Not all task-unrelated thoughts (TUT) are created equal - TUT characteristics as predictors of affective states and heart-rate variability

Not all task-unrelated thoughts (TUT) are created equal - TUT characteristics as predictors of affective states and heart-rate variability | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Not all task-unrelated thoughts (TUT) are created equal - TUT characteristics as predictors of affective states and heart-rate variability Michał S. Skorupski, Izabela Krejtz, Steven Barnes, Celine Baeyens, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7827343/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Task-unrelated thoughts (TUT) are a prevalent transdiagnostic phenomena, robustly associated with both adaptive and maladaptive outcomes. However, it is still not clear which factors determine the maladaptive outcomes of TUT. In this study, we focused on analysing the role of a wide spectrum of TUT characteristics in everyday functioning, specifically their associations with momentary affective states, depressive symptoms, heart rate variability, and sleep quality. Forty-seven participants took part in a seven-day ecological momentary protocol, completing momentary and daily questionnaires, alongside continuous ECG monitoring. Additionally, trait-level measures of mind-wandering, repetitive negative thinking, depressive and anxiety symptoms were collected using self-report questionnaires. The results show that TUT emotional appraisal—especially perceived burden and thought valence—were significant predictors of affective momentary outcomes. In contrast, characteristics linked to control over TUT (e.g., intrusiveness and freely moving features) significantly predicted depressive symptoms at the daily level. Part of the associations between TUT characteristics and outcomes were moderated by individual differences, such as tendency to engage in mind-wandering or repetitive negative thinking, as well as trait-level depressive and anxiety symptoms. Health sciences/Diseases Health sciences/Health care Biological sciences/Psychology Social science/Psychology Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Everybody knows the feeling of drifting away mid-task, suddenly realizing that their mind has wandered away from their current activity. Task-Unrelated Thoughts (TUT) – thoughts directed away from the ongoing focal activity or situation without any direct environmental requirement [ 1 , 2 ] - have been reported to occupy nearly half of our daily thought content [ 3 ]. As a complex transdiagnostic process, TUT play a role in various conditions, including anxiety [ 4 ], depression [ 5 ], ADHD [ 6 ], and dissociative disorders [ 7 ]. Past studies suggest that TUT can be associated with a decline in mood [ 3 ] and attention [ 8 ]. At the same time, they have been shown to play an important role in facilitating creative processes [ 9 ], planning future events [ 10 ], delaying gratification [ 11 ], and in the developing of socio-emotional skills [ 12 ]. Which factors are responsible for the widely varying cognitive and emotional outcomes associated with TUT? It is challenging to answer this question, in part due to the inconsistent terminology and numerous theoretical frameworks used within the field [ 13 ]. The umbrella term task-unrelated thoughts defined above [ 1 , 14 ] encompasses terms such as daydreaming [ 15 ], mind-wandering [ 16 ], spontaneous thoughts [ 17 ], self-generated thinking [ 18 ], but also rumination [ 19 ], repetitive negative thoughts [ 5 ], and perseverative cognition [ 20 ]. The discussions regarding how to distinguish between different types of TUT, which of the aforementioned terms can be used interchangeably, and which represent overarching categories that include more specific subtypes seems to be far from over [ 13 , 18 ]. Additionally, measuring TUT, particularly in the ecological context, when participants perform different sets of tasks (including resting) is a significant challenge [ 2 , 21 ]. In the heat of the ongoing debate on the definition and differentiation of TUT types, Seli et al. [ 14 ] proposed treating all TUT phenomena as one heterogeneous construct. This family resemblance approach enables us to focus on different thought characteristics as variables linked to both the adaptive and maladaptive outcomes, with less attention directed to the categorisation of the thought. Although this perspective risks overlooking some of the debates regarding TUT classification, prioritizing the detection of maladaptive TUT’s features is vital for clinical applications. It may represent the only viable way to push maladaptive TUT research forward, regardless of the continuing definitional controversy [ 22 ]. The effects of certain TUT characteristics have been relatively well explored. First, the literature suggests that thought valence is largely responsible for the affective outcome of TUT, showing that not only is TUT linked generally to poorer mood, but this effect is increased for negatively valenced thoughts [ 23 ]. However the role of TUT valence is nuanced by the studies exploring the role of TUT as an avoidance strategy suggesting that its effect on emotion regulation might be different in short vs. long term perspective [ 24 ]. Second, intentional mind-wandering is associated with lower depressive and anxiety symptoms than its unintentional version [ 25 ]. Third, thoughts considered more freely moving are associated with better mood [ 26 ]. Further, future related thoughts may be linked to more positive affect than past-related thoughts [ 27 ]. Finally, definitional characteristics of repetitive negative thinking (RNT) - repetitiveness, intrusiveness, negative valence, and uncontrollability - are all associated with depressive and anxiety symptoms [ 28 ]. In 2023, Thiemann et al. [ 22 ] examined both the intentionality and the freely moving nature of thoughts in a single study and found that each of these characteristics mitigates the negative relationship between TUT and negative affect. However, research investigating all these characteristics (or even more than two concurrently in a single study) in the perspective of affect regulation is rare [ 29 , 30 ]. Thus, the present study aims to fill this gap by considering all the relevant characteristics identified in the literature, in order to test which of them are significant predictors of mood when the others are also included in the model. Most prior research relies on self-report questionnaires and/or was conducted in laboratory settings [ 18 , 20 ]. However, recent literature emphasizes the importance of assessing psychological processes in ecological settings [ 31 , 32 ] and exploring their link not only with subjective mood evaluation, but also with psychophysiological measures [ 33 ]. This trend is also present in TUT research. A rising number of studies explore the links between task-unrelated thoughts and heart rate variability (HRV) - a measure used as a psychophysiological marker of balance of the autonomic nervous system [ 34 ] or even general psychologicalwell-being [ 35 ]. Studies by Ottaviani and colleagues [ 20 , 36 ] suggest that perseverative cognition, but not mind-wandering, can be associated with lowered HRV. Rumination [ 37 ], worry [ 38 ], and repetitive negative thinking [ 39 ] seem to also have a detrimental effect on HRV. However, to the best of our knowledge, no studies exist linking a wider set of TUT characteristics to changes in HRV in ecological settings. In the present study, we used ecological momentary assessment (EMA) methodology to investigate the relationship between TUT characteristics (e.g. intentionality, emotional valence, controllability) and (1) affect, (2) heart rate variability, (3) depressive symptoms, and (4) sleep quality. As existing studies often focus on thought characteristics that are particularly salient within one of the TUT subtypes, we aim to integrate characteristics commonly associated with specific TUT types (e.g. repetitiveness and lack of control in RNT [ 40 ]) into a more universal framework, consistent with the family resemblance approach [ 14 ]. All TUT characteristics were grouped into three factors following the approach previously used by Rosenkranz et al. [ 28 ] for measuring rumination with the experience sampling method. The content factor was directly adapted from Rosenkranz et al. [ 28 ] and includes features describing the thematic focus of thoughts (e.g., problems, feelings, temporal orientation). The second factor, labelled control , was based on core processual characteristics of RNT as defined by Rosenkranz and colleagues [ 28 ], but was restricted to variables capturing aspects of perceived mental control - a factor identified as one of the most relevant in TUT (e.g. [ 24 , 29 , 32 , 41 ]) namely, uncontrollability, repetitiveness, intrusiveness, intentionality, and the freely moving nature of TUT. The variable assessing emotional burden (originally included in the process characteristics in Rosenkrantz et al.[ 28 ]) was removed from this cluster and moved to the emotional appraisal factor. Distinguishing appraisal factor in the present study was directly linked to the results of recent studies underlying the importance of beliefs and meta-cognitions about TUT in their maladaptive outcomes [ 32 , 42 , 43 ]. We hypothesises that TUT characteristics linked to lack of control, negative valence and negative beliefs (i.e. uncontrollability, repetitiveness, intrusiveness, focus on problems, past and future orientation, emotional burden) are positively related to negative affect; while freely moving feature, intentionality and positive valence will be negatively related to negative affect. The current literature is not clear about the link between focus on feelings link to negative affect – while this component is a inherent part of rumination evaluation [ 28 ], literature suggest also that it might be adaptive – negatively related to negative affect [ 40 ]. Informed by the theoretical framework [ 44 , 45 ] of mind-wandering and prior research (e.g. [ 24 ]), we controlled for ongoing task characteristics, particularly task valence. This builds on prior research indicating that TUT can be maladaptive when used as an avoidance strategy [ 46 , 47 ], suggesting that specific TUT characteristics may be especially detrimental when one is trying to escape a negatively valenced task [ 24 ]. Finally, we provide exploratory analyses on whether the trait tendency to ruminate or mind wander, as well as the level of depressive and anxiety symptoms, can be a moderator of momentary and daily associations. Methods Participants A total of 71 students volunteered to take part in the EMA study.Fifteen participants withdrew before the EMA stage of the study began, and three withdrew during the study. Five participants were excluded for not meeting the minimum compliance criterion (60% compliance for EMA) orfor providing inconsistent ID across different parts of the study. As the minimum compliance required from participants varies strongly between studies [ 48 ], we chose a mid-to-high compliance cut-off of 60% as used in previous studies [ 49 ], aiming to achieve an average compliance rate around 80% [ 50 ]. One participant was excluded from the study following inspection of the HRV data, which revealed irregularities (frequent sudden stops in measurement, strong declines and increases in ECG signal – beyond the expected thresholds)rendering the dataunsuitable for further analysis. A total of 47 participants were included in the final sample ( M age = 24.83, SD age =5.96), of which 33 were female, 11 male and 2 were non-binary.One participant decided to withhold information regarding their gender. Procedure The participants were recruited through the university’s internal research recruitment system. After signing up for the study, the participants were asked to provide and informed consent and fill out a series of questionnaires in Qualtrics. They were assigned an assistant, who guided them throughout the procedure. On the first meeting with the assistant, the participants installed the Movisens XS application and were given the chest ECGMove4 Movisens sensors together with thorough instruction on how to use them. For 7 consecutive days and nights the participants wore the sensors and filled out momentary and daily questionnaires. The sensors were removed daily for around 20 minutes for charging. The sensors were also removed during activities that could damage them (bathing, particular forms of physical exercise, using sauna etc.). At the beginning of the study, participants chose one of 3 daily activity windows (7am-9pm, 9am-11pm or 11am-1am). Each day, participants received: (1) a short questionnaire assessing their sleep quality (30 minutes after the start of the daily activity windows); (2) a total of 8 questionnaires assessing current thought and task characteristics, as well as changes in mood (7 of which appeared semi-randomly in 2-hour windows covering the daily activity window - additionally, 1 questionnaire was activated by the participants themselves just before sleep); (3) a short questionnaire assessing daily depressive symptoms (30 minutes before the end of the daily activity window). After the measurement period, participants returned the devices to the assistants during a previously scheduled appointment, during which they were also given the opportunity to give feedback on their study experience. Participants who finished the study and met the pre-specified requirements (wearing the sensor for the duration of the study and answering at least 60% of momentary questionnaires) were awarded university credit points. Th procedure was approved by the Ethic Committee of Psychology Department in Katowice, SWPS University, Poland (WKEB69/03/2021). All methods described below were performed in accordance with the relevant guidelines and the study was run in accordance with the Declaration of Helsinki. Measures Momentary measures Each momentary questionnaire consisted of a total of 22 short items. All items were available to participants only in Polish (untranslated item content available in supplementary materials). Thought characteristics. One item measured the level of TUT [ 29 ] (“To what extent, just before the signal, were you mind-wandering/your thoughts were divagating/ you were daydreaming” (using the term TUT would not be intuitive in the language of the study) with visual analog scale (VAS) from 0-“no at all”, to 100-“strongly”). Eight items adapted from Rosenkrantz and colleagues [ 28 ], measured characteristics needed to assess repetitive negative thinking (RNT). These included items assessing the focus of thoughts on (1) feelings, (2) problems, (3) unpleasant memories, (4) unpleasant situations that might occur in the future, as well as items addressing (5) perceived burden caused by them, and the (6) repetitiveness, (7) intrusiveness, and (8) uncontrollability of those thoughts. These items were answered using a 7-point Likert scale, as in the original questionnaire. Six items were used to measure thought characteristics not included in the RNT measures, but relevant, based on the literature in evaluating TUT: (1) intentionality (adapted from Banks and colleagues [ 51 ]), (2) emotional valence [ 52 ], (3) the extent to which thoughts were freely moving [ 53 ]. The last item, measuring thought usefulness (“To what extent are you satisfied that this thought appeared?”) was proposed to address the possibility that the thought might be perceived by the participant as useful, regardless of its specific characteristics. Based on the theoretical background described in the introduction, thought characteristics were divided into 3 factors: Content (4 items: future/past orientation, focus on feelings/problems), Control (5 items: uncontrollability, repetitive, intrusive, freely moving, intentionality) and Emotional appraisal (3 items: valence, emotional burden, usefulness). Task characteristics. To fully assess the context of the occurring thoughts, two items measuring task characteristics were used. The task-focused items were preceded by a message “Think about the task or activity that you have been doing just before the signal…”. One item measured the emotional valence of the task (“What emotions does this task evoke in you?” with VAS from 0-negative, to 100-positive). Another item measured the level of engagement (“To what extent does this task require your focus or engagement”? with VAS from 0-“not at all” to 100-“strongly”). Mood. Momentary mood was measured using four items, each structured as: “To what extent were you feeling [emotion] just before the signal?”. The specific emotions inserted into the item stem were (1) “happy”, (2) “angry”, and (3) “sad” [ 54 ], and “nervous, anxious, or on edge”, as a measure of anxiety [ 55 ]. The mood items were answered using a VAS with answers from 0-“not at all” to 100-“strongly”. Daily measures Sleep quality. Measured with a single item (“How would you rate your sleep quality” (referring to last night) with VAS from 0-“terrible”, 50-fair to 100-“excellent” [ 56 ]. Daily depressive symptoms. Measured with three items [ 57 ] – “Thinking about today in general: (1) How positive were your thoughts about yourself? (2) How well did things go today? (3) Today, how optimistic are you about how your life (in general) will be tomorrow?” with VAS ranging from 0-“very negative”(for items 1 and 3) or “very badly” for item 2, to 100-(1)“very positive”,(2)”very well”, (3)”very optimistic”. The final daily depressive symptoms score was calculated by reversing and adding the scores of the 3 scales together. Heart Rate Variability measure ECG signal was recorded at 1024 Hz using the Movisens ECG Move 4 sensors. The Root Mean Square of Successive Differences (RMSSD) was calculated using the Movisens dataAnalyzer software (Movsiens GmbH, 2024). During the calculation process, the software provided automated artifact correction. The RMSSD values were obtained for 1-minute epochs aligned with the beginning of each of the momentary questionnaires answered by participants. Trait measures Daydreaming Frequency Scale (DDFS). Frequency of daydreaming/mind-wandering was evaluated using a Polish adaptation of Daydreaming Frequency Scale (DDFS [ 1 ])- a single-factor, 12-item scale. Perseverative Thinking Questionnaire (PTQ). The PTQ is a 15-item scale measuring the level of perseverative thinking [ 58 ]. It includes one higher-order factor (repetitive negative thinking; RNT) and three lower-order factors: core characteristics of RNT, unproductiveness of RNT, and RNT-related cognitive interference. The Polish version of the PTQ [ 59 ] was used. Hospital Anxiety and Depression Scale (HADS). The HADS is a 14-item scale, with seven items assessing anxiety (HADS A) and seven assessing depression (HADS D) [ 60 ]. Participants indicate how frequently they experience each item on a 4-point Likert-type scale, with anchors varying depending on the item. The Polish version of the HADS [ 61 ] was used. Statistical analysis plan As the data were collected using an EMA method, providing multiple measurement points per day for multiple days, for multiple users the data were analysed using three-level models to appropriately account for the nested data structure and to fully exploit the analytical possibilities provided by intensive repeated measures [ 62 ]. The momentary observations (level 1) were nested within days (level 2), and days nested within participants (level 3). Random intercepts were included for both days and participants to capture unobserved heterogeneity at these levels. An example of the model can be viewed below: Anxiety ijk ​=β 0jk ​+β1​⋅Usefulness ijk ​+β2​⋅Burden ijk ​+β3​⋅Thought_valence ijk ​+ε ijk ​ β 0jk ​=γ 0k ​+u 0jk ​ γ 0k ​=γ 00 ​+v 0k ​ In models measuring daily outcomes (daily depressive symptoms and sleep quality), a 2-level data structure was used, with daily measurements (level 1) nested within participants (level 2). Random intercepts were included for participants. To analyse models predicting daily outcomes, momentary measures were aggregated to compute daily means. This approach was chosen to prevent mismatches in temporal granularity between predictors and outcomes, and to avoid non-independence of observations (multiple momentary predictor entries would otherwise share the same daily outcome, biasing standard errors and possibly inflating significance). All predictors were centered at the participant level to isolate within-person fluctuations from between-person differences and reduce potential multicollinearity, particularly in the presence of random slopes [ 63 ]. To focus the analysis on task-unrelated thoughts, measurement points at which participants' thoughts were either fully or strongly related to the current activity (answers 7 and 6 in item measuring task-relatedness) were excluded from the analyses – these points corresponded to 38.6% of the initial dataset. This criterion positions our threshold on the more inclusive end of those commonly employed in previous studies [ 14 ], allowing for the examination of a broad range of thoughts - from fully to only somewhat task-unrelated. Given that the primary focus of this study lies in examining thought characteristics rather than distinct thought types, adopting a liberal classification criterion minimizes the risk of overlooking data on partially task-unrelated thoughts. For each of the models, the reduction in deviance and the significance of the likelihood ratio test are reported relative to a null model. This allows for the evaluation of whether the inclusion of predictors significantly improved model fit compared to a baseline model [ 64 ]. Moderation analyses for all models were conducted with trait level variables (HADS D, HADS A, DDFS, PTQ) and task valence as moderators of the relationship between thought characteristics and all outcomes. In addition to simple slopes analyses, Johnson-Neyman intervals [ 65 ] were computed for all significant interaction effects. This was done, firstly, because not all moderators showed a normal distribution, making analyses at ± 1 SD potentially unrepresentative. Moreover, Johnson-Neyman intervals (J-N intervals) offer more detailed information than probing at ± 1 SD. Rather than estimating the effect at two arbitrary points, they indicate the exact range of moderator values outside of which the predictor-outcome association becomes significant. This allows for a more precise interpretation of moderation effects and helps avoid potentially misleading conclusions based on fixed cutoffs. All raw values of J-N intervals were re-calculated and presented as the number of standard deviations from the mean value of the moderator, for an easier interpretation of the results. For improved readability, only significant moderation effects are reported (the results for non-significant moderation models are presented in supplementary materials Tables S3-S23). An example of moderation model: Anger ijk = β 0jk + β 1 ·Thought_valence ijk + β 2 ·Usefulness ijk + β 3 ·Burden ijk + β 4 ·DDFS ijk + β 5 ·(cThought_valence ijk × DDFS ijk ) + β 6 ·(Usefulness ijk × DDFS ijk ) + β 7 ·(Burden ijk × DDFS ijk ) + ε ijk β 0jk = γ 0k + u 0jk γ 0k = γ 00 + v 0k The analyses were conducted in R (version 4.1.2; R Core Team, 2021), using esmpack (version 0.1–21 [ 65 ]) for data management, lme4 (version 1.1-3 [ 67 ]) for model building, sjPlot (version 2.8.17[ 68 ]) and jtools (version 2.2.0; [ 69 ]) for obtaining results, Interactions (version 1.2.0; Long, 2024) for the computation of simple slopes, lavaan (version 0.6–19; [ 70 ]) for confirmatory factor analysis, and reghelper (version 1.1.2 [ 71 ]) for plotting preliminary figures (the scripts are available in supplementary materials at: https://osf.io/tw9hx/ ). As the moderation analyses were conducted for exploratory purposes, no corrections for multiple comparisons were used [ 72 ]. Results Descriptive statistics and correlations between measured variables are presented in supplementary materials Tables S1 and S2. TUT control predicting affect and HRV Regarding the control factor of TUT, uncontrollability, intrusiveness and freely moving characteristics were found to be significant predictors of all affective states (see Table 1 , panel A). Repetitiveness of TUT was positively linked to anxiety and sadness, and negatively to happiness. Intentionality was only linked to happiness and anger - the association was positive in both cases. None of the characteristics included in the control factor were linked to HRV. TUT content characteristics predicting affect and HRV Among content characteristics, thinking about one’s problems seems to be a significant predictor of all self-reported affective outcomes (see Table 1 Problems, panel B). Future oriented thinking positively predicted anxiety and sadness (but not anger), and negatively happiness, while past oriented thinking was positively linked to anger, sadness and negatively to happiness. Thinking about one’s feelings was positively related to happiness and did not present any significant relation to other affect measures. None of the content TUT characteristicswere linked to HRV measures. Additionally, the deviance drop compared to the null model was not significant for HRV (see Table 1 , panel B - HRV). Emotional appraisal of TUT as a predictor of affect and HRV Among emotional appraisal characteristics of TUT, their perceived valence was negatively linked to sadness, anxiety and anger, positively to happiness (see Table 1 , panel C). Emotional burden caused by TUT was linked to all the measured outcomes - positively to anxiety, sadness, anger and negatively to happiness. Perceived usefulness of TUT was only linked to happiness. Overall, characteristics from the emotional appraisal factor—especially perceived burden and thought valence—emerged consistently as significant predictors across affective momentary outcomes. Perceived burden of TUT was the only significant predictor of HRV – higher burden was linked to lower HRV values. Table 1 Results of multilevel models predicting momentary affect and HRV Outcome Anxiety Anger Sadness Happiness HRV β SE t-ratio β SE t-ratio β SE t-ratio β SE t-ratio β SE t-ratio (A) Control Uncontrollability 0.12 *** 0.03 3.68 0.12 *** 0.03 3.39 0.17 *** 0.03 5.26 -0.13 *** 0.03 -3.83 -0.08 0.04 -1.76 Repetitive 0.07 * 0.03 2.18 0.04 0.03 1.21 0.06 * 0.03 2.13 -0.09 ** 0.03 -2.76 -0.08 0.04 -1.80 Intrusive 0.15 *** 0.03 4.69 0.08 * 0.03 2.41 0.14 *** 0.03 4.34 -0.11 *** 0.03 -3.31 0.06 0.04 1.42 Freely moving -0.17 *** 0.02 -7.26 -0.09 *** 0.02 -3.66 -0.12 *** 0.02 -5.63 0.24 *** 0.02 10.21 -0.02 0.03 -0.70 Intentionality 0.04 0.02 1.64 0.05* 0.02 1.91 -0.00 0.02 -0.22 0.10 *** 0.02 4.42 0.04 0.03 1.05 Deviance drop 261.66 109.57 269.9 319.28 27.42 P LRT < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 (B) Content Future 0.13 *** 0.03 4.08 0.05 0.03 1.65 0.15 *** 0.03 4.96 -0.14 *** 0.03 -4.30 -0.06 0.05 -1.36 Feelings -0.02 0.03 -0.68 -0.01 0.03 -0.11 0.02 0.02 0.84 0.08 ** 0.03 3.11 -0.01 0.04 -0.15 Problems 0.25 *** 0.03 7.62 0.16 *** 0.03 4.60 0.15 *** 0.03 4.72 -0.21 *** 0.03 -6.24 -0.02 0.05 -0.38 Past 0.06 0.03 1.94 0.12 *** 0.03 3.93 0.15 *** 0.03 5.38 -0.14 *** 0.03 -4.65 0.02 0.04 0.57 Deviance drop 263.64 141.54 300.34 261.19 4.44 P LRT < 0.001 < 0.001 < 0.001 < 0.001 0.349 (C) Emotional appraisal Usefulness -0.01 0.02 -0.25 0.04 0.02 1.88 -0.01 0.02 -0.44 0.08 *** 0.02 3.84 -0.01 0.03 -0.32 Burden 0.33 *** 0.03 12.58 0.21 *** 0.03 7.58 0.34 *** 0.03 13.02 -0.22 *** 0.03 -8.94 -0.10 * 0.04 -2.36 Thought valence -0.19 *** 0.03 -7.08 -0.19 *** 0.03 -6.89 -0.11 *** 0.03 -4.25 0.37 *** 0.02 15.03 -0.02 0.04 -0.54 Deviance drop 397.01 220.93 327.21 592.04 22.32 P LRT < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 Note . All results are given as standardized betas and standardised SE. * p < 0.05; ** p < 0.01; *** p < 0.001 Trait-level tendency to use TUT as moderator of the link between TUT features and affective outcomes In the next step, we analysed how trait level tendency to use TUT might moderate the relations between TUT characteristics and affective outcomes. The significant moderations are presented in Table 2 . The tendency to use repetitive negative thinking was a significant moderator of the link between repetitiveness of TUT and anger (repetitiveness of TUT was associated with elevated levels of anger in participants high in RNT (PTQ), while the opposite pattern was observed for individuals low in this trait (1.06 SD or more below average)). Similarly, thoughts focused on problems predicted higher levels of anger and sadness among individuals with high daydreaming frequency (DDFS) but showed no association in participants scoring low on this trait. An interaction also emerged between thought usefulness and DDFS, with greater usefulness predicting higher happiness only in individuals with low levels of daydreaming frequency. Similar patterns of results were observed for HRV as an outcome, potentially explaining the lack of the relation between TUT characteristics and HRV when moderators were not taken into account. Both PTQ and DDFS were significant moderators of the link between TUT focused on problems and HRV (see Table 3 and Fig. 1). The focus on problems predicted decreased HRV among individuals high in the respective trait, while the opposite pattern—higher HRV—was observed at low trait levels. In sum, it seems that individuals with high trait levels of TUT have stronger links between some maladaptive TUT features (particularly focus on problems from content factor and usefulness from mental control factor) and affective outcomes. Figure 1 Visualization of interactions between thoughts’ focus on problems and PTQ/DDFS on HRV Depressive and anxious symptoms as moderators of the link between TUT features and affective outcomes and HRV Perceived usefulness of thoughts interacted with depressive symptoms (HADS-D), with thoughts considered as more useful predicting higher anger and sadness among individuals with elevated depressive symptoms (see Table 2 and Fig. 2, panel A & B). No such effect was present in those with low depressive symptoms (HADS-D) for predicting anxiety. For sadness as outcome in participants low in depressive symptoms (at -1.63 SD or more), the association was negative. Depressive symptoms also moderated the link between thought affective burden: sadness levels were generally higher in participants with higher depressive symptoms, but the association between burden and sadness was stronger for participants experiencing low depressive symptoms (Fig. 2, panel C). Figure 2 Visualization of selected significant interactions between HADS-D and thought characteristics We observed similar patterns of interactions for almost all content TUT characteristics. Thoughts focused on future negative situations, those focused on problems and emotions were associated with decreased HRV in participants experiencing high depressive (HADS-D) and anxiety (HADS-A) symptoms. The same three thought characteristic was linked to increased HRV for participants with low depressive symptoms (with marginal statistical significance) and had no significant effect on HRV for participants with low anxiety symptoms. For thoughts focused on the past as a predictor, HADS-D score was the only significant moderator of the relationship with HRV levels. Among participants with high depressive symptoms, past-focused content was related to increased HRV, whereas the association was non-significant at low levels of depression. For positive affect, thought intrusiveness interacted with depressive symptoms. Among individuals low in HADS-D, higher intrusiveness was associated with lower happiness, whereas no significant effect was present for those with high depressive symptoms. Overall, these results suggest that the relations between particular TUT feature and its negative correlates might operate differently in participants with higher anxiety or depressive symptoms. It is however important to note that those data were collected in a non-clinical sample and that participants, even if presenting with subclinical or potentially clinical scores on scales evaluating depression (corresponding to 6 participants with subclinical and 4 with potentially clinical scores) and anxiety (corresponding to 10 participants with subclinical and 7 with potentially clinical scores) had non diagnosis of one of those disorders. Table 2 Significant trait-level moderators of relationships between TUT features and self-reported affect Interaction results Moderator M -1 SD Moderator M + 1 SD J-N interval (in SD ) Outcome Predictor Moderator Coeff SE t-ratio p Coeff p Coeff p Low High Anger Usefulness HADS D 0.02 0.01 2.09 0.037 -0.04 0.498 0.14 0.022 -8.12 0.34 Anger Problems DDFS 0.16 0.07 2.33 0.02 1.21 0.151 4.16 < 0.001 -11.21 -0.83 Anger Repetitive PTQ 0.17 0.06 2.96 0.003 -2.08 0.06 2.38 0.009 -1.06 0.63 Sadness Usefulness HADS D 0.02 0.01 2.4 0.017 -0.08 0.124 0.12 0.049 -1.63 0.93 Sadness Burden HADS D -0.32 0.16 -1.97 0.05 8.09 < 0.001 5.35 < 0.001 2.33 2593.18 1 Sadness Usefulness PTQ 0.01 0.01 2.16 0.031 -0.05 0.288 0.09 0.05 -4.52 1.05 Sadness Problems DDFS 0.2 0.07 2.9 0.004 0.86 0.311 4.54 < 0.001 -4.58 -0.72 Happiness Usefulness DDFS -0.01 0.01 -2.15 0.032 0.13 < 0.001 0.03 0.395 0.47 17.89 Happiness Intrusive HADS D 0.38 0.14 2.61 0.009 -2.97 0.001 0.3 0.753 -0.05 3.75 Note. 1 The Johnson-Neyman (J-N) analysis treats the moderator as a continuous variable, identifying regions where the relationship is significant, even beyond the observed data range. Boundary values that are theoretically possible but fall outside the practical range of the data, or are exceptionally high, indicate that the "region of significance" does not exist in practice. Methodologists advise against interpreting such solutions [ 73 ]. Table 3 Significant trait-level moderators of relationships between TUT features and HRV(RMSSD) Interaction results Moderator M -1 SD Moderator M + 1 SD J-N interval (in SD ) Outcome Predictor Moderator Coeff SE t-ratio p Coeff p Coeff p Low high HRV Problems DDFS 0.17 0.07 2.31 0.021 -1.8 0.077 1.28 0.147 -1.42 1.82 HRV Future HADS A -0.4 0.2 -2.02 0.045 0.48 0.68 -2.72 0.018 -28.96 0.38 HRV Problems HADS A 0.49 0.19 2.52 0.012 -1.72 0.126 2.17 0.045 -1.66 0.93 HRV Future HADS D -0.48 0.17 -2.76 0.006 1.43 0.219 -2.71 0.011 -1.99 0.49 HRV Feelings HADS D -0.38 0.13 -2.87 0.004 1.94 0.029 -1.34 0.095 -0.79 1.21 HRV Problems HADS D 0.35 0.16 2.15 0.032 -1.66 0.14 1.35 0.169 -2.69 2.31 HRV Past HADS D 0.35 0.17 2.08 0.039 -1.45 0.212 1.6 0.112 -5.45 1.79 HRV Problems PTQ 0.15 0.06 2.46 0.014 -1.88 0.074 2 0.052 -1.24 1.07 Task valence as a moderator of relationship between TUT characteristics and affective outcomes Based on the results of the previous studies we also tested how task valence might interact with the TUT characteristics (see Table 4 ). Task valence was a significant moderator of the link between TUT burden and anxiety, and future orientation and anxiety (see Fig. 3 , panel A and B). It also moderated the link between focus on problem and sadness, and uncontrollability and sadness (see Fig. 3 , panel C and D). Finally, there was a significant interaction between TUT and task valence in predicting anger (see Fig. 3 , panel E). Table 4 Task valence as a significant moderator of relationships between TUT features and self-reported affect Interaction results Moderator M -1 SD Moderator M + 1 SD J-N interval (in SD ) Outcome Predictor Moderator Coeff SE t-ratio p Coeff p Coeff p Low High Anger Thought valence Task valence 0.01 0.01 2.55 0.011 -0.27 < 0.001 -0.11 0.027 1.11 10.67 Anxiety Burden Task valence 0.06 0.02 2.43 0.015 5.74 < 0.001 8 < 0.001 -30.58 -3.35 Anxiety Future Task valence 0.05 0.03 1.96 0.05 1.2 0.12 3.32 < 0.001 -3931.59 1 -0.81 Sadness Problems Task valence 0.07 0.03 2.37 0.018 1.1 0.147 3.68 < 0.001 -10.44 -0.80 Sadness Uncontrollability Task valence -0.07 0.03 -2.24 0.025 4.69 < 0.001 1.97 0.03 1.09 20.05 Note. 1 The Johnson-Neyman (J-N) analysis treats the moderator as a continuous variable, identifying regions where the relationship is significant, even beyond the observed data range. Boundary values) that are theoretically possible but fall outside the practical range of the data, or are exceptionally high, indicate that the "region of significance" does not exist in practice. Methodologists advise against interpreting such solutions [ 73 ]. TUT characteristics predicting daily depressive symptoms and sleep quality In the models predicting daily depressive symptoms and sleep quality (Table 5 ), few thought characteristics emerged as significant predictors than in models predicting affective outcomes. Thought content focused on feelings was the only variable significantly associated with both outcomes—positively predicting sleep quality and negatively related to daily depressive symptoms—suggesting it may serve as a protective factor across domains. No predictors other than feelings significantly predicted sleep quality, making it the most relevant and consistent thought characteristic in relation to this outcome. The strongest predictors of daily depressive symptoms were intrusive thoughts (positive association) and freely moving thoughts (negative association). Additionally, future-oriented thought content and perceived emotional burden were significant positive predictors, though with smaller effect sizes. Table 5 Results of multilevel models predicting daily depressive symptoms and sleep quality Outcome Daily Depressive Symptoms Sleep Quality β SE t-ratio β SE t-ratio (A)Control Uncontrollability -0.01 0.08 -0.06 -0.11 0.11 -0.98 Repetitive -0.06 0.07 -0.88 0.12 0.11 1.01 Intrusive 0.23 ** 0.07 3.05 0.13 0.11 1.22 Freely moving -0.15 *** 0.04 -3.50 0.04 0.06 0.63 Intentionality -0.03 0.04 -0.77 0.04 0.06 0.72 Deviance drop 40.75 6.72 Significance of likelihood ratio test < 0.001 0.241 (B)Content Future 0.17 * 0.07 2.39 -0.08 0.09 -0.83 Feelings -0.12 * 0.05 -2.18 0.20 ** 0.07 2.72 Problems 0.02 0.07 0.31 0.03 0.10 0.36 Past 0.08 0.06 1.3 -0.01 0.08 -0.18 Deviance drop 21.83 0.95 Significance of likelihood ratio test < 0.001 0.041 (C)Emotional appraisal Usefulness -0.08 0.05 -1.45 0.05 0.07 0.71 Burden 0.13 * 0.05 2.55 0.09 0.08 1.19 Thought valence -0.09 0.06 -1.43 0.05 0.09 0.54 Deviance drop 35.14 2.14 Significance of likelihood ratio test < 0.001 0.544 Note . All results are given as standardized betas. * p < 0.05; ** p < 0.01; *** p < 0.001 Table 6 Statistically significant interaction terms between predictors and trait-level measures in models predicting daily depressive symptoms and sleep quality Interaction results Moderator M -1 SD Moderator M + 1 SD J-N interval (in SD ) Outcome Predictor Moderator Coeff SE t-ratio p Coeff p Coeff p low high Daily depressive symptoms Thought valence HADS A -0.37 0.14 -2.68 0.008 1.69 0.033 -1.23 0.089 -0.79 1.27 Daily depressive symptoms Thought valence HADS D -0.3 0.13 -2.24 0.027 1.35 0.075 -1.25 0.12 -1.35 1.98 Daily depressive symptoms Burden PTQ -0.94 0.37 -2.52 0.012 25.85 0.001 1.7 0.762 0.45 4.68 Daily depressive symptoms Thought valence PTQ -0.07 0.03 -2.16 0.032 0.68 0.281 -1.06 0.046 -5.30 1.00 Daily depressive symptoms Repetitive DDFS 2.09 0.81 2.63 0.009 -22.59 0.035 17.14 0.062 -0.83 1.05 Daily depressive symptoms Freely moving DDFS 0.09 0.03 2.61 0.01 -1.78 < 0.001 -0.12 0.73 0.42 4.05 Sleep quality Past HADS A 2.04 1 2.04 0.044 -5.28 0.296 11.02 0.051 -10.22 1.00 Sleep quality Past PTQ 0.54 0.25 2.14 0.034 -5.56 0.159 8.39 0.091 -2.34 2.08 Trait tendency to use TUT, depressive and anxiety symptoms as moderators of the link between TUT characteristics, daily depressive symptoms and sleep quality Several significant interactions emerged between momentary thought characteristics and trait-level tendencies in predicting daily depressive symptoms. The relationship between repetitiveness and depressive symptoms was moderated by daydreaming frequency (DDFS): among individuals high in DDFS (at + 1.05 SD or more), repetitiveness was associated with increased symptoms, while for those low in DDFS, the effect was reversed. Freely moving thoughts were associated with reduced depressive symptoms among participants low in DDFS, with no effect observed at higher trait levels. Perceived burden associated with thought interacted with PTQ scores. While it was associated with higher daily depressive symptoms among individuals low in PTQ, it was not significantly related to symptoms among those with high trait-level tendency to engage in RNT. Trait level anxiety, depression and tendency to engage in RNT all moderated the relationship between thought valence and daily depressive symptoms. Interestingly, in participants scoring low in HADS-A, mean daily thought valence was positively linked to daily depressive symptoms (see Fig. 4 ). For participants scoring high in PTQ, a negative link between thought valence and daily depressive symptoms was uncovered (the J-N interval values show significance at + 1.27 and + 1.98 SD for HADS A and HADS D respectively). Although it seems that participants scoring high on depressive, anxiety symptoms and PTQ self-reported retrospective questionnaires do not differ significantly from those with lower scores on the link between positively valence TUT and daily depressive symptoms, they present higher daily depressive symptoms when their TUT are focused on negative content (see Fig. 4 ). In models predicting sleep quality, past-focused thoughts interacted with both HADS-A and PTQ scores. Individuals high in either anxiety (HADS-A) or tendency to engage in RNT (PTQ) experienced poorer sleep quality in association with past-focused thoughts, while these effects were absent (for HADS-A) or reversed (for PTQ) among individuals with low levels of these traits. Discussion The primary aim of the present study was to investigate which characteristics of task-unrelated thought (TUT), as identified in existing literature, are associated with participants' momentary affect (assessed through EMA and ambulatory-assessed HRV), daily depressive symptoms, and sleep quality. Furthermore, we examined whether individual differences in trait tendencies towards repetitive negative thinking or daydreaming and task valence moderate these associations. To our knowledge, this study is the first to test a comprehensive set of TUT characteristics within an EMA framework, integrated with objective psychophysiological measures. The marked difference in results between the models predicting momentary negative affect and daily depressive symptoms may suggest that only some of the thought characteristics associated with momentary negative affect (e.g. freely moving feature or intrusiveness) have effects that extend into broader patterns of daily functioning. Other characteristics however, such as uncontrollability, impact momentary affect but not daily measures. Certain factors, especially the extent to which thoughts focus on feelings, may have distinctly stronger effects on a broader, day-level scale than momentarily. This highlights the possibility that the impact of particular TUT characteristics may accumulate over time, influencing daily emotional well-being, while others might be limited to momentary outcomes. It's also possible they have differential short-term (positive) and long-term (negative) consequences, particularly when used as an avoidance strategy. Therefore, it seems crucial to evaluate different temporal dynamics when assessing the link between TUT features and their consequences on affect regulation. The present study is the first to measure the characteristics applying to a large definition of TUT, including repetitive negative thinking, mind-wandering and daydreaming. Although most of our findings related to RNT-derived predictors followed the expected direction of associations between RNT and affect, not all of them are consistent. Interestingly, while focus on feelings is considered a core feature of RNT [ 28 ], and a maladaptive form of TUT linked to depression and anxiety [ 74 ], in our study it consistently showed adaptive associations (as positively linked to momentary positive affect and lower daily depressive symptoms and the only characteristic associated with better sleep quality). These results are consistent with the processing mode theory [ 5 ] further included in the HExAGoN model of RNT [ 40 ] and supported also by previous studies (e.g. [ 75 , 76 ]). Attention to concrete emotional experiences, as opposed to their appraisal, meaning or consequences, may be beneficial for psychological well-being [ 5 ]. Similarly, affect labelling has been proposed to trigger implicit emotion regulation processes [ 77 ]. The effects of this process are thought to accumulate over time, even without immediate affective relief, which could explain the stronger links with daily outcomes observed in our study. In line with this interpretation, future studies should consider including measures of thought concreteness versus abstract processing mode. Freely moving thoughts emerged as a robust and consistent negative predictor of momentary negative affect and daily depressive symptoms, and a positive predictor of happiness. These findings are in line with the assumption that mind-wandering - being an adaptive form of TUT - is characterised by freely moving thoughts, while maladaptive ruminations are more restrained [ 78 ]. Previous studies also indicated that the freely moving characteristic plays an important role in the adaptive function of task-unrelated thoughts [ 22 , 26 ]. Thought usefulness was generally associated with higher happiness, corroborating earlier findings linking this characteristic to positive mood [ 79 ]. However, no significant negative associations were observed for negative affect or daily-level outcomes. These findings underline the added value of using emotion-specific measurements in the study of task-unrelated thoughts. Such measures can capture differential effects that might be overlooked by relying solely on aggregated negative affect scales or a positive–negative affect continuum, highlighting the necessity of treating specific emotions as distinct points of measurement. Interestingly, participants with higher depressive and anxiety symptoms and a greater trait tendency toward repetitive negative thinking appeared to perceive their TUT as more useful. However, this perceived usefulness of thought was also associated with increased negative affect. One possible explanation coming from worry and rumination theory (e.g. S-Ref model [ 80 ]) is that those participants had positive meta-cognitions about TUT and may appraise thoughts characteristic typical of worry or rumination as more useful, in spite of deleterious consequences on affect regulation [ 81 , 82 ]. One of the more appealing results presented in this study is the association between positively valence thoughts and daily depressive symptoms in participants with low depression, anxiety and tendency to ruminate (Fig. 3 ). While these results were not strongly significant - they call for further exploration and replication in future studies (for depression and rumination the simple slopes at +/- 1 SD were not significant, but J-N intervals showed significance at + 1.27 and + 1.98 SD). The key question is: could positively valenced TUT have served as a form of emotion regulation on more difficult days [ 47 ] and if so, what is the causal direction of this relation? Could participants use TUT focused on positive content as a distraction [ 41 ] therefore meaning the high level of depressive symptoms at the end of the day is explained by the situational factors or rather that TUT is used as an avoidance strategy [ 46 ] and their negative consequences emerges at the end of the day? An alternative explanation is that engaging in daydreaming might underline the discrepancy between ideal and actual self [ 83 ]. This discrepancy might operate at two levels - when participants are comparing the content of their daydreams with the actual situation and/or when they realize in the evening that engaging in positive daydreaming made them neglect some important activities. This discrepancy and the resulting negative affect could serve as an adaptive motivator for action to reduce it. Finally, it is necessary to explore in further studies whether the lack of these mechanisms in participants with high repetitive negative thinking and depressive or anxious symptoms might be due to the use of TUT as a habitual response [ 84 ] and thus reducing the effect of situational variables. The results of our study show clear and distinct patterns in how daily depressive symptoms are linked with negatively valenced TUT, depending on participants' HADS scores and rumination levels. This suggests that clinical and subclinical populations may engage with TUT differently compared to healthy participants. Surprisingly, in the perspective of a recent literature review suggesting that mind-wandering propension might predict sleep quality [ 85 ], we did not find any link between TUT and sleep quality. However, only one study in this review was linking sleep quality to mind-wandering assessed through EMA [ 86 ], and, in line with our results, failed to find a link between TUT and sleep quality measures (although the individual differences in TUT predicted sleep quality). Overall, our findings highlight the imperative to assess the perceived consequences of TUT, considering both their momentary (short-term), more enduring (e.g., daily) and trait-level impacts. This evaluation is especially vital when examining TUT function or the dynamic relationship between its function and characteristics. While the link between TUT and HRV was previously explored in laboratory studies (e.g. [ 87 ]) and scarce ambulatory studies (e.g. [ 36 ]), this study was, to our knowledge, the first to combine ambulatory HRV assessment with key TUT characteristics. While only emotional burden was associated with momentary HRV measures, our investigation of moderation effects provided valuable insights. It suggests that both depressive and anxiety symptoms may modulate the link between TUT features and HRV. These findings imply that TUT might differentially affect HRV in healthy, subclinical, and clinical populations. The innovative use of HRV ambulatory measures, relatively new to the TUT field, also brings some limitations. Participants' self-management of sensor placement and removal, limited external control over non-wear episodes, and non-inclusion of control variables such as physical activity or substance intake could significantly affect HRV. Our dataset included some irregular measurement gaps and periods where collected data deviated from expected patterns. Currently there is no clear recommendation on collecting and cleaning HRV data sets collected in ambulatory settings. Moreover, the measurement windows around EMA prompts vary across studies. In future studies, we plan to (1) implement Bluetooth-based alerts to notify participants when data are not being recorded or are being recorded incorrectly; (2) include prompts about physical activity and substance use between measurements; and possibly (3) link a portion of participant compensation to the amount of HRV data collected (even if symbolically, as a motivating factor to react to alerts described in point 1). Findings in the literature focusing on TUT remain inconsistent, and few studies examine multiple thought characteristics and outcomes simultaneously, yet even fewer use EMA designs with data collected across momentary, daily, and person levels over extended periods. Our study demonstrates that such an approach captures meaningful variability which may otherwise be overlooked. Recent recommendations emphasize aligning sampling frequency with the temporal dynamics of the studied process [ 88 ]. Our results support this view, showing that effects vary across timescales and some emerge only under intensive, ecologically valid designs. This is particularly relevant in TUT research, where thought characteristics and the context in which they arise interact in complex, highly time-sensitive ways. Declarations Acknowledgments The authors would like to thank the team of assistants responsible for data gathering and guiding participants through the study process: Jan Skorupski, Natalia Cichecka, Martyna Mielnik, Olga Szkodzińska, Karol Kawik. Supplementary materials https://docs.google.com/document/d/1arjmipLGAhEd0nzZpyRz_djv_IkBSptKHCkE0XaucL4/edit?tab=t.0 Funding This project is funded by the SONATA Grant “Toward an Integrative Model of Maladaptive Spontaneous Task-Unrelated Thoughts (STUT): A Processual and Functional Approach” (2019/35/D/HS6/02364) from the National Science Centre, Poland, awarded to MK. Data availability statement Data is available on request from the first and from the corresponding authors. Competing interests’ statement The authors have no competing interest to declare. Authors’ contribution according to CRediT (Contributor Roles Taxonomy). MSS: Conceptualization, Methodology; Writing – Original Draft Preparation; Investigation, Visualization, Data Curation, Formal Analysis, Software. IK: Methodology, Supervision, Writing – Review & Editing; SB: Conceptualisation, Methodology, Writing – Review & Editing; CB: Conceptualization, formulation of research goal; Writing – Review & Editing; TA: Conceptualization, formulation of research goal, Writing – Review & Editing; MK: Conceptualization: idea, Methodology, Writing – Review & Editing, Formal Analysis, Validation, Supervision, Project Administration; Resources, Funding Acquisition. References Giambra LM. The influence of aging on spontaneous shifts of attention from external stimuli to the contents of consciousness. 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Available from: https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2024.1441565/full Marcusson-Clavertz D, Persson SD, Davidson P, Kim J, Cardeña E, Kuehner C. Mind wandering and sleep in daily life: A combined actigraphy and experience sampling study. Conscious Cogn. 2023 Jan 1;107:103447. Kocsel N, Köteles F, Szemenyei E, Szabó E, Galambos A, Kökönyei G. The association between perseverative cognition and resting heart rate variability: A focus on state ruminative thoughts. Biol Psychol. 2019 Jul 1;145:124–33. Löchner J, Carlbring P, Schuller B, Torous J, Sander LB. Digital interventions in mental health: An overview and future perspectives. Internet Interv. 2025 Jun;40:100824. Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":103036,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eVisualization of interactions between thoughts’ focus on problems and PTQ/DDFS on HRV\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7827343/v1/3eef3132b509dad232a9ec47.png"},{"id":94198493,"identity":"ad13d28e-a3d1-400e-8d25-ab356e482910","added_by":"auto","created_at":"2025-10-23 13:33:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":53090,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eVisualization of selected significant interactions between HADS-D and thought characteristics\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7827343/v1/cd9368b08348d7e96347ee95.png"},{"id":94197914,"identity":"dc978710-9595-4130-875d-64ef804e5e67","added_by":"auto","created_at":"2025-10-23 13:25:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":107794,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eVisualisation of the significant interactions between task valence and thought characteristics.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7827343/v1/ba60f32c73bbb270429526ab.png"},{"id":94197916,"identity":"471932cf-b080-4d88-8606-6c343c435f9f","added_by":"auto","created_at":"2025-10-23 13:25:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":73123,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eVisualisation of the interactions between mean daily thought valence, depressive (HADS D), anxiety (HADS A) symptoms, and rumination (PTQ) measured through self-reported retrospective questionnaire on daily depressive symptoms\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7827343/v1/6e4429da13bb07d506ff07a8.png"},{"id":104739974,"identity":"2ecbdab3-75f5-4c7c-b1cd-2ad56ca261e1","added_by":"auto","created_at":"2026-03-16 16:14:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2149993,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7827343/v1/84e4dc5e-33da-42f7-a830-1d90c9501946.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Not all task-unrelated thoughts (TUT) are created equal - TUT characteristics as predictors of affective states and heart-rate variability","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEverybody knows the feeling of drifting away mid-task, suddenly realizing that their mind has wandered away from their current activity. Task-Unrelated Thoughts (TUT) \u0026ndash; thoughts directed away from the ongoing focal activity or situation without any direct environmental requirement [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] - have been reported to occupy nearly half of our daily thought content [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. As a complex transdiagnostic process, TUT play a role in various conditions, including anxiety [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], depression [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], ADHD [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and dissociative disorders [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Past studies suggest that TUT can be associated with a decline in mood [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and attention [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. At the same time, they have been shown to play an important role in facilitating creative processes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], planning future events [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], delaying gratification [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and in the developing of socio-emotional skills [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhich factors are responsible for the widely varying cognitive and emotional outcomes associated with TUT? It is challenging to answer this question, in part due to the inconsistent terminology and numerous theoretical frameworks used within the field [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The umbrella term task-unrelated thoughts defined above [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] encompasses terms such as daydreaming [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], mind-wandering [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], spontaneous thoughts [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], self-generated thinking [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], but also rumination [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], repetitive negative thoughts [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and perseverative cognition [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The discussions regarding how to distinguish between different types of TUT, which of the aforementioned terms can be used interchangeably, and which represent overarching categories that include more specific subtypes seems to be far from over [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Additionally, measuring TUT, particularly in the ecological context, when participants perform different sets of tasks (including resting) is a significant challenge [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn the heat of the ongoing debate on the definition and differentiation of TUT types, Seli et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] proposed treating all TUT phenomena as one heterogeneous construct. This \u003cem\u003efamily resemblance approach\u003c/em\u003e enables us to focus on different thought characteristics as variables linked to both the adaptive and maladaptive outcomes, with less attention directed to the categorisation of the thought. Although this perspective risks overlooking some of the debates regarding TUT classification, prioritizing the detection of maladaptive TUT\u0026rsquo;s features is vital for clinical applications. It may represent the only viable way to push maladaptive TUT research forward, regardless of the continuing definitional controversy [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe effects of certain TUT characteristics have been relatively well explored. First, the literature suggests that thought valence is largely responsible for the affective outcome of TUT, showing that not only is TUT linked generally to poorer mood, but this effect is increased for negatively valenced thoughts [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However the role of TUT valence is nuanced by the studies exploring the role of TUT as an avoidance strategy suggesting that its effect on emotion regulation might be different in short vs. long term perspective [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Second, intentional mind-wandering is associated with lower depressive and anxiety symptoms than its unintentional version [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Third, thoughts considered more freely moving are associated with better mood [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Further, future related thoughts may be linked to more positive affect than past-related thoughts [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Finally, definitional characteristics of repetitive negative thinking (RNT) - repetitiveness, intrusiveness, negative valence, and uncontrollability - are all associated with depressive and anxiety symptoms [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In 2023, Thiemann et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] examined both the intentionality and the freely moving nature of thoughts in a single study and found that each of these characteristics mitigates the negative relationship between TUT and negative affect. However, research investigating all these characteristics (or even more than two concurrently in a single study) in the perspective of affect regulation is rare [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Thus, the present study aims to fill this gap by considering all the relevant characteristics identified in the literature, in order to test which of them are significant predictors of mood when the others are also included in the model.\u003c/p\u003e\u003cp\u003eMost prior research relies on self-report questionnaires and/or was conducted in laboratory settings [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, recent literature emphasizes the importance of assessing psychological processes in ecological settings [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and exploring their link not only with subjective mood evaluation, but also with psychophysiological measures [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This trend is also present in TUT research. A rising number of studies explore the links between task-unrelated thoughts and heart rate variability (HRV) - a measure used as a psychophysiological marker of balance of the autonomic nervous system [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] or even general psychologicalwell-being [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Studies by Ottaviani and colleagues [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] suggest that perseverative cognition, but not mind-wandering, can be associated with lowered HRV. Rumination [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], worry [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], and repetitive negative thinking [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] seem to also have a detrimental effect on HRV. However, to the best of our knowledge, no studies exist linking a wider set of TUT characteristics to changes in HRV in ecological settings.\u003c/p\u003e\u003cp\u003eIn the present study, we used ecological momentary assessment (EMA) methodology to investigate the relationship between TUT characteristics (e.g. intentionality, emotional valence, controllability) and (1) affect, (2) heart rate variability, (3) depressive symptoms, and (4) sleep quality. As existing studies often focus on thought characteristics that are particularly salient within one of the TUT subtypes, we aim to integrate characteristics commonly associated with specific TUT types (e.g. repetitiveness and lack of control in RNT [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]) into a more universal framework, consistent with the family resemblance approach [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAll TUT characteristics were grouped into three factors following the approach previously used by Rosenkranz et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] for measuring rumination with the experience sampling method. The \u003cem\u003econtent\u003c/em\u003e factor was directly adapted from Rosenkranz et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and includes features describing the thematic focus of thoughts (e.g., problems, feelings, temporal orientation). The second factor, labelled \u003cem\u003econtrol\u003c/em\u003e, was based on core processual characteristics of RNT as defined by Rosenkranz and colleagues [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], but was restricted to variables capturing aspects of perceived mental control - a factor identified as one of the most relevant in TUT (e.g. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]) namely, uncontrollability, repetitiveness, intrusiveness, intentionality, and the freely moving nature of TUT. The variable assessing emotional burden (originally included in the process characteristics in Rosenkrantz et al.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]) was removed from this cluster and moved to the \u003cem\u003eemotional appraisal\u003c/em\u003e factor. Distinguishing appraisal factor in the present study was directly linked to the results of recent studies underlying the importance of beliefs and meta-cognitions about TUT in their maladaptive outcomes [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. We hypothesises that TUT characteristics linked to lack of control, negative valence and negative beliefs (i.e. uncontrollability, repetitiveness, intrusiveness, focus on problems, past and future orientation, emotional burden) are positively related to negative affect; while freely moving feature, intentionality and positive valence will be negatively related to negative affect. The current literature is not clear about the link between focus on feelings link to negative affect \u0026ndash; while this component is a inherent part of rumination evaluation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], literature suggest also that it might be adaptive \u0026ndash; negatively related to negative affect [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eInformed by the theoretical framework [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] of mind-wandering and prior research (e.g. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]), we controlled for ongoing task characteristics, particularly task valence. This builds on prior research indicating that TUT can be maladaptive when used as an avoidance strategy [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], suggesting that specific TUT characteristics may be especially detrimental when one is trying to escape a negatively valenced task [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Finally, we provide exploratory analyses on whether the trait tendency to ruminate or mind wander, as well as the level of depressive and anxiety symptoms, can be a moderator of momentary and daily associations.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eA total of 71 students volunteered to take part in the EMA study.Fifteen participants withdrew before the EMA stage of the study began, and three withdrew during the study. Five participants were excluded for not meeting the minimum compliance criterion (60% compliance for EMA) orfor providing inconsistent ID across different parts of the study. As the minimum compliance required from participants varies strongly between studies [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], we chose a mid-to-high compliance cut-off of 60% as used in previous studies [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], aiming to achieve an average compliance rate around 80% [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. One participant was excluded from the study following inspection of the HRV data, which revealed irregularities (frequent sudden stops in measurement, strong declines and increases in ECG signal \u0026ndash; beyond the expected thresholds)rendering the dataunsuitable for further analysis. A total of 47 participants were included in the final sample (\u003cem\u003eM\u003c/em\u003e\u003csub\u003eage\u003c/sub\u003e = 24.83, \u003cem\u003eSD\u003c/em\u003e\u003csub\u003eage\u003c/sub\u003e=5.96), of which 33 were female, 11 male and 2 were non-binary.One participant decided to withhold information regarding their gender.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003eThe participants were recruited through the university\u0026rsquo;s internal research recruitment system. After signing up for the study, the participants were asked to provide and informed consent and fill out a series of questionnaires in Qualtrics. They were assigned an assistant, who guided them throughout the procedure. On the first meeting with the assistant, the participants installed the Movisens XS application and were given the chest ECGMove4 Movisens sensors together with thorough instruction on how to use them.\u003c/p\u003e\u003cp\u003eFor 7 consecutive days and nights the participants wore the sensors and filled out momentary and daily questionnaires. The sensors were removed daily for around 20 minutes for charging. The sensors were also removed during activities that could damage them (bathing, particular forms of physical exercise, using sauna etc.).\u003c/p\u003e\u003cp\u003eAt the beginning of the study, participants chose one of 3 daily activity windows (7am-9pm, 9am-11pm or 11am-1am). Each day, participants received: (1) a short questionnaire assessing their sleep quality (30 minutes after the start of the daily activity windows); (2) a total of 8 questionnaires assessing current thought and task characteristics, as well as changes in mood (7 of which appeared semi-randomly in 2-hour windows covering the daily activity window - additionally, 1 questionnaire was activated by the participants themselves just before sleep); (3) a short questionnaire assessing daily depressive symptoms (30 minutes before the end of the daily activity window).\u003c/p\u003e\u003cp\u003e After the measurement period, participants returned the devices to the assistants during a previously scheduled appointment, during which they were also given the opportunity to give feedback on their study experience. Participants who finished the study and met the pre-specified requirements (wearing the sensor for the duration of the study and answering at least 60% of momentary questionnaires) were awarded university credit points. Th procedure was approved by the Ethic Committee of Psychology Department in Katowice, SWPS University, Poland (WKEB69/03/2021). All methods described below were performed in accordance with the relevant guidelines and the study was run in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eMomentary measures\u003c/h2\u003e\u003cp\u003eEach momentary questionnaire consisted of a total of 22 short items. All items were available to participants only in Polish (untranslated item content available in supplementary materials).\u003c/p\u003e\u003cp\u003e\u003cb\u003eThought characteristics.\u003c/b\u003e One item measured the level of TUT [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] (\u0026ldquo;To what extent, just before the signal, were you mind-wandering/your thoughts were divagating/ you were daydreaming\u0026rdquo; (using the term TUT would not be intuitive in the language of the study) with visual analog scale (VAS) from 0-\u0026ldquo;no at all\u0026rdquo;, to 100-\u0026ldquo;strongly\u0026rdquo;).\u003c/p\u003e\u003cp\u003eEight items adapted from Rosenkrantz and colleagues [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], measured characteristics needed to assess repetitive negative thinking (RNT). These included items assessing the focus of thoughts on (1) feelings, (2) problems, (3) unpleasant memories, (4) unpleasant situations that might occur in the future, as well as items addressing (5) perceived burden caused by them, and the (6) repetitiveness, (7) intrusiveness, and (8) uncontrollability of those thoughts. These items were answered using a 7-point Likert scale, as in the original questionnaire.\u003c/p\u003e\u003cp\u003eSix items were used to measure thought characteristics not included in the RNT measures, but relevant, based on the literature in evaluating TUT: (1) intentionality (adapted from Banks and colleagues [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]), (2) emotional valence [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], (3) the extent to which thoughts were freely moving [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The last item, measuring thought usefulness (\u0026ldquo;To what extent are you satisfied that this thought appeared?\u0026rdquo;) was proposed to address the possibility that the thought might be perceived by the participant as useful, regardless of its specific characteristics.\u003c/p\u003e\u003cp\u003eBased on the theoretical background described in the introduction, thought characteristics were divided into 3 factors: Content (4 items: future/past orientation, focus on feelings/problems), Control (5 items: uncontrollability, repetitive, intrusive, freely moving, intentionality) and Emotional appraisal (3 items: valence, emotional burden, usefulness).\u003c/p\u003e\u003cp\u003e\u003cb\u003eTask characteristics.\u003c/b\u003e To fully assess the context of the occurring thoughts, two items measuring task characteristics were used. The task-focused items were preceded by a message \u0026ldquo;Think about the task or activity that you have been doing just before the signal\u0026hellip;\u0026rdquo;. One item measured the emotional valence of the task (\u0026ldquo;What emotions does this task evoke in you?\u0026rdquo; with VAS from 0-negative, to 100-positive). Another item measured the level of engagement (\u0026ldquo;To what extent does this task require your focus or engagement\u0026rdquo;? with VAS from 0-\u0026ldquo;not at all\u0026rdquo; to 100-\u0026ldquo;strongly\u0026rdquo;).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMood.\u003c/b\u003e Momentary mood was measured using four items, each structured as: \u0026ldquo;To what extent were you feeling [emotion] just before the signal?\u0026rdquo;. The specific emotions inserted into the item stem were (1) \u0026ldquo;happy\u0026rdquo;, (2) \u0026ldquo;angry\u0026rdquo;, and (3) \u0026ldquo;sad\u0026rdquo; [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], and \u0026ldquo;nervous, anxious, or on edge\u0026rdquo;, as a measure of anxiety [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. The mood items were answered using a VAS with answers from 0-\u0026ldquo;not at all\u0026rdquo; to 100-\u0026ldquo;strongly\u0026rdquo;.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDaily measures\u003c/h3\u003e\n\u003cp\u003e\u003cb\u003eSleep quality.\u003c/b\u003e Measured with a single item (\u0026ldquo;How would you rate your sleep quality\u0026rdquo; (referring to last night) with VAS from 0-\u0026ldquo;terrible\u0026rdquo;, 50-fair to 100-\u0026ldquo;excellent\u0026rdquo; [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eDaily depressive symptoms.\u003c/b\u003e Measured with three items [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] \u0026ndash; \u0026ldquo;Thinking about today in general: (1) How positive were your thoughts about yourself? (2) How well did things go today? (3) Today, how optimistic are you about how your life (in general) will be tomorrow?\u0026rdquo; with VAS ranging from 0-\u0026ldquo;very negative\u0026rdquo;(for items 1 and 3) or \u0026ldquo;very badly\u0026rdquo; for item 2, to 100-(1)\u0026ldquo;very positive\u0026rdquo;,(2)\u0026rdquo;very well\u0026rdquo;, (3)\u0026rdquo;very optimistic\u0026rdquo;. The final daily depressive symptoms score was calculated by reversing and adding the scores of the 3 scales together.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eHeart Rate Variability measure\u003c/h2\u003e\u003cp\u003eECG signal was recorded at 1024 Hz using the Movisens ECG Move 4 sensors. The Root Mean Square of Successive Differences (RMSSD) was calculated using the Movisens dataAnalyzer software (Movsiens GmbH, 2024). During the calculation process, the software provided automated artifact correction. The RMSSD values were obtained for 1-minute epochs aligned with the beginning of each of the momentary questionnaires answered by participants.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eTrait measures\u003c/h3\u003e\n\u003cp\u003e\u003cb\u003eDaydreaming Frequency Scale (DDFS).\u003c/b\u003e Frequency of daydreaming/mind-wandering was evaluated using a Polish adaptation of Daydreaming Frequency Scale (DDFS [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e])- a single-factor, 12-item scale.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePerseverative Thinking Questionnaire (PTQ).\u003c/b\u003e The PTQ is a 15-item scale measuring the level of perseverative thinking [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. It includes one higher-order factor (repetitive negative thinking; RNT) and three lower-order factors: core characteristics of RNT, unproductiveness of RNT, and RNT-related cognitive interference. The Polish version of the PTQ [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] was used.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHospital Anxiety and Depression Scale (HADS).\u003c/b\u003e The HADS is a 14-item scale, with seven items assessing anxiety (HADS A) and seven assessing depression (HADS D) [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Participants indicate how frequently they experience each item on a 4-point Likert-type scale, with anchors varying depending on the item. The Polish version of the HADS [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] was used.\u003c/p\u003e\n\u003ch3\u003eStatistical analysis plan\u003c/h3\u003e\n\u003cp\u003eAs the data were collected using an EMA method, providing multiple measurement points per day for multiple days, for multiple users the data were analysed using three-level models to appropriately account for the nested data structure and to fully exploit the analytical possibilities provided by intensive repeated measures [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. The momentary observations (level 1) were nested within days (level 2), and days nested within participants (level 3). Random intercepts were included for both days and participants to capture unobserved heterogeneity at these levels. An example of the model can be viewed below:\u003c/p\u003e\u003cp\u003eAnxiety\u003csub\u003eijk\u003c/sub\u003e​=β\u003csub\u003e0jk\u003c/sub\u003e​+β1​\u0026sdot;Usefulness\u003csub\u003eijk\u003c/sub\u003e​+β2​\u0026sdot;Burden\u003csub\u003eijk\u003c/sub\u003e​+β3​\u0026sdot;Thought_valence\u003csub\u003eijk\u003c/sub\u003e​+ε\u003csub\u003eijk\u003c/sub\u003e​\u003c/p\u003e\u003cp\u003eβ\u003csub\u003e0jk\u003c/sub\u003e​=γ\u003csub\u003e0k\u003c/sub\u003e​+u\u003csub\u003e0jk\u003c/sub\u003e​\u003c/p\u003e\u003cp\u003eγ\u003csub\u003e0k\u003c/sub\u003e​=γ\u003csub\u003e00\u003c/sub\u003e​+v\u003csub\u003e0k\u003c/sub\u003e​\u003c/p\u003e\u003cp\u003eIn models measuring daily outcomes (daily depressive symptoms and sleep quality), a 2-level data structure was used, with daily measurements (level 1) nested within participants (level 2). Random intercepts were included for participants.\u003c/p\u003e\u003cp\u003eTo analyse models predicting daily outcomes, momentary measures were aggregated to compute daily means. This approach was chosen to prevent mismatches in temporal granularity between predictors and outcomes, and to avoid non-independence of observations (multiple momentary predictor entries would otherwise share the same daily outcome, biasing standard errors and possibly inflating significance). All predictors were centered at the participant level to isolate within-person fluctuations from between-person differences and reduce potential multicollinearity, particularly in the presence of random slopes [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo focus the analysis on task-unrelated thoughts, measurement points at which participants' thoughts were either fully or strongly related to the current activity (answers 7 and 6 in item measuring task-relatedness) were excluded from the analyses \u0026ndash; these points corresponded to 38.6% of the initial dataset. This criterion positions our threshold on the more inclusive end of those commonly employed in previous studies [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], allowing for the examination of a broad range of thoughts - from fully to only somewhat task-unrelated. Given that the primary focus of this study lies in examining thought characteristics rather than distinct thought types, adopting a liberal classification criterion minimizes the risk of overlooking data on partially task-unrelated thoughts.\u003c/p\u003e\u003cp\u003eFor each of the models, the reduction in deviance and the significance of the likelihood ratio test are reported relative to a null model. This allows for the evaluation of whether the inclusion of predictors significantly improved model fit compared to a baseline model [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eModeration analyses for all models were conducted with trait level variables (HADS D, HADS A, DDFS, PTQ) and task valence as moderators of the relationship between thought characteristics and all outcomes. In addition to simple slopes analyses, Johnson-Neyman intervals [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] were computed for all significant interaction effects. This was done, firstly, because not all moderators showed a normal distribution, making analyses at \u0026plusmn;\u0026thinsp;1 SD potentially unrepresentative. Moreover, Johnson-Neyman intervals (J-N intervals) offer more detailed information than probing at \u0026plusmn;\u0026thinsp;1 SD. Rather than estimating the effect at two arbitrary points, they indicate the exact range of moderator values outside of which the predictor-outcome association becomes significant. This allows for a more precise interpretation of moderation effects and helps avoid potentially misleading conclusions based on fixed cutoffs. All raw values of J-N intervals were re-calculated and presented as the number of standard deviations from the mean value of the moderator, for an easier interpretation of the results. For improved readability, only significant moderation effects are reported (the results for non-significant moderation models are presented in supplementary materials Tables S3-S23). An example of moderation model:\u003c/p\u003e\u003cp\u003eAnger\u003csub\u003eijk\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;β\u003csub\u003e0jk\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e1\u003c/sub\u003e\u0026middot;Thought_valence\u003csub\u003eijk\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e2\u003c/sub\u003e\u0026middot;Usefulness\u003csub\u003eijk\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e3\u003c/sub\u003e\u0026middot;Burden\u003csub\u003eijk\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e4\u003c/sub\u003e\u0026middot;DDFS\u003csub\u003eijk\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e5\u003c/sub\u003e\u0026middot;(cThought_valence\u003csub\u003eijk\u003c/sub\u003e \u0026times; DDFS\u003csub\u003eijk\u003c/sub\u003e) + β\u003csub\u003e6\u003c/sub\u003e\u0026middot;(Usefulness\u003csub\u003eijk\u003c/sub\u003e \u0026times; DDFS\u003csub\u003eijk\u003c/sub\u003e) + β\u003csub\u003e7\u003c/sub\u003e\u0026middot;(Burden\u003csub\u003eijk\u003c/sub\u003e \u0026times; DDFS\u003csub\u003eijk\u003c/sub\u003e) + ε\u003csub\u003eijk\u003c/sub\u003e\u003c/p\u003e\u003cp\u003eβ\u003csub\u003e0jk\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;γ\u003csub\u003e0k\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;u\u003csub\u003e0jk\u003c/sub\u003e\u003c/p\u003e\u003cp\u003eγ\u003csub\u003e0k\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;γ\u003csub\u003e00\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;v\u003csub\u003e0k\u003c/sub\u003e\u003c/p\u003e\u003cp\u003eThe analyses were conducted in R (version 4.1.2; R Core Team, 2021), using \u003cem\u003eesmpack\u003c/em\u003e (version 0.1\u0026ndash;21 [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]) for data management, \u003cem\u003elme4\u003c/em\u003e (version 1.1-3 [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]) for model building, \u003cem\u003esjPlot\u003c/em\u003e (version 2.8.17[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]) and \u003cem\u003ejtools\u003c/em\u003e (version 2.2.0; [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]) for obtaining results, Interactions (version 1.2.0; Long, 2024) for the computation of simple slopes, \u003cem\u003elavaan\u003c/em\u003e (version 0.6\u0026ndash;19; [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]) for confirmatory factor analysis, and \u003cem\u003ereghelper\u003c/em\u003e (version 1.1.2 [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]) for plotting preliminary figures (the scripts are available in supplementary materials at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/tw9hx/\u003c/span\u003e\u003cspan address=\"https://osf.io/tw9hx/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ). As the moderation analyses were conducted for exploratory purposes, no corrections for multiple comparisons were used [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDescriptive statistics and correlations between measured variables are presented in supplementary materials Tables S1 and S2.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eTUT control predicting affect and HRV\u003c/h2\u003e\u003cp\u003eRegarding the control factor of TUT, uncontrollability, intrusiveness and freely moving characteristics were found to be significant predictors of all affective states (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, panel A). Repetitiveness of TUT was positively linked to anxiety and sadness, and negatively to happiness. Intentionality was only linked to happiness and anger - the association was positive in both cases. None of the characteristics included in the control factor were linked to HRV.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eTUT content characteristics predicting affect and HRV\u003c/h2\u003e\u003cp\u003eAmong content characteristics, thinking about one\u0026rsquo;s problems seems to be a significant predictor of all self-reported affective outcomes (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e Problems, panel B). Future oriented thinking positively predicted anxiety and sadness (but not anger), and negatively happiness, while past oriented thinking was positively linked to anger, sadness and negatively to happiness. Thinking about one\u0026rsquo;s feelings was positively related to happiness and did not present any significant relation to other affect measures. None of the content TUT characteristicswere linked to HRV measures. Additionally, the deviance drop compared to the null model was not significant for HRV (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, panel B - HRV).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eEmotional appraisal of TUT as a predictor of affect and HRV\u003c/h2\u003e\u003cp\u003eAmong emotional appraisal characteristics of TUT, their perceived valence was negatively linked to sadness, anxiety and anger, positively to happiness (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, panel C). Emotional burden caused by TUT was linked to all the measured outcomes - positively to anxiety, sadness, anger and negatively to happiness. Perceived usefulness of TUT was only linked to happiness. Overall, characteristics from the emotional appraisal factor\u0026mdash;especially perceived burden and thought valence\u0026mdash;emerged consistently as significant predictors across affective momentary outcomes. Perceived burden of TUT was the only significant predictor of HRV \u0026ndash; higher burden was linked to lower HRV values.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eResults of multilevel models predicting momentary affect and HRV\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"17\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"15\" nameend=\"c17\" namest=\"c3\"\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eAnxiety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eAnger\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003eSadness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c14\" namest=\"c12\"\u003e\u003cp\u003eHappiness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c17\" namest=\"c15\"\u003e\u003cp\u003eHRV\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003et-ratio\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003et-ratio\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003et-ratio\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cem\u003et-ratio\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cem\u003et-ratio\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003e(A) Control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUncontrollability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.12\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.12\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.17\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e5.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.13\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-3.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e-1.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRepetitive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.07\u0026nbsp;*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.06\u0026nbsp;*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.09\u0026nbsp;**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-2.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e-1.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntrusive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.15\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.08\u0026nbsp;*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.14\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e4.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.11\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-3.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFreely moving\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.17\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-7.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.09\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-3.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.12\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-5.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.24\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e10.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e-0.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntentionality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.05*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.10\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e4.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eDeviance drop\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e261.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e109.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e269.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e319.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e27.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eLRT\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e(B) Content\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFuture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.13\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.15\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e4.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.14\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-4.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e-1.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFeelings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.08\u0026nbsp;**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e3.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e-0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProblems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.25\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.16\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.15\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e4.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.21\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-6.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e-0.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.12\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.15\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e5.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.14\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-4.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eDeviance drop\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e263.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e141.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e300.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e261.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e4.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eLRT\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e0.349\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e(C) Emotional appraisal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUsefulness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.08\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e3.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e-0.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBurden\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.33\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.21\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.34\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e13.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.22\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-8.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-0.10\u0026nbsp;*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e-2.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThought valence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.19\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-7.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.19\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-6.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.11\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-4.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.37\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e15.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e-0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eDeviance drop\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e397.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e220.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e327.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e592.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e22.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eLRT\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"17\"\u003e\u003cem\u003eNote\u003c/em\u003e. All results are given as standardized betas and standardised SE.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"17\"\u003e*\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTrait-level tendency to use TUT as moderator of the link between TUT features and affective outcomes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn the next step, we analysed how trait level tendency to use TUT might moderate the relations between TUT characteristics and affective outcomes. The significant moderations are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The tendency to use repetitive negative thinking was a significant moderator of the link between repetitiveness of TUT and anger (repetitiveness of TUT was associated with elevated levels of anger in participants high in RNT (PTQ), while the opposite pattern was observed for individuals low in this trait (1.06 SD or more below average)). Similarly, thoughts focused on problems predicted higher levels of anger and sadness among individuals with high daydreaming frequency (DDFS) but showed no association in participants scoring low on this trait. An interaction also emerged between thought usefulness and DDFS, with greater usefulness predicting higher happiness only in individuals with low levels of daydreaming frequency.\u003c/p\u003e\u003cp\u003eSimilar patterns of results were observed for HRV as an outcome, potentially explaining the lack of the relation between TUT characteristics and HRV when moderators were not taken into account. Both PTQ and DDFS were significant moderators of the link between TUT focused on problems and HRV (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;1). The focus on problems predicted decreased HRV among individuals high in the respective trait, while the opposite pattern\u0026mdash;higher HRV\u0026mdash;was observed at low trait levels. In sum, it seems that individuals with high trait levels of TUT have stronger links between some maladaptive TUT features (particularly focus on problems from content factor and usefulness from mental control factor) and affective outcomes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure 1\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eVisualization of interactions between thoughts\u0026rsquo; focus on problems and PTQ/DDFS on HRV\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eDepressive and anxious symptoms as moderators of the link between TUT features and affective outcomes and HRV\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePerceived usefulness of thoughts interacted with depressive symptoms (HADS-D), with thoughts considered as more useful predicting higher anger and sadness among individuals with elevated depressive symptoms (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;2, panel A \u0026amp; B). No such effect was present in those with low depressive symptoms (HADS-D) for predicting anxiety. For sadness as outcome in participants low in depressive symptoms (at -1.63 SD or more), the association was negative. Depressive symptoms also moderated the link between thought affective burden: sadness levels were generally higher in participants with higher depressive symptoms, but the association between burden and sadness was stronger for participants experiencing low depressive symptoms (Fig.\u0026nbsp;2, panel C).\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure 2\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eVisualization of selected significant interactions between HADS-D and thought characteristics\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe observed similar patterns of interactions for almost all content TUT characteristics. Thoughts focused on future negative situations, those focused on problems and emotions were associated with decreased HRV in participants experiencing high depressive (HADS-D) and anxiety (HADS-A) symptoms. The same three thought characteristic was linked to increased HRV for participants with low depressive symptoms (with marginal statistical significance) and had no significant effect on HRV for participants with low anxiety symptoms.\u003c/p\u003e\u003cp\u003eFor thoughts focused on the past as a predictor, HADS-D score was the only significant moderator of the relationship with HRV levels. Among participants with high depressive symptoms, past-focused content was related to increased HRV, whereas the association was non-significant at low levels of depression.\u003c/p\u003e\u003cp\u003eFor positive affect, thought intrusiveness interacted with depressive symptoms. Among individuals low in HADS-D, higher intrusiveness was associated with lower happiness, whereas no significant effect was present for those with high depressive symptoms.\u003c/p\u003e\u003cp\u003eOverall, these results suggest that the relations between particular TUT feature and its negative correlates might operate differently in participants with higher anxiety or depressive symptoms. It is however important to note that those data were collected in a non-clinical sample and that participants, even if presenting with subclinical or potentially clinical scores on scales evaluating depression (corresponding to 6 participants with subclinical and 4 with potentially clinical scores) and anxiety (corresponding to 10 participants with subclinical and 7 with potentially clinical scores) had non diagnosis of one of those disorders.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eSignificant trait-level moderators of relationships between TUT features and self-reported affect\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\u003cp\u003eInteraction results\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eModerator \u003cem\u003eM\u003c/em\u003e-1 \u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eModerator \u003cem\u003eM\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1 \u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eJ-N interval\u003c/p\u003e\u003cp\u003e(in \u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eCoeff\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003et-ratio\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eCoeff\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003eCoeff\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnger\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUsefulness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHADS D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.498\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-8.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnger\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProblems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDDFS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e4.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-11.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e-0.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnger\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRepetitive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePTQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-2.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSadness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUsefulness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHADS D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-1.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSadness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBurden\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHADS D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e5.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e2.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e2593.18\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSadness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUsefulness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePTQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-4.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSadness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProblems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDDFS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e4.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-4.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e-0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHappiness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUsefulness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDDFS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e17.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHappiness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntrusive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHADS D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-2.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.753\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e3.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"13\"\u003e\u003cem\u003eNote.\u003c/em\u003e \u003csup\u003e1\u003c/sup\u003eThe Johnson-Neyman (J-N) analysis treats the moderator as a continuous variable, identifying regions where the relationship is significant, even beyond the observed data range. Boundary values that are theoretically possible but fall outside the practical range of the data, or are exceptionally high, indicate that the \"region of significance\" does not exist in practice. Methodologists advise against interpreting such solutions [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e].\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eSignificant trait-level moderators of relationships between TUT features and HRV(RMSSD)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\u003cp\u003eInteraction results\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eModerator \u003c/p\u003e\u003cp\u003e\u003cem\u003eM\u003c/em\u003e-1 \u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eModerator \u003cem\u003eM\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1 \u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eJ-N interval\u003c/p\u003e\u003cp\u003e(in \u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eCoeff\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003et-ratio\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eCoeff\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003eCoeff\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHRV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProblems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDDFS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHRV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFuture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHADS A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-2.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-28.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHRV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProblems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHADS A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-1.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-1.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHRV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFuture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHADS D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-2.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-1.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHRV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFeelings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHADS D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-1.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHRV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProblems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHADS D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-1.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-2.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e2.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHRV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHADS D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-5.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHRV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProblems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePTQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-1.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003eTask valence as a moderator of relationship between TUT characteristics and affective outcomes\u003c/h2\u003e\u003cp\u003eBased on the results of the previous studies we also tested how task valence might interact with the TUT characteristics (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Task valence was a significant moderator of the link between TUT burden and anxiety, and future orientation and anxiety (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e, panel A and B). It also moderated the link between focus on problem and sadness, and uncontrollability and sadness (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e, panel C and D). Finally, there was a significant interaction between TUT and task valence in predicting anger (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e, panel E).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eTask valence as a significant moderator of relationships between TUT features and self-reported affect\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\u003cp\u003eInteraction results\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eModerator \u003cem\u003eM\u003c/em\u003e-1 \u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eModerator \u003cem\u003eM\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1 \u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eJ-N interval\u003c/p\u003e\u003cp\u003e(in \u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eCoeff\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003et-ratio\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eCoeff\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003eCoeff\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cem\u003eHigh\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnger\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThought valence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTask valence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e10.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnxiety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBurden\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTask valence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-30.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e-3.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnxiety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFuture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTask valence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e3.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-3931.59\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e-0.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSadness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProblems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTask valence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e3.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-10.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e-0.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSadness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUncontrollability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTask valence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e20.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"13\"\u003e\u003cem\u003eNote.\u003c/em\u003e \u003csup\u003e1\u003c/sup\u003eThe Johnson-Neyman (J-N) analysis treats the moderator as a continuous variable, identifying regions where the relationship is significant, even beyond the observed data range. Boundary values) that are theoretically possible but fall outside the practical range of the data, or are exceptionally high, indicate that the \"region of significance\" does not exist in practice. Methodologists advise against interpreting such solutions [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e].\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eTUT characteristics predicting daily depressive symptoms and sleep quality\u003c/h2\u003e\u003cp\u003eIn the models predicting daily depressive symptoms and sleep quality (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), few thought characteristics emerged as significant predictors than in models predicting affective outcomes. Thought content focused on feelings was the only variable significantly associated with both outcomes\u0026mdash;positively predicting sleep quality and negatively related to daily depressive symptoms\u0026mdash;suggesting it may serve as a protective factor across domains. No predictors other than feelings significantly predicted sleep quality, making it the most relevant and consistent thought characteristic in relation to this outcome.\u003c/p\u003e\u003cp\u003eThe strongest predictors of daily depressive symptoms were intrusive thoughts (positive association) and freely moving thoughts (negative association). Additionally, future-oriented thought content and perceived emotional burden were significant positive predictors, though with smaller effect sizes.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eResults of multilevel models predicting daily depressive symptoms and sleep quality\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eDaily Depressive Symptoms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eSleep Quality\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003et-ratio\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003et-ratio\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003e(A)Control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUncontrollability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRepetitive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntrusive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.23\u0026nbsp;**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFreely moving\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.15\u0026nbsp;***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntentionality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eDeviance drop\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eSignificance of likelihood ratio test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.241\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e(B)Content\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFuture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.17\u0026nbsp;*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFeelings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.12\u0026nbsp;*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.20\u0026nbsp;**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProblems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eDeviance drop\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eSignificance of likelihood ratio test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e(C)Emotional appraisal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUsefulness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBurden\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.13\u0026nbsp;*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThought valence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eDeviance drop\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eSignificance of likelihood ratio test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.544\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eNote\u003c/em\u003e. All results are given as standardized betas.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e*\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eStatistically significant interaction terms between predictors and trait-level measures in models predicting daily depressive symptoms and sleep quality\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\u003cp\u003eInteraction results\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eModerator \u003c/p\u003e\u003cp\u003e\u003cem\u003eM\u003c/em\u003e-1 \u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eModerator \u003cem\u003eM\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1 \u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eJ-N interval\u003c/p\u003e\u003cp\u003e(in \u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eCoeff\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003et-ratio\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eCoeff\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003eCoeff\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDaily depressive symptoms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThought valence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHADS A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDaily depressive symptoms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThought valence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHADS D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDaily depressive symptoms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBurden\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePTQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e25.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.762\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e4.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDaily depressive symptoms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThought valence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePTQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-5.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDaily depressive symptoms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRepetitive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDDFS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-22.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e17.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDaily depressive symptoms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFreely moving\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDDFS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-1.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e4.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep quality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHADS A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-5.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e11.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-10.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep quality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePTQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-5.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-2.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e2.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTrait tendency to use TUT, depressive and anxiety symptoms as moderators of the link between TUT characteristics, daily depressive symptoms and sleep quality\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSeveral significant interactions emerged between momentary thought characteristics and trait-level tendencies in predicting daily depressive symptoms. The relationship between repetitiveness and depressive symptoms was moderated by daydreaming frequency (DDFS): among individuals high in DDFS (at +\u0026thinsp;1.05 SD or more), repetitiveness was associated with increased symptoms, while for those low in DDFS, the effect was reversed. Freely moving thoughts were associated with reduced depressive symptoms among participants low in DDFS, with no effect observed at higher trait levels.\u003c/p\u003e\u003cp\u003ePerceived burden associated with thought interacted with PTQ scores. While it was associated with higher daily depressive symptoms among individuals low in PTQ, it was not significantly related to symptoms among those with high trait-level tendency to engage in RNT.\u003c/p\u003e\u003cp\u003eTrait level anxiety, depression and tendency to engage in RNT all moderated the relationship between thought valence and daily depressive symptoms. Interestingly, in participants scoring low in HADS-A, mean daily thought valence was positively linked to daily depressive symptoms (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For participants scoring high in PTQ, a negative link between thought valence and daily depressive symptoms was uncovered (the J-N interval values show significance at +\u0026thinsp;1.27 and +\u0026thinsp;1.98 SD for HADS A and HADS D respectively). Although it seems that participants scoring high on depressive, anxiety symptoms and PTQ self-reported retrospective questionnaires do not differ significantly from those with lower scores on the link between positively valence TUT and daily depressive symptoms, they present higher daily depressive symptoms when their TUT are focused on negative content (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn models predicting sleep quality, past-focused thoughts interacted with both HADS-A and PTQ scores. Individuals high in either anxiety (HADS-A) or tendency to engage in RNT (PTQ) experienced poorer sleep quality in association with past-focused thoughts, while these effects were absent (for HADS-A) or reversed (for PTQ) among individuals with low levels of these traits.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe primary aim of the present study was to investigate which characteristics of task-unrelated thought (TUT), as identified in existing literature, are associated with participants' momentary affect (assessed through EMA and ambulatory-assessed HRV), daily depressive symptoms, and sleep quality. Furthermore, we examined whether individual differences in trait tendencies towards repetitive negative thinking or daydreaming and task valence moderate these associations. To our knowledge, this study is the first to test a comprehensive set of TUT characteristics within an EMA framework, integrated with objective psychophysiological measures.\u003c/p\u003e\u003cp\u003eThe marked difference in results between the models predicting momentary negative affect and daily depressive symptoms may suggest that only some of the thought characteristics associated with momentary negative affect (e.g. freely moving feature or intrusiveness) have effects that extend into broader patterns of daily functioning. Other characteristics however, such as uncontrollability, impact momentary affect but not daily measures. Certain factors, especially the extent to which thoughts focus on feelings, may have distinctly stronger effects on a broader, day-level scale than momentarily. This highlights the possibility that the impact of particular TUT characteristics may accumulate over time, influencing daily emotional well-being, while others might be limited to momentary outcomes. It's also possible they have differential short-term (positive) and long-term (negative) consequences, particularly when used as an avoidance strategy. Therefore, it seems crucial to evaluate different temporal dynamics when assessing the link between TUT features and their consequences on affect regulation.\u003c/p\u003e\u003cp\u003eThe present study is the first to measure the characteristics applying to a large definition of TUT, including repetitive negative thinking, mind-wandering and daydreaming. Although most of our findings related to RNT-derived predictors followed the expected direction of associations between RNT and affect, not all of them are consistent. Interestingly, while focus on feelings is considered a core feature of RNT [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and a maladaptive form of TUT linked to depression and anxiety [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e], in our study it consistently showed adaptive associations (as positively linked to momentary positive affect and lower daily depressive symptoms and the only characteristic associated with better sleep quality). These results are consistent with the processing mode theory [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] further included in the HExAGoN model of RNT [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] and supported also by previous studies (e.g. [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]). Attention to concrete emotional experiences, as opposed to their appraisal, meaning or consequences, may be beneficial for psychological well-being [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Similarly, affect labelling has been proposed to trigger implicit emotion regulation processes [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. The effects of this process are thought to accumulate over time, even without immediate affective relief, which could explain the stronger links with daily outcomes observed in our study. In line with this interpretation, future studies should consider including measures of thought concreteness versus abstract processing mode.\u003c/p\u003e\u003cp\u003eFreely moving thoughts emerged as a robust and consistent negative predictor of momentary negative affect and daily depressive symptoms, and a positive predictor of happiness. These findings are in line with the assumption that mind-wandering - being an adaptive form of TUT - is characterised by freely moving thoughts, while maladaptive ruminations are more restrained [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Previous studies also indicated that the freely moving characteristic plays an important role in the adaptive function of task-unrelated thoughts [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThought usefulness was generally associated with higher happiness, corroborating earlier findings linking this characteristic to positive mood [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. However, no significant negative associations were observed for negative affect or daily-level outcomes. These findings underline the added value of using emotion-specific measurements in the study of task-unrelated thoughts. Such measures can capture differential effects that might be overlooked by relying solely on aggregated negative affect scales or a positive\u0026ndash;negative affect continuum, highlighting the necessity of treating specific emotions as distinct points of measurement.\u003c/p\u003e\u003cp\u003eInterestingly, participants with higher depressive and anxiety symptoms and a greater trait tendency toward repetitive negative thinking appeared to perceive their TUT as more useful. However, this perceived usefulness of thought was also associated with increased negative affect. One possible explanation coming from worry and rumination theory (e.g. S-Ref model [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]) is that those participants had positive meta-cognitions about TUT and may appraise thoughts characteristic typical of worry or rumination as more useful, in spite of deleterious consequences on affect regulation [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOne of the more appealing results presented in this study is the association between positively valence thoughts and daily depressive symptoms in participants with low depression, anxiety and tendency to ruminate (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e). While these results were not strongly significant - they call for further exploration and replication in future studies (for depression and rumination the simple slopes at +/- 1 SD were not significant, but J-N intervals showed significance at +\u0026thinsp;1.27 and +\u0026thinsp;1.98 SD). The key question is: could positively valenced TUT have served as a form of emotion regulation on more difficult days [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] and if so, what is the causal direction of this relation? Could participants use TUT focused on positive content as a distraction [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] therefore meaning the high level of depressive symptoms at the end of the day is explained by the situational factors or rather that TUT is used as an avoidance strategy [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] and their negative consequences emerges at the end of the day? An alternative explanation is that engaging in daydreaming might underline the discrepancy between ideal and actual self [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. This discrepancy might operate at two levels - when participants are comparing the content of their daydreams with the actual situation and/or when they realize in the evening that engaging in positive daydreaming made them neglect some important activities. This discrepancy and the resulting negative affect could serve as an adaptive motivator for action to reduce it. Finally, it is necessary to explore in further studies whether the lack of these mechanisms in participants with high repetitive negative thinking and depressive or anxious symptoms might be due to the use of TUT as a habitual response [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e] and thus reducing the effect of situational variables. The results of our study show clear and distinct patterns in how daily depressive symptoms are linked with negatively valenced TUT, depending on participants' HADS scores and rumination levels. This suggests that clinical and subclinical populations may engage with TUT differently compared to healthy participants.\u003c/p\u003e\u003cp\u003eSurprisingly, in the perspective of a recent literature review suggesting that mind-wandering propension might predict sleep quality [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e], we did not find any link between TUT and sleep quality. However, only one study in this review was linking sleep quality to mind-wandering assessed through EMA [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e], and, in line with our results, failed to find a link between TUT and sleep quality measures (although the individual differences in TUT predicted sleep quality).\u003c/p\u003e\u003cp\u003eOverall, our findings highlight the imperative to assess the perceived consequences of TUT, considering both their momentary (short-term), more enduring (e.g., daily) and trait-level impacts. This evaluation is especially vital when examining TUT function or the dynamic relationship between its function and characteristics.\u003c/p\u003e\u003cp\u003eWhile the link between TUT and HRV was previously explored in laboratory studies (e.g. [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]) and scarce ambulatory studies (e.g. [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]), this study was, to our knowledge, the first to combine ambulatory HRV assessment with key TUT characteristics. While only emotional burden was associated with momentary HRV measures, our investigation of moderation effects provided valuable insights. It suggests that both depressive and anxiety symptoms may modulate the link between TUT features and HRV. These findings imply that TUT might differentially affect HRV in healthy, subclinical, and clinical populations.\u003c/p\u003e\u003cp\u003eThe innovative use of HRV ambulatory measures, relatively new to the TUT field, also brings some limitations. Participants' self-management of sensor placement and removal, limited external control over non-wear episodes, and non-inclusion of control variables such as physical activity or substance intake could significantly affect HRV. Our dataset included some irregular measurement gaps and periods where collected data deviated from expected patterns. Currently there is no clear recommendation on collecting and cleaning HRV data sets collected in ambulatory settings. Moreover, the measurement windows around EMA prompts vary across studies. In future studies, we plan to (1) implement Bluetooth-based alerts to notify participants when data are not being recorded or are being recorded incorrectly; (2) include prompts about physical activity and substance use between measurements; and possibly (3) link a portion of participant compensation to the amount of HRV data collected (even if symbolically, as a motivating factor to react to alerts described in point 1).\u003c/p\u003e\u003cp\u003eFindings in the literature focusing on TUT remain inconsistent, and few studies examine multiple thought characteristics and outcomes simultaneously, yet even fewer use EMA designs with data collected across momentary, daily, and person levels over extended periods. Our study demonstrates that such an approach captures meaningful variability which may otherwise be overlooked. Recent recommendations emphasize aligning sampling frequency with the temporal dynamics of the studied process [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. Our results support this view, showing that effects vary across timescales and some emerge only under intensive, ecologically valid designs. This is particularly relevant in TUT research, where thought characteristics and the context in which they arise interact in complex, highly time-sensitive ways.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the team of assistants responsible for data gathering and guiding participants through the study process: Jan Skorupski, Natalia Cichecka, Martyna Mielnik, Olga Szkodzińska, Karol Kawik.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ehttps://docs.google.com/document/d/1arjmipLGAhEd0nzZpyRz_djv_IkBSptKHCkE0XaucL4/edit?tab=t.0\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project is funded by the SONATA Grant \u0026ldquo;Toward an Integrative Model of Maladaptive Spontaneous Task-Unrelated Thoughts (STUT): A Processual and Functional Approach\u0026rdquo; (2019/35/D/HS6/02364) from the National Science Centre, Poland, awarded to MK.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is available on request from the first and from the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026rsquo; statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution according to CRediT (Contributor Roles Taxonomy).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMSS: Conceptualization, Methodology; Writing \u0026ndash; Original Draft Preparation; Investigation, Visualization, Data Curation, Formal Analysis, Software. IK: Methodology, Supervision, Writing \u0026ndash; Review \u0026amp; Editing; SB: Conceptualisation, Methodology, Writing \u0026ndash; Review \u0026amp; Editing; CB: Conceptualization, formulation of research goal; Writing \u0026ndash; Review \u0026amp; Editing; TA: Conceptualization, formulation of research goal, Writing \u0026ndash; Review \u0026amp; Editing; MK: Conceptualization: idea, Methodology, Writing \u0026ndash; Review \u0026amp; Editing, Formal Analysis, Validation, Supervision, Project Administration; Resources, Funding Acquisition.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGiambra LM. The influence of aging on spontaneous shifts of attention from external stimuli to the contents of consciousness. Exp Gerontol. 1993 Oct;28(4\u0026ndash;5):485\u0026ndash;92. \u003c/li\u003e\n\u003cli\u003eMurray S, Krasich K, Schooler JW, Seli P. What\u0026rsquo;s in a Task? 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Internet Interv. 2025 Jun;40:100824. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":true,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-7827343/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7827343/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTask-unrelated thoughts (TUT) are a prevalent transdiagnostic phenomena, robustly associated with both adaptive and maladaptive outcomes. However, it is still not clear which factors determine the maladaptive outcomes of TUT. In this study, we focused on analysing the role of a wide spectrum of TUT characteristics in everyday functioning, specifically their associations with momentary affective states, depressive symptoms, heart rate variability, and sleep quality. Forty-seven participants took part in a seven-day ecological momentary protocol, completing momentary and daily questionnaires, alongside continuous ECG monitoring. Additionally, trait-level measures of mind-wandering, repetitive negative thinking, depressive and anxiety symptoms were collected using self-report questionnaires. The results show that TUT emotional appraisal\u0026mdash;especially perceived burden and thought valence\u0026mdash;were significant predictors of affective momentary outcomes. In contrast, characteristics linked to control over TUT (e.g., intrusiveness and freely moving features) significantly predicted depressive symptoms at the daily level. Part of the associations between TUT characteristics and outcomes were moderated by individual differences, such as tendency to engage in mind-wandering or repetitive negative thinking, as well as trait-level depressive and anxiety symptoms.\u003c/p\u003e","manuscriptTitle":"Not all task-unrelated thoughts (TUT) are created equal - TUT characteristics as predictors of affective states and heart-rate variability","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-23 13:25:28","doi":"10.21203/rs.3.rs-7827343/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-02T02:51:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-01T10:18:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-24T18:02:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"282621963578151483583199094128375950511","date":"2025-11-10T14:14:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"307338319499601057491292870156628839586","date":"2025-11-03T21:57:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-20T14:00:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-20T13:58:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-20T13:14:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-17T12:32:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-17T12:28:05+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":"971b6bee-81d3-4e17-8b28-dd8406480b4e","owner":[],"postedDate":"October 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":56722954,"name":"Health sciences/Diseases"},{"id":56722955,"name":"Health sciences/Health care"},{"id":56722956,"name":"Biological sciences/Psychology"},{"id":56722957,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-03-16T16:06:40+00:00","versionOfRecord":{"articleIdentity":"rs-7827343","link":"https://doi.org/10.1038/s41598-026-42261-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-03-12 15:59:27","publishedOnDateReadable":"March 12th, 2026"},"versionCreatedAt":"2025-10-23 13:25:28","video":"","vorDoi":"10.1038/s41598-026-42261-0","vorDoiUrl":"https://doi.org/10.1038/s41598-026-42261-0","workflowStages":[]},"version":"v1","identity":"rs-7827343","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7827343","identity":"rs-7827343","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}