Sedentary behaviour (SB) is a risk factor for chronic disease and premature mortality. While many individual studies have examined the reliability and validity of various self-report measures for assessing SB, it is not clear, in general, how self-reported SB ) compares to device measures .The primary objective of this systematic review was to compare self-report versus device measures of SB in adults.Six bibliographic databases were searched to identify all studies which included a comparable self-report and device measure of SB in adults. Risk of bias within and across studies was assessed. Results were synthesized using meta-analyses.The review included 185 unique studies. A total of 123 studies comprising 173 comparisons and data from 55,199 participants were used to examine general criterion validity. The average mean difference was -105.19 minutes/day ; self-report underestimated sedentary time by ~1.74 hours/day compared to device measures. Self-reported time spent sedentary at work was ~40 minutes higher than when assessed by devices. Single item measures performed more poorly than multi-item questionnaires, EMAs and logs/diaries. On average, when compared to inclinometers, multi-item questionnaires, EMAs and logs/diaries were not significantly different, but had substantial amount of variability  with approximately half over-reporting and half under-reporting. A total of 54 studies provided an assessment of reliability of a self-report measure, on average the reliability was good (ICC = 0.66).Evidence from this review suggests that single-item self-report measures generally underestimate sedentary time when compared to device measures. For accuracy, multi-item questionnaires, EMAs and logs/diaries with a shorter recall period should be encouraged above single item questions and longer recall periods if sedentary time is a primary outcome of study. Users should also be aware of the high degree of variability between and within tools. Studies should exert caution when comparing associations between different self-report and device measures with health outcomes.PROSPERO CRD42019118755 Sedentary behaviour (SB) is considered a risk factor for chronic disease and premature mortality . This arEkelund et al. 2016) performed a large observational study to assess the interactions between SB and PA, and reported that the associated risk of prolonged sitting was nullified once individuals performed 60-75 minutes per day of moderate-to-vigorous intensity physical activity (MVPA) . Current performeAs with PA , the larThe review was prospectively registered with PROSPERO (#CRD42019118755).Adults with a mean age ≥ 18 years.Self-report measures may include: diaries or logs ; questionnaires  or along with other activity intensities ; surveys (self-report measure which may include one or more questions on SB); ecological momentary assessments , and, recall interviews . Following the TASST framework proposed by Dall et al. , self-reAccelerometers , reading, television/video watching, passive video games, screen time, computer time, passive transportation , etc. Measures needed to be comparable . Units were standardized where possible . Medians and interquartile range were included by equating the median to the mean and dividing the interquartile range by 1.35 (SD = Q3 – Q1/1.35) . MeasureObservational  and experimental  studies that compared the amount of time spent in SB between a self-report and device measure were eligible.Both published (peer-reviewed) and unpublished grey literature  were eligible. No language restrictions were imposed on the search, but only papers published in English or French were included due to translation limitations.Studies in which the mean age was < 18 years, included animals, or which did not compare two measures or in which outcome data from the two measures could not be extracted, and those from which the types of SB were not comparable  were excluded from the review.The search strategy was created by an information specialist (KF) in discussion with members from the authorship team . The search was first created in MEDLINE using a combination of index terms/unique subject headings and keywords related to the self-report and device measures and SB. Main concepts searched were “sedentary behaviours”, “self-report measures”, and “device measures”. Only adult and human populations were captured, and a comparison of the two types of measures (self-reported or device) or data to compare the two measures had to be performed for the study to be retained. Once the search was finalized  ALL (Ovid), EMBASE , PsycINFO (Ovid), CINAHL (EBSCO), SPORTDiscus (EBSCO), and Dissertations & Theses Global (Proquest). Search results were exported to EndNote X8  and duplicates removed through manual inspection using the EndNote duplicate identification function.Following the initial search, titles and abstracts were exported from EndNote X8 into Covidence  software where further duplicates were removed. Two reviewers independently reviewed all titles and abstracts  and full texts . A third reviewer was consulted if disagreements occurred. Reviewers were not blinded to the authors of the studies.Data extraction was completed by one reviewer (SAP) and verified by a second  using standardized data extraction forms in Google Sheets. Information extracted included: publication details; participant characteristics , country of study; sample size analyzed ; study design; behaviour examined ; self-report measure type  and name ; device measure type  and name and model ; quantity of behaviour within each measure and measure of variance ; units of measurement ; analytical techniques used to compare measures ; result of comparison ; and, reliability assessment details .A priori determined subgroup analyses were carried out to test differences for: males vs. females, multi vs. single item questionnaires, week/workday vs. weekend/non-workday recall, work vs. non-work time, questionnaire comparisons , healthy vs. chronic  populations, and validation against accelerometer vs. inclinometer. If a study only provided sex-stratified results, the individual male and female data were included in the meta-analyses separately, but if a total combined estimate was provided this was used rather than sex-specific estimates . Publication bias was conducted using examination of funnel plots for symmetry. Post-hoc sensitivity analyses also examined comparisons in overweight/obese and pregnant populations, by wear location , and accelerometer cut-point.A narrative synthesis, including summary tables, was used to examine all study results. Forest plots and meta-analyses were created using Review Manager Version 5.3.5 (Copenhagen: The Nordic Cochrane Centre) to compare mean differences (minutes/day) and 95% CIs in time spent sedentary between self-report and device-assessed measures. Studies that reported units that could not be converted to minutes per day, that did not provide measures of variance , or did not provide results for comparison were not included in the meta-analyses. If a study reported on weekday, weekend, leisure or occupational-specific time they were included in separate meta-analyses. A minimum of two studies were required to conduct a meta-analysis. Several studies –30 incluWe assessed the quality of the individual studies used in the meta-analyses using a modified version of the QUADAS2 tool used to examine bias in studies of diagnostic accuracy . In the Figure The included studies were published from 2004 to 2019; the majority from the past five years. A total of 36 countries were represented by at least one study. The countries with the most studies included the United States (29%) and Australia (15%). A greater number of studies reported on female-specific data/populations compared to male-only data. The IPAQ-SF (37 studies), IPAQ-LF (32 studies), GPAQ (17 studies), SBQ (13 studies), and the Occupational Sitting and Physical Activity Questionnaire  were the most common self-report measures and the ActiGraph GT3X the most used device measure followed by the activPAL. All but six of the studies , 205 thaRisk of bias results are summarized in Fig. Supplemental table 2 = 99%) indicating a great deal of variation between studies with as much as 6 hours/day discrepancy within individual studies. There was no clear pattern in under- or over-reporting based on the risk of bias associated with timing and flow. The funnel plot was largely symmetrical and there was no apparent publication bias.A total of 123 studies comprising 173 comparisons and data from 55,199 participants were used to examine the criterion validity of self-reported average sedentary or sitting time by comparison to a device measure of sitting or sedentary time , 208–2102 = 93%) and weekend/non-workday  sedentary time were both significantly different than device-assessed sedentary time, but were not significantly different from each other . Self-reported television time was significantly lower than television time derived from a combination of logs and accelerometers or television monitoring devices . On average, self-reported time spent sedentary at work was significantly greater than that assessed by device (regardless of device) . Self-reported proportion of a workday spent sedentary was not significantly different from the inclinometer-assessed proportion .Additional studies examined and compared the criterion validity of self-report measures under the following conditions: nine studies (19 comparisons) on weekday/workday (includes time at work and outside of work) vs. weekend/non-workday sedentary time , 198; 2 2 = 98%) and females  under-reported their sedentary time by a similar amount when compared to device measures . Examining only those studies which provided both male- and female-specific data did not change the findings .Subgroup analyses found that males . Those from apparently healthy populations , chronic populations , and those who were considered overweight or obese  all under-reported their sedentary time compared to the device measure. Among the three studies that included pregnant women, self-reports were not significantly different than device measures, though they trended toward under-reporting . Chronic populations appear to under-report to a greater degree than apparently healthy populations .No significant between group differences in the discrepancy between self-report and device measures were observed for participants from apparently healthy populations, chronic populations, pregnant women or those who were considered overweight or obese  whereby single item measures, on average, significantly under-reported sedentary time compared to device measures , whereas multi-item questionnaires , EMAs  and log/diaries  did not. Almost all (82%) of the single item studies significantly under-reported compared to 37% of multi-item questionnaires and 22% of logs/diaries. A sub-group analysis was also performed to examine the effect of recall duration on self-report measure performance. Current day  and previous day  recalls performed the best, while recalls over the previous seven days , previous month , usual day , and usual week  performed the worst  compared to multi-item/component questionnaires (excluding IPAQ + motorized travel), EMAs, and logs/diaries. A significant between group difference was found . The IPAQ-Short Form , IPAQ-Long Form sitting questions , IPAQ-Long Form sitting + motorized travel , and GPAQ  had the greatest discrepancy between self-reported sitting time and device-assessed sedentary time. The SBQ performed the best with an average mean difference of ~6 minutes/day . However, the SBQ studies had a high level of heterogeneity and were split with 4/9 significantly under-reporting and 3/9 significantly over-reporting, whereas the IPAQ and GPAQ consistently under-reported sitting time. On average, the Multimedia Activity Recall for Children and Adolescents (MARCA) questionnaire  and the sitting time questionnaire developed by Marshall [2 = 89%) over-reported sedentary time compared to device measures.Within questionnaires there was a significant between group difference . A significant difference between accelerometer  and a combined accelerometer + heart rate monitor  and self-report measures was observed. An analysis of different models of accelerometers  revealed no between group differences . Further, when comparing accelerometer cut-points  against a single-item measure  there were no between group differences . The majority of accelerometers with wear location reported were worn on the dominant hip or waist. A sensitivity analysis of wear location found that the self-report measures were significantly lower when compared to wrist vs. waist/hip locations . On average, self-report measures when compared to monitors worn on the upper arm (e.g. SenseWear Armband) were not significantly different , though there was a high degree of variation between and within studies , and most self-report measures were multi-component or diaries/logs. Contrary to the accelerometer results, no significant differences were observed when comparing self-report measures to an inclinometer . However, when only studies that included both an accelerometer and inclinometer  outcome compared to a self-report measure were used, no significant difference between the self-report and device measure were observed  when comparing activPAL  and accelerometers . A sensitivity analysis found that self-reported sedentary time using single-item questionnaires (including IPAQ-LF without motorized transport) was significantly lower when compared to both inclinometers  and accelerometers  . However, when multi-item questionnaires (including IPAQ-LF + motorized transport), logs/diaries and EMAs were compared to device measures, they performed better when compared to inclinometers  versus accelerometers  .A sub-group analysis identified that the difference between self-report and device measures varied depending on the type of device used  with 72% of comparisons identifying that self-reports under-reported compared to device measures. Single question measures  resulted in significant under-reporting of sedentary time compared to all device measures, whereas multi-item questionnaires, EMAs, and logs/diaries did not, and performed relatively well, especially when compared to activPALs (the gold standard of field-based sitting). Therefore, it appears that the use of multi-item questionnaires, logs/diaries and EMAs may provide more comparable results to device measures versus single-item questions. There was, however, a large and significant degree of variability within (up to 4.5 hours/day) and between studies, and this was present in most of the meta-analyses performed. In the case of diaries/logs and EMAs where fewer studies were used in the meta-analysis, this variability further limits our confidence in the reproducibility of the results. While most led to an under-reporting (though to different degrees), some provided similar estimates and some over-reported and the 95% CIs around these estimates were often quite large with the majority greater than 30 minutes. This variability has great consequences for the interpretation and use of measures and is something that users should consider when deciding on which tool to use. For example, while multi-item questionnaires, EMAs and logs/diaries had more acceptable mean differences, the variability was quite large and not generally acceptable (up to 1.8 hours/day). It is also important to recognize that most studies that employed a single item did so using the global sitting question from the IPAQ or the GPAQ (or based on these questions). The sitting time question is part of a larger questionnaire on PA and was developed for surveillance purposes, whereas multi-item SB questionnaires such as the SBQ and WSQ were designed as standalone surveys to obtain more detailed estimates of SB.Self-reported work-time spent sedentary appeared to be better recalled compared to total day sedentary time. Interestingly, questionnaires that asked participants to recall the proportion of their workday spent sedentary did not significantly differ from inclinometer-assessed sedentary time ranging from an average over-reporting of 9% to under-reporting by 4%. Chastin et al., compared 18 self-report measures of sedentary time against an activPAL and found tools that used a scale to assess the proportion of daily time spent sitting had the lowest amount of missing data and best agreement with device-assessed measures . Matsuo It is important to remember that self-report and device measures provide different and complimentary information , 212. AsEMAs, logs/diaries and previous day recalls generally performed better than single item questionnaires which ask participants to recall total sitting in the past week/month/year or on a usual day as they remove or attenuate potential recall and response biases by capturing self-report closer to ‘real-time’ and often assess details about body position and different types of behaviours. They also provide an upper threshold for reporting by asking respondents to reflect upon smaller portions of time. Similarly to what was observed by Chastin et al., multi-item questionnaires performed better than single-item questions for accuracy, but with both having poor precision (large 95% CIs) . This maContrary to what has been observed in the literature for PA , there dResearch has shown that self-report and device-based measures of SB associate differently with health outcomes , 216; deAlthough correlation between self-report and device measures was low-to-moderate, self-report measures had an acceptable level of reliability. Reliability is important when assessing SB over time whether to assess changes from an intervention or to monitor prevalence in a population .This review has limitations that should be considered when extrapolating results. First, the review was limited to studies that included directly comparable SB data between the self-report and device measures. This greatly reduced the number of studies examining self-reported activities such as television and passive transit which are difficult to compare to device measures of the same behaviour. However, it does provide an assessment of questionnaires that included multiple SBs and many that examine self-reported sitting. It should be noted that almost all the meta-analyses demonstrated a high degree of heterogeneity between and within (up to 6 hours/day) studies, limiting our confidence in the estimates. However, given the large number of included studies, we feel that the high degree of heterogeneity is likely, at least in part, explained by the potential variability in recall and response bias within study populations. While we did not identify any difference in healthy vs. chronic populations, there may be other respondent-level factors at play. Within studies that assessed television viewing, we cannot be certain that individuals were undertaking non-sedentary behaviours while the television was on.This review presents a comprehensive examination of self-report measures of SB and how they compare to device measures. The field of SB has experienced a rapid rate in growth over the last 5-10 years and with this growth has come an evolution of measurement methods. While devices have become increasingly used for assessing these behaviours, they provide limited contextual information to explain where and how individuals are sedentary. Self-report measures continue to be the most widely used methods to assess these behaviours. Evidence from this review suggests that self-report single-item measures generally underestimate sedentary time when compared to device measures. Multi-items questionnaires, EMAs and logs/diaries and tools that employ a shorter recall period perform well with respect to accuracy, especially when compared to inclinometers, but present with a high degree of variability (many over- and under- reporting) within and between studies leading to poor precision. This variability, which in individual studies could be as much as six hours/day, has great consequences for the interpretation and use of measures. It is apparent that there is an abundance of self-report tools available for researchers, making it difficult to compare findings across studies. Researchers should exert caution when comparing associations between different self-report and device measures with health outcomes.Additional file 1: Supplemental figure 1. Correlation coefficients between self-report and device measures.Additional file 2: Supplemental figure 2. Forest plot comparing self- 841 report and device measures of total sedentary or sitting time, minutes/day.Additional file 3: Supplemental figure 3. Forest plot comparing self- 843 report and device measures of total sedentary or sitting time between 844 weekday/work days and weekend/non-work days, minutes/day.Additional file 4: Supplemental figure 4. Forest plot comparing self- 846 report and device measures of television viewing time, minutes/day.Additional file 5: Supplemental figure 5. Forest plot comparing self-report and device measures of total occupational sedentary time, minutes/day.Additional file 6: Supplemental figure 6. Forest plot comparing self-report and device measures of workday spent sedentary, % of day.Additional file 7: Supplemental figure 7. Forest plot comparing self-report and device measures of total sedentary or sitting time between men and women, minutes/day.Additional file 8: Supplemental figure 9. Forest plot comparing self-report and device measures of total sedentary or sitting time between recall periods, minutes/day.Additional file 9: Supplemental figure 8. Forest plot comparing self-report and device measures of total sedentary or sitting time between population subgroups, minutes/day.Additional file 10: Supplemental figure 10. Forest plot comparing self-report and device measures of total sedentary or sitting time across questionnaires, minutes/day.Additional file 11: Supplemental figure 11. Forest plot comparing self-report and device measures of total sedentary or sitting time between devices, minutes/day.Additional file 12: Supplemental figure 12. Forest plot comparing self-report and accelerometer measures of total sedentary or sitting time across cut-points, minutes/day.Additional file 13: Supplemental figure 13. Forest plot comparing self-report and device measures of total sedentary or sitting time across wear locations, minutes/day.Additional file 14: Supplemental table 1. Ovid MEDLINE search strategy.Additional file 15: Supplemental table 2. Study characteristics.Additional file 16: Supplemental table 3. Validity characteristics.Additional file 17: Supplemental table 4. Reliability characteristics.


Current accepted cerebrovascular reactivity indices suffer from the need of high frequency data capture and export for post-acquisition processing. The role for minute-by-minute data in cerebrovascular reactivity monitoring remains uncertain. The goal was to explore the statistical time-series relationships between intra-cranial pressure (ICP), mean arterial pressure (MAP) and pressure reactivity index (PRx) using both 10-s and minute data update frequency in TBI. Prospective data from 31 patients from 3 centers with moderate/severe TBI and high-frequency archived physiology were reviewed. Both 10-s by 10-s and minute-by-minute mean values were derived for ICP and MAP for each patient. Similarly, PRx was derived using 30 consecutive 10-s data points, updated every minute. While long-PRx (L-PRx) was derived via similar methodology using minute-by-minute data, with L-PRx derived using various window lengths . Time-series autoregressive integrative moving average (ARIMA) and vector autoregressive integrative moving average (VARIMA) models were created to analyze the relationship of these parameters over time. ARIMA modelling, Granger causality testing and VARIMA impulse response function (IRF) plotting demonstrated that similar information is carried in minute mean ICP and MAP data, compared to 10-s mean slow-wave ICP and MAP data. Shorter window L-PRx variants, such as L-PRx_5, appear to have a similar ARIMA structure, have a linear association with PRx and display moderate-to-strong correlations . Thus, these particular L-PRx variants appear closest in nature to standard PRx. ICP and MAP derived via 10-s or minute based averaging display similar statistical time-series structure and co-variance patterns. PRx and L-PRx based on shorter windows also behave similarly over time. These results imply certain L-PRx variants may carry similar information to PRx in TBI. Cerebrovascular reactivity monitoring in adult traumatic brain injury (TBI) is becoming increasingly common in the critical care management of moderate and severe TBI , 2. ImpaVarious metrics for continuous monitoring of cerebrovascular reactivity have been described in adult TBI, based on the invasive/non-invasive modalities employed for monitoring cranial physiology. The intra-cranial pressure (ICP) based measures have received the most attention, with the pressure reactivity index (PRx) considered the standard by many . PRx is However, despite the promising nature of PRx, it has limitations. One important limitation is the data required for derivation. PRx is based on the concept that the correlation between slow-wave vasogenic fluctuations in a measure of cerebral blood volume (such as ICP) and a driver of cerebral blood flow ), provide a surrogate measure of cerebral autoregulation . As suchGiven this, there has been an attempt to utilize minute-by-minute ICP and MAP data to derive low resolution, or low frequency, versions of PRx, termed long-PRx (L-PRx), with the aim to increase accessibility to such monitoring –20. SuchDespite this, there exists uncertainty as to whether minute-by-minute ICP and MAP data behave similarly to higher frequency 10-s mean data, and whether L-PRx carries similar information to PRx over time. Previous works focused mainly on the correlation between large averaged time periods of data, not focusing on the statistical properties of the parent signals. In the absence of a direct gold standard reproducible measure of autoregulation or cerebral blood flow in vivo, we cannot directly compare measures such as PRx and L-PRx to flow or vascular regulation. Thus, we are left with exploring the relationships of these derived signals, their structure statistically and behavior over time, in order to make more definitive comments as to whether L-PRx metrics provide close surrogate measure of PRx and may be employed clinically. As such, the goal of this study was to evaluate the statistical time-series relationships between ICP, MAP and PRx/L-PRx using both data derived from 10-s and minute-based means of ICP and MAP in adult TBI.Patient data was accrued retrospectively from three centers with archived high resolution physiologic data, with two from Nordic countries , and one from Canada . All patients were studied during January 2015 to February 2019. All patients from Stockholm and Helsinki were also part the multi-center Collaborative European Neuro Trauma Effectiveness Research in TBI (CENTER-TBI) study high-resolution ICU sub-study cohort . These pAll patients suffered predominantly from moderate to severe TBI (moderate = Glasgow Coma Score (GCS) 9–12, and severe = GCS of 8 or less). A minority of patients suffered from mild TBI (GCS 13-15), with subsequent early deterioration leading to ICU admission for care and monitoring. All patients in this cohort had invasive ICP monitoring conducted in accordance with the BTF guidelines .Data from Stockholm and Helsinki were collected as part of the CENTER-TBI study which had individual national or local regulatory approval . The CENTER-TBI study (EC grant 602150) has been conducted in accordance with all relevant laws of the EU if directly applicable or of direct effect and all relevant laws of the country where the Recruiting sites were located, including but not limited to, the relevant privacy and data protection laws and regulations (the “Privacy Law”), the relevant laws and regulations on the use of human materials, and all relevant guidance relating to clinical studies from time to time in force including, but not limited to, the ICH Harmonised Tripartite Guideline for Good Clinical Practice (CPMP/ICH/135/95) (“ICH GCP”) and the World Medical Association Declaration of Helsinki entitled “Ethical Principles for Medical Research Involving Human Subjects”. Informed Consent by the patients and/or the legal representative/next of kin was obtained, accordingly to the local legislations, for all patients recruited in the Core Dataset of CENTER-TBI and documented in the e-CRF.Data from the Winnipeg cohort was collected in accordance with full local health research ethics board approval from the University of Manitoba .All patients had demographics prospectively recorded. Similarly, all patients had high frequency digital signals from ICU monitoring recorded throughout their ICU stay, with the goal of initiating recording within 24 h of ICU admission. All digital ICU signals were further processed . For the purpose of this study, the following admission demographic variables were retrospectively collected: age, sex, admission Glasgow Coma Scale  and admission pupillary response . CENTER-TBI data was accessed/extracted using Opal database software  for the https://www.integralife.com/) or external ventricular drain (in 3 patients). All signals were recorded using digital data transfer or digitized via an A/D converter , where appropriate, sampled at frequency of 100 Hertz (Hz) or higher, using the ICM + software  or Moberg CNS Monitor  or a combination of both. Signal artifacts were removed using both manual and automated methods prior to further processing or analysis.Arterial blood pressure (ABP) was obtained through either radial or femoral arterial lines connected to pressure transducers . ICP was acquired via an intra-parenchymal strain gauge probe , parenchymal fiber optic pressure sensor . For ICP and MAP, two sets of data were produced. First, 10-s moving averages (updated every 10 s to avoid data overlap) were calculated for all recorded signals: ICP and ABP (which produced MAP). Second, minute-by-minute averages were also created for both ICP and MAP.Next, we derived PRx and L-PRx variants for each patient. PRx was derived via the moving correlation coefficient between 30 consecutive 10 s mean windows of the parent signals (ICP and MAP), updated every minute. This methodology for PRx determination has been employed for over 20 years, with this metric having been preliminarily validated as a measure of the lower limit of autoregulation in experimental animal models –20.https://www.R-project.org/) and XLSTAT  add-on package to Microsoft Excel .All statistical analysis were conducted using R (R Core Team (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL For all testing described within, the alpha was set at 0.05 for significance. Given the bounded nature of PRx and L-PRx variables, all cerebrovascular reactivity indices were transformed using a Fisher natural logarithmic transformation. Thus, for all reference to PRx and L-PRx within the manuscript, we are referring to the Fisher transformed indices. Similarly, ICP and MAP were transformed using a logarithmic transformation, and for all reference to ICP and MAP within the manuscript we are referring to log10 transformed values.We currently lack a direct, reliable and reproducible continuous measure of cerebral autoregulation and cerebral blood flow in vivo in humans. As such, previous studies have tried to validate PRx against the Lassen curve , in partICP and MAP were analyzed in both 10-s by 10-s and minute-by-minute mean value data sheets, per patient. The time series characteristics of ICP and MAP were independently evaluated in each patient, using Box-Jenkin’s autoregressive integrative moving average (ARIMA) models –27. FirsNext, the optimal ARIMA structure for ICP and MAP were derived in both the 10-s and minute mean data sheets, for each patient. The auto.arima function was initially employed to determine the upper order limit for tested ARIMA models. Based on this, autoregressive order (p) was varied from 1 to 4, and the moving average order (q) was varied from 1 to 4, while the integrative order (d) was held at 1. All subsequent permutations of the ARIMA orders were assessed, with the model Akaike Information Criterion (AIC) and Log-Likelihood (LL) recorded for every model, for both ICP and MAP, in every patient. Using the AIC and LL, the optimal ARIMA structures for ICP and MAP variables were compared in the 10-s and minute mean data sheets, with the lowest AIC and highest LL values indicating superior models. As an example, the general Box-Jenkin’s ARMA model for ICP can be expressed as follows:Subsequently, the influence of ICP and MAP on one another over time was assessed via Granger causality using stationary first order difference ICP and MAP data, with both the impact of ICP on MAP, and the impact of MAP on ICP tested. This was tested in both the 10-s and minute mean data sheets, for every patient. Both F-test statistic value and p-values were recorded.Finally, in order to further evaluate if 10-s or minute based mean data made a difference in the behavior of ICP and MAP over time, we derived multi-variate vector ARIMA (VARIMA) models. Such models explore the behavior of two time series recorded simultaneously over time, and are derived via extending the standard Box-Jenkin’s models to multi-variate systems , 27, 28.We utilized differenced ICP and MAP signals, to eliminate trend and seasonality, and employed basic VARMA models with autoregressive order of 4 and moving average order of 4, based on the findings from individual patient ARIMA models of ICP and MAP, for both the 10-s and minute mean based data, for each patient. The VARMA model autoregressive order of 4 was chosen given the optimal ARIMA models for ICP and MAP in most patients had autoregressive orders of 2, and thus the product of these orders is 4. Taking the product of the ARIMA autoregressive orders for VARMA modelling is one suggested method of ensuring adequate model structure to cover such a multi-variate time series . Similarp value or nature of the confidence intervals should be interpreted with caution.Using minute-by-minute updated PRx and L-PRx data, the following analysis was conducted. For each patient, locally weighted scatterplot smoothing (LOESS) function plots were created between PRx and each L-PRx variant, assessing the degree of linearity between each L-PRx metric and the conventional PRx. Next, Pearson correlation coefficients between PRx and L-PRx were determined for each patient, for each variant of L-PRx. Of note, the degree of statistical significance of the linear models and Pearson correlation coefficients are artificially increased secondary to autocorrelation between minute-by-minute updated measures, and violation of the pre-conditions of linearity. As such, both the linear model plots and correlation coefficients reported are for purely descriptive purposes only, and the size of the Finally, the optimal ARIMA time-series structure was compared for PRx and all L-PRx variants for each individual patient using the following methodology. First, ACF and PACF plots were produced, and both ADF and KPSS testing were conducted, for all PRx/L-PRx measures, confirming non-stationarity. First order differencing was then undertaken to remove all trend components, confirming stationarity by repeating the above-mentioned plots and testing. Next ARIMA models were built for each PRx and L-PRx variable, keeping the differencing order of 1 (i.e. d = 1), and varying both the autoregressive and moving average orders  from 0 to 4, through all respective permutations. The AIC and LL were then tabulated for each of these models, for every patient. Using the AIC and LL, the optimal ARIMA structures for PRx and L-PRx variables were compared, with the lowest AIC and highest LL values indicating superior models.We briefly evaluated the association with outcome for PRx and the derived L-PRx metrics. Employing univariate logistic regression, we assessed the association between PRx and L-PRx measures to dichotomized Glasgow Outcome Scale Extended (GOSE) at 6 months post-injury. We evaluated both mortality and favourable/unfavourable outcome (defined as GOSE 5 or above for favourable outcome). Models were assessed by area under the receiver operating curve (AUC), Akaike Information Criterion (AIC) and 95% confidence intervals (95% CI), with alpha set at 0.05 for significance. AUC and 95% CI were determined through a bootstrap resampling methodology, employing 2000 iterations.A total of 31 patients from the three centers were included in this study. The mean age was 41.7 ± 13.6 years, with 23 (74.2%) male. The median admission total GCS score and motor sub-scores were 6 (IQR: 3 to 8) and 4 (IQR: 1 to 5), respectively. Three patients (9.7%) suffered pre-hospital hypoxic episodes, and three (9.7%) suffered hypotension episodes. Ten patients (32.3%) presented with bilaterally unreactive pupils, and one (3.2%) with unilaterally unreactive pupils. The mean duration of high frequency digital signal recording was 108.3 ± 50.7 h. Table We evaluated the ARIMA time-series structure for both ICP and MAP, in every patient, using log transformed and first order differenced data . This was conducted in both 10-s and minute mean data. Table In order to further assess for differences between 10-s and minute mean data for ICP and MAP (transformed and differenced), we performed Granger causality testing in both sets of data for all individual patients. For most patients, the directional nature of the causal relationship regardless of whether 10-s or minute mean data were assessed, favored MAP impacting ICP. A small minority of patients displayed the alternative causal relationship. Table Finally, to assess the relationship between ICP and MAP further, and whether there was a difference in information carried between 10-s and minute mean data, we employed VARIMA modelling. VARIMA models of autoregressive order 4, integrative order 1, and moving average order 4 were employed for each individual patient, for both the 10-s and minute mean data. IRF plots were produced to provide a descriptive visualization of the relationship between ICP and MAP, in both data sets. These IRF plots allowed us to visually determine the relationship between ICP and MAP, assessing the impact of one unit impulse in MAP on ICP, using transformed data.Overall, using minute mean data led to similar absolute changes in the ICP standard error, with some blunting of the response of ICP to the impulse in MAP, likely related to the data smoothing seen in the minute mean data. This was seen in all patients, and confirms that some information regarding the slow-wave relationship between ICP and MAP is lost in the minute mean data, however there is preservation of the overall general shape of response of ICP to changes in MAP. This, suggests that the minute mean data does indeed contain some information regarding cerebrovascular reactivity. Figure For each individual patient the linear relationship between PRx and L-PRx variants was assessed using LOESS plots, Pearson correlations and Bland–Altman Analysis. Table Finally, the ARIMA time-series structure for PRx and L-PRx variants were assessed in each patient. Table We performed univariate logistic regression analysis between mean PRx and L-PRx metrics with dichotomized GOSE at 6 months. In keeping with other studies, these PRx/L-PRx metrics displayed stronger association with mortality than favourable/unfavourable outcome –20. The Given the absence of direct, reliable and reproducible metrics of cerebral autoregulation and cerebral blood flow in vivo in adult TBI, to evaluate whether L-PRx is similar to PRx, we must rely on investigating the statistical relationships between high-frequency fluctuations in the derived signals. To do so requires evaluating such behavior over time between both the parent signals used to derive PRx and L-PRx, and also the relationship between PRx and L-PRx metrics themselves. As such, we employed complex time-series analyses via ARIMA, VARMA and Granger causality testing. Through in-depth evaluation of the relationship between ICP and MAP, using 10-s and minute based mean data, and comparing standard PRx to L-PRx variants, some important insights into lower resolution cerebrovascular reactivity metrics have been made. This provides some preliminary evidence to support that certain L-PRx metrics may be considered to be applied clinically in TBI monitoring, after further prospective investigation. Further, these results carry implications to expand the application of cerebrovascular reactivity monitoring, through low-frequency L-PRx metrics, to centers without biomedical signal processing expertise or ability to capture high-frequency full waveform physiology.First, minute based mean ICP and MAP data displays similar time-series relationships, Granger causality and IRF plots, compared to the conventional 10-s averaged vasogenic slow-wave ICP and MAP data. This is important because despite the smoothing effect seen with minute based ICP and MAP data, and concerns about only capturing the ultra-low end of the vasogenic slow-wave frequency spectrum (i.e. 0.005 Hz or less), minute based data appears to still carry some information regarding the ICP and MAP relationship seen in the higher frequency 10-s data set. This implies that minute based ICP and MAP data may carry information regarding cerebrovascular reactivity, supporting their use in the derivation of L-PRx measures. Though we must acknowledge, the results found in this manuscript are preliminary and require much further validation.Second, despite both the Granger testing and VARIMA IRF plots indicating that the minute based data likely carries some information regarding cerebrovascular reactivity from the ultra-low end of the vasogenic slow-wave frequency range, there are some subtle findings that also suggest this data carries other potential information. The magnitude of the Granger F-test statistic decreased for the MAP on ICP causal relationship, when evaluating the minute based data, with two patients falling out of significance for this relationship. Thus, despite the causal direction between ICP and MAP remaining in favor of MAP on ICP, when going from 10-s to minute-based data, there is suggestion that this relationship is less strong. This likely stems from the fact that minute-based data captures information on the MAP and ICP relationship outside of the ultra-low end of the vasogenic slow-wave frequency range associated with cerebral autoregulation , 17. SimThird, L-PRx variants display heterogeneous relationships with standard PRx. This is in parallel to previous retrospective analysis on low frequency cerebrovascular reactivity metrics derived from minute-by-minute data . Based oFourth, it must be re-emphasized that our results do not absolutely confirm or refute L-PRx metrics as measures of cerebrovascular reactivity. We only demonstrate preliminarily that aspects of L-PRx derivation and specific L-PRx metrics may carry similar information as standard PRx. This is important, since widespread adoption of continuous cerebrovascular reactivity monitoring is hindered by the equipment and expertise required for high-frequency data capture and derivation of standard PRx based on vasogenic slow-wave frequency data. Our results suggest a potential role for L-PRx measures as a surrogate for cerebrovascular reactivity monitoring, in the absence of the ability to derive PRx. Our results require much further validation and exploration in larger adult TBI data sets, as well as in experimental animal models. Given the results of this multi-center pilot analysis, we plan on future large prospective data collection to explore these findings in more detail, integrating patient, injury and treatment characteristics.Finally, briefly evaluating the association between PRx and L-PRx and 6 month dichotomized GOSE demonstrated corollary findings that L-PRx displays similar outcome association to PRx. This is in keeping with previously published retrospective studies –20. Our In summary, these results provide some support for low-frequency physiology in the derivation and monitoring of cerebrovascular reactivity in adult TBI. With further prospective evaluation of such metrics, L-PRx provides the ability for a larger number of centers globally to be able to monitor cerebrovascular reactivity in their critically ill cohorts, without the need for specialized biomedical signal processing capabilities, or high-frequency digital data, which is often not available in many centers. The results here imply that low-frequency physiology below the low end of the vasogenic slow wave spectrum (i.e. 0.005 Hz and below), provides at least some information regarding cerebrovascular reactivity. This does not mean it provides all, or even the most optimal, amount of information regarding cerebrovascular reactivity in TBI. The current literature body supports that the optimal slow-wave frequency range for vascular reactivity monitoring existing between 0.05 and 0.005 Hz, as demonstrated in animal models interrogating the lower-limit of autoregulation during arterial hypotension , 17. ThiDespite the promising results found, there are some limitations which deserve highlighting. First, this is retrospective analysis of prospectively collected data, in a small data set. As such, our findings can only be considered exploratory and preliminary in nature. The 31 patients were selected based on these being the only TBI patients from the 3 centers with available ICM + data for analysis. As such, the results here may not be generalizable to other TBI populations, necessitating much needed validation. Second, patient, injury and treatment heterogeneity could have influenced the physiologic signal response, and is something that can only be accounted for in larger prospective multi-center data sets. This can be highlighted by the fact that 3 of the 31 patients had EVD’s. Though in this particular cohort there was no difference found in the relationship between PRx and L-PRx, compared to the non-EVD cohort, when looking at the individual patient level, there exists the potential that such differences in monitoring devices, and therapies given, may impact the statistical signal properties. As such, the results here should be interpreted as preliminary, requiring much further validation prior to widespread adoption of L-PRx for clinical monitoring in adult TBI. Finally, the statistical methodology employed was computationally tasking. Larger prospective studies will benefit from more robust central computing services in order to accomplish analysis in a timely fashion.ICP and MAP derived via 10-s or minute based averaging display similar time-series statistical structure and co-variance patterns. PRx and L-PRx also behave similarly over time, with those L-PRx indices derived from longer time-windows displaying less association with standard PRx derived from higher frequency data. These results imply certain short window L-PRx variants may carry similar information to PRx in TBI.


Impulsivity is a common symptom in Parkinson’s disease (PD). Adaptive behavior is influenced by prepotent action-reward and inaction-avoid loss Pavlovian biases. We aimed to assess the hypothesis that impulsivity in PD is associated with Pavlovian bias, and to assess whether dopaminergic medications and deep brain stimulation (DBS) influence Pavlovian bias. A PD DBS cohort (N = 37) completed a reward-based Go/No-Go task and bias measures were calculated. This DBS cohort completed the task under three conditions: on-med/pre-DBS, off-med/off-DBS, and on-med/on-DBS. Participants also completed self-reported measures of impulsivity. Dopaminergic medication was associated with lower action-reward bias while DBS was associated with higher action-reward bias. Impulsivity was associated with higher action-reward bias but not inaction-avoid loss bias. We furthermore replicated this association in an independent, non-DBS PD cohort (N = 88). Overall we establish an objective behavioral marker of impulsivity and show that DBS affects impulsivity by amplifying automated responding. Our results point to the importance of reward rather than punishment avoidance in driving impulsive behaviors. This work provides insight into the pathophysiological underpinnings of impulsivity and especially medication and DBS-associated impulsivity in PD. Failure during this process occurs in numerous neuropsychiatric diseases, including Parkinson’s disease (PD), and can manifest as one or more impulse control disorders (ICDs)—for example, pathological gambling, hypersexuality, binge eating, compulsive shopping, and hobbyism3. Among PD patients, ICDs are frequent and underdiagnosed5. ICDs and other non-motor symptoms in PD contribute more to poor quality of life than motor symptoms6. However, it is unclear why people with PD experience high rates of ICDs2. It is also unclear why effective PD treatments, including dopaminergic medications and deep brain stimulation (DBS) of either the subthalamic nucleus (STN) or globus pallidus internus (GPi), can exacerbate existing ICDs or precipitate the appearance of de novo ICDs211. Thus, there is a pressing need to understand the nature of impulsivity in PD, both in patients with and without DBS therapy.A fundamental, evolutionary purpose of the brain is to determine whether an organism should approach or avoid stimuli in the environment13. Decades of work have investigated how such decisions materialize. For example, in 1986, researchers reported difficulty in training chickens to not approach a food reward in order to obtain it14. The automatic associations between approach and reward, and conversely, between avoidance and punishment, have been coined Pavlovian biases15. That is, an individual is more likely to act to obtain a reward than to avoid a punishment (action-reward bias); and an individual is more likely to not act to avoid a punishment than to obtain a reward (inaction-avoid loss bias)16. These anchoring biases implicate brain regions such as the striatum, ventral tegmental area, subthalamic nucleus, and frontal gyrus22.Faced with a decision, impulsivity is associated with heightened reward sensitivity and deficient cognitive control, resulting in an ill-informed outcome to either act (“Go”) or inhibit action (“No Go”)25. People with PD in particular demonstrate difficulty in learning to withhold actions especially for reward outcomes27. ICDs in PD are specifically associated with impaired stopping behavior and response inhibition30, suggesting the possibility of an exaggerated action-reward Pavlovian bias. However, no study to date has assessed whether impulsivity in PD—especially in the context of dopaminergic medication and DBS therapy—is associated with abnormal Pavlovian biases. Such investigations in this vulnerable population are valuable because they could implicate pathological brain regions, establish objective behavioral correlates for clinical translation, and uncover mechanisms for the role of medications and DBS in affecting impulsivity.Under normal circumstances, Pavlovian biases broadly influence adaptative behavior—they guide learning and form the basis for responding rapidly with little cognitive effort. However, they can become maladaptive26. In addition, given that response inhibition is impaired in individuals with PD and ICD, we hypothesized that impulsivity would be associated with increased reward bias.In the present study, we used a reward-based, Pavlovian Go/No-Go paradigm to measure the action-reward bias (referred to simply as ‘reward bias’ throughout) and the inaction-avoid loss bias (referred to simply as ‘avoid-loss bias’ throughout) in PD patients with variable degrees of impulsivity measured using a standardized self-report questionnaire. We included a DBS cohort and a separate non-DBS cohort. In the DBS cohort, we further assessed the effect of medications and DBS on Pavlovian biases. Overall we expected performance to be higher during natural action-reward or inaction-avoid loss associations as compared to unnatural inaction-reward or action-avoid loss associations. Based on prior results from the Go/No-Go task in healthy adults and in patients with PD taking dopamine medications, we expected that the on-medication (on-med) state would be associated with decreased reward biasThis is an Institutional Review Board (IRB)-approved study (IRB201601780 and IRB201702032) of two cohorts of adults with PD from the Movement Disorders Clinic at the University of Florida Norman Fixel Institute for Neurological Diseases. Written informed consent was obtained from all subjects in accordance with local guidelines and regulations. PD patients undergoing DBS at the University of Florida were recruited one day prior to their surgery. These individuals were deemed surgical candidates following an extensive multidisciplinary evaluation by neurology, neurosurgery, psychiatry, physical therapy, occupational therapy, neuropsychology, and speech and swallow therapy. DBS target (GPi or STN) and surgery type  were selected during this clinical evaluation as per standard of care. There was no randomization. We had no specific inclusion criteria for the DBS cohort beyond those used during this clinical evaluation. Exclusion criteria were severe vision or hearing impairment.Participants in the non-DBS cohort were also recruited from the Movement Disorders Clinic. Inclusion criteria were right-hand dominant adults aged 50–80 with at least 8+ years of education, Beck Depression Inventory (BDI)-II less than 20, and Montreal Cognitive Assessment (MoCA) standardized score greater than 1.5 standard deviations below the mean. Exclusion criteria were current psychostimulant or cognitive-enhancing medication use, history of substance abuse, current unstable psychiatric disturbance , other current neurological disturbance , current chronic unstable medical conditions , history of brain surgery, and severe vision or hearing impairment.The DBS cohort was enrolled in a study of intraoperative invasive electrophysiology during reward processing . They played the Go/No-Go task one day before their DBS surgery in the on-med state. The majority of the participants (those that were not withdrawn) repeated the task the following day in the operating room during their DBS surgery in the off-med/off-DBS state Table . Prior tThe non-DBS cohort was enrolled in a separate but related study of non-invasive electrophysiology during reward processing. They completed the Go/No-Go task at a single visit in the on-med state while wearing an electroencephalography cap. The data presented in this paper include the Go/No-Go behavioral results from these two cohorts.The Go/No-Go paradigm consisted of trials that included stimulus presentation, action selection, and feedback presentation  or absent (negative [ICD−]) according to established cutoffs32.To characterize the cohorts we recorded age, gender, disease duration, the Beck Depression Inventory (BDI)-II, and the PD quality of life questionnaire (PDQ-39) cognition subscore for all participants. We additionally recorded the on-med Hoehn & Yahr at the on-med timepoints. We included BDI because it is known that impulsivity correlates to depression, and we used the PDQ-39 cognition subscore as a surrogate marker of cognitive ability. For the non-DBS cohort and for the on-med conditions of the DBS cohort we quantified dopaminergic medication use with a levodopa equivalency daily dose (LEDD), which included dopamine agonists, as well as the equivalency dose when only considering dopamine agonists (DADD). The Questionnaire for Impulsive-Compulsive Disorders in Parkinson’s Disease-Rating Scale (QUIP-RS)All data analyses were completed in R 3.5.2 (r-project.org). Accuracy was computed as the proportion of trials correct and error was defined as a proportion of trials incorrect. Reaction time (RT) was computed as the time elapsed between stimulus presentation and button press on correct Go trials. Pavlovian bias was defined as the error rate of the unnatural valence-action association divided by the accuracy of the respective natural valence-action association. Namely, reward bias was defined as NGTW error divided by GTW accuracy, and avoid-loss bias was defined as GTAL error divided by NGTAL accuracy. This calculation thus represents a functional measure of a prepotent, impulsive action-reward association incorrectly applied to NGTW trials, and of a prepotent, impulsive inaction-avoid loss association incorrectly applied to GTAL trials. The numerator represents a raw bias and the denominator scales the bias by the strength of the natural association. For instance, a bias of 0 suggests that individuals were able to completely overcome prepotent associations—that is, throughout the entire task bias did not affect performance on unnatural associations. Hence, the presence of a Pavlovian bias occurring at the group level is indicated by a distribution of bias values greater than 0 . In contrast, a bias value greater than 1 suggests that the natural, prepotent association was weaker than the unnatural association. This would be highly unusual as it goes against the Pavlovian bias phenomenon and probably suggests that instructions were not properly followed during the task. We therefore removed these instances as outliers from the study . The N listed in Table To characterize the participants we first compared baseline characteristics between all visits of the DBS and non-DBS cohort using mixed model analyses of variance for continuous data and chi-square tests for categorical data. Mixed model analyses were used throughout this study to provide a method for controlling for possible confounding variables and for the random effect of participants, particularly because there were data at multiple visits from the same participants in the DBS cohort.To analyze behavioral performance, we began by examining accuracy. We used a mixed model to test whether overall accuracy depended on the cohort (DBS or non-DBS). Further analyses were then carried out as necessary to interpret significant effects from mixed models, which led us to further split analyses by cohort and by condition within the DBS cohort. For instance, within the DBS cohort, we also tested for an effect of the med/DBS condition on overall accuracy. In addition to total accuracy, we also evaluated whether Go/No-Go accuracy depended on the trial condition. To do so we included terms in a mixed model for the cohort, trial valence (reward or avoid-loss), trial action (Go or No-Go), and all their interactions. This has been done in prior studies and led us to hypothesize higher accuracies in GTW vs NGTW and in NGTAL vs GTAL in each of these one-tailed comparisons, respectively. Similarly, a predicted result of Pavlovian bias is that RT should be faster in GTW versus GTAL trials, and we therefore tested this in our data with one-tailed comparisons as well.Given the possible influence of learning on Go/No-Go performance over repeated completions of the task in the on-med/on-DBS condition, we also included a variable in the mixed models for an effect of time since the first visit. In our models we also included terms for brain target (GPi or STN) and surgery type  as fixed effects for analyses within the on-med/on-DBS condition.2. In a related analysis, we compared both reward and avoid-loss biases across participants categorized as ICD-negative and ICD-positive.The primary measures of interest in this study were the Pavlovian biases. We evaluated reward and avoid-loss biases similar to accuracies—first checking for an effect of cohort, then for an effect of med/DBS condition within the DBS cohort, and finally for effects of brain target or stimulation type in the on-med/on-DBS cohort. This also led us to split reward and avoid-loss bias analyses by cohort and condition within the DBS cohort and to compare reward but not avoid-loss bias across different conditions within the DBS cohort see “” sectionThroughout our analyses specific comparisons across conditions or cohorts were completed by student t-tests or Wilcoxon signed-rank tests for normal or non-normal data, respectively. Normality was assessed using Shapiro–Wilk tests. Lastly, we use Pearson or Spearman correlations for normal or non-normal data, respectively, to evaluate the relationship between impulsivity and Pavlovian biases as well as other clinical factors.A total of 37 people with PD in the DBS cohort completed the Go/No-Go task Table . Each pa12 = 4.97, P < 0.05). Participants in the DBS cohort tended to use more dopaminergic medications  despite showing similar QUIP-RS scores and similar proportions of participants with any ICD(s) . Within the DBS cohort, there was no effect of med/DBS condition on overall accuracy across all trials (P = 0.29). In addition, within the on-med/on-DBS condition of the DBS cohort, overall accuracy did not depend on brain target , surgery type  (P = 0.82), time since the first visit (P = 0.82), or any of their interactions.Overall accuracy across all trials during the Go/No-Go task was significantly lower in the DBS cohort compared to the non-DBS cohort , a valence by action interaction , and a three-way valence by action by cohort interaction . We therefore separated this analysis for each cohort. Within the DBS cohort, accuracy depended on a valence by action interaction , and an action by med/DBS condition interaction . Thus for the DBS cohort we also separated accuracy data across the different med/DBS conditions. Within the on-med/on-DBS group, accuracy depended on an interaction between action and valence  but not on any other main effects or interactions including brain target or surgery type. Considering these effects overall, we compared accuracy across GTW, GTAL, NGTW, and NGTAL trials and across cohorts, as well as across med/DBS conditions within the DBS cohort, but not across brain targets or surgery type in the on-DBS condition , trial valence (reward vs avoid-loss), and trial action (Go vs No-Go) through a cohort main effect (FP < 0.001) as well as lower NGTW versus NGTAL accuracy . This effect was further seen specifically in the off-med/off-DBS condition .Performance in the DBS cohort overall reflected prepotent Pavlovian behavior with higher GTW versus GTAL accuracy . Taken together, accuracy and RT results suggest that participants performed the task as expected, but they likely prioritized accuracy over speed. Additionally, the task was clearly less challenging for the non-DBS cohort.Another predicted result of the Pavlovian bias in this task is that RT should be faster during GTW compared to GTAL trials. This effect was observed in the on-med/pre-DBS condition Fig. b, the of1,146 = 20.86, P < 0.001) and avoid-loss biases  differed between the DBS and non-DBS cohorts . Reward but not avoid-loss bias further depended on the med/DBS condition within the DBS cohort . Reward and avoid-loss bias within the on-med/on-DBS condition did not depend on brain target or stimulation type. Given these effects we also separated biases by the different cohorts and conditions within the DBS cohort . Similarly, across all participants and conditions, higher reward biases were significantly associated with higher avoid-loss biases .Next we computed reward and avoid-loss biases during the Go/No-Go task. Similar to accuracies across Go/No-Go conditions, both reward  and the cohort —whereas avoid-loss bias only depended on the cohort  and BDI . Both reward and avoid-loss biases did not depend on age, levodopa dose, disease duration, gender, or PDQ-39 cognition subscore. Reward bias was slightly lower in the on-med/pre-DBS condition compared to the on-med/on-DBS condition  STN stimulation53. The isolated effects of DBS on Pavlovian biases should be ascertained in future work with a postoperative DBS cohort in the off-med/on-DBS setting.In contrast to the association of medications with decreased reward biases, we found that the on-DBS state resulted in higher reward biases relative to the on-med/pre-DBS state. In addition to being in the off-med state, it is also possible that a temporary lesion effect following DBS implantation contributed to higher reward biases seen in the off-med/off-DBS setting, and this will require further studies to carefully tease apart. Pavlovian biases in the on-DBS state did not depend on whether the stimulation was in the GPi or STN. It is known that DBS is a risk factor for development of ICDsAn important observation from this study was that impulsivity was associated with increased Pavlovian bias, and in particular, action-reward association bias. This effect was present independent of age, LEDD, disease duration, gender, PDQ-39 cognition, and BDI-II. Interestingly, across all participants, higher reward biases were also associated with higher avoid-loss biases—however, QUIP-RS was only correlated to reward biases. The only condition for which this was not the case was the off-med/off-DBS state. This is an atypical state for PD patients which does not usually occur in circumstances outside of clinical assessment. The QUIP-RS assessment used at this time point captures individuals in their day-to-day activities (on-med) and so in hindsight it was unsurprising that this relationship did not hold. Despite a notable ceiling effect of accuracy in the non-DBS group, which led to low action-reward biases, the variability in those bias values were still related to QUIP-RS. This is an encouraging finding which points to the potential sensitivity of this task in deriving an objective marker of impulsivity even in PD populations that perform particularly well.68. Therefore, hypersensitivity to reward anticipation and reward feedback, which drive impulsive choices, probably underpin the persistence of ICDs.Overall, throughout the different groups and conditions in this study, the fact that reward but not avoid-loss bias was higher in people with ICD points to the importance of reward, and not loss avoidance, in driving impulsive behaviors. This finding also suggests that ICDs in PD are not simply a motor or a general executive dysfunction phenomenon, in which we would expect both reward and avoid-loss biases to be equally impacted. This distinction can only be appreciated in tasks such as the one used here that orthogonalizes positive and negative valence with action and inaction conditions. Whereas punishment drives faster motor learning, reward more so enhances retention of learned motor sequences23. On the other hand, traumatic stress is associated with increased Pavlovian biases24 while anxiety is specifically associated with increased avoidance bias in the face of threats25. People with depression show similar Pavlovian bias to healthy controls69, though interestingly in this study we found an association between avoid-loss bias and BDI. The functional meaning of this association is unclear and will require further studies, though we suspect it could point to an abnormal loss sensitivity in depression. Given that the Pavlovian bias fundamentally influences basic human behavior, it is likely that Pavlovian dysfunction will be assessed in future work across many more diseases.This work contributes to a growing literature on Pavlovian biases in neuropsychiatric diseases. For example, people with schizophrenia exhibit reinforcement learning deficits with an associated reduction in the Go biasThis study has important limitations. First, the off-med/off-DBS condition of the DBS cohort was carried out in an operating room setting whereas the other conditions involved participants sitting comfortably at a desk. The operating room environment can induce discomfort, which could affect performance during the task. Second, the on-DBS condition involved different numbers of visits across participants. However, this was intentional to study a possible learning effect, and we included a random effect term in our mixed model analyses to control for this methodological consideration. Third, stimulation configuration in the on-DBS condition followed from therapeutic settings and we did not systematically stimulate specific DBS contacts. However, this improves the generalizability of our results to PD DBS patients . Fourth, we did not specifically recruit balanced numbers of unilateral and bilateral GPi and STN participants, and the study may not have been sufficiently powered to fully appreciate effects of these variables. For example, the GPi vs STN effect of overall mean accuracy was trending, but did not reach statistical significance. Similarly, we also did not specifically recruit patients with certain PD ICDs, and it is possible that the Pavlovian biases relate to some ICDs more than others. Fifth, the Go/No-Go task in the non-DBS cohort was slower paced than the task used in the DBS cohort, though participants still had 1000 ms to respond to the stimuli. The slower paced design probably contributed to higher accuracies. Nonetheless, we were able to replicate the relationship between QUIP-RS and reward bias seen in the DBS cohort. Lastly, the different conditions of the DBS cohort in this study were all completed in the same sequence: on-med/pre-DBS, then off-med/off-DBS, then on-med/on-DBS. Future work could include an off-med/off-DBS condition postoperatively, as well as an off-med/on-DBS condition.In conclusion, this is the first study to establish Pavlovian bias abnormalities in PD patients with ICDs. We demonstrate that simple and objective behavioral measures during a Go/No-Go task in part captures subjective impulsivity scores in people with PD. We found that Pavlovian biases are affected by therapies used in PD including dopaminergic medication and DBS of either the STN or GPi. Finally, total impulsivity severity was associated with increased reward bias, but not avoid-loss bias, hence pointing to the importance of reward-guided behaviors for ICDs. This finding was replicated in a separate cohort of PD patients. Future studies should further clarify why these deficits occur in people with PD.Supplementary file 1.Supplementary file 2.


Phototherapy is widely applied to various human diseases. Nanomedicine-based phototherapy can be classified into photodynamic therapy (PDT) and photothermal therapy (PTT). Activated photosensitizer kills the target cells by generating radicals or reactive oxygen species in PDT while generating heat in PTT. Both PDT and PTT have been employed for treating various diseases, from preclinical to randomized controlled clinical trials. However, there are still hurdles to overcome before entering clinical practice. This review provides an overview of nanomedicine-based phototherapy, especially in non-oncologic diseases. Multiple clinical trials were undertaken to prove the therapeutic efficacy of PDT in dermatologic, ophthalmologic, cardiovascular, and dental diseases. Preclinical studies showed the feasibility of PDT in neurologic, gastrointestinal, respiratory, and musculoskeletal diseases. A few clinical studies of PTT were tried in atherosclerosis and dry eye syndrome. Although most studies have shown promising results, there have been limitations in specificity, targeting efficiency, and tissue penetration using phototherapy. Recently, nanomaterials have shown promising results to overcome these limitations. With advanced technology, nanomedicine-based phototherapy holds great potential for broader clinical practice. Phototherapy is the use of light to treat disease. Although sunlight was used to treat disease (Heliotherapy) from BC 1400s, phototherapy′s scientific documentation could be found at the end of the 19th century , for whiThe current phototherapy with exogenous PSs can be broadly classified into photodynamic therapy (PDT) and photothermal therapy (PTT) ,6,7. In PDT was used to localize lesions and assess therapeutic effects with hematoporphyrin derivatives in the 1960s ,11. AftePTT has recently emerged as an important and efficient strategy for cancer treatment with its short treatment time of a few minutes and reduced patient pain . Most PTRecent emerging nanotechnology enables advanced PSs for PDT and PTT to enhance therapeutic performance with reduced adverse effects. However, most PDT and PTT with the state-of-the-art PSs have been applied to oncology to eliminate tumor cells ,19,20. OPhototherapy-based nanomedicine can be broadly classified into PDT and PTT. In the section, we present the mechanism of phototherapy how to cause selective damage to the target cells with PSs. In addition, we describe nanocarriers which can be conjugated with PSs to enhance phototherapy performance.PDT is phototherapy to kill target cells involving light and photosensitizing chemical agents. PDT involves three principle components: (1) PS, a substance, which induces a chemical alteration in the photochemical process, (2) light, and (3) oxygen ,22,23. A0) into a relatively long-lived electronically excited state  via a short-lived excited singlet state (S1). The lifetime of the triplet state enables the interaction of the excited PS with the surrounding molecules. The activated PS can undergo two types of reactions  ,18,19,20PDT kills cells by necrosis or apoptosis. Necrotic cells swell and disrupt the plasma membrane, resulting in the release of intracellular components that lead to the inflammatory reaction. Apoptotic cells bleb and shrink with nuclear fragmentation via endonuclease that degrades DNA into oligonucleosomal fragments . PDT canThe categories of light sources for PDT include halogen lamps, light-emitting diodes (LEDs), and lasers. The selection of light sources depends on the location of the target and the type of PSs. LEDs have been widely applied with low cost and diverse illumination spectra options due to broad illumination areas, especially for dermatological diseases with 5-aminolevulinic acid (5-ALA) sensitizers in clinics. However, LEDs have poor coupling efficiency for optical fiber delivery or beam collimation to access distant lesions such as the gastrointestinal tract with endoscopy. However, lasers can be used for both superficial and deep tissue PDT applications. Lasers provide high optical power and monochromatic light delivered to a distal irradiation location via optical fiber coupling.The criteria for selecting an optimal light source are as follows: (1) absorption spectra of reactive PSs, (2) the state of the disease (location and characteristics of the tissue), and (3) cost. Proper dosimetry can affect the treatment efficiency of PDT ,32. DosiSinglet oxygen, the major reactive oxygen species, causes physicochemical damage to intracellular organelles, such as mitochondria and membrane systems. This leads to target cell death from PDT. Also, only cells proximal to ROS production are directly affected by PDT due to high reactivity and short half-life of ROS. As the half-life of singlet oxygen in biological tissue is <0.04 μs with a limited radius effect (0.20 μm), ROS is limited to the lesion tissue area, where the PS accumulates ,36. HowePS is a light-sensitive material that is an essential factor for PDT. PSs can be used because of their toxicity at a specific wavelength . In PDT,Currently, in clinical trials, PS can be divided into porphyrin-based and non-porphyrin-based types. Thus far, the most extensively studied PSs are porphyrins, which were identified in the mid-nineteenth century. 5-Aminolevulinic acid (5-ALA) is a prodrug that converts into protoporphyrin IX in tumor tissues. 5-ALA has been used extensively for PDT in Europe, where it is readily available and inexpensive . To perfPTT is a treatment that selectively eliminates lesions through a photothermal transducer which converts light into heat ,41,42.PTT agents are active substances that generate heat in response to light exposure. When the PTT agents absorb light, electrons transition occurs from the ground state to the excited state . The eleGold nanoparticles have attracted significant interest among PTT agents for nearly 20 years, owing to their unique physicochemical properties such as bacteriostatic, anticorrosive, and antioxidative characteristics . ResearcThe selection of photothermal agents is based on their strong absorption cross-sections and highly efficient light-to-heat conversion. Recently, several nanoparticles were used for photothermal treatment. Metal nanoparticles show four to five times more light absorption than light-absorbing dyes. This strong absorption can be effectively treated using a laser with less energy. Therefore, it is possible to reduce the destruction of normal surrounding tissues. Besides, metal nanoparticles have high stability against light, and there is no loss of fluorescence. The gold nanoparticles in the body adhere to the target cells. When the laser is irradiated into cells, heat is generated killing the target cells ,52. It cThe gold nanospheres react to light in the visible region to create strong surface plasmon resonance (SPR). As the particle size increases, the wavelength of the reacting light moves to the longer side and reacts to light in the NIR region ,53. In aA nanocarrier is a nanomaterial with 1–100 nm to deliver substances such as photosensitizers. The intrinsic characteristics of PSs with low water solubility and aggregating tendency lowers the therapeutic efficacy of phototherapy . In ordeThe colloidal carriers are frequently used in the drug delivery system by protecting the drug against degradation while preventing the drug’s adverse side effects and toxicity . The polLiposomes are spherical-shaped, small artificial vesicles created from phospholipids and cholesterol . They haco-glycolic acid) (PLGA) is the US Food and Drug Administration (FDA)-approved polymers for human use [Polymeric nanocapsule (NC) consists of a liquid/solid core coated with a polymeric shell . NC can uman use . PLGA hauman use ,73. Moreuman use .Carbon-based materials are popular in chemistry and biomaterials due to their environmental friendliness . They po3O4) can be delivered to the desired region under an external magnetic field [Metal-based nanomaterials have drawn great attention with their tunability in electronic and electro-optical properties and high luminescence . The golic field . PS-conjic field . In partic field . A recenic field .The upconversion nanoparticles (UCNP) are another promising nanocarrier in the phototherapy field. Especially, the rare-earth-doped near-infrared (NIR)-to-visible UCNP has a promising potential . Due to With nanotechnology, conjugated PDT or PTT agents showed enhanced therapeutic efficacy with improved drug delivery to the target tissue. Also, nanocarrier-conjugated PSs can be visualized with multiple imaging modalities, including fluorescence. The recent representative examples of PS-conjugated nanocarriers for phototherapy is shown in While PDT has been used in many cancer treatments, here we describe PDT’s non-oncologic applications focusing on clinical study. Some preclinical studies which can be translatable to clinical practice are also included. We illustrate various non-cancerous human diseases that can be treated with PDT in Acne is a disease of pilosebaceous units . This isYang et al. performed a prospective clinical trial with 75 patients with acne conglobata, a severe form of cystic acne that is difficult to manage . The 5-AConventional therapeutic methods  for hand and foot warts may cure up to 70% of warts in 3 months . However2 laser therapy group . However, owing to the absence of the optimized therapeutic protocol, PDT is less frequently used in clinics, despite the positive results.Genital warts are related to human papillomavirus (HPV) infection and sexually transmitted diseases. Liang et al. performed a randomized clinical trial in patients with condylomata acuminata (CA) . The 5-AAging affects all skin constituents, resulting in reduced generation of the dermal matrix . NonablaPsoriasis is a chronic inflammatory skin disease mediated by increased keratinocyte proliferation and T-cell infiltration . PDT wit2. The therapeutic efficacy was evaluated eight weeks after PDT. The PDT group showed a higher improvement rate than the placebo group .Vascular malformations are abnormalities of vasculatures, including venous, arteriovenous, capillary, and lymphatics. Port-wine stain (PWS) is a congenital vasculopathy owing to an abnormal capillary network in the upper dermis with a normal overlying epidermis . It becoJerjes et al. performed a clinical trial for patients with vascular tumors (hemangioma) or vascular malformation . The mTHp < 0.001). Also, all lesions that underwent PDT showed a parasitological cure. Therefore, PDT with topical PS could be an alternative therapeutic modality in CL patients.Cutaneous leishmaniasis (CL) is a parasitic disease of the skin. It is caused by female sandflies infected by Leishmania species . The goaOnychomycosis is a fungal infection that causes discoloration, thickening, and separation from the nail bed . PDT hasHirsutism is excessive hair growth in women in places usually associated with androgen-dependent areas of the body, including the face, chest, abdomen, lower back, upper arms, and thighs . GeneralThe development of keloids and hypertrophic scars is related to impaired fibroblastic proliferation and collagen deposition after trauma, inflammation, surgery, or burns ,128. TheAlopecia areata (AA) is a complex genetic, immune-mediated disease that affects hair follicles and results in nonscarring hair loss . Severalp < 0.001). In addition, visual acuity and retinal sensitivity were improved in the PDT-treated group.Central serous chorioretinopathy (CSC) is characterized by a localized, serous detachment of the neurosensory retina in the macular region, and occasionally associated with detachment of the retinal pigment epithelium . AlthougAge-related macular degeneration (AMD) is a chronic structural change in the macular area under multifactorial interaction of metabolism, functions, genetics, and the environment . The mosCorneal neovascularization is characterized by abnormal proliferation of preexisting blood vessels and lymphatic vessels into the corneal stroma . IncreasThe application of PDT for atherosclerotic plaque treatment has limitations, including (1) nonspecific accumulation of the PS in the skin, leading to cutaneous photosensitivity ; (2) relThe esophageal varix is the dilated veins that bulge into the lumen, producing an uneven wormlike surface inside the esophagus . PDT canPeriodontitis is an inflammatory disease caused by dysbiotic dental biofilm and characterized by progressive destruction of the periodontium . PDT wasOral lichen planus (OLP) is a common T-cell-mediated inflammatory disorder that affects the oral mucosa . AlthougAlzheimer’s disease (AD) is a common progressive neurogenerative disorder with abnormal accumulation of beta-amyloid (Aβ) plaque as a characteristic finding . The inhC) into its misfolded infectious form (PrPTSE) [Prion disease is a fatal neurodegenerative disease including Creutzfeldt-Jacob disease (CJD) and kuru in humans, scrapie in sheep, and bovine spongiform encephalopathy (BSE) in cattle . They ar(PrPTSE) . The pre(PrPTSE) . PDT inhRheumatoid arthritis (RA) is a systemic, inflammatory autoimmune disease currently considered a disease of the joints . SynovecChronic synovitis is a pathologic feature of RA, osteoarthritis, spondylarthritis, and villonodular synovitis . The resCrohn’s disease is a relapsing inflammatory disorder that potentially affects the entire gastrointestinal tract and presents with abdominal pain, fever, bowel obstruction, or bloody or mucus diarrhea . Fabre eHelicobacter pylori is a human pathogen that colonizes the gastric mucosa and causes chronic infection. Baccani et al. used porphyrin-PDT as an adjuvant with conventional doxycycline therapy [ therapy . The resBacteroides fragilis, which causes chronic infection of the colon. Therefore, PDT may be used with targeted PSs in the gastrointestinal tract to kill specific pathogens.Cassidy et al. performed targeted PDT to treat colon-residing bacteria . The h-AVentilator-associated pneumonia (VAP) is a life-threatening infectious disease related to patients who require mechanical ventilation . The endCOVID-19 is caused by an infection related to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus strain. Although there are few relevant in vitro or in vivo studies of COVID-19, PDT can be a potential therapeutic strategy . MoghissOverall, we reviewed the various kinds of non-oncologic PDT applications in clinical and potentially promising preclinical studies summarized in Reducing the burden of atherosclerosis below the Glagov threshold is a therapeutic target for cardiovascular disease . KharlamThe dry eye occurrence has increased due to the substantial screen time watching a computer monitor, tablet, or smartphone. To treat dry eye syndrome, Pang et al. developed a gold nanoparticle-based hydrogel patch that can attach to the skin of the lacrimal gland . After wCurrent PS agents suffer from low target sensitivity and specificity with off-target toxicity. In addition, clinical PDT/PTT efficacy is restricted by limited tissue penetration of photosensitizing light due to absorption and scattering within the tissue. The application of phototherapy will expand when these limitations are resolved. In this section, we introduce strategies with the state-of-the-art nanomaterials, that have been mostly applied to oncology, to improve phototherapy in non-oncologic diseases as a future direction.The residual tissue after PTT tend to regrow by acquired thermal resistance. To resolve this, there has been an attempt to enhance the therapeutic effect by combining PTT and PDT . The res2+ into black phosphorus@Cu nanostructures enabled chemodynamic therapy and enhanced PTT to improve photothermal stability with positron emission tomography (PET), allowing in vivo real-time and quantitative tracking for diagnosis [To increase both diagnostic and therapeutic efficacy, multifunctional theranostic nanomaterial platform has been developed. Cheng et al. developed a core-shell nanohybrid for multimodal image-guided combined PTT/PDT in CT26 tumor-bearing mice . The naniagnosis . TherefoSeveral nanomaterials show poor signal-to-noise ratio (SNR) with side effects, owing to nonspecific biodistribution and “always-on” pharmacological activities ,198. StiThere remain various challenges for clinical translation with sufficient therapeutic efficacy. Many photoresponsive linker’s biocompatibility and their degradation byproducts are currently less understood and require further investigation. Besides, as UV and visible light has limited tissue penetration, NIR-I (700–950 nm) light can be used instead. However, NIR-I nanomaterials have relatively low sensitivity and effective therapy requires high photoirradiation power . With fuThe induction of immunogenic cell death (ICD) presents a therapeutic modality, which is attributed to immune system’s ability to eradicate target cells through a “bystander effect” . This ICMoreover, the conventional PDT family lacks hydrophilicity. Aza-boron-dipyrromethene (aza-BODIPY) molecules were fabricated into hydrophilic nanoassemblies, contributing to enhanced target tissue accumulation with prolonged blood circulation. The aza-BODIPY family comprises organic PSs with NIR optical characteristics . Chen etConventional PDT and PTT cannot reach deep-seated target lesions due to insufficient light penetration into the tissue ,211. AlsThe strategies used to improve phototherapy such as PDT and PTT with novel nanomaterials are summarized in The nanomedicine-based phototherapy for treating various diseases shows promising results in clinical and preclinical studies. While there exist many promising phototherapy results, several limitations still hinder the wide-spread use of phototherapy. (1) The therapeutic phototherapy protocols have not been well established. A standardized protocol is necessary to obtain consistent therapeutic responses; (2) Some clinical studies did not directly compare with the control (or placebo) group. The number of enrolled patients in the randomized controlled trial was too small to prove clinical significance . A well-


Financial well-being describes when people feel able to meet theirfinancial obligations, feel financially secure and are able to makechoices that benefit their quality of life. Financial strain occurswhen people are unable to pay their bills, feel stressed about moneyand experience negative impacts on their quality of life and health.In the face of the global economic repercussions of the COVID-19pandemic, community-led approaches are required to address thesetting-specific needs of residents and reduce the adverse impacts ofwidespread financial strain. To encourage evidence-informed bestpractices, a provincial health authority and community-engagedresearch centre collaborated to conduct a rapid review. We augmentedthe rapid review with an environmental scan and interviews. Our datafocused on Western Canada and was collected prior to the pandemic(May–September 2019). We identified eight categories of community-ledstrategies to promote financial well-being: systems navigation andaccess; financial literacy and skills; emergency financial assistance;asset building; events and attractions; employment and educationalsupport; transportation; and housing. We noted significant gaps in theevidence, including methodological limitations of the included studies, a lack of reporting on themechanisms leading to the outcomes and evaluation of long-termimpacts, sparse practice-based data on evaluation methods andoutcomes, and limited intervention details in the publishedliterature. Critically, few of the included interventions specificallytargeted financial strain and/or well-being. We discuss theimplications of these gaps in addition to possibilities and prioritiesfor future research and practice. We also consider the results inrelation to the COVID-19 pandemic and its economic consequences. The novel coronavirus disease COVID-19) will have long-lasting repercussionson people’s health and well-being worldwide. Beyond immediate healthconsequences, there will be serious physical, social and mental healthimpacts of added financial strain 9 will ha and globFinancial strain occurs when people cannot pay their bills, feel worried aboutmoney and experience negative impacts on their quality of life and health,9. FinanFinancial strain disproportionately impacts underserved groups  ,7 who exFinancial strain and well-being and their connection to health are emergingresearch areas ,9,10. Fi22https://together4health.albertahealthservices.ca/FinancialWellness/news_feed/building-financial-well-being-a-community-planning-toolkit).To achieve this, we conducted a rapid review of the academic literature. Our primary objective was to create a toolkit for useby community organisations and municipal/regional governments withimplementation details for interventions to promote financial well-being-trained InformationSpecialist helped to develop the search terms and conducted theacademic literature searches using two online databases  as well as Google and Google Scholar(on 11 May 2019). For the environmental scan, she created 15 distinctsearch strategies that were run by research assistants in Google . The searches includedterminology related to three concepts: (a) financial strain and/orwell-being; (b) intervention; and (c) community.A single reviewer used a two-step screening process to identify relevantarticles for the rapid review. Inclusion criteria were:community-level interventions targeting financial well-being and/orstrain; process or outcome studies that had been evaluated; primaryresearch studies, reviews, practice-based reports; published inEnglish or French between 2014 and 2019; United Nations (UN) developedeconomy . PrimaryThe environmental scan searches were run, and the first 200 records werescreened by two independent reviewers. Websites that containedadditional relevant links, such as Alberta-based municipal websites,were ‘hand-searched’ by following embedded links and applyinginclusion criteria. Inclusion criteria were: Canadian interventionstargeting financial well-being and/or strain among families,individuals or communities; English or French. A reviewer bilingual inFrench and English conducted all of the French language searches,screening and data extraction. A second reviewer conducted the Englishlanguage searches, screening and data extraction.A total of 194 unique interventions emerged from the scan. We conductedfull data extraction on 7 Alberta-based poverty reduction strategiesand 96 Alberta-based community-level interventions. The remaining 91interventions were national/provincial-level or community-level fromoutside Alberta, which underwent abbreviated data extraction and didnot inform the findings of this rapid review.Our interview guide focused on gaps in our rapid review and environmentalscan results, and gathered information on intervention implementationand tailoring by setting or population. We identified interviewparticipants through the environmental scan, prioritisingAlberta-based organisations running unique interventions or operatingoutside of urban centres. A total of 12 semi-structured telephoneinterviews occurred with 15 participants between 3 September and 17September 2019. These represented 12 distinct interventions and 10organisations –39.AddiA single reviewer extracted (i.e. coded) detailed content from thearticles, websites and interview transcripts using NVivo software.This inWe first synthesised the findings from the rapid review and environmentalscan to identify specific intervention strategies (e.g. housing) andsub-strategies (housing policies). Our analysis of the interviewtranscripts focused on additional details (e.g. tips for tailoring)relevant to the intervention (sub-)strategies identified. It alsoinvolved pinpointing new sub-strategies that emerged from theseconversations .Two co-authors reviewed the data extracted and identified key practiceimplications and gaps in the evidence that aligned with each of theeight intervention strategy areas. These were then discussed with thefull team until we reached a consensus.Our study results are contextualised by our methodological limitations.To maintain the narrow scope required of a rapid review, and toprioritise the literature most relevant to the needs of our practicepartners, articles that only described interventions, reported onstudy protocols or discussed theoretical or predictive models in theabsence of intervention-related outcomes or process measures wereexcluded. These may have contained detailed implementationinformation; therefore, their exclusion represents a possiblelimitation of the rapid review method. Limiting academic articles toUN developed economies , the envSupplementary Table S1.Based on the rapid review and environmental scan, we identified eightdistinct intervention strategies for communities to act on financialwell-being and/or financial strain. They included: (a) systemsnavigation and access; (b) financial literacy and skills; (c)emergency financial assistance; (d) asset building; (e) events andattractions; (f) employment and educational support; (g) affordableand accessible transportation; and (h) housing. We noted interventionsub-strategies for six of the eight strategies, which are detailedalong with a description of the intervention strategies, relatedtarget populations and outcomes in Of the 21 articles retrieved through the rapid review, 11 reported onmulti-component interventions strategies was lacking because themajority of the interventions assessed in the rapid review weremulti-component or included wrap-around support –53, were546165Few research articles reported on follow-up assessments to evaluate thesustained impacts of the interventions. The results of theseassessments were mixed. In one case, the effects mostly dissipated,while iAlthough interventions commonly targeted social determinants of health or connections to thissupport if it is required. This includes the provision ofbasic needs , which is particularlykey for underserved populations and people living outsideof resource-dense urban centres.Robust evaluations, that collect information  on the needs, processes, outcomes  and impacts of interventionsimplemented. Reporting data on socially ascribedcategories will be key to understanding what works for whom and howit does so, the possible unintended consequences of theinterventions and also equity impacts. A plan for sharinglessons learned and evaluation findings with othersworking in practice and researchers [is necessary] toinform the creation of best practices.Financial strain is a socio-economic pandemic that will echo each wave ofCOVID-19. Promoting financial well-being at this time necessitatesadequate preparation and swift, strategic community-led action tosupport societal recovery, particularly for underserved populationswho are already at heightened risk of the negative impacts of bothpandemics.Click here for additional data file.Supplemental material, sj-pdf-1-ped-10.1177_1757975920984182 forCommunity intervention strategies to reduce the impact of financialstrain and promote financial well-being: a comprehensive rapid reviewby Nicole M. Glenn, Lisa Allen Scott, Teree Hokanson, Karla Gustafson,Melissa A. Stoops, Brynn Day and Candace I. J. Nykiforuk in GlobalHealth Promotion