all data exclusions, manipulations, measures, procedures, and results transparently, and we follow JARS\u0026nbsp;\u003csup\u003e62\u003c/sup\u003e. All analyses were run in SPSS Version 28\u0026nbsp;\u003csup\u003e63\u003c/sup\u003e and R version 4.3.2.\u0026nbsp;\u003csup\u003e64\u003c/sup\u003e using packages \u003cem\u003eesmpack\u003c/em\u003e \u003csup\u003e65\u003c/sup\u003e, \u0026nbsp; \u003cem\u003enlme\u003c/em\u003e \u003csup\u003e66\u003c/sup\u003e, \u003cem\u003elavaan\u003c/em\u003e \u003csup\u003e67\u003c/sup\u003e, and \u003cem\u003eggplot2\u003c/em\u003e \u003csup\u003e68\u003c/sup\u003e. This study was not preregistered on OSF before data collection started due to tight project timelines. However, all hypotheses, study design, and planned analyses were outlined in detail in a grant proposal and approved before data collection started.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthical Approval\u003c/h2\u003e\n\u003cp\u003eThe study was approved by the university ethics committee of KU Leuven [G-2022-5629-R2(MAR)]. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.\u003c/p\u003e\n\u003ch2\u003eInformed Consent\u003c/h2\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements and funding\u003c/h2\u003e\n\u003cp\u003eWe would like to express our gratitude to all the participants. Sofie Weyn was supported by a postdoctoral fellowship by KU Leuven (PDMT2/22/017).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u003c/h2\u003e\n\u003cp\u003eAuthor S.W. \u0026nbsp;is the corresponding author and was responsible for the funding acquisition, conceptualization, methodology, data collection, analyses, project administration, and writing the original draft. Author C.R.G. was responsible for supervision, conceptualization, project administration, and reviewing and editing the written manuscript. Authors C.U.G. and S.J.S. were responsible for supervision, reviewing and editing the written manuscript.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eConflict of Interest declaration\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eAll authors declare that there are no conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eA link to the anonymized data and analysis code is made available on Open Science Framework (OSF): https://osf.io/598fh/?view_only=00c6acc7361249fbbf24f042985752a7.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAron, E. N. \u0026amp; Aron, A. Sensory-processing sensitivity and its relation to introversion and emotionality. \u003cem\u003eJ. Pers. Soc. 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(2016).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Sensory Processing Sensitivity, Overstimulation, Experience Sampling Methodology, Triggers, Fluctuations","lastPublishedDoi":"10.21203/rs.3.rs-7030961/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7030961/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIndividuals differ in how they perceive and process social and sensory information in their environment, a personality trait known as \u003cem\u003eSensory Processing Sensitivity\u003c/em\u003e (SPS). Approximately 30% of the general population scores high on this trait making them more responsive to both negative and positive environmental influences than individuals lower in SPS. \u003cem\u003eOverstimulation\u003c/em\u003e is one of the biggest challenges associated with SPS. Associations between SPS, triggers, and fluctuations of overstimulation in everyday life were examined using an experience sampling method study in 139 healthy adults. Results showed that overstimulation peaked in the early evening and in the presence of others. Furthermore, more sensitive individuals reported \u003cem\u003ehigher\u003c/em\u003e levels of overstimulation when auditory and visual stimuli were rated as unpleasant, when fatigued, or in a negative mood. Yet, more sensitive individuals reported \u003cem\u003elower\u003c/em\u003e levels of overstimulation with momentary pleasant auditory and visual stimuli, when not fatigued, and in a positive mood at the current moment. Everyone, but especially individuals high in SPS, might benefit from interventions preventing high levels of fatigue, increasing positive mood and the pleasantness of sensory stimuli in their daily life to reduce feelings of overstimulation.\u003c/p\u003e","manuscriptTitle":"Sensory Processing Sensitivity and Overstimulation in Daily Life: An Experience Sampling Method Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-28 13:50:55","doi":"10.21203/rs.3.rs-7030961/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-10T06:55:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-06T19:59:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205160300897004420529591843135400522017","date":"2025-09-18T16:48:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-11T08:34:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"257687282591338458269848202853788557711","date":"2025-07-28T08:39:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"284824443520569179374264880577490627056","date":"2025-07-28T06:09:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16892459783240723490114394165279246255","date":"2025-07-27T22:50:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-25T18:15:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-25T18:07:42+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-11T06:10:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-09T13:40:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-09T13:36:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fdc3e839-c5ca-4d27-a618-669cab58b30f","owner":[],"postedDate":"July 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":52190887,"name":"Health sciences/Health care"},{"id":52190888,"name":"Biological sciences/Neuroscience"},{"id":52190889,"name":"Biological sciences/Psychology"},{"id":52190890,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2025-12-22T16:04:59+00:00","versionOfRecord":{"articleIdentity":"rs-7030961","link":"https://doi.org/10.1038/s41598-025-31629-3","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-18 15:57:47","publishedOnDateReadable":"December 18th, 2025"},"versionCreatedAt":"2025-07-28 13:50:55","video":"","vorDoi":"10.1038/s41598-025-31629-3","vorDoiUrl":"https://doi.org/10.1038/s41598-025-31629-3","workflowStages":[]},"version":"v1","identity":"rs-7030961","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7030961","identity":"rs-7030961","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}