Users’ emotions may influence the formation of presence in virtual reality (VR). Users’ expectations, state of arousal and personality may also moderate the relationship between emotions and presence. An interoceptive predictive coding model of conscious presence (IPCM) considers presence as a product of the match between predictions of interoceptive emotional states and the actual states evoked by an experience . The present paper aims to test this model’s applicability to VR for both high-arousal and low-arousal emotions. The moderating effect of personality traits on the creation of presence is also investigated. Results show that user expectations about emotional states in VR have an impact on presence, however, expression of this relationship is moderated by the intensity of an emotion, with only high-arousal emotions showing an effect. Additionally, users’ personality traits moderated the relationship between emotions and presence. A refined model is proposed that predicts presence in VR by weighting emotions according to their level of arousal and by considering the impact of personality traits. In the past few decades, VR technology has evolved significantly in its technical capabilities, producing ever more realistic environments and increasingly sophisticated virtual worlds. We will specifically refer to VR as the head-mounted displays (HMDs) that are able to immerse users in virtual worlds. Presence has been described as the sense of “being there” , which sThis paper will thus focus on understanding the creation of presence in VR. For clarity, we distinguish between presence and immersion which, although related, are distinct concepts and therefore should not be used interchangeably , 19. SlaIn previous studies, presence has often been regarded as a unitary variable defined as the sensation of ‘being there’ , this would render the VE less believable as a real space, and thus social and self presence may in turn be hampered.Studies have often treated presence as a unitary concept, which may have contributed to the myriad of contrasting theories on the formation of presence. Descriptive models of presence focus on distinguishing the components of presence and put less emphasis on the actual process that creates presence, hence their name. Thus, descriptive models are heavily impacted by technological approaches that strive to improve VR through technical advances. Early evidence of this trend is exemplified by Schubert et al.’s  Igroup PThis model predicts that spatial presence is facilitated by the existence, believability and accuracy of spatial cues present in a VE. This is in turn dependent on whether the user is able to perceive the VE as a plausible space in the first place.Despite the useful contribution of descriptive models, structural models of presence may offer a better understanding of self presence and social presence because structural models focus on how presence is created in the mind. The user is considered a mediator between bodily interoceptive states and real world exteroceptive signals . ExterocBased on this concept of interoception, Seth et al.  formulatApplied to VR, the IPCM postulates that prior to experiencing a VE, the user would predict the emotions that the VE would elicit . For exaThe IPCM is supported by neurophysiological and neuroimaging studies investigating the role of brain regions such as the anterior insular cortex (AIC). Most importantly, the AIC has been shown to be responsible for the comparison between predicted and actual interoceptive signals , 54. ThiThe relationship between presence and emotion has mostly been studied by means of correlations, so the directionality of any causation is impossible to derive . For exaSeveral studies have assumed that presence drives emotion and not the other way around , 61, 62.The IPCM, like other presence models, does not distinguish between different components of presence in VR, preferring to simplify it into a unitary concept. This is problematic because, as mentioned, predictive coding can be expected to apply mainly to social and self presence and less to the formation of spatial presence. There is a need to test how these components of presence are achieved individually and to what extent emotions and expectations contribute to their creation.Secondly, emotions differ in characteristics such as arousal, with feelings of fear and anxiety characterised by high levels of arousal increasing with sense of presence  and emotThirdly, distinct highly arousing emotions could manifest differently. Fear, unlike happiness, is an important evolutionary survival function that triggers fight or flight responses . Due to Finally, the IPCM does not account for individual differences derived from personality traits. It has been shown that personality traits such as neuroticism, conscientiousness and extraversion have a significant moderating effect on emotion regulation, notably suppression . In addiIt is apparent that presence as a construct is more complex than previously thought, and the exact role of emotions, expectations and personality traits for the formation of different components of presence remains to be explained. Arousal may moderate this process and so also may prior perceptual or conceptual priming. The potential moderating effect of personality factors also bears investigation. To address these points, four research questions were formulated.Research Question 1: Does the IPCM predict the different types of presence  in VR?Research Question 2: Are different emotions affecting presence differently in VR?Research Question 3: Do conceptual and perceptual processes lead to different levels of presence?Research Question 4: Do personality traits moderate the relationship between emotion and presence?The IPCM treats presence as a unitary concept but, as discussed, it can be more informative to treat its components separately. Because the impact of emotion is expected to be more prominent in social and self-presence, we predict the IPCM to apply in their cases but not in the case of spatial presence. As discussed above, we would expect high-arousal emotions to have a more pronounced effect on presence compared to low-arousal emotions. In addition, within highly-arousing emotions, fear should have the most pronounced effect due to its evolutionary survival role. As argued above, perceptual priming should elicit stronger emotions compared to conceptual priming, because actually encountering a stimulus elicits a stronger response than information about said stimulus . We mighHypothesis 1: We predict that smaller mismatches between predicted and felt emotion (IV) will lead to higher levels of social and self presence, but not spatial presence (DV).Hypothesis 2: We predict that highly-arousing emotions  will have a more pronounced effect than low-arousal emotions (relaxation) (IV) on social presence and self-presence (DV). We expect fear to have the most pronounced effect of all.Hypothesis 3: We predict that perceptual priming (IV) will affect presence (DV) more than conceptual priming (IV), and we expect this effect to be strongest for fear.Hypothesis 4: We predict that the personality traits of openness, introversion and agreeability (IV) will moderate the relationship between emotion and presence (DV).The experiment adopted a between groups design with seven participant groups. Three groups were primed with perceptual stimuli (PP) while another three received conceptual priming (CP). The seventh group received no priming and acted as the control group. Thus, the seven experimental conditions were as follows: PP + relaxation, PP + happiness, PP + fear, CP + relaxation, CP + happiness, CP + fear and the control group. From hereon we will distinguish between ‘predicted’ emotions, referring to the intensity of a given emotion that the user expected they would feel within the VR experience prior to being exposed to it, and ‘felt’ emotions, which represent the retrospective subjective appreciation of the intensity of emotions they felt in said VE. Within the experimental design, the predictor variables were the type of priming  and the scores for predicted and felt emotions (relaxation/happiness/fear). Additional predictor variables were computed as differences between predicted emotion scores and felt emotion scores for the three emotions. The five personality traits measured via the Big Five Inventory (BFI) were also used as predictors . The outIn order to test the IPCM, we computed scores from predicted and felt emotion intensity scores. Thus, predicted and felt emotion intensities had to be assessed in a similar manner and on a similar scale. We opted to collect self-report measures of emotion rather than, for example, physiological measures because the IPCM is based on subjective expectations of emotions. Previous studies such as that of Riva et al.  successfThe questions measuring emotions felt during the VR experience were phrased as: ‘For the following emotions, how intensely did you feel them during the VR experience, 10 being the highest and 1 being the lowest?’. Below the question, relaxation, happiness and fear were listed, each with a scroll bar set by default in the middle of the scale, at 5. The scroll bar could be dragged to indicate the level of emotion felt. To measure predicted emotions before VR the same questions were rephrased: . The items about emotion predictions were administered so as to later compute the difference between predicted and felt emotions.To assess presence, we used a battery of questionnaires. We administered the UCL Presence Questionnaire as it is a well-established measure of presence . It alsovs introversion, agreeableness vs antagonism, conscientiousness vs lack of direction, neuroticism vs emotional stability, and openness vs closedness to experience.The Big Five Inventory (BFI) which contains 44 items  was usedFor the perceptual priming conditions, three pieces of classical music were chosen for their ability to induce the three desired emotions. For relaxation, Chopin’s ‘Nocturne, op. 15: 6 in G minor’ was chosen . For hapFor the conceptual priming conditions, three ‘reviews’ were written about the VR experience we used, portraying it respectively as relaxing, happy or fearful. The text of the three reviews was kept the same but key words related to emotions were changed accordingly . The reviews were pilot tested on a sample of five people who estimated that the reviews reflected similar levels of relaxation, happiness and fear respectively. The reviews were then read aloud by a female voice and recorded using a high quality microphone. This was done to ensure that the music and reviews were presented via the same sensory modality, i.e. auditory.An Oculus Rift HMD was used for presenting the VR environment. This headset has a maximum refresh rate of 90Hz and uses OLED panels with a pixel density of 1080x1200 per eye. To enhance sound quality, a pair of Sennheiser HD 380 Pro headphones was used instead of the built in Oculus Rift headphones. The HMD was powered by an Alienware Area 51 desktop computer running Windows 10, with a 3.4 GHz Intel Core i7, 16 gigabytes of RAM, and an Nvidia 1080 Ti graphics card with 11 gigabytes of GDDR5 memory.A VR experience from the Oculus Store named ‘Senza Peso’  see Fig was chosThe experience lasted for approximately 7 minutes. This has been shown to be the ideal duration for a passive VR experience as the level of presence is expected to reach maximum levels around the 7 minute mark, after which boredom can ensue . Becausea priori expectations of emotions.Participants were met by the experimenter and were asked to read the information sheet and complete the consent form. Participants were randomly assigned to one of the seven experimental groups. The study adopted a between-subjects design. A pre-questionnaire on demographics was then completed by the participants. This was followed by the assessment of their initial levels of relaxation, happiness and fear, i.e. before any intervention. They were also asked to indicate their expectations of the emotions they would feel in the VR experience. This was done to allow assessment of potential biases resulting from participants’ existing knowledge of VR which could have influenced their Next, participants were fitted with the Oculus Rift and listened to the priming material while a Unity default infinite horizon was played to control for visual input. The only difference between the seven experimental conditions was the auditory content that participants were exposed to. Participants in the three perceptual priming experimental conditions listened to music either eliciting relaxation, happiness or fear. The other three conceptual priming experimental groups, in contrast, listened to the priming material eliciting relaxation, happiness or fear. Participants in the control condition only experienced the same infinite horizon for the same amount of time, but did not receive any priming prior to the VR experience. The VR infinite horizon also served to familiarize the participants with the Oculus Rift head tracking and allowed them to accommodate to any VE-specific distance compression . The latAfter the priming phase, levels of felt and expected emotions were measured a second time for the three emotions, using the same questions as before. This second measurement of emotion expectations was conducted in order to test the effect of the priming interventions. Next, the ‘Senza Peso’ VR experience was presented. Before commencing the experience, participants were verbally encouraged to make use of the head tracking and look around as much as they liked and to anything in the VE capturing their attention. After this VR experience, the headset was removed and the levels of felt emotion were measured, followed by the battery of presence questionnaires. After the experimental phase, participants were administered the BFI via the Qualtrics platform and were debriefed.p = .751), education (p = .916) and experience with VR (p = .426). No participants withdrew from the study before its end.A total of 126 participants  over the age of 18  were recruited via opportunity sampling. Each of the seven experimental groups consisted of 18 participants with counterbalanced numbers of males and females. Participants were randomly assigned to one of the experimental conditions. They were a mixture of undergraduate (39), masters (35) and PhD (19) students as well as university staff members (30) and others, who were university alumni (three). All participants had normal or corrected to normal vision and hearing. In terms of previous experience with VR, 42 declared they had ‘none at all’, 63 had ‘a little’, 17 had ‘a moderate amount’, while only one had ‘a lot’ and three ‘a great deal’. There was also no significant difference between the seven groups in terms of their age , a level of power of 0.80, nine predictors , and an α-level of 0.05. This analysis returned a total necessary sample size of 114, i.e., 16 participants per group. The study was approved by the Psychology Research Ethics Committee (PREC) of the University of Bath (ethics code: PREC 18–301). All participants were adults and gave written informed consent prior to participating and were debriefed accordingly at the end of the study. Participants were also informed they were free to withdraw from the study at any point, but none chose to do so.The sample size was calculated with an ower 3.1 . For theScores were computed for the three emotions by subtracting the felt emotion score (third measurement which was collected after the VR experience) from the predicted emotion score that had been recorded after the priming phase but before the VR experience (second measurement). These scores are referred to hereafter as the computed relaxation, happiness and fear scores. Negative scores represent an overestimation of emotion levels in the participant’s prediction, and positive scores represent an underestimation. The closer a computed score is to zero, the greater the match between the predicted emotion score and its corresponding experienced emotion score. The IPCM predicts that the closer the computed emotion scores are to zero, the higher the level of presence should be.To verify our hypotheses, several statistical tests were considered. Due to the continuous covariates, we could not use simple ANOVAs, but had to employ either univariate ANCOVAs or multiple regressions. These two are statistically equivalent and would have yielded similar results. However, there was a larger number of continuous variables compared to categorical, which suggests that regression analysis would be more suited. Second, our continuous predictors were of primary interest rather than being covariates to control for, hence we preferred using a multiple regression analysis. We finally ran a comprehensive multiple regression analysis rather than separate analyses to address each hypothesis as we wanted to consider the possible interaction effects among all the different predictors, which could have been lost if running separate analyses.In consequence, a total of five multiple linear regressions were carried out to test the effect of all predictors on the measures of presence, i.e. on spatial, social and self presence, on the presence question (Q2) from the questionnaire by Egan et al.  and on tFor clarity, we will split the results section based on the predicted variables, namely the five measures of presence that were used, rather than per hypothesis. For each section we mention the implications of the presented results for our hypotheses. At the end of the section, we discuss the extent to which we can accept each hypothesis.F = 1.153, p = .321). Only agreeability acted as a significant predictor. This yielded a significant regression equation  = 7.044, p = .009), with an R2 of.054. Spatial presence was equal to 5.328 +.051 (p = .009) (computed fear score). This suggests that computed emotion scores cannot predict spatial presence; cf. social and self presence in H1. Agreeability predicting spatial presence partially supports H4.For spatial presence, as expected, no significant regression was found  = 1.80, p = .046), with an R2of.185. Two significantly contributing factors were found, namely the computed fear score (p = .026) and agreeableness score (p = .034). Next, we examined whether there was any interaction effect between fear and agreeableness scores. To correct for collinearity between the interaction scores  and the original scores for fear and agreeableness, we centered the data for all these predictors and checked for correlations. No correlation was found among the centered predictors (p > = .338), thus showing lack of multicollinearity. A regression with agreeableness, computed fear score and the interaction variable was run. This yielded a significant regression equation  = 5.778, p = .001), with an R2 of.124. Participants’ predicted social presence was equal to 4.732 +.169 (p = .006) (computed fear score) +.073 (p = .002) (agreeability score). No interaction effects were found between agreeableness and fear scores (p = .894). Computed fear scores predicting social presence partially supports H1 and H2. The lack of an effect of perceptual or conceptual priming means that H3 is rejected. H4 is partially supported as only agreeableness predicted social presence, but not openness and introversion.In the case of social presence, a significant regression equation was found  = 1.127, p = .343), with an R2of.124. However, two significantly contributing factors were found, namely computed happiness scores (p = .036) and computed fear scores (p = .036). This prompted a regression analysis with just these two predictors, as for social presence. No interaction variable was calculated between happiness and fear computed scores as we did not hypothesize any interaction between them. The analysis showed a significant regression equation  = 3.213, p =.044), with an R2 of.050. Participants’ predicted self presence was equal to 4.909 +.177 (p =.041) (computed happiness score) +.136 (p =.023) (computed fear score). These results suggest that self presence is predicted by both computed fear and computed happiness scores, unlike spatial and social presence. No personality traits predicted self presence. The computed happiness and fear scores predicting self presence supports H1. The fact that computed happiness and computed fear, but not computed relaxation, predicted self presence supports H2. The lack of an effect of perceptual or conceptual priming means that H3 is rejected. H4 is not supported as no personality traits predicted self presence.For self presence, a non-significant regression equation was found  = 2.768, p <.001), with an R2 of.259. Two significant contributors were found for the model, namely computed happiness score (p =.046) and openness (p =.033). Next, we examined whether there was any interaction effect between happiness and openness scores. To correct for collinearity between the interaction scores  and the original scores for happiness and openness, we centered the data for all these predictors and checked for correlations. No correlation was found among the centered predictors (p > =.161), indicating a lack of multicollinearity. A significant regression equation was found  = 9.252, p <.001), with an R2 of.259. Participants’ presence was equal to 6.428 +.028 (p = 0.49) (openness score) +.166 (p <.001) (computed happiness score)—.018 (p =.020) (openness and happiness score interaction variable). An interaction was found between computed openness and happiness scores  = 1.274, p =.235), with an R2 of.138. Only the computed fear scores acted as a significant predictor, which partially supports H2. This yielded a significant regression equation  = 5.228, p =.024), with an R2 of.040. Presence as measured by the UCL questionnaire was equal to 4.887 +.088 (p =.024) (computed fear score), suggesting that computed fear scores predicted presence. The UCL presence questionnaire thus showed different results to presence as assessed by Egan et al.’s Q2  achieved significantly less presence than those who overpredicted emotion ; t(87) = −4.032, p <.001, d = −.319. There was no significant difference between the underpredicting group and the group who predicted correctly , or between the group who predicted correctly and the overpredicting group (p > =.066). Results were similar for fear, in that the only significant difference found was between the underpredicting group  and the overpredicting group ; t(89) = 2.535, p =.039, d =.011. For relaxation, no differences were found between any of the groups (p > =.051)  see . These rMean = 5.73, SD = 2.37) and happiness  = −7.387, p <.001, d = −.658, between relaxation and fear ; t(125) = 7.923, p <.001, d =.706, and between happiness and fear; t(125) = 13.631, p <.001, d = 1.214. Thus, happiness scores were overall highest, followed by relaxation and lastly by fear.Paired-samples t-tests were conducted to compare the felt emotion scores (third measurement) for relaxation, happiness and fear for the entire sample size. There was a significant difference between the scores for relaxation (p >.051). No significant correlation was found between general comfort and presence measured by any of our measures (p >.382). Hence, physical discomfort or lack of satisfaction with the VR hardware did not affect our results.Because post-experience emotion scores are emotional appraisals of the VR app, we took into account that negative experience due to cybersickness, which can cause sensations of dizziness or nausea, could impact these scores. Discomfort could be due to motion sickness, or general dissatisfaction with the system hardware , 74. To H1, the IPCM predicted the formation of social and self presence but not spatial presence. Moreover, H2 was supported because the IPCM predicted presence based on computed happiness and computed fear but not computed relaxation. The lack of an effect of perceptual or conceptual priming on any of the emotions or components of presence means that H3 is rejected. Lastly, results showed that some personality traits, namely openness and agreeableness moderate the relationship between emotion and presence, and so we partially accept H4. A schematic presenting the significant interactions found can be seen in Our results showed that, as hypothesized in Our results show for the first time that the IPCM can explain the formation of presence in VR environments. As expected, the IPCM predicted the formation of social and self-presence, but not spatial presence, supporting H1. The model can predict presence based on computed happiness and fear scores, but not relaxation scores, depending on the measure of presence which supports H2. H3 was rejected since priming via the perceptual and conceptual pathways did not yield any changes in presence. Another novel finding is that some personality traits influenced the relationship between emotion and presence, supporting H4. These results prompt a proposed revision to the IPCM, presented below.The IPCM was found to predict social presence but only fear was a significant predictor. This may be explained through an evolutionary typology of fear. Mayr  proposedThis suggestion is supported by the other results. Self presence was predicted by both the computed happiness and computed fear scores, while relaxation did not yield significance. It seems, therefore, that users drew on high-arousal emotions when constructing self presence. Ratan and Hasler  created Spatial presence refers to the ability of the user to locate themselves relative to features in the environment . It is dWhen Overall presence was measured using Egan et al.’s Q2 , we founWhen comparing presence scores between participants who underpredicted and those who overpredicted levels of happiness, we found that the underpredicting group achieved higher presence. In other words, participants who felt more happiness than they had predicted in the VR experience achieved higher presence than those who overestimated the level of happiness they would feel. An inverse result was found for fear in that overpredicting fear led to higher presence compared to underpredicting it. Thus, it seems that unexpectedly high intensity emotions are detrimental to presence if they are negative but can be beneficial if positive. In other words, the level of presence seems to be linked to the level of positive emotional experience, which is not unexpected. No effect was found for relaxation, which strengthens the idea that low-arousal emotions are not heavily weighted in determining presence. This pattern of results is inconsistent with the IPCM which predicts a uniform decrease in presence independent of whether participants underestimate or overestimate emotion.Openness, or openness to experience as it is often referred to, significantly predicted presence as assessed by the question used previously by Egan et al. . We founThis finding supports the mechanism proposed by the IPCM. Seth et al.  suggest Agreeability was found to be a predictor for social presence but there was no interaction between agreeability and computed fear. Hence, the effects that agreeability and computed fear had on social presence were independent of each other. The finding that more agreeable people seem to achieve higher levels of social presence is not surprising given that social presence itself is defined as the impression of ‘being amongst others’ or, as found by Shin , of beinSimilarly to the findings for emotion, not all personality traits consistently moderated or predicted all types of presence. In line with our findings, Kober and Neuper  found thBased on our findings, we propose several refinements to the IPCM. First, the model should not treat presence as a unitary concept but rather should consider its components separately. Our findings suggest that this adapted model could be effectively applied to understanding the formation of social presence and self presence but not spatial presence. Secondly, the model should include the role of emotions and weight them with respect to their influence on presence. This should be done according to the level of arousal of the emotions elicited by the VE as well as to the type of emotion . For example, the higher the level of arousal, the greater impact a specific emotion should have on presence. And the direction (not only the magnitude) of the mismatch should be considered depending on the valence of the emotion when predicting presence. Finally, the model should take into account that users’ personality, in particular openness to experience, moderates the degree to which emotions determine presence.Although not affecting our results for H1, H2 and H4, priming participants with conceptual or perceptual stimuli did not show an effect, thus not supporting H3. One possible explanation for this is our use of audio-only stimuli for the priming. VR is a rich, multisensory medium and thus the priming we attempted via a unisensory stimulus may not have had a large effect. Future research could attempt priming using multisensory stimuli, preferably presented in VR. This could clarify whether the use of unisensory priming was indeed the reason why priming did not succeed. Still, it is worth noting that because the computed emotion scores were calculated using the scores collected after the priming phase and those collected after the VR experience, the lack of a priming effect has no impact on our other findings.An important limitation related to social presence in this study is the lack of user interaction with the virtual avatars present in the experience. This is because social presence is not only determined by the mere presence of other sentient beings, but also interactions with them which would lead to more complex inferences about their thoughts or emotions . This meUsing self-report measures for both emotion and presence could also be a limitation as judging the intensity of felt emotions could be considered when reporting presence and vice-versa. As observed by IJsselsteijn  an emotiMeasuring either emotions or presence via an objective measure could be a solution and seems promising, but has so far been unsuccessful in the context of VR presence , 79. AddApart from the UCL Presence Questionnaire , the prePrevious studies have often focused on the formation of spatial presence within VR, but the processes that lead to social and self-presence remain relatively under researched. Furthermore, the role played by emotions in the formation of these components of presence has been largely unexplored. Here we show that an interoceptive predictive coding model can be applied to VR to understand how presence is created and the role that emotions play in this process. We show that the model does apply for a selection of highly-arousing emotions and that the results can depend on how presence is measured. We propose that, depending on the type of presence assessed and VR content presented, highly-arousing emotions may be prioritized when creating presence.We have also shown that users’ expectations of the emotions they will experience in a VR application can be of crucial importance in the creation of presence. In turn, users’ expectations of high-arousal emotions such as happiness and fear may be manipulated to enhance presence in VEs. Overprediction and underprediction of such emotions do not have symmetric effects on presence, and we propose that the IPCM should be refined to account for this asymmetry. We have also shown how some personality factors can moderate the creation of presence. Incorporating this range of findings, we propose the creation of a refined, integrated model of presence that encompasses predictive coding of high-arousal emotions and accounts for individual differences.S1 Data(XLSX)Click here for additional data file.


UV–vis–NIRand X-ray spectroscopies show differences in both susceptibility tooxidative doping and extent of oxidation for the P(OE3) series, withP(OE3)-E being the most doped. Wide-angle X-ray scattering measurementsindicate that P(OE3)-E generally demonstrates the lowest paracrystallinityvalues in the series, as well as relatively small π–πstacking distances. The significant  increasein electrical conductivity of doped P(OE3)-E films versus doped P(OE3)-Dor P(OE3)-Ph films can therefore be attributed to P(OE3)-E exhibitingboth the highest carrier ratios in the P(OE3) series, along with goodπ–π overlap and local ordering . Furthermore, these trends in the extent of doping and paracrystallinityare consistent with the reduced Fermi energy level and transport functionprefactor parameters calculated using the semilocalized transport(SLoT) model. Observed differences in carrier ratios at the transportedge (ct) and reduced Fermi energies [η(c)] suggest a broader electronic band  for the EDOT-incorporating P(OE3)-E polymer relativeto P(OE3)-D and P(OE3)-Ph. Ultimately, we rationalize improvementsin electrical conductivity due to microstructural and doping enhancementscaused by EDOT incorporation, a structure–property relationshipworth considering in the future design of highly electrically conductivesystems.This study investigatesthe solid-state charge transport propertiesof the oxidized forms of dioxythiophene-based alternating copolymersconsisting of an oligoether-functionalized 3,4-propylenedioxythiophene(ProDOT) copolymerized with different aryl groups, dimethyl ProDOT(DMP), 3,4-ethylenedioxythiophene (EDOT), and 3,4-phenylenedioxythiophene(PheDOT), respectively, to yield copolymers P(OE3)-D, P(OE3)-E, andP(OE3)-Ph. At a dopant concentration of 5 mM FeTos Additionally,oligoether (OE) side chains have been increasingly employed with CPs because of their ability to: (1)improve doping stability of CPs through enhanced polymer-ion miscibility,23 (2) lower CP bandgaps, and (3) improve charge transport throughclose π–π stacking distances .25Understandingsolid-state charge transport in chemically doped,electrically conductive conjugated polymers (CPs) is important toemerging applications which include organic thermoelectrics,26 The EDOT-based homopolymer serves as a primary component of poly:poly(styrenesulfonate)(PEDOT:PSS), a commercially available dispersion broadly used in researchranging from organic solar cells, to electrochromic and bioelectronicdevices.31 PEDOT(OH), an EDOT-containing polymer derived from a soluble precursor,has recently been reported to demonstrate high, metal-like conductivity(>1000 S cm–1) as well as potentially usefulstability(90% retention of σ after 2 months) under ambient conditions.32 Finally, a dioxythiophene copolymer composedof 2,2′-bis-thiophene (biEDOT) and 3,4-propylenedioxythiophene(ProDOT) substituted with branched oligoether side chains, PE2-biOE2OE3, exhibits an exceptionally high σ value foran OE-functionalized CP (∼400 S cm–1),15 a value 3–10× higher than comparableOE-functionalized thiophene-based analogues.35While many XDOT-basedunits have been investigated for the developmentof stable, highly electrically conductive films, none have been asinfluential or as successful as 3,4-ethylenedioxythiophene (EDOT).b]thiophene (DOTT)] yield lower σ values. P(ProDOT-EG),an OE-functionalized ProDOT-based homopolymer, was reported to havea σ value of 1 S cm–1 with the dopant F4TCNQ.36 Similarly, a PheDOT-basedpolymer  was reportedto have a σ value of 1 S cm–1.37 The homopolymer of DOTT (HD) displayed σvalues <10–2 S cm–1 when dopedwith either Magic Blue or F4TCNQ.38 Incorporation of unsubstituted EDOT into copolymer structures withmonomers containing solubilizing groups, however, has been shown toincrease the σ values of resultant oxidatively doped materials.38 While this EDOT incorporationis known to lower oxidation onsets by increasing the relative electronrichness of copolymers relative to homopolymers ,39 it is not well known to what extent EDOT incorporation dictatesthe relationships between solid-state electrical conductivity, electronicstructure, and microstructural properties in these conjugated copolymers.By comparison,homopolymers composed of alternate XDOT units . To rationalize these differences in thermoelectric properties,spectroscopic and microstructural characterizations are used in conjunctionwith the semilocalized transport (SLoT) model,17 which accounts for varying localized and delocalized  contributions to the observabletransport properties. Doping processes are first probed by UV–vis–NIRspectroscopy, electrochemistry , and X-ray photoelectronspectroscopy (XPS). EDOT-containing P(OE3)-E demonstrates the greatestsusceptibility to oxidative doping and yields the highest carrierratios (ratio of charges per aromatic ring) of the P(OE3) series.Doping processes can furthermore be related to the resonance stabilizationenergies of the comonomer oxygen lone pairs with respect to: (1) thetilt of the oxygen p-orbital relative to the thiophene ring  and . Summations for P(OE3)-Phreveal similar charge delocalizations over P(OE3) and PheDOT . Significant charge carrier localization on aparticular unit, as demonstrated for copolymers with donor-acceptordesign motifs,57 is therefore not observed forthe P(OE3) copolymer series. This can be attributed to all of theunits being XDOT-based and therefore not as strongly contrasting inelectron-donating or electron-accepting character from each other.Additionally, high planarity between rings on the polymer chain  is observed for the P(OE3) seriesin both the neutral and oxidized states , consistent with the literature.61 The lack oftwisting along the backbone for any of the polymers therefore contributesto the strong charge carrier delocalization observed for all polymers.Charge carrier delocalizations were investigatedusing the modelhexadecamer systems. Increased charge carrier delocalization contributesto decreased charge carrier binding energy , thereby maximizing chargetransport.39 do play a role in the subtle variation of theoxidation potentials by DPV and the more drastic differences in polymerthin-film bleaching by solid-state UV–vis–NIR. DFT cantherefore rationalize the development of higher charge carrier ratiosat lower oxidation potentials/doping concentrations for P(OE3)-E dueto this polymer’s low IE relative to P(OE3)-D and P(OE3)-Ph.However, Hirshfeld charge carrier per fragment calculations seem toindicate that developed charges delocalize to the same extent overthe polymer chain. This similarity across the polymer series suggeststhat differences in solid-state microstructure  are likely to be a greater factor in determiningelectrical conductivity than the electronic properties calculatedfor a single polymer chain, which is limited in representing the energeticlandscape of an oxidatively doped film. To elucidate differences inmeasured electrical conductivity, we therefore turn to microstructuralanalysis of these materials.In conclusion, observed redox trends are consistent with IE valuescalculated by DFT, indicating that differences in electron donationinto the polymer backbone, as robustly discussed in our previous study,3.4Figure S13a). The lack of any distinctthermal features in the DSC traces  indicates no significant microstructural changes occurringwhen heating or cooling the samples , consistent with other OE-functionalized CP systems.58 Local ordering observed in the GIWAXS of the pristine P(OE3) films(vide infra) is therefore ascribed to aggregationin the polymer solution prior to processing the film, a point supportedby the similarity of the solid-state UV–vis–NIR in 39Thermogravimetric analysis (TGA) and differentialscanning calorimetry(DSC) were first employed to probe changes to thermal transitionsassociated with changes to molecular structure. Each material displayedthermal stability up to 325 °C under an inert argon atmosphereby TGA S13a. TheFigures S14–S16 show the two-dimensional GIWAXS diffractograms of pristine filmsand films doped at varying FeTos3 concentrations. Figures S17 and To provide insight intothe structure of these materials, GIWAXS analysis was performed onpristine and doped films. Figure S17 (tabulated in Table S6). In the pristine state, the P(OE3)-Dlamellar spacing (24.0 Å) is larger than that of either P(OE3)-E(22.4 Å) or P(OE3)-Ph (21.2 Å). Upon doping at 0.125 mMFeTos3, we observe an initial increase in lamellar stackingdistances for P(OE3)-E (an increase of 2.1 Å) and P(OE3)-Ph (anincrease of 3.6 Å), consistent with dopant insertion into theside chains.62 At higher dopant concentrations,we observe diverging trends in the lamellar spacings. From the 0.125mM doped film to the 50 mM doped film, the lamellar spacing of P(OE3)-Ddecreases by 3.5 Å and P(OE3)-E decreases by 2.1 Å. Overthe same range of dopant concentrations, however, P(OE3)-Ph maintainslamellar spacings close to 25 Å. Simultaneously, we note theemergence of additional features at ∼0.5 Å–1 for the P(OE3) series at dopant concentrations of 5 and 50 mM, withthe features of P(OE3)-Ph at this position being especially prominent peak. Instead, thesefeatures are attributed to the formation of ordered dopant domains,akin to what is observed in some F4TCNQ-doped films.12We first consider the lamellar spacings (100) ofthe P(OE3) seriesshown in Table S8). In the pristine state, the π–π spacings ofP(OE3)-E (3.79 Å) and P(OE3)-Ph (3.85 Å) are smaller thanthe P(OE3)-D π–π spacing (4.11 Å), consistentwith the high unit planarity of EDOT and PheDOT relative to DMP -D, 0.20 Å for P(OE3)-E,and 0.28 Å for P(OE3)-Ph. At the different doping levels, however,closer π–π spacings are maintained for P(OE3)-Eand P(OE3)-Ph relative to P(OE3)-D. As decreased π–πspacings lead to increased molecular overlap as well as carrier delocalizationand mobility between chains,12 the observedtrends are in good agreement with electrical conductivity trends observedin We next consider the π–π spacings(020) of theP(OE3) series shown in g), which represents the statistical fluctuation in d-spacing (in this case π–π spacing)in the crystallite regions of a polymer which can prevent long-rangeorder.63 Statistical fluctuation in crystallineorder affects transport and charge-trapping properties of conductingand semiconducting materials,64 and increasedcrystallite ordering  is positivelycorrelated with polaron delocalization, higher mobilities, and increasedσ.65 Paracrystallinity values, associated withintermolecular π–π stackings for pristine and dopedfilms of polymers in the P(OE3) copolymer series, were calculatedusing a pseudo-Voigt peak fit  and tabulated in Table S8. While the P(OE3) films, both pristine and doped, do not exhibitsignificant long-range order (g ≥ 10%),64 trends may be observed for the different polymers at different dopantconcentrations . For P(OE3)-D, g values hover between 15 and 16% for both pristine anddoped films, indicating that molecular doping has relatively littleeffect on the statistical fluctuation of the π–πspacings. The g values for P(OE3)-E, in contrast,decrease from ∼16 to ∼12% from a pristine to a 0.125mM FeTos3 doped film, indicating an ordering effect inthe crystallite regions upon doping. The g valuesfor P(OE3)-Ph also decrease from ∼19% for the pristine filmto ca. 13–14% for the doped films, suggesting a similar orderingeffect to that observed in the P(OE3)-E system. Ultimately, the lowoverall g values of the P(OE3)-E films (both pristineand doped) indicate lower statistical fluctuation of π–πspacings and a higher degree of crystallite ordering for P(OE3)-Erelative to P(OE3)-D and P(OE3)-Ph. Increased order of the P(OE3)-Esystem further contributes to the higher σ values observed forthis system relative to P(OE3)-D and P(OE3)-Ph.65Finally, we evaluate the paracrystalline disorder parameter  polymer serieshave been understood in the context of susceptibility to oxidativedoping, carrier ratio, π–π spacings, and paracrystallinityvalues. P(OE3)-E has the highest electrical conductivities of theP(OE3) series, likely because it obtains the highest carrier ratios,close π–π spacings, and the lowest paracrystallinityupon doping. Under similar doping conditions, P(OE3)-D and P(OE3)-Phtend toward similar and lower electrical conductivity values due totheir lower carrier ratios, larger π–π spacings,and higher paracrystallinity values. While these chemical and structuralchanges are sufficient to explain the higher σ values observedfor P(OE3)-E relative to P(OE3)-D and P(OE3)-Ph, further analysisof temperature-dependent Seebeck coefficients (S),electrical conductivities (σ), and carrier ratios (c) through the SLoT model allows for differences in charge transportparameters  to becalculated and contextualized.17Finally,the SLoT model is used to evaluate the charge transport propertiesof the P(OE3) copolymers doped with FeTos17The SLoT model takes the formE  is the chargetransport function, and the chargetransport function is a function of the charge carrier energy (E), temperature (T), and carrier ratio(c). A full description of the SLoT model can befound in refs (S) propertiesby evaluating the transport function using the Boltzmann transportequation, yieldingwhere σ in refs , 16, 17.S values to By fittingmeasured σ and 3 doped filmsof the P(OE3) series. We observe the expected S–σanticorrelation,17 showing that σvalues increase and S values decrease with increasingFeTos3 doping concentrations; increasing FeTos3 doping concentrations increases the carrier ratio (c), as quantified using XPS (c) and σ0 are evaluated.sing XPS 4. At low0 (g(E)) can be made using the reduced Fermi energy relationship, η(c), using c) relationshipthat depends on the electronic states allowed by the structure ofthe polymer.16 As c increases,charge carriers increasingly populate the electronic band, g(E), at higher and more mobile energylevels, thereby increasing η and σ. In contrast, the SLoTtransport prefactor (σ0), calculated from 17 discussion on localization energy (WH) values for the P(OE3) series  are left to the Additional Discussionin the SI as the differences in WH are not sufficient to explain the differencesin σ in this P(OE3) series. Ultimately, the SLoT model providesa quantitative framework for isolating these complex interrelationshipsby isolating η(c) and σ0 expressions.The high σ values exhibited by P(OE3)-E, relative toP(OE3)-Dand P(OE3)-Ph, can be better contextualized by examining key chargetransport expressions: the reduced Fermi energy as a function of carrierratio ctor, σ0 6b.Inferc) relationshipin c). For every measured Seebeck coefficient in Table S9). c) increases, η increases, with each material system havinga unique η(c) relationship, as indicated bythe differences in curve slope and shape. The transport edge (Et) is denoted by the dashed horizontal line,and the carrier ratio needed to exceed the transport edge is referredto as ct (the fit intercept at η= 0). The transport edge is oftentimes synonymous with the band edgeand marks the onset of significant transport.66 Here, the ct value is specific to thepolymer: P(OE3)-D (ct = 0.07), P(OE3)-E(ct = 0.20), and P(OE3)-Ph (ct = 0.11). For context, P(OE3)-D and P(OE3)-Ph have ct values close to P3HT (ct ∼ 0.05), while P(OE3)-E has ct values more consistent with PEDOT (ct ∼ 0.22).17 With regardsto the η(c) relationship, P(OE3)-E achievesη values ranging from 30 to 50 at the highest doping levelswhile P(OE3)-D and P(OE3)-Ph achieve η values ranging from 10to 20 at their highest doping levels. Physically, this signifies P(OE3)-Echarge carriers accessing more mobile, higher-energy states than P(OE3)-Dand P(OE3)-Ph charge carriers. The steeper η(c) curve for P(OE3)-E is further attributed to increased resonancestabilization of the EDOT unit vs DMP or PheDOT 67 for the EDOT-incorporating P(OE3)-E polymer relative to P(OE3)-Dand P(OE3)-Ph . Densityof states calculated for the hexadecamers using DFT  appear to corroborate this claim,as the highest energy band for P(OE3)-E does appear to be a littlebroader relative to the highest energy bands for P(OE3)-D and P(OE3)-Ph.However, this analysis is complicated by the onset of the lower energyband centered around −7 eV for P(OE3)-Ph as well as likelydifferences in electronic structure between a gas-phase hexadecamerDFT calculation and a solid-state polymer film.First, we examine the η series; notably, P(OE3)-Ph andP(OE3)-D have σ0 values that are ∼2 S cm–1 while P(OE3)-E has σ0 values thatare ∼20 S cm–1. These significant differencesindicate that P(OE3)-E has the potential to be 10× more conductivethan P(OE3)-D and P(OE3)-Ph in the absence of localization effectsand at equal η values. In Figure S24, we show that this difference in σ0 is consistentwith calculated drift mobility  and weighted mobility  values, which showthat P(OE3)-E has mobilities an order of magnitude higher than P(OE3)-Eand P(OE3)-Ph. Physically, we believe this difference in σ0 may be related to the combination of increased ordering  and smaller π–π spacingsin the crystalline domains of the P(OE3)-E doped films. Ultimately,this ∼10× increase in σ0 with a ∼2×increase in η is consistent with the 10–20× largerσ values observed for EDOT-incorporating P(OE3)-E relative toP(OE3)-D and P(OE3)-Ph.Second, theSLoT model additionally enables the calculation ofσ4In thiswork, we investigate design rules toward enhanced solid-statetransport properties in OE-functionalized dioxythiophene-based alternatingcopolymers, providing a mechanistic understanding of observed increasesin σ through spectroscopic, computational, and scattering measurementsas well as further contextualization through the SLoT model. We observethat P(OE3)-D and P(OE3)-Ph exhibit similar electrical conductivities, while P(OE3)-E electricalconductivities are significantly higher at all doping concentrations.By XPS, doped P(OE3)-E films yield the highest charge carrier ratiosin the series, followed by P(OE3)-D and then P(OE3)-Ph. P(OE3)-E’ssusceptibility to oxidative doping is additionally supported by UV–vis–NIRspectroscopy (significant suppression of the π–πtransition and growth of the charge carrier bands at low doping concentrations)and electrochemical measurements (lower onset of oxidation of P(OE3)-Erelative to P(OE3)-D and P(OE3)-Ph). GIWAXS is further employed toprobe differences in π–π overlap and local ordering. We note that P(OE3)-D demonstrates thelargest π–π spacings (poorest π–πoverlap) in the P(OE3) series, while P(OE3)-E and P(OE3)-Ph trendtoward similar π–π spacings at the different dopingconcentrations. Oxidatively doped P(OE3)-D therefore exhibits highercharge carrier ratios, but poorer π–π overlap relativeto P(OE3)-Ph, resulting in P(OE3)-D and P(OE3)-Ph yielding similarmacroscopic electrical conductivities. In contrast, P(OE3)-E exhibitsboth the highest carrier ratios in the P(OE3) series as well as goodπ–π overlap and local ordering , elucidating the significant  increasein electrical conductivity of doped P(OE3)-E films versus doped P(OE3)-Dor P(OE3)-Ph films.S and σ), withan ∼10× increase in σ0 and an ∼2×increase in η quantifying the 10–20× larger σvalues observed for P(OE3)-E relative to P(OE3)-D and P(OE3)-Ph. EDOTincorporation ultimately enables the development of high charge carrierdensities and local order in the resultant polymers, significantlybroadening the electronic band structure of EDOT-containing polymers.Ultimately, this work quantifies our previously qualitative understandingof how EDOT incorporation significantly changes material properties,informing the design of future high-performing CP systems for solid-statethermoelectric and electronic properties.Finally, the SLoT model is used to furthercontextualize the observabletransport properties (i.e.,


Because active asteroids are generally discovered by chance only after their tails have fully formed, the process of how impact ejecta evolve into a tail has, to our knowledge, not been directly observed. The Double Asteroid Redirection Test (DART) mission of NASA2, in addition to having successfully changed the orbital period of Dimorphos3, demonstrated the activation process of an asteroid resulting from an impact under precisely known conditions. Here we report the observations of the DART impact ejecta with the Hubble Space Telescope from impact time T + 15 min to T + 18.5 days at spatial resolutions of around 2.1 km per pixel. Our observations reveal the complex evolution of the ejecta, which are first dominated by the gravitational interaction between the Didymos binary system and the ejected dust and subsequently by solar radiation pressure. The lowest-speed ejecta dispersed through a sustained tail that had a consistent morphology with previously observed asteroid tails thought to be produced by an impact5. The evolution of the ejecta after the controlled impact experiment of DART thus provides a framework for understanding the fundamental mechanisms that act on asteroids disrupted by a natural impact6.Some active asteroids have been proposed to be formed as a result of impact events Observations with the Hubble Space Telescope reveal a complex evolution of the ejecta produced by the Double Asteroid Redirection Test (DART) spacecraft impacting Dimorphos. T + 0.4 h  because of the optical depth effect. The ejecta cone showed many distinct morphological features  observed the ejecta once every 1.6 h during the first 8 h after the DART impact  was considerably more massive than the 151-m diameter Dimorphos2. At the scale of the HST, the Deep Impact ejecta were diffuse and mostly featureless, expanding at an average speed of about 100 m s−1 and a maximum speed of about 300 m s−1 (refs. 14). This difference in ejecta morphology is probably because of the different target compositions and subsurface structures. Whereas Tempel 1 has a highly porous subsurface15 composed of fine-grained dust and is rich in volatiles17, the bouldery surface and potential rubble-pile interior of Dimorphos2 could perturb the ejecta curtain and produce inhomogeneous structures in the ejecta19.The ejecta of Dimorphos were different from the ejecta of comet 9P/Tempel 1 produced by Deep ImpactT + 0.7 days to T  + 2.1 days, the ejecta features composed of slower dust escaping at less than around 1 m s−1 emerged from the base of the ejecta cone , situated roughly orthogonal to the sunward direction, increasingly widened to form the observed wing-like shape, with a diffuse antisunward edge and a relatively sharp sunward edge , we observed the ejecta roughly once every 12 h, and then once every day in the subsequent three orbits (orbits 16–18). In the final phase (orbits 21–24), observations were made once every 3 days. The observations concluded 18.5 days after impact. In each orbit, images were collected at several exposure levels, in which the central core of Didymos was unsaturated in short exposures and long exposures saturated Didymos to image the relatively faint ejecta and tail. All images were collected through filter F350LP 30.We used a total of 19 HST orbits (period 95 min) over about 19 days to observe the Dimorphos ejecta . The centroid was defined by a two-dimensional Gaussian fit with a 5 × 5 pixel box centred at the photocentre. The pixel area map was used to correct pixel area variations in the image31. The total counts were measured with circular apertures of a radius of 1–130 pixels (0.04–5.2 arcseconds). We converted the total counts to flux density and Vega magnitude based on the photometric calibration constants  provided in the image headers and the HST photometric calibration website. The total brightness of Didymos including the ejecta and the total brightness of ejecta are shown in Extended Data Fig. Aperture photometry was measured in all short, unsaturated exposures that have been corrected for charge transfer efficiencyWe used the images corrected for charge transfer efficiency and geometric distortion (.drc files) to study the morphology of the ejecta. To increase the signal-to-noise ratio of the faint ejecta features, we stacked all long exposures in each orbit because no change is visible in the ejecta morphology with each orbit. The centroid of long exposures that are saturated in the centre was determined by the cross-section of the diffraction spikes. Some long exposures with pointing-induced drift were included in the stack, but those with more than ten pixels of drift were discarded. The effects of this drift are accounted for as extra positional uncertainties to the measurements of features, which are mostly larger than the length of the drift. Cosmic rays and background stars were removed in the stacking process. Because different numbers of good long exposures were available for each orbit, the total exposure times varied from 25 to 50 s in most stacked long exposures and reached 155 s for the orbit 21 stack and 110 s for the orbit 23 stack.32 were used to assist the identification of ejecta features, including azimuthal median subtraction and division, azimuthal and radial reprojection, different brightness stretching and the use of various colour tables. All identified features were confirmed by several techniques.Various image-enhancement techniques commonly used for studies of comets−1) or for features affected by solar radiation pressure. The trajectories of the features in those cases are notably affected by the gravity of Didymos  define the two cone edges with approximately a Gaussian distribution. The mean and the 1σ uncertainty of the position angles of the two cone edges are derived from the histograms. On average, the northern and southern cone edges are at position angles of 4º ± 8º and 131º ± 8º, respectively, consistent with the measurements described above. We then generated simulated images from the model ejecta cone and computed the corresponding histogram following the same approach for the actual images. This histogram was compared with the measured cone edge position angles to calculate a score, defined asσi and μi are the standard deviation and mean of the northern or southern edges , respectively, xj is the position angle of the histogram bin j for the simulated image and sj is the pixel count in bin j. We searched the cone axis in the full range of RA and dec and the opening angle in 100º–160º for the highest score. Because the HST images alone could not determine whether the cone faced towards or away from Earth, this approach resulted in a pair of best-fit cone-axis solutions that were symmetric with respect to the image plane. We thus considered both as feasible cone-axis directions. The uncertainties of the solutions were estimated with 500 random samples of the measured cone edge position angles distributed in two Gaussians with the measured means and standard deviations. The best-fit cone-axis directions were  =  and , both with an opening angle of 125º ± 10º (1σ uncertainties). Both solutions are about 12º from the image plane, with the former pointing towards Earth and the latter pointing away.To further constrain the ejecta cone geometry, we constructed a three-dimensional numerical cone model parameterized by the direction of the cone axis in right ascension (RA) and declination (dec), as well as an opening angle, to compare with the images. We first projected the six early post-impact images Fig.  in an az22, in which the motion of dust is determined by βsrp. βsrp, which is defined as the ratio of the solar radiation pressure force to the solar gravitational force, depends on particle radius, r, and density, ρ, asK = 5.7 ×  10−4 kg m−2 is a constant, Qpr is the radiation pressure coefficient averaged over the solar spectrum, which is usually assumed to be 1. We assumed a grain density of 3.5 × 103 kg m−3 for the dust in the ejecta, following the density of ordinary chondrite meteorites33, considering that the Didymos–Dimorphos system shows an S-type spectrum that is associated with LL ordinary chondrite material34.The position angle of the tail and its uncertainty were determined by the radial directions from the asteroid that define the visible boundary of the tail at the furthest point along the tail in all (short and long exposures) stacked images that contain the tail. The dust dynamics model under the influence of solar radiation pressure follows a previous model35. These small particles are pushed out of the binary system in less than 10 h. The gravity of Didymos is predominant within about 3 km for millimetre-sized particles and 10 km for centimetre-sized particles.Pre-impact modelling suggested that the acceleration of solar radiation pressure always exceeds that of the gravitational acceleration of the Didymos system for ejecta particles smaller than 100 µm in size36, in which synchrones are the loci of dust particles ejected with zero initial velocity at the same time but with various βsrp. The measured position angles of the tail of Dimorphos coincide to within 4º of the direction suggested by the synchrones associated with the time of impact in all images, suggesting that solar radiation pressure dominates the tail formation . The relatively sharp southern edge and the more diffuse northern edge are consistent with the expectation from the ejecta mass–speed relationshipβsrp with particle size means that small particles experience stronger solar radiation pressure and are pushed away from the asteroid faster after ejection than large particles. Because the duration of our HST observations is much shorter than the orbital period of Didymos around the Sun (2.1 years), the motion of particles along the tail relative to the asteroid under solar radiation pressure can be approximated by a constant acceleration motion. As the length of the tail grows, particles of various sizes spread out along the tail, with the smallest particles remaining near the far end of the tail from the asteroid and the larger particles dominating the end near the asteroid. Assuming a power-law differential particle-size distribution with an exponent of α for the tail, we derived that the brightness of the tail is expected to have a power-law relationship with the distance to the asteroid with an exponent b = −4 − α.The inverse proportionality of T + 5 h until the last stack at T + 18.5 days  in various images, corresponding to a range of βsrp from 0.2 × 10−4 to 8 × 10−4, to be between −2.2 and −3.1, with an average of −2.7 and a standard deviation of 0.2. The range of βsrp indicates that particle sizes range from 1 µm to a few millimetres. In images after about T + 6 days, the tail brightness displays two regions with different power-law slopes. The inner region appears to be influenced by the particles in the curved ejecta streams that started to overlap with the tail. The outer region has best-fit slopes close to −2.7 as in the early images, whereas the slope of the inner region ranges from −3.6 to −3.9. The range of βsrp for the inner region is 7 × 10−4 to 1 × 10−5, corresponding to millimetre- to centimetre-sized particles. The lack of small particles in the curved streams is expected because particles with a size of 100 µm or smaller should have been removed a few hours after the impact. The apparent increasing steepness of the particle-size distribution in this size range also seems to indicate that the bulk of ejecta particles have a size cut-off of a few centimetres. If the particle-size distribution of the tail represents that of all ejecta, then a power-law index of −2.7 means that the total ejecta mass is dominated by the largest particles.We extracted the brightness profiles of the tail from stacked long exposures from 38 and millimetre-sized particles39, along with supporting models of scattering efficiency40, the albedo of micrometre-sized particles is about 70% that of millimetre-size-grains at the phase angle of our early observations (54º). This brightness ratio is reversed at the phase angle corresponding to the final images (74º), in which micrometre-sized particles become about 16% brighter. Our calculation indicates that the small difference between the albedos of micrometre- and millimetre-sized particles changes the best-fit power-law index of the particle-size distribution by less than 2%. Our assumption of the same albedo for micrometre-to centimetre-sized particles holds.The above treatment assumes that the albedo is independent of particle size, which needs to be examined. On the basis of laboratory measurements of the phase function of micromtre-sized aerosolsT + 5 days and T + 7 days indicates an increase in the total scattering cross-section in the ejecta within 10 km of the system . Mass shedding from the surface of Dimorphos because of rotation is not likely given its slow rotation if its spin is tidally locked. However, mass movement and shedding from Didymos could potentially be triggered by ejecta re-impact because its fast rotation causes a net outward acceleration at its equator, although no clear indication of this has been confirmed yet3. Once the dust is lifted from the surface of Dimorphos or Didymos using these mechanisms, solar radiation pressure will quickly sweep the dust into the antisunward direction, forming a secondary tail.The possible mechanisms of the secondary dust emission could include the re-impact of ejecta blocks onto Dimorphos or Didymos41, gravitational scattering for the ejecta dust when they are turned back by solar radiation pressure and pass the binary system, or photon-charged dust particles under the influence of interplanetary magnetic field42 could also result in the unusual tail morphology that leads to the appearance of a secondary tail. Our dynamics simulations suggested that a secondary dust emission is not necessary to form a secondary tail that has a morphology consistent with the one observed. However, these scenarios may not be accompanied by the increase in ejecta dust as suggested by the fading lightcurve of the Didymos system.Other mechanisms, such as the dynamic interaction between the slow ejecta dust and the binary systemAny methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-023-05811-4.Supplementary Video 1Animation of the HST image sequence of evolution of ejecta from Dimorphos. In all frames, north is up and east to the left—that is, the same orientation as all of the figures in the Article. All frames are displayed with the same logarithmic brightness scale.


Refusal self-efficacy protects against risky health behavior. Digital interventions have the potential to support self-efficacy due to the enactive experience provided by digital technologies. The aim of this systematic literature review was to evaluate the evidence of digital interventions to support refusal self-efficacy in child and adolescent health promotion. Following the Cochrane Collaboration guidelines, five electronic databases were searched from 2009 to 2020. The studies were assessed by two independent reviewers according to the eligibility criteria. Eligible studies were included in the review, assessed for risk of bias, synthesized narratively and assessed for evidence quality with the GRADE approach. Twenty-three studies, that examined 18 different interventions, were included in the review. The interventions included various digital elements as means to support the child and adolescent refusal self-efficacy . The interventions improving refusal self-efficacy were more often used at home setting and addressed the four sources of self-efficacy with different digital elements regardless of intervention duration and intensity. Although the results on intervention effects varied and the evidence quality remained low, the overall evidence concerning these interventions was encouraging. Based on the subgroup analysis, the results were mainly encouraging among girls. When these interventions are implemented in health promotion, their benefits and weaknesses need to be considered comprehensively. The results provide information for designing and developing digital interventions to support child and adolescent refusal self-efficacy. Further research with larger sample sizes and more rigorous study designs is needed to strengthen the evidence of these interventions. When approaching adolescence, children begin to explore and adopt new health behaviors , and is Refusal self-efficacy, among some other factors, determines child and adolescent health behavior in different domains of health: substance abuse, smoking, healthy nutrition and sexual behavior (In child and adolescent (later child) health promotion, there is a need for innovative and effective interventions that develop refusal self-efficacy . DigitalSynthesized information from different studies concerning the evidence and elements of digital interventions is needed for evaluating and further developing the interventions  AND  AND (adolescen* OR teen* OR preadolescen* OR preteen* OR mid-adolescen* OR mid-teen* OR child* OR youth* OR youngster* OR young people* OR young person* OR “Adolescent”[Mesh] OR “Child”[Mesh] OR “Minors”[Mesh]) AND (anti OR refus* OR non OR prevent* OR avoid* OR declin*) AND (self-efficacy [tiab] OR efficacy expectation* [tiab] OR “Self Efficacy”[Mesh]) AND . No other filters were used than those concerning publication date. In addition to the electronic database search, the reference lists of relevant publications were searched manually.The studies that met the following eligibility criteria were included in the review:The majority (≥ 50 %) of participants are children or adolescents up to the age of 18.Digital health interventions that are in a virtual form and aim to support refusal self-efficacy and promote health.Both intervention and control conditions enable evaluating the effectiveness.Refusal self-efficacy was measured with instruments before and after the intervention.Experimental or quasi-experimental study design.After performing the searches, all the duplicates were removed. Titles of all the remaining records were screened by one reviewer based on the inclusion criteria, and all potentially relevant publications were included for abstract screening. All publications that were potentially relevant were finally assessed based on the full-text. After full-text examination, all publications that met the eligibility criteria were included in the review and all included publications were reviewed by two independent reviewers. The full-text assessments were mainly consistent but there were a few discrepancies related to the definitions of digital intervention and refusal self-efficacy which were solved to resolved.The risk of bias was assessed by two independent reviewers using the Cochrane Collaboration’s tool for assessing the risk of bias based on the following criteria: random sequence generation (selection bias), allocation concealment (selection bias), blinding of participants and personnel (performance bias), blinding of outcome assessment (detection bias), blinding of outcome assessment (detection bias), incomplete outcome data addressed (attrition bias), and selective reporting (reporting bias)  approach was applied to evaluate the quality of the evidence of the refusal self-efficacy outcomes based on five criteria: study limitations (risk of bias related to the study design and execution), inconsistency of results (unexplained heterogeneity), indirectness of evidence , imprecision (in the estimates of effect), and publication bias (selective study publication). Although the GRADE approach is most applicable to meta-analyses, it has been used to evaluate the evidence quality of systematic reviews.  reported having used randomization, only eight studies reported how randomization was done  and substance use (n = 6), alcohol (n = 3) and smoking (n = 2) prevention.There were 18 interventions examined in the 23 studies, and over half of the interventions had a specific name see . The hean = 16) were delivered by computers or online. Two interventions were delivered by smartphones or tablets , such as Bandura’s social cognitive theory, the theory of planned behavior, the social learning theory, and the theory of reasoned action. Also, other theoretical frameworks that acknowledge the role of self-efficacy in influencing human behavior were used, for example, the family interaction theory or social influence models  in which other children or avatars representing them faced challenging situations and demonstrated their skills, and faced different consequences based on their behavior. Social persuasion was addressed in the interventions by providing encouraging feedback on tasks and exercises. The children could also earn points, scores, prices or incentives based on the decision they make in a game or other activity. Elements that acknowledged the regulation of emotions and physiological states emphasized the positive sides of healthy behaviors, provided activities for regulating or coping with feelings, or provided a platform for reflection and personalization.The effects of the digital interventions on child refusal self-efficacy varied with six studies showing only statistically significantly favorable results and six studies showing statistically nonsignificant results and others showing both statistically significant and nonsignificant results at different time points see . Out of n/N = 4/5), and either addressed to support only girl’s refusal self-efficacy or refusal self-efficacy outcomes were measured only from girls (n/N = 4/5)  see . These i4/5) see .In our analysis with the GRADE approach, the main outcome refusal self-efficacy was divided into three subtypes: substance use refusal self-efficacy, sex refusal self-efficacy and peer resistance self-efficacy see . More toThis systematic review describes digital interventions to support refusal self-efficacy in child health promotion as well as the evidence of these interventions. The results suggest that different interventions with several digital elements have been developed and used.mastery experiences and vicarious experiences were most commonly used to support child refusal self-efficacy  of refusal self-efficacy. Although there are common aspects of refusal self-efficacy across different health domains , there is evidence that refusal self-efficacy beliefs differ across domains  or let them use the interventions freely. According to According to our results, the digital interventions that improved child refusal self-efficacy were mostly used in home settings and used various digital elements to support refusal self-efficacy. Although school-based interventions targeting other health areas have had positive results on self-efficacy  had participated (the Fume game is owned by the University of Turku and available to use free of charge). To provide an objective assessment, it was ensured that these two did not participate in the eligibility assessment as well as the evaluation of the quality of their own study. To balance the limitations, the Cochrane Collaboration guidelines for conducting systematic reviews were followed including assessing the risk of bias in the selected studies. In our assessment, the risk of bias remained predominantly unclear. Because of this, the high methodological quality of the studies cannot be ensured which undermines the reliability of our review results. Nevertheless, an adequate amount of studies was included in our review which bolsters the generalizability of the results.The digital interventions improving refusal self-efficacy were more often used in home setting and addressed the four sources of self-efficacy with different digital elements regardless of intervention duration and intensity. The overall evidence concerning the digital interventions was encouraging, but the results on intervention effects varied and the evidence quality was low. Based on the subgroup analysis the results were mainly encouraging among girls. Although the evidence quality remained low, it does not indicate that digital interventions are poor methods to support child refusal self-efficacy. When these interventions are implemented in health promotion, their benefits and weaknesses need to be considered comprehensively. This review provides information for designing and developing digital health interventions to support child refusal self-efficacy by summarizing how refusal self-efficacy can be addressed with various digital elements. To strengthen the evidence, further research on the effectiveness of these interventions could use larger sample sizes and more rigorous study designs and make more elaborated subgroup analysis, for example, based on the age of the participants. This could improve the quality of evidence of these interventions and their applicability in child health promotion.daac085_suppl_Supplementary_Table_S1Click here for additional data file.daac085_suppl_Supplementary_Table_S2Click here for additional data file.daac085_suppl_Supplementary_Table_S3Click here for additional data file.


Advance care planning (ACP) is the process supporting individuals with life-limiting illness to make informed decisions about their future healthcare. Ethnic disparities in ACP have been widely highlighted, but interpretation is challenging due to methodological heterogeneity. This review aims to examine differences in the presence of documented ACP in individuals’ care records for people with advanced disease by ethnic group, and identify patient and clinician related factors contributing to this.Mixed-methods systematic review. Keyword searches on six electronic databases were conducted (01/2000–04/2022). The primary outcome measure was statistically significant differences in the presence of ACP in patients’ care records by ethnicity: quantitative data was summarised and tabulated. The secondary outcome measures were patient and clinician-based factors affecting ACP. Data was analysed qualitatively through thematic analysis; themes were developed and presented in a narrative synthesis. Feedback on themes was gained from Patient and Public Involvement (PPI) representatives. Study quality was assessed through Joanna Briggs Institute Critical Appraisal tools and Gough’s Weight of Evidence.N=35papers were included in total; all had Medium/High Weight of Evidence. Fifteenpapers (comparing two or more ethnic groups) addressed the primary outcomemeasure. Twelve of the fifteen papers reported White patients had statisticallyhigher rates of formally documented ACP in their care records than patientsfrom other ethnic groups. There were no significant differences in the presenceof informal ACP between ethnic groups. Nineteen papers addressed the secondaryoutcome measure; thirteen discussed patient-based factors impacting ACPpresence with four key themes: poor awareness and understanding of ACP; financialconstraints; faith and religion; and family involvement. Eight papers discussedclinician-based factors with three key themes: poor clinician confidence aroundcultural values and ideals; exacerbation of institutional constraints; andpre-conceived ideas of patients’ wishes.This review found differences in the presence of legal ACP across ethnic groups despite similar presence of informal end of life conversations. Factors including low clinician confidence to deliver culturally sensitive, individualised conversations around ACP, and patients reasons for not wishing to engage in ACP  may begin to explain some documented differences.PROSPERO-CRD42022315252.The online version contains supplementary material available at 10.1186/s12904-023-01168-7. Advance Care Planning (ACP) is an internationally recognised term describing conversations around an individual’s future healthcare preferences. The aim is to support individuals to “live well and die well, in the place of their choosing” . These cThe first national guidance for healthcare professionals in the UK was developed in 2007 . Since tThe international literature on race, ethnicity and ACP is complex due to variations in the definition and measurement of ACP, the wide range of disease groups and differences in labels for ethnic groups used across different countries. In this study, ACP is defined as “a process that supports adults at any age or stage of health in understanding and sharing their personal values, life goals, and preferences regarding future medical care” . The curThere are long recognised ethnic inequalities in healthcare , 11. OveA review of reviews  highlighBazargan & Bazargan-Hejazi (2021)’s review investigated disparities in ACP document completion for non-Hispanic Black patients . They foOther reviews identify ethnicity as a factor influencing presence of ACP, though the underlying reasons for this require further exploration. For example, Lovell and Yates (2014) found that “being African American” affected uptake of ACP in palliative care , and SpeThe heterogeneity of research studies  elicits the need for an up-to-date review encapsulating global literature on ACP for all ethnically diverse groups. This review aims to investigate the existing literature around ethnic disparities in the documented presence of ACP and to illustrate the patient and clinician factors affecting this, to improve understanding of culturally competent ACP and promote progression towards this.This study will review published literature around ACP and ethnicity. Firstly, we will explore and illustrate disparities in the documented presence of ACP by ethnicity; secondly, we will conduct thematic analysis of extracted data to illustrate key clinician and patient-based factors affecting this.This is a mixed-methods systematic literature review including 1) tabulation and narrative exploration of the primary outcome measure  and (2) a Thematic Analysis  of quali tabulatiThe full eligibility criteria are outlined in Table Searches were conducted on six key databases on 16/03/2022: PubMed ; AMED (2000 – March 2022); EMBASE (2000 – March 2022); PsychINFO (2002 – present); CINAHL (2000 – present). Additional literature was sought via manual journal index searching on 06/04/22 (using criteria 2000 – 06/04/22) of: Palliative Medicine; BMJ Supportive and Palliative Care; BMC Palliative Care; Ethnicity and Healthcare.Search outputs were imported into a reference managing software (EndNote) and records deduplicated. Two members of the research team  independently screened the remaining titles for inclusion/exclusion; decisions were reviewed  and discrepancies resolved through discussion . Abstracts and subsequently full texts were selected through the same process.Data extraction of full texts was conducted independently by two members of the research team  into a pre-designed form, extracting study data, cohort data and data relating to both the primary and secondary outcome measures. Output was compared and combined. Discrepancies were resolved through discussion with a third team member (GC).The primary outcome measure was statistically significant differences in the documented presence of advance care planning in individuals’ care records for people with advanced disease by ethnicity; this is illustrated in the tabulation  to enable a comprehensive exploration. This was a qualitative analysis considering factors in two key categories: patient-related factors and clinician-related factors.Other data extracted included study data  and cohort data .Risk of bias assessment was conducted using the Joanna Briggs Institute Critical Appraisal Tools . The appTo consider the suitability of each included paper, Gough’s (2007) Weight of Evidence (WoE) assessment was conducted  with the JBI outcome integrated into this as WoE criteria A . The othTo decide which studies were eligible for each synthesis, study characteristics were tabulated Table  and compData extracted for the primary outcome measure  was quantitatively tabulated to demonstrate the key findings. To represent confidence in the results and compare results of similar quality papers, output was organised by Weight of Evidence ratings. No numerical data conversions occurred. Inter-related ethnic groups were tabulated together for the summary analysis Table  for concFor the secondary outcome measures (clinician and patient -based factors affecting presence of ACP for different ethnic groups), extracted data was imported into a qualitative coding software (NVivo Plus™). Analysis followed thematic analysis methodology as described by Braun & Clarke . InitialTwo Patient and Public Involvement (PPI) contributors provided feedback to inform this review: a British-South Asian individual with personal and professional experience of ACP, and a White Other, Jewish individual with personal experience of ACP. Early iterations of themes illustrating the secondary outcome measure were presented to the PPI contributors, who offered comments and opinions based on their own personal and professional experiences. This feedback and critique shaped the final iterations of the themes presented. PPI contributors were also invited to be co-authors for this review, and were involved in reviewing and giving feedback for drafts of the review manuscript.The electronic searches yielded 1390 papers of which 907 remained eligible after de-duplication. The screening process diagrammed in Fig. The characteristics of the 35 studies included in the analysis, along with the Risk of Bias and Weight of Evidence ratings, are presented in Table Fifteen papers were included in the primary outcome measure analysis , 68, 73.All fifteen papers included drew comparison between two or more ethnic groups. The operationalisation of ACP was variable, as illustrated in Table All fifteen papers used legal measures of ACP: Six papers measured DNACPR completion , 56, 57,Of the thirteen papers finding statistically significant ethnic differences in the presence of legal ACP, four were rated High Weight of Evidence , 49, 51.Five studies operationalised ACP in terms of informal discussion or end of life conversations , 51, 65.The secondary outcome measures investigated ethnicity-related patient and clinician factors affecting presence of ACP in patients’ healthcare record. Nineteen papers addressed these outcomes and were included in the analysis , 69-72. Thirteen of the nineteen papers discussed patient-based factors affecting ACP presence; four key themes were identified: lack of awareness and understanding; financial constraints; faith and religion; and family involvement.“All patients were predominantly unaware of concepts related to ACP, and none of the cases, nor their family caregivers, had prior experience making life-sustaining treatment (LST) decisions themselves.” [selves.” .One frequently cited barrier to ACP was a general lack of awareness and understanding surrounding ACP , 60, 63;There are multiple intersectional factors which impact upon a person’s awareness of ACP. The evidence suggests that a lack of previous experience with, and exposure to, death and dying may contribute to an individual’s lack of awareness surrounding ACP. Beltran (2022) in an American study of Latino patients and relatives found that minimal exposure to other relatives’ ageing and dying limits the opportunity to learn about ACP and facilitate conversations about one’s own preferences . Highliglimitations in their ability to seek out and understand health information and services” [An individual’s health literacy and education levels may also impact their understanding of ACP. Patients perceived “ervices” . Li . For some patients, this meant entrusting all end-of-life decisions to family members , 69. ForIn some cultures, caring for loved ones was considered the family’s responsibility – a “fundamental family obligation” —particulOf the nineteen papers addressing the secondary outcome measure, eight examined clinician-based factors affecting ACP presence  was statistically significantly more common amongst White patients than amongst patients from minoritised ethnic groups , 66, 68.All five papers that operationalised ACP in terms of informal discussion or conversation about end of life found no statistically significant ethnic differences in this measure , 51, 65,Four key themes pertaining to patient-related factors affecting presence of ACP were identified: lack of awareness and understanding; financial constraints; faith and religion; family involvement and dynamics.The evidence showed that lack of awareness and understanding of ACP by patients was a barrier. This is consistent with a review by Hong et al. (2018), which set out that lack of knowledge is a principal barrier to ACP in minoritised ethnic groups . This cuFaith and religion were found to frequently impact the acceptability of ACP. For some participants, the end of life was pre-ordained by God; planning for the end of life was perceived to undermine God’s authority, or to be unnecessary as their end of life had already been planned by a higher being. Consequently, some faith-centred ethnic groups are choosing not to engage with ACP. Religion was not specifically measured in this study, though it should be noted that existing studies often consider ethnicity and religion as intertwined . This maThe role of a patient’s family was found to vary within and across ethnicities: some individuals preferred to make decisions alone and avoid ‘burden’ to their family, while others placed importance on collaborative familial decision-making. The position of individuals and their families regarding familial or self-expression of end of life preferences may impact the perceived relevance and importance of ACP, in turn impacting engagement with ACP conversations and documentation.Confidence and competence were important factors for clinicians undertaking conversations about ACP. Papers often reported clinicians having poor understanding around cultural values and ideals; related to this was poor cultural competence where clinicians held preconceptions that influenced rates of initiation of conversations about ACP .Previous work suggests that White doctors are less likely than doctors from minoritised ethnic groups to believe that ethnic l inequality in healthcare exists , 81. SimClinicians were generally willing to discuss ACP with patients but lacked the confidence and cultural understanding to do so . The eviContributions from Patient and Public Involvement representatives informed the analysis of the secondary outcome measure. They largely agreed with the shaping of the six key themes representing patient and clinician factors affecting ACP presence. It is important to note that contributors were shocked to read findings around clinicians’ pre-conceived ideas of patients wishes; they explained that it was unnerving that this stereotyping occurs, particularly when they have not knowingly been subject to it themselves.PPI contributions emphasised the importance of early ACP intervention. Timing was highlighted as a challenging clinician-related factor, whereby clinicians wanted to introduce ACP at a prognostically-appropriate juncture; in contrast, PPI representatives suggested introducing ACP much earlier, to prevent it from occurring too late to be a viable and effective option for patients. A South Asian representative highlighted how ACP should encompass broader questions, including questions around wills and other legal issues; they described poor understanding of the need for a will in their community due to the misplaced assumption that Sharia Law would be in place. Exposure to death and dying was highlighted as an important factor in awareness of ACP; a White Jewish representative shared their emotive experiences around the deaths of two close friends, and how this led them to develop and discuss openly with their family their own wishes and plans for death.The collection of data regarding ethnicity is often heterogeneous and incomplete. Research literature often does not specify the methodology used to assign participants to specific ethnic categories (72% of papers had an absence of explanation ). MoreovIt should also be highlighted that this review was limited to differences based on ethnicity. As discussed above, ethnicity and religion are often considered intertwined; these facets also relate to broader individuality. This review aims to highlight learning points and areas where culturally competent ACP can be improved; it is important to note that a patient-centred approach is key.As noted by Manohar et al. (2017), a researcher’s background, experiences and positionality will undoubtedly impact their perspective and interpretation of the data, even if this is implicit ; it can Although this review demonstrated no statistically significant differences between ethnicities in informal ACP conversations, “presence of conversation” is quite a crude measure. Furthermore, the current review did not look at content of conversation nor how successful it was deemed by the clinician subsequently, nor at the effect on the patient subsequently.A potential weakness of this review is the exclusion of any non-English language papers. This was a decision of necessity based on the authors’ skillset. It should also be noted that the majority of included papers were from the USA ; thus the current dataset may not be representative of ACP universally. Expansion of the eligibility criteria to include non-English language papers may have identified more high quality, rich datasets that are important to this review.The current review investigated potential differences in the documented presence of ACP in patients’ care records by ethnicity. Assessment of the primary outcome measure found statistically significant differences in the documented presence of legal ACP measures by ethnicity in countries where majority is White; people from White groups were more likely to have legally documented ACP in their health record compared to people from other groups. However, no statistically significant differences were found in presence of discussions around informal ACP.From the clinicians’ perspective, factors which were considered barriers to ACP included resource limitations (particularly of time) and a lack of confidence in holding culturally sensitive discussions about ACP. From the patients’ perspective, factors affecting ACP included a lack of understanding or awareness of ACP; resource limitations ; and factors such as religion, faith, and family. Further research is needed to understand how to deliver individualised, culturally sensitive ACP conversations to minoritised ethnic groups, to provide equitable opportunity to make informed decisions.Increased research on intersectionality in advance care planning internationally, particularly within the UK: Research identified within this review was mainly based within the USA, meaning it may have limited generalisability across other countries, particularly due to differences in sociocultural contexts and healthcare systems. An increase in research  in a variety of understudied regions would give insight into the transferability of findings and improve understanding of ACP values in relation to different cultural values.Greater policy and healthcare engagement to allow earlier introduction of culturally safe and sensitive ACP to all patients: There was a lack of awareness and understanding of ACP as an ‘unfamiliar’ idea amongst participants of incorporated studies. Reinforced by feedback from PPI contributions, normalising ACP conversations through early culturally-sensitive introduction may promote equitable access and information about ACP, allowing people time to engage with conversations, if they choose to do so, and recognising where, when and for whom it may not be appropriate.Greater patient-centredness using a cultural humility model: This study identified a lack of understanding of different cultural ideals and values as a clinician-based barrier to discussion about ACP. There is potential for more education and training around patient-centred ACP; the cultural humility model and Platinum Rule could be helpful here.In order to move towards more equitable ACP, we propose the following recommendations for research, policy and practice:Additional file 1:Appendix Table 1. PRISMA 2020 item checklist.