The HADITHI study is a cluster-randomized trial of children living with HIV and their caregivers in Kenya that aimed to increase rates of caregiver disclosure of their child's HIV status, encourage earlier status disclosure, and improve pediatric mental health and HIV outcomes. This analysis identified characteristics predicting caregiver non-responsiveness and compared outcomes among children based on disclosure status.A penalized logistic regression model with lasso regularization identified the most important predictors of disclosure. The two-stage least squares instrumental variable approach was used to assess outcomes accounting for non-compliance to disclosure.Caregiver non-isolation and shorter time on antiretroviral therapy were predictive of HIV status disclosure. There were no statistically significant differences found in CD4 percentage, depression status, or mental and emotional status based on disclosure status up to 24 months-post intervention.These findings have implications for specialists seeking to tailor disclosure interventions to improve caregiver-child dyad responsiveness. In 2021, there were approximately 1.7 million children living with HIV and 160,000 children newly infected with HIV globally –8, knowlDespite the clear importance of disclosure for children living with HIV, most adolescents in resource-limited settings remain undisclosed. Up to 50% of adolescents across studies in low and middle-income countries were told non-HIV related reasons for HIV illness and healthcare visits . These dInterventions focused on HIV disclosure have been developed across the world—including in Kenya , 23, GhaBased on this literature, it is well-understood that HIV disclosure interventions are successful in improving disclosure rates and play an important role in supporting the overall health and wellbeing of adolescents living with HIV. Yet, an important assumption remains unquestioned from existing research: is the action of disclosure itself, or participation in a disclosure intervention, more predictive of improved health outcomes? In other words, does disclosure matter after a dedicated HIV disclosure intervention, or could these same outcomes be found from participation in the intervention even without disclosure? Relatedly, are there common characteristics among caregivers who decide to disclose post-intervention, and are these relevant to their child's post-disclosure outcomes?This study attempts to answer these questions by employing instrumental variable analysis to isolate disclosure as an independent variable separate from intervention participation using data from a cluster-randomized controlled trial of an HIV disclosure intervention conducted in Kenya from 2013–2015 . Using tThe Academic Model Providing Access to Healthcare (AMPATH) Consortium is a partnership established in 2001 between 14 universities and academic health centers across North America and Moi University and Moi Teaching and Referral Hospital in Eldoret, Kenya that aims to provide comprehensive and preventative care, advance research findings, and educate medical students, residents, and community healthcare workers. Based on over a decade of medical practice and research related to pediatric HIV/AIDS in western Kenya, the AMPATH consortium developed a culturally adapted, multicomponent intervention to support disclosure of HIV status to perinatally infected children referred to cumulatively as the HADITHI  intervention. The disclosure intervention included patient-centered materials to guide disclosure, disclosure counselors, and post-disclosure child support groups to supplement usual care resources .A cluster randomized controlled trial  conducted between 2013 and 2015 evaluated the effectiveness of the HADITHI intervention on 285 caregiver-child dyads recruited from eight facilities in Eldoret, Kenya using an as-treated approach with intensive clinical and psychosocial assessments at 6-month intervals until 2 years post-intervention. The primary outcome was prevalence of HIV disclosure and pre-specified secondary outcomes included mental and behavioral outcomes for children living with HIV. The study was approved by the Institutional Review Board at Indiana University School of Medicine in Indianapolis, Indiana, USA and by the Institutional Research Ethics Committee at Moi University School of Medicine in Eldoret, Kenya. Additional design and results of the primary HADITHI study have been previously reported .post-hoc study was to examine the effect of non-adherence to disclosure among HADITHI intervention participants. In this study, we applied instrumental variable estimation to the intention-to-treat analysis previously published in 2019  as reported by either child or caregiver via disclosure questionnaires. Of the 285 caregiver-child dyads, 146 children (51.2%) were non-disclosed at baseline by either caregiver or child report.Disclosure post-intervention was defined as a binary variable of whether or not the child knew his/her HIV status as reported by both child and caregiver via disclosure questionnaires. Disclosure was assessed at 6-month intervals from baseline (immediately post-intervention) to 24 months post-intervention.Eighty demographic and clinical covariates were extracted from children's medical files or compiled from baseline questionnaires provided to caregivers and children during the HADITHI trial, including the Pediatric AIDS Clinical Trials Group General Health Assessment for Children Quality of Life Questionnaire, Strengths and Difficulties Questionnaire- Youth Version (SDQ), Patient Health Questionnaire nine-item depression instrument (PHQ-9), and locally developed and validated adherence and Stigma in AIDS family adherence (SAFI) stigma questionnaires . DetailsOutcome measurements for instrumental variable analysis were assessed using data from post-intervention follow-up at 6 month intervals for 24 months. CD4 percentage was extracted from the children's medical files by trained study team members. Depression symptoms were measured by the PHQ-9, and due to low frequencies of children reporting depression, the scales were transformed into three severity categories: no depression (score of 0), minimal symptoms (score of 1–4), and clinically significant depression (score of 5–19). Overall emotional and behavioral symptoms were measured by the SDQ, and scores were categorized into three categories: normal (score of 0–15), borderline (score of 16–19) and abnormal (score of 20–40).t tests, and two-sample Wilcoxon rank sum tests, as appropriate to test for statistical significance.Sample characteristics and distributions of categorical predictors were summarized using numbers and percentages for categorical variables, mean and standard deviation for normalized continuous variables, and median and interquartile ranges for non-normalized continuous variables. Baseline characteristics of children in the intervention and control groups were compared using Pearson chi-square tests, Fisher's exact tests, two-sample The least absolute shrinkage and selection operator (LASSO) penalized regression was used to select the best subset of predictors of disclosure for the 60 caregiver-child dyads who were randomized into the intervention group. We defined disclosure as a binary response of “disclosed” vs. “not disclosed,” with disclosure defined as caregiver-reported disclosure at any point within the 24-month follow-up period. Caregiver-child dyads who participated in the intervention were analyzed to select the best subset of multi-level predictors  of disclosure. LASSO selects a subset of predictors by shrinking the coefficients of the least contributive variables to zero, thereby excluding them from the model. LASSO combines the benefits of multiple regularizations and is particularly useful in studies such as this one where the number of observations is less than the number of variables and there are groups of correlated variables.To identify relevant predictors, all explanatory variables available in the data set were entered into the LASSO procedure. To calculate a reliable measure for the model validity, we used the conventional validation technique and randomly split the data into two data sets: 50% as the training data on which variable selection via the LASSO was done and 50% as the test data on which the logistic regression and corresponding pseudo R-squared were calculated. The dependent variable was disclosure after baseline. Independent variables included 80 demographic and clinical covariates described above. In the testing data, the most important predictors were selected using validation alpha standard error (ASE). To reduce the random effect arising out of the random split of the data, we used the bootstrapping method to repeat the random partition 1,000 times with replacement, and the average value of the obtained ASEs was calculated to produce the best regularization model .Instrumental variable (IV) methods were used to assess outcomes of children with HIV to account for non-compliance to disclosure among intervention participants . While sRandomization at the clinic level was used as the instrument for IV analysis because this instrument satisfied the two required assumptions: (1) the instrument affects the processes patients receive and (2) the instrument is not correlated with unmeasured factors or directly related with outcomes , 38. Ran*intervention status. In the second stage of 2SLS, the predicted number of disclosures and the same set of control variables were used to estimate the process effects on the outcome. The second stage 2SLS model was run independently for data at post-intervention time points of 6, 12, 18, and 24 months. The regression coefficient for the predicted treatment received in the second stage of the model is a consistent estimator of the LATE if the first stage model is a linear regression containing all the variables appearing in the second stage (IV models were estimated using the two-stage least squares (2SLS) approach . The fulnd stage , 42.As a comparison, ordinary-least-squares (OLS) linear regression models were utilized to estimate the effects of the processes on outcomes using an as-treated approach. The outcome model was explained by intervention status and the control variables again run independently at all four post-intervention time points.SAS Studio 3.8 (Enterprise Edition) was used in managing data, performing descriptive statistics, comparisons, and diagnostic tests, and completing the regression analysis. A statistical significance level of 5% was utilized for all analyses, and clinical significance was defined as a 5-fold change in the test statistic.Of the 146 non-disclosed caregiver-child dyads who participated in the HADITHI intervention, 130 (89%) completed all follow-up assessments and were included in this secondary analysis.n = 86) were the child's biological mother. Forty eight children (37.5%) had Stage 3 HIV disease at baseline, while 37 children (28.9%) had Stage 2 disease and 39 children (30.5%) had Stage 1 disease. Eighty-five percent of children were on ART at the initiation of the intervention. Forty-five percent of children had minimal depressive symptoms below clinical levels of depression, and 20% of children had clinically mild to moderately severe depression.Among all non-disclosed participants, the median child age was 11.42 years old and 55% were girls . The majp < 0.0001). The plurality of non-disclosed dyads in the control were members of the Luhya tribe (45%), while the plurality of non-disclosed dyads in the intervention were members of the Kalenjin tribe (40%).Demographic characteristics were not significantly different between non-disclosed caregiver-child dyads in the control and intervention subgroups except for tribe (p = 0.0030) and length of time on ARVs —were retained as significant in the regression model  of non-isolated caregivers disclosed HIV status after the intervention, only 3 out of 8 (37.5%) of isolated caregivers disclosed. In addition, children who had been on ARVs for an increased time prior to the intervention were less likely to be disclosed to about their HIV status post-intervention. The mean (SD) length of time on ARVs for children who were disclosed to was 1.3 (1.5) years, while that of those who were not disclosed to was 2.5 (2.4) years.p-values were >0.16, indicating no statistical significance.IV regression was conducted to compare clinical outcomes between intervention participants who disclosed HIV status post-intervention and those who did not. There were no significant differences in CD4 count, PHQ-9 scores, or SDQ scores between the children of intervention compliers and non-compliers at 6 months, 12 months, 18 months, or 24 months post-intervention . All p-vp-values were >0.12.OLS regression was also conducted for comparison using the predetermined subset of non-disclosed caregiver-child dyads at baseline. There were also no significant differences in CD4 count, PHQ-9 scores, or SDQ scores between those who completed the intervention vs. those who were in the control group at any point post-intervention. All st year for adolescents who were post-disclosure as compared to those who were post-intervention. At the same time, IV analysis also found a clinically significant increase in PHQ-9 scores at 12 months  and in SDQ scores at 24 months  as compared to OLS analysis, indicating worsened depression and behavioral health for post-disclosure adolescents as compared to post-intervention adolescents at 1 and 2 years post-intervention.When comparing OLS and IV methodology, a clinically significant difference between models was defined as a 5-fold difference in the test statistic, or the difference in outcomes between intervention participation (OLS methodology) and disclosure (IV methodology). IV analysis found a clinically significant decrease in SDQ scores at 6 months  and 12 months  as compared to OLS analysis, indicating improved behavioral health over the 1This study defined caregiver-child characteristics that predicted caregiver compliance to a disclosure intervention for children living with HIV and their families in western Kenya. We also used instrumental variable methods to explore local average treatment effects of disclosure post-intervention on HIV, mental health, and behavioral health outcomes. In completing this analysis, we aimed to isolate disclosure as a variable separate from intervention participation to independently assess the independent prediction and impact of disclosure.Two caregiver-child characteristics were found to be predictive of compliance to the HIV disclosure intervention: caregiver isolation and the length of time that the child had been taking ARVs.While almost 85% of non-isolated caregivers disclosed their child's HIV status to them after the intervention, less than 40% of caregivers who self-reported feeling isolated complied with HIV disclosure. This finding substantiates qualitative research conducted within this population prior to the intervention when caregivers cited their own isolation and fears of their child's isolation post-HIV status disclosure as reasons for not previously disclosing their child's HIV status . It alsoIn combination, this research suggests that HIV-related stigma may isolate caregivers and impact their willingness to disclose HIV status, even after specific curriculum within a disclosure intervention to address and attempt to combat HIV stigma. These isolated caregivers may continue to have substantial fear about the potential for a disclosed child to share about HIV status with others and subsequently be subject to further stigma. It also proposes the need for increased focus on caregiver HIV status and stigma within pediatric HIV disclosure interventions, which has already been implemented in various disclosure models , 48.Caregivers of children who were on ARVs for a longer time were also less likely to disclose post-intervention in this study. The average length of time on ARVs for children who were disclosed was more than 1 year less than those who were not disclosed. This result is surprising, since other studies have found that increased length of time on ARV medication was significantly associated with HIV disclosure , 15, 17.Of note, this study did not find that any of the other 78 baseline caregiver-child characteristics were significantly associated with disclosure post-intervention The full list of baseline characteristics included in the regression model can be found in the Our study did not find statistically significant differences in CD4 percentage, PHQ-9 score, or SDQ score between children based on disclosure status across the intervention. Disclosure was shown to not impact mental health or HIV outcomes, which contradicts caregiver fears that disclosing HIV status would worsen children's mental health and control over HIV treatment , a reporThese outcomes fit within growing literature that suggests that disclosure of HIV status to children in resource-limited settings may lead to improved HIV, psychological, and quality of life outcomes. One unmatched case control study of 309 children living with HIV in Tanzania found that patients who had their HIV status disclosed to them were more likely to have improved ART adherence as measured by a treatment adherence manual and improved quality of life as measured by World Health Organization Quality of Life standard tool . A seconAlthough the HADITHI trial found improved outcomes among children living with HIV when comparing children who completed the disclosure intervention to those who did not , these rOur study contained numerous limitations. First, this analysis was limited by its sample size since the study was not sufficiently powered for effect sizes. Almost 50% of children who participated in the HADITHI intervention were already aware of their HIV status at baseline, significantly limiting the number of caregiver-child pairs in this analysis and thus the study's power. Second, the definition of disclosure used in this quantitative analysis could not capture the nuances of disclosure studied in this and other settings. Some caregiver-child dyads reported divergent answers for disclosure across the study—with the caregiver stating that they disclosed to the child but the child not expressing knowledge of their HIV status or vice-versa. For this analysis, we defined disclosure as both the parent and the child reporting disclosure, but this may underestimate study results. In addition, it is well-studied that disclosure is not a binary variable but a longitudinal process , 55, whiOur study found that caregiver isolation status and the length of time a child had been on antiretroviral therapy were predictive of disclosure of HIV status to children living with HIV in western Kenya after participation in a disclosure intervention. We also found that children who had their HIV status disclosed to them and those who did not had no statistically significant different outcomes in CD4 percentage, depression status, or mental and emotional status up to 24 months post-intervention. The results of this study can inform future adaptation of the HADITHI intervention as well as disclosure interventions in other low-resource contexts. Disclosure interventions should integrate additional mental health and treatment adherence counseling aimed at continuing to stabilize mental health post-disclosure and improving mental health and HIV outcomes over time. Educational components of disclosure interventions can also be tailored to include concepts related to HIV-related stigma and caregiver and family isolation and target new ARV patients. Future research should replicate this study design elsewhere to understand factors contributing to disclosure compliance in other settings and further explore the impact of disclosure on health outcomes at further timepoints post-disclosure.The original contributions presented in this study are included in the article/The studies involving human participants were reviewed and approved by Institutional Review Board at Indiana University School of Medicine in Indianapolis, Indiana, USA, and Institutional Research Ethics Committee at Moi University School of Medicine in Eldoret, Kenya. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.EM, AB, and RV designed the research. WN, JA, CA, JL, TN, FS, JN, MS, and JH proved substantial contributions to the conception of the project and the acquisition of project data. EM completed data analysis and manuscript drafting. WN, AB, AC, and RV contributed to manuscript revision. All authors contributed to the article and approved the submitted version.


Human frontocentral event-related potentials (FC-ERPs) are ubiquitous neural correlates of cognition and control, but their generating multiscale mechanisms remain mostly unknown. We used the Human Neocortical Neurosolver(HNN)’s biophysical model of a canonical neocortical circuit under exogenous thalamic and cortical drive to simulate the cell and circuit mechanisms underpinning the P2, N2, and P3 features of the FC-ERP observed after Stop-Signals in the Stop-Signal task (SST). We demonstrate that a sequence of simulated external thalamocortical and cortico-cortical drives can produce the FC-ERP, similar to what has been shown for primary sensory cortices. We used this model of the FC-ERP to examine likely circuit-mechanisms underlying FC-ERP features that distinguish between successful and failed action-stopping. We also tested their adherence to the predictions of the horse-race model of the SST, with specific hypotheses motivated by theoretical links between the P3 and Stop process. These simulations revealed that a difference in P3 onset between successful and failed Stops is most likely due to a later arrival of thalamocortical drive in failed Stops, rather than, for example, a difference in effective strength of the input. In contrast, the same model predicted that early thalamocortical drives underpinning the P2 and N2 differed in both strength and timing across stopping accuracy conditions. Overall, this model generates novel testable predictions of the thalamocortical dynamics underlying FC-ERP generation during action-stopping. Moreover, it provides a detailed cellular and circuit-level interpretation that supports links between these macroscale signatures and predictions of the behavioral race model. Frontocentral event-related potentials (FC-ERPs) from human electroencephalography (EEG) are ubiquitous proxy signatures of higher-order cognition and control e.g., . For exaneural signature of the Stop process that starts earlier should lead to more successful stopping. Consequently, the onset of the Stop-Signal-aligned frontocentral P3 has been proposed to index the timing of this Stop process, since it onsets earlier on successful than failed Stop trials , whose foundation is a biophysical model of the canonical neocortical column under thalamocortical and cortico-cortical drive, to predict multiscale cell and circuit dynamics producing the FC-ERP in the SST. We specifically tested theorized links between these neural mechanisms and predictions of the well-known “horse-race” model, a computational model of behavior in the SST. This model assumes the outcome of Stop trials is determined by the winner of a race between pro-kinetic Go and anti-kinetic Stop processes , 1984b. ls e.g., . Howeverls e.g., . Based oHNN was explicitly designed to simulate the multiscale biophysical origin of human M/EEG signals. HNN accounts for both local circuit activity and exogenous driving influences from subcortical thalamic and higher/lower order cortical areas . As suchhttps://osf.io/v3a78/). The data collection procedures are described in full in https://github.com/darcywaller/HNN_StopSignal_FCERP.FC-ERPs used to fit HNN models were extracted from an open-source EEG dataset collected during a SST  who participated in research studies at the University of Iowa (IRB #201511709). Participants were compensated for participation monetarily or with course credit.All participants performed a computerized SST  in our data that accounts for the successful versus failed Stop condition difference in the onset of the P3 in the SST . The literature on receptor concentration within layers of medial frontal cortex  suggests that this area of the cortex differs from other regions in several ways. In particular, there is an abundance of AMPA-facilitated interconnections in pyramidal cells, resulting in networks that are “hyper-reciprocally connected” . There iThe net current dipole is simulated by summing the intracellular current flow in the pyramidal neuron dendrites directed parallel to the apical dendrite. A multiplicative scaling factor is applied to simulated net current dipole to estimate the number of cells contributing to the recorded signals, under the assumption that the recorded macroscale signals reflect nearly synchronous pyramidal neuron activity across larger populations of cells than those simulated. Here, we used a scaling factor of 150. Though the scaling factor relates roughly to the number of neurons producing the dipole, this correlation is indirect in the context of sensor-level data, as discussed further below. Additionally, raw evoked responses were smoothed by convolving with a 30ms Hamming window to account for spatiotemporal averaging that occurs when recording the response over a larger heterogeneous network in the data as compared with the model.There are two types of exogenous excitatory synaptic drive that can activate the local network. One drive is designed to represent feedforward input from the lemniscal thalamus that targets Layer 4 (not explicitly modeled) and is then relayed to the inhibitory neurons and pyramidal neuron basal  dendrite compartments in Layers 2/3 and Layer 5, referred to as Proximal drive in As described above, HNN is designed to simulate current dipole activity from a single source in the brain. Here, we applied ICA to the sensor-level signals as described above to estimate the activity from a single neural generator. This method does not provide information about the anatomical location of the source, and the units are in mV rather than current dipole units of nAm.The primary assumptions in comparing HNN current dipole output to these ICA applied sensor signals is that that signal reflects activity from a single brain source, here assumed to be in mPFC. As such, positive and negative deflections in the sensor level signal can be related to the primary current generator coming from intracellular current flow up and/or down the pyramidal neuron dendrites see also  section.HNN contains a complex dynamical systems model representing a neocortical column under exogenous driving influences. Due to the large-scale nature of the model, which has hundreds of parameters, in practice users leave most of the parameters fixed and adjust only a user-defined subset of parameters based on hypotheses motivated by prior studies and literature. The graphical user interface and quantification of the root mean squared error (RMSE) between simulated and empirical evoked response waveforms allows users to examine if and how adjustments in these parameters can account for recorded current dipole source data.Given the HNN neocortical template model , all simThe process for parameter estimation begins by starting with the hypothesized sequence of drives, using the drive parameters distributed with the software, and manually hand-tuning the parameters defining the timing and strength of these drives to get an initial close representation to the current dipole waveform, or, small RMSE between the empirical current dipole and simulated trial-mean current dipole. Once an initial close representation to the data is found, automated parameter estimation algorithms distributed with the software that leverages the COBYLA algorithm  can be uAs briefly discussed in the Introduction, theorized links between inhibitory processes and neural signatures such as the FC-ERP in the SST have been motivated in large part by assumptions and predictions of the horse-race model of SST behavior . This moonset occurred significantly earlier in successful compared to failed Stop trials (t(233) = −4.94, p < .0001, d = 0.29). The amplitude of the P3 did not differ between conditions (t(233) = 0.84, p = 0.40, d = 0.02). In addition, P2 peak amplitude was larger on failed Stop trials than successful Stop trials (t(233) = −8.53, p < .0001, d = 0.44), while the N2 peak amplitude was not significantly different between conditions (t(233) = −1.68, p = 0.09, d = 0.07). However, N2 peak latency was significantly earlier in successful Stops than in failed Stops (t(233) = −5.09, p < .0001, d = 0.34). The presence of the N2 and P3 peaks and their relation to the success of Stop trials here replicated prior work. Compared to the N2/P3 complex, the P2 is not typically theorized to relate to the activity of the Stop process itself per se, but represents a canonical feature of the FC-ERP that impacts preceding network dynamics and was therefore modeled alongside the N2 and P3 features in our HNN models (see next section).Participants performed a SST in which Stop-Signals (red arrows) were presented following the Go signal on one–third of trials. Grand-average ERPs  were calA main goal of this study was to use HNN to simulate the thalamocortical and cortico-cortical mechanisms generating differences in the FC-ERP between successful and failed Stop trials, and specifically to evaluate whether predicted mechanisms of P3 onset latency differences are consistent with race model-derived assumptions of an earlier Stop process onset in successful Stops. To begin, we modeled the mechanisms by which the FC-ERP could be generated, focusing first on recapitulating the key features of the Stop-Signal-locked FC-ERP observed during successful Stop trials: namely, the polarity and timing of the P2, N2, and P3 peaks. For more logic behind hypothesis testing with HNN and a discussion of how this was accomplished procedurally, we refer the reader to the We began with the HNN default evoked response parameters distributed with the software that define a sequence of thalamocortical and corticol-cortical feedforward and feedback input to the local network through Proximal and Distal projection pathways e.g., . We firsThe optimized HNN model predicted that an initial thalamocortical  drive at 115ms, a cortico-cortical  drive at 190ms, and a re-emergent thalamocortical  drive at 306ms led to the FC-ERP observed following Stop-Signals during successful Stop trials  but is not effectively stronger  than in failed Stops. The process for fitting the HNN model to the failed Stop FC-ERP was to start with the model fit to the -ERP see . Once molater but not weaker latent Stop process in failed Stops, in the optimized failed Stop model, the onset of the Proximal 2 drive was later and the strength of the drive did not need to be adjusted. Specifically, the best fit model  Proximal 1 drive. Although no significant differences in N2 amplitude were observed on average in our data, the failed Stop model predicted both a later and weaker (Layer 5 AMPA and NMDA weight reductions) Distal drive during failed Stop trials. These changes were necessary to compensate for differences in the earlier Proximal 1 drive underlying the P2 deflection, which in our simulations generated a different network state at the time of the N2 across conditions. Of note, the patterns of layer-specific spiking activity were similar across the successful Stop and failed Stop models, with the notable exception of more bursty spiking activity early in the trial in the Layer 5 pyramidal neurons in the failed Stop model . One intstrength of re-emergent thalamocortical  inputs could account for the later P3 onset, as opposed to a later onset time.To avoid model bias stemming from our main hypothesis about the cause of the P3 onset latency difference across conditions, we also simulated two alternative hypotheses of mechanisms that could potentially account for the later P3 onset in failed Stops. Both alternatives tested whether decreases in the effective In one alternative, we decreased the effective strength of the Proximal 2 input by reducing the number of spikes representing this drive from two to one to simulate a weaker underlying Stop process in failed Stop trials. We then optimized the conductance strength  and timing parameters of all input drives - except for the timing of Proximal 2 - to fit the recorded ERP see . The besIn the second alternative, rather than assuming the effective strength of the Proximal 2 input was weaker in the failed Stop model, we tested if a change in the conductance strength of this drive (in either direction) instead of its timing could also fit the data. More specifically, as in the first alternative, we optimized the conductance strength  and timing parameters of all input drives except for the timing of Proximal 2, albeit in this case without reducing the number of drive spikes. Though the best fit model recapitulated some of the features of the empirical waveform , it could not fit all of the waveform characteristics as well as before  while requiring minimal changes to Distal drive (generating N2). Within HNN’s framework, a reduced efficacy in Distal drive and consequently smaller-amplitude N2 could arise from two primary mechanisms: 1) a reduction in distal depolarizing currents, such as due to increased synaptic weights of the Distal drive onto pyramidal cells, or 2) hyperpolarization of the pyramidal cell soma, resulting from inhibition produced in response to a preceding Proximal drive. Both of these mechanisms reduce downward current flow in apical dendrites during Distal drive. Our timing-change model of failed Stops  predictsFirst, we tested if the compensatory reduced efficacy of Distal drive in the failed Stop model could also result from increased pyramidal inhibition at the time of the N2 peak. An increase in inhibition could emerge from changes in the preceding thalamocortical  dynamics e.g., , and spee. The resulting model simulated a better fit to the empirical FC-ERP  and P3  deflections separately, fitting two different models  to the two parts of the waveform. This yielded two models that accurately reproduced the (1) P2 and N2 deflections and (2) the P3 deflection, respectively. Further, when summed together the output from the two models reproduced the entire time course of the successful Stop FC-ERP, suggesting that it is not necessary that these individual waveforms all arise from the same underlying source see , A–C.To examine if the predicted mechanisms underlying successful vs failed Stops still hold, we next optimized these two models to the failed Stop FC-ERP, where the empirical data was similarly artificially split into early and late components. To be consistent with our prior hypotheses and simulations, we allowed all parameters of the first column’s model - which included the Proximal 1 and Distal drives - to vary during optimization, but only allowed the timing parameters in the second model - which contained the Proximal 2 drive - to vary. As before, we found that changes to timing and strength (in the same direction) of the parameters associated with the first Proximal and Distal drive were needed to account for early differences in the P2 and N2, but that a timing change alone in the second Proximal drive could account for the later P3 in failed Stops. Again, when summed together the output from the two models reproduced the entire time course of the failed Stop FC-ERP see , D–F. Tha priori hypotheses and post-hoc investigation of simulated alternative mechanisms. Our investigation resulted in a number of specific model-based predictions on the mechanisms of FC-ERP generation in each condition, as shown in In this section so far we have described the logic we followed to optimize the successful and failed Stop FC-ERP models based on Modeled thalamocortical  drive to the pyramidal neurons was stronger in failed compared to successful Stops, driving more early spiking at the time of the P2. Proximal 1 drive was stronger to Layer 5 pyramidal cells in both failed Stop models shown in Modeled cortico-cortical  drive producing the N2 in failed Stops occurred later than in successful Stops in both models. In the failed Stop model in In both models of failed Stop trials, modeled re-emergent thalamocortical  drive occurred on average later, but did not differ in effective strength, compared to successful Stop trials. This result implies that successful stopping relies upon tightly timed firing of Layer 2/3 and Layer 5 pyramidal cells, which are recruited earlier in successful Stops but do not otherwise differ in their activity compared to failed Stops.We used HNN’s biophysical model of the canonical neocortical column under exogenous drive to model the cell and circuit mechanisms underpinning the Stop-Signal-locked FC-ERP. Motivated by prior studies on the mechanisms generating sensory evoked responses , we demoOur modeling results predicted that the FC-ERP during successful stopping was generated by a sequence of thalamocortical , cortico-cortical , and re-emergent thalamocortical  drives, which sequentially interacted with local network dynamics to produce the P2, N2, and P3 peaks. In the model, the origins of these drives to the neocortex are abstract, and assumed to originate in thalamus and higher order cortex , representing “feedforward” and “feedback” drive . This moSeveral of our model-based predictions about mechanistic underpinnings of the Stop-Signal-locked FC-ERP align with emerging theories of motor inhibition as a two-stage process . The PauThe precise sources for the presumed thalamocortical and cortical-cortical drives to the frontal cortex generating the human FC-ERP are unknown. Based on cytoarchitectural principles e.g.,  and tracEfferent projections from mPFC to thalamic and/or cortical targets are also important to consider, as these projections provide possibilities for re-emergent thalamocortical signaling  and ultimately contribute to network activity supporting the Stop process during successful action-stopping. Projections from Layer 2/3 and 5 of mPFC may target dorsal motor areas such as the premotor or pre-supplementary motor areas , and Layseveral circuit mechanisms generating Stop-Signal-locked ERP features that together support inhibitory control processes in the SST. Our models predicted that the N2 was generated by Distal input to superficial layers that generated downward currents followed by spiking in L5 pyramidal cells. In failed Stop trials, this drive occurred later and in one model  being appropriately timed and spaced with respect to each other.In summary, both the N2 and the P3 are generated by mechanisms that vary with the success of stopping and have the potential to impact stopping accuracy through communication with downstream regions. These results also highlight that the N2 and P3 are underpinned by inputs from different origins to different cortical layers. More work is needed to disentangle the separate but parallel contributions of these mechanisms and resultant network states to stopping. Because the precise processes indexed by the N2 and P3 are still subject to debate, these models and subsequent work may help the field generate new ideas about the computations each feature reflects.Here, we focused on the Stop-Signal-locked FC-ERP, whose defining characteristics include the negative-positive N2/P3 complex. Several other inhibitory task contexts are known to elicit similar frontocentral negative-positive complexes including action errors ERN/P3; , unexpeca priori hypotheses in this study, with several alternatives examined, but we cannot rule out that additional dynamics may be involved in FC-ERP generation or stopping process. Overall, our results lead to many detailed predictions that provide targets for follow-up testing with invasive recordings (e.g., thalamic deep brain stimulation and recording, single-unit and laminar recordings in mPFC; The predictions in this study rely on the assumptions on the underlying HNN neocortical column model, which are based on generalizable features of neocortical circuitry and not specific to the potentially unique aspects of frontal cortical circuits. How much the finer details of microcircuit structure across brain areas contribute to macroscale EEG signals is unknown. Our results show that the FC-ERP shares similar features with sensory evoked ERPs from granular primary auditory and somatosensory areas  and sugg


Long-acting injectable cabotegravir (CAB-LA) for preexposure prophylaxis (PrEP) has proven efficacious in randomized controlled trials. Further research is critical to evaluate its effectiveness in real-world settings and identify effective implementation approaches, especially among young sexual and gender minorities (SGMs).ImPrEP CAB Brasil is an implementation study aiming to generate critical evidence on the feasibility, acceptability, and effectiveness of incorporating CAB-LA into the existing public health oral PrEP services in 6 Brazilian cities. It will also evaluate a mobile health  education and decision support tool, digital injection appointment reminders, and the facilitators of and barriers to integrating CAB-LA into the existing services.This type-2 hybrid implementation-effectiveness study includes formative work, qualitative assessments, and clinical steps 1 to 4. For formative work, we will use participatory design methods to develop an initial CAB-LA implementation package and process mapping at each site to facilitate optimal client flow. SGMs aged 18 to 30 years arriving at a study clinic interested in PrEP (naive) will be invited for step 1. Individuals who tested HIV negative will receive mHealth intervention and standard of care (SOC) counseling or SOC for PrEP choice . Participants interested in CAB-LA will be invited for step 2, and those with undetectable HIV viral load will receive same-day CAB-LA injection and will be randomized to receive digital appointment reminders or SOC. Clinical appointments and CAB-LA injection are scheduled after 1 month and every 2 months thereafter (25-month follow-up). Participants will be invited to a 1-year follow-up to step 3 if they decide to change to oral PrEP or discontinue CAB-LA and to step 4 if diagnosed with HIV during the study. Outcomes of interest include PrEP acceptability, choice, effectiveness, implementation, and feasibility. HIV incidence in the CAB-LA cohort (n=1200) will be compared with that in a similar oral PrEP cohort from the public health system. The effectiveness of the mHealth and digital interventions will be assessed using interrupted time series analysis and logistic mixed models, respectively.During the third and fourth quarters of 2022, we obtained regulatory approvals; programmed data entry and management systems; trained sites; and performed community consultancy and formative work. Study enrollment is programmed for the second quarter of 2023.ImPrEP CAB Brasil is the first study to evaluate CAB-LA PrEP implementation in Latin America, one of the regions where PrEP scale-up is most needed. This study will be fundamental to designing programmatic strategies for implementing and scaling up feasible, equitable, cost-effective, sustainable, and comprehensive alternatives for PrEP programs. It will also contribute to maximizing the impact of a public health approach to reducing HIV incidence among SGMs in Brazil and other countries in the Global South.Clinicaltrials.gov NCT05515770; https://clinicaltrials.gov/ct2/show/NCT05515770PRR1-10.2196/44961 As of Oe [SUS]) .Tenofovir-based oral PrEP has been effective in dramatically reducing population-level HIV incidence in multiple regions -11. HoweOwing to user adherence challenges, PrEP has not decreased HIV incidence among some population, particularly among populations with higher susceptibility  -20. AlthIn multiple studies, reasons for discontinuing or never initiating daily oral PrEP include low HIV perceived risk, concerns about medication side effects, desire not to take a daily pill, stigma, competing life events, and challenges accessing the medication ,21-23. LLong-acting injectable cabotegravir (CAB-LA) is an integrase strand-transfer inhibitor, and its effectiveness for PrEP has been demonstrated in phase 2b/3 studies  ,26. HPTNAlthough it is reassuring that CAB-LA has demonstrated comparable effectiveness among youth, Black MSM, and TGW in large clinical trials, the advantages of bypassing daily adherence behavior may not be realized if individuals cannot access injectable PrEP or do not return for repeat injections. Implementation studies are needed to provide crucial information about how interventions that are proven efficacious under randomized clinical trial conditions can be optimized for delivery within real-world service settings. The ImPrEP CAB Brasil implementation study aims to generate critical evidence to inform national policies and program implementers about how injectable CAB-LA PrEP can be provided to MSM, nonbinary persons, and transgender persons within public health PrEP services in Brazil. Its implementation objectives are to describe the facilitators of and barriers to integrating CAB-LA into routine public health clinics and to evaluate whether a mobile health  intervention optimizes decision-making on the best PrEP regimen among PrEP users and WhatsApp text messaging improves the uptake of and adherence to CAB-LA injection appointments. The primary clinical objective is to evaluate the effectiveness of CAB-LA PrEP in reducing the risk of HIV acquisition in a study cohort of participants in a context where participants can exercise choice in their HIV prevention method .The ImPrEP CAB Brasil  is an imThis is a type-2 hybrid implementation-effectiveness open-label cohort study with a convergent mixed methods approach . A type-2 hybrid design examines both effectiveness and implementation strategies within the same study . The stuIndividuals may participate in up to 4 different steps . In stepParticipants will be included if they (1) are MSM, nonbinary person , TGW, or transgender men; (2) are aged 18 to 30 years; (3) attend one of the study clinics looking for PrEP; (4) are CAB-LA and TDF and FTC PrEP naive; (5) are willing and able to provide written informed consent and adhere to the study requirements; (6) have nonreactive or negative HIV test results, including both third-generation HIV rapid tests and an undetectable HIV RNA at enrollment for individuals choosing injectable CAB-LA; (7) have no report of hepatic dysfunction; and (8) are willing to undergo all required study procedures. The full list of exclusion criteria is described in We will conduct formative work using participatory design methods  to develProcess mapping ,34 at eaFocus group discussions (FGDs) will be held with health care providers experienced in CAB-LA PrEP and members of the young SGM community to gather their input for the adaptation of an mHealth education and decision support tool. This tool, intended to be accessed via an electronic tablet with self-guided instructions, will be designed to convey information about both oral and CAB-LA PrEP in an engaging way, describing the benefits and drawbacks of each method, to help participants decide whether and which PrEP method may be appropriate for them. A preliminary storyboard will be developed and shared with FGD participants, and they will be asked to provide feedback on images, content, and sequence. Feedback will be incorporated into the programming of the mHealth education intervention. FGD participants will be also asked to review and refine the content of WhatsApp appointment reminders and provide input regarding the best timing for receiving the WhatsApp reminders. In addition, FGD participants will be asked about barriers and facilitators for CAB-LA PrEP implementation. Health care providers at the 2 study sites with experience in CAB-LA PrEP will be inquired about frequent questions asked by CAB-LA PrEP users about the medication as well as examples of education and counseling strategies they have used previously to facilitate CAB-LA user comprehension.Step 1 focuses on evaluating the mHealth intervention and participants’ choice of PrEP method . PotentiChlamydia trachomatis and Neisseria gonorrhoeae nucleic acid amplification testing. If a reactive or positive result is obtained for HIV test, the respective person is deemed ineligible to receive PrEP. Additional testing to confirm suspected HIV infection will be performed in accordance with local guidelines. If an HIV infection is confirmed, the participants will receive counseling and be immediately referred for HIV treatment and care. All participants will be evaluated for signs or symptoms of a possible acute HIV infection. Those with suspected acute HIV infection will be referred for follow-up investigation within the public health system.After signing step 1 informed consent, participants will undergo rapid tests for HIV, syphilis, and hepatitis B and C. Rectal, oral, and urine swab samples will be collected for Participants at each site will receive counseling and education on the use of PrEP options per the local SOC from a health care provider until 100 participants indicate that they would like to start CAB-LA PrEP and enroll in step 2. Additional participants will receive an mHealth education and counseling intervention plus the SOC education and counseling until 100 participants exposed to both SOC and the mHealth intervention enroll in step 2 of the study at each site (200 participants enrolled per site).Upon completing their exposure to SOC or SOC plus mHealth, participants will be asked to complete a brief survey administered by a study member that will capture the following information: (1) participants’ knowledge and beliefs about the dosing, advantages, and disadvantages of the 2 PrEP options, (2) their method of choice for HIV prevention, and (3) the reasons for their preference for oral or CAB-LA PrEP. Participants who received the mHealth intervention will be asked additional questions about its acceptability and its value in supporting decision making on PrEP choice.verbatim by study staff.A small subset of step 1 participants from each site  will be asked to participate in a qualitative interview to evaluate their perceived advantages and disadvantages of different PrEP products and reasons for their choice. Those who received SOC plus mHealth will be asked to share their feedback on the acceptability of the mHealth intervention. Audio recordings will be transcribed Following the completion of step 1 procedures, participants will be asked by a health provider whether they choose oral PrEP or CAB-LA PrEP. Participants who indicate a preference for oral PrEP will be referred to receive it in the routine HIV prevention service and will not be navigated by the study team. The study team will not actively follow-up on these participants but will retain their participant ID in case they express interest in switching to CAB-LA PrEP later in the study period. Participants who choose CAB-LA PrEP will be invited on the same day to participate in step 2 of the study and will undergo additional consent, screening, HIV viral load testing, enrollment, and data collection procedures.Step 1 participants who chose CAB-LA PrEP after receiving either SOC counseling or SOC counseling plus mHealth will be eligible to participate on the same day in step 2  and 2. SHIV viral load testing will be performed in step 2 participants using the GeneXpert platform to confirm their eligibility. With a detectable HIV result, the person will be deemed ineligible to receive PrEP and will be referred to treatment and care through the public health system. Those who do not have detectable HIV results and do not meet any other exclusion criteria will receive their first CAB-LA injection.The first follow-up visit will happen in 1 month, and the subsequent follow-up visits will happen once in every 2 months. At every follow-up visit, sites will perform HIV testing using the third-generation (antibody) HIV rapid tests before providing the next CAB-LA injection. Blood-based HIV self-testing will be performed by the study participant 24 hours before all follow-up visits, and the results will be reported by the participant during the study visit. If a participant does not bring the HIV self-testing report, the test will be performed at the clinic in a private space and before the study visit procedures. Regularly, additional HIV testing with stored samples will be conducted retrospectively, in batches, with fourth-generation HIV rapid test, Abbot ARCHITECT HIV Ag/AB combo assay, and the HIV-1 and HIV-2 RNA Viral Load COBAS 5800 platform. For positive or inconclusive results, the Geenius HIV1/2 confirmatory assay will be used. In all study visits, samples will be stored for additional retrospective HIV testing at the study central lab at INI-Fiocruz, as necessary. At any point of the study follow-up, participants with suspected HIV infection will be referred for follow-up investigation under study protocol. If HIV infection is confirmed at any point, CAB-LA injection will be stopped, and the participant will be immediately linked to care and ART initiation (according to the Brazilian guidelines) and will be invited to maintain a 1-year follow-up in the study with quarterly visits (step 4). Resistance testing will also be performed .As part of the consent process for enrollment in step 2, participants will be informed that they may be selected to receive a WhatsApp appointment reminder 2 days before each scheduled injection visit. Participants will be randomly assigned (1:1) to either the SOC  or SOC plus WhatsApp arm using simple or block randomization and will be informed of which arm they are assigned to during the step 2 enrollment visit. During the month 5 visit, participants who were randomized to receive WhatsApp appointment reminders will be asked to complete a brief participant survey to assess the acceptability of receiving WhatsApp appointment reminders. If a participant does not return for their month 5 visit, the survey will be administered at their next clinic visit.Step 1 participants may choose to switch from oral TDF and FTC PrEP to CAB-LA PrEP only once during the study period. These participants will undergo all step 2 procedures prior to receiving the first CAB-LA injection. Individuals with hepatitis B surface antigen–positive results and indication of hepatitis B treatment will be counseled to remain on oral TDF and FTC for PrEP. If hepatitis B treatment is not indicated and the individual chooses to switch to CAB-LA, enrollment will be allowed unless exclusionary hepatic criteria are identified .Participants included in step 2 to receive CAB-LA PrEP who decide to discontinue participation before completing the minimum 1 year and maximum 2 years of study follow-up will complete early discontinuation visit procedures . These pIf a participant misses an appointment but wishes to continue with CAB-LA PrEP, reloading with CAB-LA may be necessary, and the participant will need repeat HIV rapid tests before receiving a new CAB-LA injection. Oral bridging with TDF and FTC will be offered to participants who anticipate not being able to attend their scheduled injections (planned missed injections) in the +7 or −7-day window, starting approximately 2 months after the last injection dose of CAB-LA.Participants from step 2 will be invited to participate in step 3 if they meet the following eligibility requirements: (1) the participant decides to switch from CAB-LA to oral TDF and FTC during study follow-up or (2) the participant decides to early discontinue CAB-LA injections but does not want to start oral PrEP.Participants who choose to switch from CAB-LA to oral TDF and FTC will need to complete the procedures related to the daily oral TDF and FTC enrollment visit . The proParticipants who choose to keep off PrEP will be invited to be followed up in step 3. Participants will have quarterly visits with sexual risk assessment, HIV and STI testing and counseling, and the provision of condoms and lubricants.seroconversion visit  , Reach, The major clinical and implementation outcomes, levels of analysis, methods, and timing are described in Our major clinical outcome  will adopt an intent-to-treat approach and compare HIV incidence (and 95% CI) among individuals in the CAB-LA cohort with HIV incidence (and 95% CI) among individuals on daily PrEP within the Brazilian SUS. We will estimate the rate of HIV incidence with 95% CI in each group using Kaplan-Maier statistics. We will use a Cox proportional hazard model to assess the relative hazard (hazard ratio with 95% CI) between the 2 groups, overall and by site, age category, and gender.It is anticipated that there will be an average follow-up of 1.5 years per person with at least 75% retention each year (N=1350 person-years of follow-up) and that incidence will be higher than that observed in HPTN 083 in Latin America (0.60 per 100 person-years). If the observed incidence is approximately 50% higher than that observed in HPTN 083, the study will be able to estimate an incidence of 0.85 (95% CI of 0.47-1.42) per 100 person-years . If the To reach the primary implementation objective, we will report descriptive statistics  of Likert-scale responses to the TFA constructs  among health care providers.The study protocol and informed consent forms were reviewed and approved by the Evandro Chagas National Institute of Infectious Diseases (INI), Fundação Oswaldo Cruz (Fiocruz) institutional review board  and the Research Ethics Review Committee at the WHO with respect to scientific content and compliance with applicable research and human participants’ regulations. The protocol, site-specific informed consent forms, participant education and recruitment materials, and any requested documents and their subsequent modifications were also be reviewed and approved by the ethical review bodies responsible for the oversight of the research conducted at each of the study sites. Informed consent will be obtained in 2 study steps. In step 1, consent will be obtained from participants who visit the site looking for PrEP. In step 2, the participants will provide consent for the use of injectable CAB-LA PrEP. This study may be paused or stopped at any time by the local institutional review board or the study’s principal investigator.During the third and fourth quarters of 2022, we obtained regulatory approvals; programmed data entry or management systems; trained sites; and performed community consultancy, process mapping, and formative work. Study enrollment is anticipated to happen in the first quarter of 2023. The planned activities are presented in ImPrEP CAB Brasil is the first study to evaluate CAB-LA PrEP implementation in Latin America. Latin America is one of the regions where PrEP scale-up, as well as the reduction of inequalities in PrEP access, is most needed. We hereby describe the study protocol, providing details of the study’s objectives, procedures, outcomes, and statistical analysis to aid researchers from Brazil, Latin America, and other regions during CAB-LA implementation within public health systems.To date, no clinical or implementation results are available for injectable PrEP with CAB-LA in real-life settings. We expect the acceptability of CAB-LA in Brazil to be high. Data from a web-based survey and descriptive choice experiment have shown that injectable PrEP was preferred over other PrEP modalities in Brazil -37. Our Our study will also contribute to the expansion of the knowledge on the best HIV testing algorithm to be implemented in low- and middle-income countries when using CAB-LA for PrEP, which is a major need.This study is coordinated by INI-Fiocruz, an internationally recognized research institution and the leading reference center in Latin America for infectious diseases. INI-Fiocruz has been working closely with the Brazilian Ministry of Health (BMOH), the Pan American Health Organization, Unitaid, and the WHO to address evidence gaps and identify key learnings to inform the introduction of injectable CAB-LA PrEP among the most susceptible populations. The INI-Fiocruz team has extensive experience in engaging SGMs in demand creation, providing HIV combination prevention services that foster PrEP uptake and retention and advocating for enhanced HIV prevention policies ,40. In aThis study is poised for informing policy makers, building public health staff capacity, and creating environments for successfully implementing the proposed prevention strategies in collaboration with the BMOH. Moreover, we expect the mHealth component to be highly acceptable and possibly implemented within the Brazilian public health system.The ImPrEP CAB Brasil study will contribute to the generation of valuable information to inform the design of programmatic strategies for implementing CAB-LA PrEP in Brazil, maximizing the public health impact of PrEP in reducing HIV incidence among young SGMs in Brazil, Latin America, and other countries in the Global South.


This article proposes a CBAM-ASPP-SqueezeNet model based on the attention mechanism and atrous spatial pyramid pooling (CBAM-ASPP) to solve the problem of robot multi-target grasping detection. Firstly, the paper establishes and expends a multi-target grasping dataset, as well as introduces and uses transfer learning to conduct network pre-training on the single-target dataset and slightly modify the model parameters using the multi-target dataset. Secondly, the SqueezeNet model is optimized and improved using the attention mechanism and atrous spatial pyramid pooling module. The paper introduces the attention mechanism network to weight the transmitted feature map in the channel and spatial dimensions. It uses a variety of parallel operations of atrous convolution with different atrous rates to increase the size of the receptive field and preserve features from different ranges. Finally, the CBAM-ASPP-SqueezeNet algorithm is verified using the self-constructed, multi-target capture dataset. When the paper introduces transfer learning, the various indicators converge after training 20 epochs. In the physical grabbing experiment conducted by Kinova and SIASUN Arm, a network grabbing success rate of 93% was achieved. Robot grasping technology based on depth vision has become a critical direction of current robot industry research. It is widely used in application scenarios such as assembly, grasping, and palletizing . GraspinThis article establishes multi-target grasping datasets and proposes a crawling detection method based on the attention mechanism and atrous spatial pyramid pooling (CBAM-ASPP) network model. CBAM module can focus on important information with a high weight, ignore irrelevant information with a low weight, and constantly adjust the weight so that important information can be selected in different situations. The ASPP module expands the receptive range by increasing the receptive field size without affecting the image’s resolution or adding additional computational burden. Various dilated convolutions have different convolution ranges and effectively extract multi-scale information. Because of this, the paper chose these two modules to improve the SqueezeNet network. The main contributions of this article can be summarized as follows:(1) This article proposes a modular robot system for predicting, planning, and executing grabs and introduces camera calibration methods. It establishes a multi-object capture dataset, and preprocessing methods such as random cropping, flipping, adjusting contrast, and adding noise were used to expand the dataset;(2) A grasping detection method based on the SqueezeNet network is proposed based on the attention mechanism and atrous spatial pyramid pooling. The paper introduces the attention mechanism network to weight the transmitted feature map in the channel and spatial dimensions. At the same time, it uses a variety of parallel operations related to atrous convolution with different atrous rates to increase the size of the receptive field and expand the receptive range. Preserving features from different ranges effectively reduces feature loss;(3) Using the single-object dataset, the paper introduces transfer learning to pre-train CBAM-ASPP-SqueezeNet. It slightly adjusts the model parameters using the self-constructed, multi-target dataset to evaluate the CBAM-ASPP-SqueezeNet model;(4) The CBAM-ASPP-SqueezeNet model is deployed on Kinova and SIASUN UR robots. This article evaluates the CBAM-ASPP-SqueezeNet model and successfully applies the CBAM-ASPP-SqueezeNet method to grab detection tasks on multi-objective object grab datasets. This network crawling method achieves a balance between crawling accuracy and running speed.Realsense D435 Depth camera has a shooting range of up to 10 m, uses Intel’s sense SDK 2.0, and offers cross-platform support.As shown in (1) First, move the Kinowa to a suitable location by using a program or with an operator;(2) Select the markers shown in (3) As shown in Formula (1), each calibration detection can calculate the coordinate system of the marker calibration board. By taking 20 calibration detection samples, easy_handeye can calculate the transformation matrix The following coordinate systems are defined: The base and end are the base coordinate system and the end coordinate system of Kinova robot. The camera is the coordinate system of the Realsense D435 depth camera. Markers are part of the coordinate system of the calibration board, and the position of the calibration board is kept fixed during the calibration process. The position and posture of the camera are adjusted automatically or manually. The marker calibration plate and robot base remain unchanged during multiple sampling. The least square method is used to fit the calibration data of multiple sampling. The form of the least-square problem is Solve The hand–eye calibration results determine the position and posture representation of the camera coordinate system camera_color_optical_frame in the robot six-axis coordinate system m1n6s300_link_6. The visualization results of the hand–eye calibration results are shown in In real scenarios, robots use five-parameter representation to grab and detect targets. This article focuses on the five-dimensional grasping representation of the two-dimensional image of the robot gripper, and the grasping posture in the two-dimensional image is shown in x, y) represents the position information of the end of the mechanical arm in the plane, where  represents the centroid of the grasping rectangle. The notation w and h represent the width and height of the parallel two-finger hand, and gh and gw represent the height and width of the rectangle of the grasping frame, respectively. Where θ represents the grasping angle, for the mechanical arm, it needs to rotate a certain angle in the horizontal direction to reach the grasping point; gθ is the grasping direction obtained using the angle between the grasping height hg and the horizontal axis of the image. Grab characterization is shown in For the two-fingered hand grasping of the Kinova robot, where g represents a plane grasping pose. The notation .In the field of robotics, grasp perception is an important task. In order to solve this problem, scholars have abstracted the perception model into grasping parameter detection in a two-dimensional plane . They poCurrently, the targets for publicly grabbing datasets are all single targets, making it necessary to create multi-target object-grabbing datasets. As shown in Due to the relatively small number of multi-objective datasets, image preprocessing and data enhancement are two standard methods used to solve this problem. As shown in In object detection tasks, preprocessing ensures that multiple targets do not exceed the boundary and remain in the field of view. Resize and padding operations of the image are required to eliminate object deformation and ensure that the object is in the field of view. At the same time, it is necessary to remove invalid grabbing boxes to retain the object to be grabbed. The specific operation steps are as follows: Fill the original 640 × 480 image with 80 pixels around each side to obtain an 800 × 640 image; then, resize it to restore its original size, and crop it square from the center to the surrounding area, resulting in an image size of 351 × 351. Next, expand by 75 pixels around each area, use the replicate mode in OpenCV, and increase the image size to 501 × 501. Perform several pixel translations along the horizontal and vertical directions and five random rotations within 180 °. Perform a final image resizing with a final image size of 320 × 320. These operations obtain a 100-fold enhancement relative to the original dataset.CBAM (attention mechanism): As shown in Atrous spatial pyramid pooling (ASPP): SqueezeNet has similar accuracy rates to AlexNet, and the number of parameters can be reduced by 50 times. The ReLU activation function is introduced between the compression and expansion layers. The basic module of SqueezeNet is Fire Module, and Squeezenet reduces network parameters using the following three aspects:(1) Use a 1 × 1 convolution kernel to replace most 3 × 3 convolution kernels because the number of parameters of a 3 × 3 convolution kernel is nine times that of a 1 × 1;(2) Reduce the number of inputs to 3 × 3 convolution kernel channels. Since the number of input channels is equal to the number of convolution kernels, the number of channels is also critical to the number of network parameters;(3) After downsampling, in the convolutional network, each convolutional layer will generate an output feature map, which is mainly controlled by the following factors: the size of input data and the location of the downsampling layer. Larger feature maps contain richer spatial information, which can maximize classification accuracy; therefore, the downsampling layer is placed at the back. Suppose downsampling occurs in a network relatively near the front. In that case, the feature map of the overall network will shrink, which is not conducive to the performance of image classification tasks. Therefore, delayed downsampling can enrich the features in the network and improve the overall performance.cls).The CBAM-ASPP-SqueezeNet model contains one ASPP group to increase the receptive field’s size and to preserve features from different ranges. ASPP essentially consists of a 1 × 1 convolution + pooling pyramid + ASPP Pooling composition. The CBAM-ASPP-SqueezeNet model contains two groups relating to channel attention and spatial attention to obtain channel and spatial dimension information. The first set of CBAM occurs after the first convolution, BatchNorm2d, and ReLU; the second set of CBAM is sampled before the upsample. Eight Fire Module modules increase the maximum pooling value with a step size of 2 after Fire Module 4 and Fire Module 8 are used for downsampling. The additional compositing of features was performed with the same number of channels, which are located after , , , and . Since the number of channels is different, features cannot be added directly; therefore, a 1 × 1 convolution is used to change the number of channels to achieve feature fusion. After the fusion of the feature maps, the parameters of the capture frame are finally predicted. The CBAM-ASPP-SqueezeNet output includes five dimensions corresponding to the capture parameters  Xeon(R) CPU E5-2620 v4 @ 2.10 GHz3. GPU: Titan X Python version: 3.7.134. torch version: 1.10.1+cu111 torch-vision version: 0.11.2++cu111The multi-target object grasping datasets are divided into train sets and test sets, which have been divided into folders. Because of transfer learning, the ratio of the train and test set is 7:3; the epoch is 100; the learning rate is initially set at 0.0025; batch_size is usually 1000.The critical elements of the robot grasping system include visual perception, grasping pose estimation, trajectory planning, and grasping execution. The grabbing system platform includes robotic arms, D435 depth cameras, and laptops. The software platform includes camera acquisition, grasping position and posture estimation, grasping planning, and motion control.This article mainly studies the multi-objective object grasping problem found in Kinova and SIASUN UR robotic arms. There are two evaluation indicators for robot grasping: point metric and moment metric. At present, moment measurement is commonly used for grasping evaluation, while grasping angle and Jaccard index standards are used to evaluate robot grasping. The CBAM-ASPP-SqueezeNet model used in this article uses rectangular metrics for evaluation.1. The difference between the predicted capture frame angle and the effective capture frame angle is within 30;2. The Jaccard index between the predicted capture frame When the candidate grab box meets the above criteria, the candidate grab box is considered valid. According to this definition, the Jaccard index is equivalent to the Region of Interest (IOU) threshold determined during object detection research, which includes information on the center point of grasping and the width of the gripper opening. Since a real value rectangle can define an ample grabbable rectangular space, a predicted rectangle that overlaps 25% with the ground truth rectangle is still a good truth rectangle, as long as its direction is correct.The CBAM-ASPP-SqueezeNet model is pre-trained using the Cornell dataset to achieve grasping prediction. The extended version of the Cornell grab dataset includes 1035 RGB D images with a resolution of 640 × 480 pixels, including 240 positive grabbing samples of 5100 and 2909 negative grab samples of different real objects. Due to the sufficient size of the dataset, this paper pre-trains the CBAM-ASPP-SqueezeNet model. The paper introduces transfer learning to pre-train CBAM-ASPP-SqueezeNet using a single-object Cornell dataset and slightly adjusts the model parameters using the self-constructed, multi-target object capture dataset to evaluate the CBAM-ASPP-SqueezeNet model. As shown in As shown in The CBAM-ASPP-SqueezeNet network, which introduces the attention mechanism and atrous spatial pyramid pooling, increases the computational complexity of the model to a certain extent. However, CBAM-ASPP-SqueezeNet is higher by about 3–5 percentage points than Squeeze on IOU, proving that the attention mechanism and atrous spatial pyramid pooling have a specific contribution to improving network performance.It can be seen from This study detected the capture of real targets. The experimental results show the widespread applicability of this target-grasping algorithm in robotic arms. This algorithm has a promising universal performance and can be easily ported to other robot systems. The CBAM-ASPP-SqueezeNet algorithm can perform well in various testing environments, providing an effective solution for achieving efficient grabbing tasks.This article utilizes the attention mechanism and atrous spatial pyramid pooling module to optimize and improve the SqueezeNet model’s crawling network. Transfer learning is used to solve the problem of insufficient small sample datasets for multiple targets. The CBAM-ASPP-SqueezeNet model can be used for multiple target capture detection models. The proposed crawl detection method, whether on a single target Cornell dataset or a self-built, multi-target object dataset, has only 7.5% more parameters than the Squeeze parameter in the CBAM-ASPP-SqueezeNet model. However, the accuracy and IOU of the CBAM-ASPP-SqueezeNet network are significantly better than existing SqueezeNet crawl detection network methods, with better generalizability. Having been verified using Kinova and SIASUN crawling platforms, the CBAM-ASPP-SqueezeNet network crawling method has a reasoning speed of 315 ms, achieving a balance between crawling accuracy and running speed.This research involved the construction of a multi-target dataset and CBAM-ASPP-SqueezeNet lightweight network model to bring several significant advantages: filling the gap in multi-objective datasets, more efficient distributed training, lower model migration costs, and easier deployment in embedded devices. The investigation also needs more research on multi-objective stacking, multi-objective sorting, and crawling, and further improvements will be made. There is still a particular gap between applying this algorithm in real and laboratory environments because there is a significant difference between unstructured and laboratory environments.


Which intervention characteristics are associated with the efficacy of acceptance and commitment therapy (ACT) in improving the psychological health of informal caregivers of people with chronic health conditions, and what is the acceptability of ACT?This systematic review and meta-analysis of 29 studies with 2010 participants suggested that ACT with mixed delivery format, face-to-face modality, and more intervention sessions was associated with greater improvements in psychological health. ACT was acceptable according to attrition rate, adherence, and caregivers’ satisfaction.In this study, ACT seemed to achieve better therapeutic outcomes when it was individualized by including more face-to-face encounters and when intensive interventions with a mixed delivery format to facilitate peer support were applied. This systematic review and meta-analysis identifies intervention characteristics of ACT that are associated with improved psychological health and assesses the acceptability of ACT among informal caregivers. Although there is substantial evidence to suggest the health benefits of acceptance and commitment therapy (ACT) among informal caregivers of people with chronic health conditions, the great variation in intervention designs among published studies limits its application.To identify intervention characteristics of ACT that are associated with improved psychological health and to assess the acceptability of ACT among informal caregivers.Seven English- and 3 Chinese-language databases without limits on publication dates, the reference lists of previous reviews, and gray literature were searched up to February 2023.Randomized clinical trials comparing the effect of ACT vs control groups on improving psychological health among informal caregivers.Two reviewers independently screened searched records and extracted data from eligible studies. Random-effects meta-analysis and mixed-effects metaregression were performed. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline was followed.Psychological health outcomes  measured by valid measurements and the acceptability of ACT based on identified parameters.g range, −0.55  to −1.14 ) and at 1-to-3–month and 4-to-6–month follow-ups . Multivariable metaregression analysis regarding intervention characteristics found that ACT delivered in a mixed individual- and group-based format, face-to-face, or through more intervention sessions was associated with greater improvements for experiential avoidance , depressive symptoms , or anxiety symptoms . In general, ACT had low attrition rates (11%), and participants’ adherence (51%-80%) and satisfactory ratings (72%-95%) lend support to its acceptability.A total of 29 studies with 2010 participants, published between 2015 and 2023, were identified. ACT showed moderate to large effect sizes for improving psychological health at postintervention assessments (Hedges This systematic review and meta-analysis found that ACT was consistently associated with improvements in psychological health, supporting its application to improve informal care for chronic disease management. This review provides specific details on the design parameters of ACT for achieving greater efficacy. As such, the overall aim of this review was to identify the crucial intervention characteristics that contributed to the success of ACT and users’ acceptance of the intervention. The evidence is urgently needed to promote the adoption of ACT in clinical practice.Two previous meta-analyses involving randomized clinical trials (RCTs) and quasi-experimental designs have indicated the beneficial effects of ACT on psychological health and quality of life among informal caregivers.30 and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines.31 Study protocol was registered a priori on the PROSPERO (CRD42022352783).This review followed updated guidance of the Cochrane Handbook for Systematic Reviews of Intervention32 regardless of when it was initially diagnosed or what stage it was in. Studies must report psychological health outcomes of informal caregivers, including psychological flexibility  and other psychological health outcomes  using any validated measurement. A detailed description is available in eMethods 1 in This review included RCTs that examined the effects of ACT on improving psychological health among informal caregivers, including family members, friends, or neighbors. Eligible care recipients should have any chronic health condition  that requires particular attention,Paired reviewers (F.Y. and D.D.X.) systematically searched for RCTs in PubMed, Embase, PsycInfo, British Nursing Index, CINAHLPlus, Web of Science, Cochrane Library, and 3 Chinese databases  without limits on publication dates (updated in February 2023) and with language restrictions to English or Chinese. Search strategies are available in eTable 1 in 33 The quality of intervention implementation was assessed using the following criteria outlined by Chambless and Hollon34: (1) treatment protocol , (2) facilitator training, and (3) checking integrity by supervision, recording, or screening of the intervention or protocol adherence. Disagreements were settled through discussion among 2 reviewers.Paired reviewers independently assessed the quality of included studies using the Cochrane Collaboration’s Risk of Bias Tool for RCTs.All analyses comprised RCTs with available data and were performed with R Studio, version 2022.07.0 + 548 . Further explanations are available in eMethods 4 in 36 Hedges g was used to adjust the SMD due to small sample sizes, and heterogeneity was indicated using I2 and Q statistics, with interpretations in accordance with the Cochrane Handbook.30 A 95% prediction interval (95% PI) was generated to show the effect size ranges within which future studies would fall.37 Publication bias was assessed visually and quantitatively using a funnel plot and Egger test, respectively.38 Post hoc sensitivity analyses were performed using the leave-one-out method to test the robustness of the findings by excluding studies with the largest and smallest sample size and effect size.39The pooled pre-post effect size for psychological health outcome is reported as a standardized mean difference (SMD) with 95% CI after performing random-effects meta-analyses.30 subgroup analyses and metaregression were performed to explore heterogeneity and examine how intervention characteristics were associated with the outcomes. Subgroup analyses for prespecified categorical moderators were conducted using random-effects models. Potential continuous moderators were explored through univariable metaregression analysis using the mixed-effects model with restricted maximum likelihood estimation. To prevent overfitting and ensure a stable model, intervention-level characteristics were included in the primary multivariable metaregression analysis. Sensitivity analyses that incorporated all study-level characteristics into multivariable metaregression models were also conducted. The multivariable metaregressions were performed using the forward selection method, adding factors with P < .10, starting with the factors the smallest P value derived from the results of univariable analyses.40 Multicollinearity was checked and controlled using the variance inflation factor (VIF < 2.5). To ensure robustness of findings and to adjust for multiple comparisons, thereby reducing the risk of type I error, the Holm-Bonferroni method was applied. Additionally, P values using the Higgins and Thompson permutation test method while accounting for multiplicity adjustment (10 000 permutations) were calculated.41When at least 10 RCTs were identified for specific psychological health outcomes at postintervention and follow-up assessments,42 GLMM, as a 1-step approach of combining proportions and fully accounting for within-study variances, is particularly critical for limited sample sizes, zero counts, and result interpretation.43 This review set a 2-sided alpha at .05 to determine statistical significance.Attrition rate as an indicator of acceptability was defined as the percentage of informal caregivers who had not completed the postintervention assessment after being randomized to different groups. Estimated attrition rates with 95% CIs and PIs were pooled using random-effects meta-analyses with the generalized linear mixed model (GLMM).72 comprising 2010 participants. The median sample size was 59 , the mean age of participants was 46.4 years , and the mean percentage of women was 77% .The systematic selection procedures  resulted71 and trained students ,70 whereas the other studies (7 [24%])72 adopted a self-help approach. Individual-based (16 studies [55%]),72 group-based (10 studies [34%]),69 and mixed 71 formats were used to deliver ACT. Most studies (22 [76%])72 used a face-to-face modality, while the others used telephone calls, online platform, and email. Approximately 55% of included studies (16 studies)72 used an active control group. The median intervention duration, number of intervention sessions, and time of therapeutic encounter were 8 weeks , 6 sessions , and 300 minutes , respectively. Further details are available in eResults 1 and 2 and eTables 2, 3, and 4 in The main facilitators of ACT included trained professionals 65 16 had some concerns,72 and 7 had a high risk of bias.71 The risk of bias was mainly due to minimal baseline differences in the participants’ characteristics, uncertainty about concealment, the use of an inappropriate analysis method without dealing with missing data, the unavailability of data for almost all participants, and the lack of a published protocol or trial registration  and at 1-to-3–month and 4-to-6–month follow-up assessments , with low to considerable heterogeneity (2I = 0%-95%). However, no significant effect sizes were found for mindfulness (postintervention assessment) or value-based living (follow-ups). eFigures 10 and 11 in 48 heterogeneity decreased, but significant results remained for value-based living, mindfulness, cognitive fusion, and stress symptoms .eTables 6 and 7 in P = .01) and every increase in the number of sessions . Greater effect size for depressive symptoms at the postintervention assessment was observed for a mixed individual- and group-based format  and face-to-face modality . A greater effect size of ACT on anxiety symptoms at the postintervention assessment was found for face-to-face modality .Multivariable metaregression analysis results regarding intervention characteristics are reported in 2 =I 83%). Adherence to all intervention sessions  was reported in 8 studies.72 Of the 10 studies that provided participants’ feedback,72 4 studies72 included self-reported satisfaction rates (72%-95%). Participants reported that ACT had beneficial effects on caregiving, improved psychological health, and provided helpful intervention content. They also provided feedback on the limitations and made suggestions for improving existing interventions  and study and participant characteristics  are relevant to the efficacy of ACT, which is a prospective and viable psychological therapy that is well-accepted by informal caregivers.25 this review identified moderate to large effect sizes of ACT on depressive, anxiety, and stress symptoms by adding 19 more RCTs than the number included in the prior reviews of which we are aware. This finding is comparable with the effect sizes of general medicines and psychiatric drugs reported in a frequently cited meta-analyses overview of psychological health,73 even though the longer-term effect of ACT beyond 6 months is yet to be established. This result is encouraging, as 75% of people with psychological distress prefer nonpharmacological treatment with minimal adverse effects.76 In addition, the current study identified positive effects of ACT on psychological flexibility of caregivers, including experiential avoidance, value-based living, mindfulness, and cognitive fusion. These outcomes suggest that ACT may cultivate psychological flexibility by improving the acceptance of emotions, clarifying a values-oriented life, promoting living in the present moment, distancing from negative feelings, and eventually, mediating the effects of the intervention on depressive, anxiety, and stress symptoms. However, ACT did not demonstrate a longer-term effect on value-based living, defined as the continuous embodiment and expression of chosen purposeful and meaningful action. As this construct is more about the existential aspect of being a caregiver, the stress-coping paradigm of the reviewed ACT may imply the inadequacy of the intervention content in fostering the positive aspects of caregiving (PAC), such as a sense of goal accomplishment, enhanced family dynamics, and finding meaning in life. A review on PAC indeed reported its role in improving the existential well-being of caregivers.77Converging with the substantial evidence for the therapeutic effects of ACT on psychological health outcomes among informal caregivers,57 The physical context also allows for sincere therapeutic interaction, enabling sensitive counseling and feedbacks, empathetic care, and partnership relationships to enrich the implementation of ACT. However, caregivers’ high role demands may limit applicability of ACT in a face-to-face modality due to accessibility issues.79 Although in-person interventions showed promising outcomes, further studies are needed to expand the evidence base for innovative interventions addressing accessibility obstacles, such as blending remote and face-to-face modalities.The reviewed studies showed that using an in-person method to deliver ACT was associated with higher ACTs’ efficacy than non–face-to-face modalities. The benefit may imply the increased relevance of an in-person encounter to optimize the caregivers’ engagement through therapeutic counseling techniques such as confrontation, reflection, and behavioral activation.80 Incorporating a group format facilitates shared experience, peer learning and support, and collective commitment to change.81 Furthermore, this review highlighted that ACT efficacy was associated with higher intensity  and face-to-face modality in ACT delivery. This suggests that greater exposure to ACT principles and practices can yield more significant results. This aligns with the narrative synthesis results of caregivers’ feedback, which showed that they have difficulties with certain ACT aspects , emphasizing the necessity for sufficient intervention exposure.This review identified a significant association of a mixed delivery format with ACT’s efficacy. This format may underline the crucial interplay between personal introspection and group interaction, connecting caregivers’ personal psychological processes to their engagement with external realities.48 These can also help diminish associated stigma. However, ensuring quality through adequate training and supervision is critical when using such task-shifting approaches. Health promoters with different backgrounds may effectively scale up ACT interventions to improve the psychological health of caregivers, especially in low- and middle-income countries with a scarcity of qualified psychologists.82Although the limited coverage of different health professionals among the reviewed studies did not allow an investigation of the effect of interveners’ professional backgrounds on outcomes, individual studies revealed that ACT can be delivered effectively by trained health professionals  and students  as well as by a self-help approach. Additionally, peer-led interventions, leveraging lived experiences and empathy, are emerging as a promising approach for delivering ACT.83 This review also identified that caregiving context, characterized by the diverse experiences and unique demands associated with different diseases of care recipients3 were associated the efficacy of ACT. Particularly, caregivers of children with chronic health conditions and adults with CNS diseases demonstrated greater benefits from ACT. This could potentially be due to their adaptability to ACT techniques, prolonged caregiving durations, and ACT’s role in enhancing psychological flexibility in the face of disease-specific challenges.Univariable analysis found that ACT’s efficacy significantly varied by caregivers’ age and health context for caregiving. Younger informal caregivers in particular benefitted from ACT, indicating that they may be more responsive to the intervention because they have a higher education level and are more receptive to learning new knowledge.84 As the attrition rate is significantly associated with efficacy of ACT for experiential avoidance and stress symptoms, it is important to examine how to optimize the program design to best fit the caregivers’ preferences. Among the reviewed studies, codesign, as an important approach that actively involved the end-users in meeting their needs for psychological services,85 has seldom been used to guide the ACT program development. This may be an important perspective to optimize ACT design and improve its acceptability among caregivers.Overall, ACT was acceptable among informal caregivers, with similar attrition rates for other psychotherapies (ranging from 17%-20%).22 The combined application prevents the examination of single and different combinations of ACT processes. Future research should examine how each of the 6 psychological flexibility processes individually contributes to improving caregivers’ psychological health, and how these processes interact and influence each other on a session-by-session basis. This understanding can inform the development of tailored ACT interventions, optimizing the sequence or combination of processes for maximum therapeutic efficacy. Additionally, the current review indicated that nonspecialists could master the techniques of administering ACT .58 Therefore, future studies could investigate effective enabling mechanisms for trainers to increase the availability of this important therapy for supporting psychological recovery and adaptation to challenging situations, which is feasible since no official certification is needed for an ACT intervener.Our findings suggest that ACT is acceptable and effective in the caregiving context. However, high-quality trials should expand the research base to include caregivers with more challenging encounters in role fulfillment and to investigate the longer-term efficacy of ACT and the underlying mechanism explaining the psychological benefits of ACT. The RCTs included in this review applied 4 to 6 psychological flexibility processes together, due to the interconnected nature of these processes.The findings of this review need to be interpreted in the light of several limitations. First, the effectiveness of ACT in improving caregivers’ psychological health is still questionable when compared with an active psychological intervention . Second, the effective ACT intervention characteristics for low-income countries with heavy caregiver burdens and for other psychological flexibility processes  remain unknown. Third, the effect sizes of some subgroups also rendered the results uncertain because of an insufficient number of studies and unexplained heterogeneity. Despite an extensive multivariable metaregression analysis considering all study-level characteristics to capture potential confounder interactions in the clinical setting, the limited number of included studies necessitates caution in result interpretation due to the potential risk of overfitting. Finally, there were inadequate descriptions of how dropouts and missing data were managed with a statement on whether per-protocol or intention-to-treat analyses were applied.This review synthesized the available empirical evidence base, and the findings suggest that ACT has beneficial effects on the psychological health of informal caregivers of people with chronic health conditions. In accordance with WHO-recommended task sharing, it is feasible and beneficial to engage nonpsychological specialists  in the delivery of ACT for informal caregivers. However, the existing evidence does not explain the underlying mechanisms. The lack of evidence regarding engagement experience emphasizes the need for a vigorous process evaluation. Effective population-level psychotherapy is gaining prominence, and this review should serve as further impetus for the widespread adoption of effective ACT for informal caregivers to help them manage various forms of psychological distress.