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Founded Year

1998

Stage

Acquired | Acquired

Total Raised

$31.35M

About Reactivity

Companies rely on Racktivity for quick and efficient deployment of SOA applications. Racktivity provides organizations with the technology to secure, accelerate and manage XML and Web Services environments. With Racktivity, customers realize the cost saving and time-to-market promise of Web services and SOA. Racktivity's products deliver security, reliability, and scalability functions in an application oriented networking layer.

Headquarters Location

1301 Shoreway Road Suite 425

Belmont, California, 94002,

United States

650-551-7800

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Reactivity Patents

Reactivity has filed 2 patents.

patents chart

Application Date

Grant Date

Title

Related Topics

Status

2/14/2006

8/11/2009

Federated identity, Cryptography, Identity management, Units of information, Identity management systems

Grant

Application Date

2/14/2006

Grant Date

8/11/2009

Title

Related Topics

Federated identity, Cryptography, Identity management, Units of information, Identity management systems

Status

Grant

Latest Reactivity News

Psychological Resilience Factors and Their Association With Weekly Stressor Reactivity During the COVID-19 Outbreak in Europe: Prospective Longitudinal Study

Oct 17, 2023

February 15, 2023 . Psychological Resilience Factors and Their Association With Weekly Stressor Reactivity During the COVID-19 Outbreak in Europe: Prospective Longitudinal Study Psychological Resilience Factors and Their Association With Weekly Stressor Reactivity During the COVID-19 Outbreak in Europe: Prospective Longitudinal Study Authors of this article: 2Leibniz Institute for Resilience Research (LIR), Mainz, Germany 3Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany 4Research Division of Mind and Brain, Department of Psychiatry and Psychotherapy, Charité Campus Mitte, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany 5Neuroimaging Center (NIC), Focus Program Translational Neuroscience (FTN), Johannes Gutenberg University Medical Center, Mainz, Germany 6Berlin School of Mind and Brain, Faculty of Philosophy, Humboldt-Universität zu Berlin, Berlin, Germany 7Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany 8Freiburg Center for Data Analysis and Modelling, Institute of Physics, University of Freiburg, Freiburg, Germany 9Division of Experimental Psychopathology and Psychotherapy, Department of Psychology, University of Zurich, Zurich, Switzerland 10Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital (PUK), University of Zurich, Zurich, Switzerland 11Faculty of Psychology, University of Warsaw, Warsaw, Poland 12Center for Contextual Psychiatry, Department of Neurosciences, KU Leuven, Leuven, Belgium 13College of Inter-Faculty Individual Studies in Mathematics and Natural Sciences, University of Warsaw, Warsaw, Poland 14Concentris Research Management GmbH, Fürstenfeldbruck, Germany 15Research Group of Quantitative Psychology and Individual Differences, Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium 16Institute of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany 17Department of Psychiatry and Psychotherapy, Johannes Gutenberg University Medical Center, Mainz, Germany 18Institute of Molecular Biology (IMB), Mainz, Germany 19Department of Developmental Psychology, University of Amsterdam, Amsterdam, Netherlands *these authors contributed equally Radboud University Medical Center Abstract Background: Cross-sectional relationships between psychosocial resilience factors (RFs) and resilience, operationalized as the outcome of low mental health reactivity to stressor exposure (low “stressor reactivity” [SR]), were reported during the first wave of the COVID-19 pandemic in 2020. Objective: Extending these findings, we here examined prospective relationships and weekly dynamics between the same RFs and SR in a longitudinal sample during the aftermath of the first wave in several European countries. Methods: Over 5 weeks of app-based assessments, participants reported weekly stressor exposure, mental health problems, RFs, and demographic data in 1 of 6 different languages. As (partly) preregistered, hypotheses were tested cross-sectionally at baseline (N=558), and longitudinally (n=200), using mixed effects models and mediation analyses. Results: RFs at baseline, including positive appraisal style (PAS), optimism (OPT), general self-efficacy (GSE), perceived good stress recovery (REC), and perceived social support (PSS), were negatively associated with SR scores, not only cross-sectionally (baseline SR scores; all P<.001) but also prospectively (average SR scores across subsequent weeks; positive appraisal (PA), P=.008; OPT, P<.001; GSE, P=.01; REC, P<.001; and PSS, P=.002). In both associations, PAS mediated the effects of PSS on SR (cross-sectionally: 95% CI –0.064 to –0.013; prospectively: 95% CI –0.074 to –0.0008). In the analyses of weekly RF-SR dynamics, the RFs PA of stressors generally and specifically related to the COVID-19 pandemic, and GSE were negatively associated with SR in a contemporaneous fashion (PA, P<.001; PAC,P=.03; and GSE, P<.001), but not in a lagged fashion (PA, P=.36; PAC, P=.52; and GSE, P=.06). Conclusions: We identified psychological RFs that prospectively predict resilience and cofluctuate with weekly SR within individuals. These prospective results endorse that the previously reported RF-SR associations do not exclusively reflect mood congruency or other temporal bias effects. We further confirm the important role of PA in resilience. JMIR Ment Health 2023;10:e46518 Background Outcome-based resilience refers to the maintenance or quick recovery of mental health despite exposure to adversity, presumably resulting from a dynamic process of adaptation [ 1 ]. While resilience has been primarily studied in the context of natural disasters, accidents, terror attacks, and other potentially traumatizing events [ 1 - 3 ], the outbreak of the COVID-19 pandemic in 2020 has brought up new types and levels of stressors that have impacted a vast majority of the global population. This is illustrated by the surge in stress-related mental disorders such as depression and anxiety during the pandemic [ 4 ]. In particular, people without mental health disorders before the pandemic exhibited significant increases in symptoms during the crisis compared with those who were already affected by a mental disorder [ 5 ]. Since 2021, the focus on the COVID-19 pandemic has shifted, and both media coverage and national policy responses have decreased substantially [ 6 , 7 ]. However, this study provides information on predictors, processes, and potential intervention targets for strategies to promote mental resilience, not only during the COVID-19 pandemic [ 1 , 8 ] but also in anticipation of increasingly frequent future global stressors [ 9 ]. Many studies worldwide have addressed questions of mental resilience during the COVID-19 pandemic via online surveys, conducted in China and other Asian countries [ 10 - 16 ], Iraq [ 17 ], Turkey [ 18 , 19 ], Israel [ 20 ], European countries [ 21 - 28 ], the United States [ 29 - 31 ], and Canada [ 32 ]. Increased levels of depressive symptoms and anxiety were frequently reported compared with population norms, while higher scores on trait resilience measures, behavioral coping (BC) strategies, and social support were cross-sectionally associated with lower symptoms of distress or better mental health. However, with the exception of our previous cross-sectional survey study “DynaCORE-C” (DynaMORE cross-sectional study on psychological resilience to the mental health consequences of the COVID-19 pandemic) [ 27 ], none of these studies considered individual-level stressor exposure, which is crucial for operationalizing resilience as the ability to maintain mental health despite exposure to such stressors [ 33 , 34 ]. In DynaCORE-C [ 27 ], we used a residualization approach [ 34 - 36 ], regressing internalizing mental health problems, retrospectively reported for a past 2-week time window, onto stressor exposure during that same time window. Using this method, individuals with a negative regression residual (a negative stressor reactivity [SR] score) can be seen as showing lower-than-expected symptom severity given their level of stressor exposure (ie, an indication of higher resilience), while individuals with positive residuals (positive SR score) show higher-than-expected stressor-related symptom severity (ie, an indication of lower resilience). This approach addresses the issue that individuals may well exhibit different degrees of mental health impairments in the COVID-19 pandemic; however, these differences may also be trivially explained by varying degrees of adversity experienced by the individuals rather than differences in their resilience capacities. Positive Appraisal Using this methodology, DynaCORE-C tested predictions put forth by the Positive Appraisal Style Theory of Resilience (PASTOR) [ 33 ]. According to PASTOR, individuals with a positive appraisal style (PAS) generally tend to set values for stressors, which they attribute to potential threats to their goals and needs, at levels that realistically reflect the threat. In some cases, they may even slightly underestimate the threat on key appraisal dimensions such as threat magnitude or cost, threat probability, and coping potential. Positive appraisers typically avoid catastrophizing on the magnitude/cost dimension, pessimism on the probability dimension, and helplessness on the coping dimension. However, they also tend not to generate unrealistically positive (delusional) threat perceptions, which could lead to trivialization, blind optimism (OPT), or overconfidence. As a result, their average stress reactions tend to be optimally regulated, in the sense that positive appraisers are well adept at generating stress reactions when necessary while also avoiding the unnecessary expenditure of resources, such as overly strong, prolonged, or repeated stress responses. This gives them enough time for recovery, resource rebuilding, and exploration and limits deleterious allostatic load effects and resource depletion as much as possible. The DynaCORE-C study found a self-report measure of PAS [ 27 ], along with the related constructs OPT and self-efficacy, to be positively associated with resilience (as approximated by a negative cross-sectional SR score). In addition to these measures of habitual appraisal styles, situational positive appraisal (PA), specifically related to the COVID-19 pandemic, was associated with resilience. Another claim of PASTOR is that the effects of other social, biological, and psychological resilience factors (RFs) on outcome-based resilience are mediated by PAS, that is, RFs other than PAS are only beneficial for resilience to the extent that they shape someone’s appraisal style toward the positive [ 33 , 37 ]. For instance, certain genetic or biological factors may render the brain circuits mediating PA and reappraisal processes more effective; spirituality may help find meaning in hardships; or trust in one’s social networks may allow one to perceive many stressors as manageable. In this regard, DynaCORE-C observed that the effects of perceived social support (PSS) were mediated by PAS [ 27 ]. Finally, DynaCORE-C found a weak cross-sectional association between BC style and resilience; additionally, it confirmed the well-known role of neuroticism (NEU) as a negative RF (ie, risk factor) [ 27 ]. These RF-SR associations from the cross-sectional DynaCORE-C study would be substantiated if one could show that (1) RFs also prospectively predict SR, ideally over an extended time window; and that (2) fluctuations in RFs are accompanied by fluctuations in SR, contemporaneously or prospectively (ie, with a time lag). Prospective associations, in particular, would help control for mood congruency or other state-dependent effects that may have exaggerated the previously reported cross-sectional associations [ 27 ]. Current Study To achieve this, we conducted a longitudinal study (DynaCORE-L or DynaMORE longitudinal study on psychological resilience to the mental health consequences of the COVID-19 pandemic) with repeated weekly measures of above RFs and of stressor exposure and mental health (to repeatedly calculate SR) over 5 consecutive weeks ( Figure 1 ). With this approach, we addressed the following 5 sets of hypotheses (H): First (H1), we aimed to replicate the associations of RFs and SR found in DynaCORE-C [ 27 ] using the cross-sectional data assessed at baseline. Second (H2), we aimed to extend the cross-sectional DynaCORE-C findings [ 27 ] by exploring whether RFs at baseline prospectively predict resilience, as approximated by the average SR score over all follow-up time points. Third (H3), we investigated the relation between RFs and SR scores within individuals longitudinally across weekly time points, predicting contemporaneous cofluctuations. Fourth (H4), in our primary hypothesis, we aimed to investigate the temporal dynamics of RFs and SR scores, namely, whether the use of RFs is prospectively associated with the SR score assessed 1 week later (lagged association). For all analyses, we hypothesized negative associations between RFs and SR (except NEU). In line with PASTOR [ 33 ] and previous results [ 27 ], we further hypothesized that the statistical effect of PSS on SR is positively mediated by PA. The mediation hypothesis was tested for each type of association, that is, cross-sectional (H1_MED), prospective (H2_MED), contemporaneous (H3_MED), and lagged (H4_MED). Fifth (H5), and based on the consideration that the experience of stressors may compromise or, as in the phenomenon of stress inoculation [ 38 - 40 ], potentially also strengthen RFs, we longitudinally investigated stressor exposure–dependent fluctuations in the RFs measured in the subsequent week, hypothesizing that stressor exposure would be associated either negatively or positively with RFs in a time-lagged fashion. All hypotheses, except H2, were preregistered at the Center for Open Science (OSF) registries [ 41 ]. For simplification and better explanation of concepts, we changed the numbering of hypotheses relative to the preregistration. ‎ Figure 1. Study design and hypotheses. To test the described hypotheses, the variables of interest were assessed at baseline (BL) and at 5 weekly follow-ups. The arrows indicate the hypothesized directions of statistical effects between the variables. At BL, resilience factors (RFs) were mostly assessed as general styles (subscript S) or traits (subscript T), while at the follow-ups, RFs were assessed as weekly modes (subscript M), that is, how frequent or extensively a certain RF was expressed during the preceding week. For each RF assessed as mode, an average weekly mode was also calculated, as the mean value across time points. Abbreviations: PAS: positive appraisal style; OPTT: optimism (trait); RECS: perceived good stress recovery (style); PSSS: perceived social support (style); CSSM: perceived change in social support during the COVID-19 pandemic (mode); BCS: behavioral coping style; NEUT: neuroticism (trait); PACM: positive appraisal specifically of the COVID-19 pandemic (mode); GSEM: general self-efficacy (mode); PSSM: perceived social support (mode); PAM: positive appraisal (mode); BCM: behavioral coping (mode); SR: stressor reactivity. Methods Sample Participants were recruited by snowball sampling via social media and mailing lists. The only inclusion criterion was a minimum age of 18 years. Data were collected across 6 time points per participant, comprising 1 baseline questionnaire and 5 weekly follow-ups ( Figure 1 ). Data collection took place between April 17 and August 10, 2020. Ethical Considerations Participants were not financially reimbursed, but those who completed all assessments were included in a raffle to win an Amazon voucher worth €100 (US $90). Data collection was pseudonymous and informed consent was given electronically via the smartphone app m-Path [ 42 ]. The study was approved by the Ethics Committee of the State Medical Board of Rhineland-Palatinate, Mainz, Germany (2020-14967) and was conducted in accordance with the Declaration of Helsinki. A total of 576 participants aged 18 years and above (mean age 31.7 years, SD 12.1 years, range 18-71 years, of which n=438 [76.1%] female) enrolled in the study, of which 210 participants (mean age 33.8 years, SD 13.3 years, range 18-68 years, of which n=160 [76.2%] female) completed at least four follow-up questionnaires. Follow-ups that were less than 5 or more than 9 days apart from the previous sampling time point were excluded from analysis, thus allowing a deviation of up to 2 days before and after the intended follow-up time point. Participants who answered less than 4 follow-up questionnaires or did not complete the baseline questionnaire were excluded from the longitudinal sample. We further excluded participants who reported demographic characteristics with exceptionally low frequencies compared with the rest of the sample, to prevent a statistically unreliable selection of covariates. After data cleaning, 558 participants were finally included in the cross-sectional (H1) and 200 participants in the longitudinal (H2-H5) analyses. Measured Variables Overview An overview of the items and inventories used for the measured variables is provided in Table S1 in Multimedia Appendix 1 . Demographic and Physical Health Variables Demographic variables assessed at baseline included age and gender, as well as geographic, educational, and social variables. Health status variables were current or previous mental health diagnoses, as well as COVID-19 risk and infection status. Resilience Factors To be able to address the potentially dynamic associations of RFs with SR over time, RFs were assessed on 2 different timescales: typical characteristics (RF styles) and current modes (RF modes). At baseline, most RF questions asked about the participant’s typical or usual behavior. They presumably reflect properties or qualities that are relatively durably associated with a person or constitute a typical way or tendency in which a person reacts to life experiences, but may still gradually change over time, for instance, through learning experiences and environmental changes. To demarcate these RFs from more trait-like RFs, we termed them “styles,” in keeping with [ 33 ], and denoted them with the subscript S. Compared with traits, which are here denoted with the subscript T, styles are more likely to show adaptation over time and can be hypothesized as the basis for allostatic resilience processes [ 34 ]. Next to styles, at the weekly follow-ups, these same RFs were assessed as “modes” (denoted with the subscript M). With this new measurement approach, we assessed to what extent a particular RF was used or experienced in a given week. Complementary to RF style measures, RF mode measures may be more sensitive to changes in the strength of an RF, which would not become apparent from inquiring about typical or usual behavior. Thereby, repeated RF mode assessments allow for examining how an RF potentially is associated with SR in a shorter time frame. RFs were PA [ 27 , 43 , 44 ], PA specifically of the COVID-19 pandemic (PAC) [ 27 ], OPT, general self-efficacy (GSE) [ 45 ], perceived good stress recovery (REC) [ 46 ], PSS [ 47 ], perceived change in social support during the COVID-19 pandemic (CSS) [ 27 ], and BC [ 27 , 43 ], complemented by NEU as a negative RF, or risk factor [ 48 ]. PA, PSS, and BC were assessed as both general styles (at baseline: PAS, PSSS, and BCS, respectively) and weekly modes (at follow-ups: PAM, PSSM, and BCM, respectively). PACM and GSEM were assessed as weekly modes only (at both baseline and follow-ups). OPTT, RECS, CSSM, and NEUT were assessed as personality traits/general styles/weekly modes at baseline only ( Figure 1 ). Stressor Exposure Participants reported the occurrence and severity of 11 general and 29 COVID-19 pandemic–specific stressors within the last 14 (baseline) or 7 days (follow-ups) on a 6-point scale ranging from 0 (did not happen) via 1 (not at all burdensome) to 5 (very burdensome). As in DynaCORE-C [ 27 ], E (ie, stressor exposure) was calculated as the total sum of all severity ratings. Mental Health Problems Internalizing symptoms were assessed for the past 14 days (baseline) or 7 days (follow-ups) using the 12-item General Health Questionnaire (GHQ-12) [ 49 ] total sum score. Stressor Reactivity and Resilience The SR score was computed as the residual of an individual’s P score on the sample’s E-P regression line [ 43 ]. E-P lines were fitted separately for the cross-sectional and longitudinal samples. For the cross-sectional analysis (H1), the E-P regression line was fitted over all 558 participants who completed the baseline questionnaire (similar to DynaCORE-C [ 27 ]). For the longitudinal analyses (H2-H5), the E-P line was fitted over all 200 participants who were included in the longitudinal analysis and over all time points, using a mixed effects model with random slopes and intercepts for participants. To reduce bias in the SR score introduced by outliers, Mahalanobis distance [ 50 ] was used for outlier detection for the E-P distribution. Cases with a chi-square value corresponding to P<.001 were excluded from the analysis. The E-P regression line was then determined by the fixed effects estimates of the slope and intercept, providing an estimate of normative SR in the sample over the whole observation period. Adding a second-order polynomial term did not improve model fit either in the cross-sectional or in the longitudinal sample (F1,555=3.35, P=.07 and χ12=0.88, P=.35, respectively, when comparing the model fit with and without the polynomial term). Subsequently, individual SR scores per time point were determined as residuals of individual P scores on the linear E-P line, by entering participants’ P and E scores from the respective week into the normative E-P line equation. SR scores were calculated separately for the cross-sectional and longitudinal samples. Covariate Selection In all models, age, gender, and survey language were included as covariates. Further covariates were selected based on their estimated effect on SR, which was assessed using univariate regression analyses separately for the cross-sectional and longitudinal samples. Variables surviving a likelihood ratio test at P<.2 were included in statistical analyses. The key covariates selected in both samples were education, general health, previous or current mental health diagnosis, belonging to a risk group, and opinion about the authorities’ measures to curtail the spread of the virus (for further details, see section 1.2 in Multimedia Appendix 1 ). Statistical Analyses The cross-sectional sample (N=558) was used to replicate the multiple regression and mediation results from the DynaCORE-C study [ 27 ] (H1, H1_MED) using the same analysis procedure (see section 1.3.1 in Multimedia Appendix 1 ). Separate multiple regression analyses were performed to assess the effects of each baseline RF style on the baseline SR score. Each model included the selected covariates (see Table S2 in Multimedia Appendix 1 ). Mediation analyses were conducted following the Baron and Kenny approach [ 51 ] and indirect paths were determined with the distribution-of-the-product method. The prospective association between baseline RF styles and the average weekly SR score (H2) as well as the corresponding mediation (H2_MED) was calculated analogously, yet in the longitudinal sample (n=200). All dynamic hypotheses (H3-H5) were tested in the longitudinal sample (n=200) by linear mixed model analyses (see sections 1.3.3-1.3.5 in Multimedia Appendix 1 ), using the lme4 package [ 52 ] in R (version 4.0.4; R Core Team). Each model included the selected covariates (see Table S3 in Multimedia Appendix 1 ) as well as the participant-level mean of the independent variable (for details, see section 1.2 in Multimedia Appendix 1 ). Random intercepts were assumed for each participant, and random slopes were fitted for the demeaned time-varying independent variable. To test model assumptions, visual checks of residual distributions were performed (see section 2.4.5 in Multimedia Appendix 1 ). As preregistered and for consistency, an α level of P<.05, 2-tailed, was used for all analyses, including the directional tests. To correct for multiple testing, a Bonferroni correction was applied to the analyses addressing our primary hypotheses about the time-lagged effects of PA (H4: PAM and PACM). These hypotheses were considered significant when passing the adjusted α level (Pcorr) <(.05/2)=.025. All reported β estimates are standardized. Any time-lagged model that revealed significant associations was followed up with an analysis of the association between the independent variable and the change in the dependent variable. For example, in the hypothetical association between any RF (time t) and the lagged SR (t+1), the SR at time t would be added as an additional predictor to the model. To this end, the model would account for the variance shared with the previous measurement (t) of the dependent variable. Finally, all analyses were repeated for participants in the top 2 tertiles (368/558, 65.9%, for the cross-sectional sample and 132/200, 66%, for the longitudinal sample) of stressor exposure (mean E counts over the observation period), to make sure that our results also apply when excluding participants with low stressor exposure. Results Sample Characteristics Demographic characteristics of the cross-sectional baseline sample after exclusions (N=558) are provided in Table 1 and Tables S4-S6 in Multimedia Appendix 1 . In this sample used for the cross-sectional replication analyses (hypothesis H1), the most frequently reported stressors were COVID-19–related media coverage (547/558, 98%), not being able to carry out leisure activities (535/558, 95.8%), and loss of social contact (522/558, 93.5%). On average, the most severely rated stressors were the inability to attend the funeral of a loved one (mean severity 4.03), the death of a loved one (mean severity 3.87), and the inability to return to the country one lives (mean severity 3.65). See Table S7 in Multimedia Appendix 1 for frequencies and severity ratings of all stressors. Participants who reported a past or present psychiatric diagnosis had significantly higher SR scores (mean 0.29, SD 1.01) than those who did not (mean –0.15, SD 0.97, t556=–4.92; P<.001). Baseline characteristics of the longitudinal sample (n=200) used for all other analyses (hypotheses H2-H5) are provided in Table 2 . Baseline characteristics of the baseline and longitudinal samples demonstrate notable similarities. Further details, including E, P, SR, and RFs per time point, are given in Tables S8-S10 in Multimedia Appendix 1 . Frequencies and severity ratings of all stressors are provided in Table S11 in Multimedia Appendix 1 . Table 1. Characteristics of the cross-sectional sample, assessed at baseline.a Characteristics

Reactivity Frequently Asked Questions (FAQ)

  • When was Reactivity founded?

    Reactivity was founded in 1998.

  • Where is Reactivity's headquarters?

    Reactivity's headquarters is located at 1301 Shoreway Road, Belmont.

  • What is Reactivity's latest funding round?

    Reactivity's latest funding round is Acquired.

  • How much did Reactivity raise?

    Reactivity raised a total of $31.35M.

  • Who are the investors of Reactivity?

    Investors of Reactivity include Cisco, Accel, JK&B Capital, Diamondhead Ventures, Austin Ventures and 4 more.

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