Toward Tailoring Just-in-Time Adaptive Intervention Systems for Workplace Stress Reduction: Exploratory Analysis of Intervention Implementation.

BACKGROUND: Integrating stress-reduction interventions into the workplace may improve the health and well-being of employees, and there is an opportunity to leverage ubiquitous everyday work technologies to understand dynamic work contexts and facilitate stress reduction wherever work happens. Sensing-powered just-in-time adaptive intervention (JITAI) systems have the potential to adapt and deliver tailored interventions, but such adaptation requires a comprehensive analysis of contextual and individual-level variables that may influence intervention outcomes and be leveraged to drive the system's decision-making. OBJECTIVE: This study aims to identify key tailoring variables that influence momentary engagement in digital stress reduction microinterventions to inform the design of similar JITAI systems. METHODS: To inform the design of such dynamic adaptation, we analyzed data from the implementation and deployment of a system that incorporates passively sensed data across everyday work devices to send just-in-time stress reduction microinterventions in the workplace to 43 participants during a 4-week deployment. We evaluated 27 trait-based factors (ie, individual characteristics), state-based factors (ie, workplace contextual and behavioral signals and momentary stress), and intervention-related factors (ie, location and function) across 1585 system-initiated interventions. We built logistical regression models to identify the factors contributing to momentary engagement, the choice of interventions, the engagement given an intervention choice, the user rating of interventions engaged, and the stress reduction from the engagement. RESULTS: We found that women (odds ratio [OR] 0.41, 95% CI 0.21-0.77; P=.03), those with higher neuroticism (OR 0.57, 95% CI 0.39-0.81; P=.01), those with higher cognitive reappraisal skills (OR 0.69, 95% CI 0.52-0.91; P=.04), and those that chose calm interventions (OR 0.43, 95% CI 0.23-0.78; P=.03) were significantly less likely to experience stress reduction, while those with higher agreeableness (OR 1.73, 95% CI 1.10-2.76; P=.06) and those that chose prompt-based (OR 6.65, 95% CI 1.53-36.45; P=.06) or video-based (OR 5.62, 95% CI 1.12-34.10; P=.12) interventions were substantially more likely to experience stress reduction. We also found that work-related contextual signals such as higher meeting counts (OR 0.62, 95% CI 0.49-0.78; P<.001) and higher engagement skewness (OR 0.64, 95% CI 0.51-0.79; P<.001) were associated with a lower likelihood of engagement, indicating that state-based contextual factors such as being in a meeting or the time of the day may matter more for engagement than efficacy. In addition, a just-in-time intervention that was explicitly rescheduled to a later time was more likely to be engaged with (OR 1.77, 95% CI 1.32-2.38; P<.001). CONCLUSIONS: JITAI systems have the potential to integrate timely support into the workplace. On the basis of our findings, we recommend that individual, contextual, and content-based factors be incorporated into the system for tailoring as well as for monitoring ineffective engagements across subgroups and contexts.

The day-to-day relationship between posttraumatic stress symptoms and social support after sexual assault.

Background: Experiencing sexual assault is associated with a significant increase in risk for developing posttraumatic stress disorder and related concerns (e.g. alcohol misuse). Cross-sectional and longitudinal evidence suggests that social support may be both broadly protective against and eroded by posttraumatic stress symptoms. However, little is known about how different aspects of social support and posttraumatic stress symptoms influence each other in the weeks and months immediately following sexual assault, when posttraumatic stress (PTS) symptoms first emerge.Objective: The present study assessed the day-to-day relationship between social support and PTS in a sample of distressed, alcohol-using, recently-assaulted female survivors participating in a clinical trial of an app-based intervention (N = 41).Method: Participants completed 3 weeks of daily diaries starting within 10 weeks of sexual assault. Mixed-effects models were used to examine prior-day and same-day relationships between PTS and four social support constructs (social contact, emotional support, pleasantness of social interactions, and talking about sexual assault).Results: Results indicate that higher quantity and pleasantness of social interactions over the full sampling period was associated with lower PTS symptoms on any given day. Experiencing better-than-typical social interactions on one day was associated with lower than typical PTS symptoms on that day and the next day. On days when participants discussed their sexual assault with others, they tended to be having higher than usual PTS symptoms.Conclusions: Findings suggest that increasing the quantity and pleasantness of social interactions soon after sexual assault might protect against worsening posttraumatic stress symptoms.Trial registration: ClinicalTrials.gov identifier: NCT03703258.

In N = 41 distressed and alcohol-using female survivors of recent sexual assault, having a higher quantity of social interactions and more pleasant social interactions within 10 weeks of assault was associated with lower posttraumatic stress symptoms.When survivors' social interactions were more pleasant than average on one day, their posttraumatic stress symptoms tended to be lower than average the next day, and recent survivors were more likely to talk about sexual assault on days when their posttraumatic stress symptoms were higher than usual.Interventionists should take note that increasing the quantity and pleasantness of social interactions soon after sexual assault might protect against worsening posttraumatic stress symptoms.

Bidirectional Associations Between Pain and Perceived Stress Among Veterans: Depressive Disorder as a Predisposing Factor.

OBJECTIVE: Military veterans who were injured in combat very often report pain along with co-occurring perceived stress and preexisting depressive disorder. The systems model of pain is a theoretical model suggesting that pain and perceived stress are bidirectionally associated at the within-person level, and associations are heightened among those with depressive disorder. However, the systems model of pain has not been adequately tested. Testing the systems model of pain could illuminate salient treatment targets for combat-injured veterans with pain and co-occurring psychological problems. METHODS: The present study empirically tests the systems model of pain among a sample of combat-injured veterans ( N = 902) surveyed five times during an 18-month period. We used a multigroup, autoregressive latent trajectory with structured residual statistical model to test the within-person associations between pain and perceived stress and determine whether associations differ between veterans with and without a positive screen for depressive disorder. RESULTS: In line with the systems model of pain, pain and perceived stress were bidirectionally associated only among combat-injured veterans with depressive disorder. Among such veterans, perceived stress was positively associated with subsequent pain ( b = 0.12; 95% confidence interval = 0.06-0.17), and pain was positively associated with subsequent perceived stress ( b = 0.44; 95% CI = 0.11-0.77). CONCLUSIONS: Our work highlights the interplay between pain and its psychological correlates among a particularly at-risk population. Clinicians addressing pain and perceived stress among combat-injured veterans should be prepared to identify and address depressive disorder.

Examining the Concurrent and Predictive Validity of Single Items in Ecological Momentary Assessments.

Although single items can save time and burden in psychology research, concerns about their reliability have made the use of multiple-item measures the default standard practice. Although single items cannot demonstrate internal reliability, their criterion validity can be compared with multiple-item measures. Using ecological momentary assessment data, we evaluated repeated measures correlations and constructed multilevel cross-lagged models to assess concurrent and predictive validity of single- and multiple-item measures. Correlations between the single- and multiple-item measures ranged from .24 to .61. In 27 of 29 unique single-item predictor models, single items demonstrated significant predictive validity, and in one of eight sets of comparisons, a single-item predictor exhibited a larger effect size than its multiple-item counterpart. Although multiple-item measures generally performed better than single items, the added benefit of multiple items was modest in most cases. The present data provide support for the use of single-item measures in intensive longitudinal designs.

Design and Implementation of a Clinical Science Specialty Clinic for Adults with Neurological Disorders and Their Caregivers.

Mental health problems are common for persons with neurological disorders (PWNDs) and their caregivers (CGs) but often are not adequately treated. Despite this growing need, the training of clinical psychologists typically does not include coursework or practicum experience working with these populations. To address this, a team of faculty, supervisors, and doctoral students in UC Berkeley's Clinical Science program undertook a year-long process that consisted of building a training curriculum that integrated coursework and consultation with visiting experts; providing supervised practicum training with PWNDs and CGs and evaluating training and clinical outcomes. We hoped to prepare students to train other mental health professionals to work with these populations in the future. In this article, we describe the Specialty Clinic with special attention given to the training provided, challenges faced and solutions found, clinic operations and logistics, and lessons learned. We also review key clinical issues and report key indicators of client outcomes. Finally, we evaluate the success of the Specialty Clinic and offer recommendations for others interested in providing these kinds of much needed training and clinical services in this important area.

Identifying and predicting posttraumatic stress symptom states in adults with posttraumatic stress disorder.

Between-person heterogeneity of posttraumatic stress disorder (PTSD) is well established. Within-person analyses and the DSM-5 suggest that heterogeneity may also be evident within individuals across time as they move through social contexts and biological cycles. Modeling within-person symptom-level fluctuations may confirm such heterogeneity, elucidate mechanisms of disorder maintenance, and inform time- and person-specific interventions. The present study aimed to identify and predict discrete within-person disorder presentations, or symptom states, and explore group-level patterns of these states. Adults (N = 20, 60.0% male, M age = 38.25 years) with PTSD responded to symptom surveys four times per day for 30 days. We subjected each individual's dataset to Gaussian finite mixture modeling (GFMM) to uncover latent, within-person classes of symptom levels (i.e., states) and predicted those states with idiographic elastic net regularized regression using a set of time-based and behavioral predictors. Next, we conducted a GFMM of the within-person GFMM outputs and tested idiographic prediction models of these states. Multiple within-person states were revealed for 19 of 20 participants (Mdn = 4; 66 for the full sample). Prediction models were moderately successful, M AUC = .66 (d = 0.58), range: .50-1.00. The GFMM of the within-person model outputs revealed two states: one with above-average and one with below-average symptom levels. Prediction models were, again, moderately successful, M AUC = .66; range: .50-.89. The findings provide evidence for within-person heterogeneity of PTSD as well as between-person similarities and suggest that future work should incorporate additional contextual variables as symptom state predictors.

Unsupervised clustering of autonomic temporal networks in clinically distressed and psychologically healthy individuals.

The present study recruited psychologically healthy individuals and individuals with clinically-severe Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition diagnoses, including generalized anxiety disorder, major depressive disorder, social anxiety disorder, posttraumatic stress disorder, panic disorder, persistent depressive disorder, and specific phobia. During the course of a structured clinical interview, 200 individuals provided continuous electrocardiogram and impedance cardiography data. Of these N = 150 were used for exploratory analyses and N = 50 for confirmatory analyses. From these time series, we modeled heart period (i.e. interbeat interval), pre-ejection period, respiratory sinus arrhythmia, and respiration rate. The group iterative multiple model estimation (GIMME) model was used to generate group and individual-level network models which, in turn, were used to conduct unsupervised classification of individual-level models into subgroups. Four subgroups were identified, comprising N = 22, N = 25, N = 26, and N = 61 individuals, with an additional 16 individuals left unclassified. The subgroup models were then used to estimate directed network models, from which out-degree and in-degree centrality were estimated for each group. Two groups, Group 2 and Group 4 exhibited elevated symptoms of depression and anxiety relative to the remaining sample. However, only one of these, Group 2, exhibited additional physiological risk features, including a significantly elevated average heart rate, and significantly reduced parasympathetic regulation (measured via respiratory sinus arrhythmia). We discuss the implications for utilizing network models for conducting systems-level analyses of physiological systems in clinically-distressed and psychologically healthy individuals.

Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors.

Background: While preliminary evidence suggests that sensors may be employed to detect presence of low mood it is still unclear whether they can be leveraged for measuring depression symptom severity. This study evaluates the feasibility and performance of assessing depressive symptom severity by using behavioral and physiological features obtained from wristband and smartphone sensors. Method: Participants were thirty-one individuals with Major Depressive Disorder (MDD). The protocol included 8 weeks of behavioral and physiological monitoring through smartphone and wristband sensors and six in-person clinical interviews during which depression was assessed with the 17-item Hamilton Depression Rating Scale (HDRS-17). Results: Participants wore the right and left wrist sensors 92 and 94% of the time respectively. Three machine-learning models estimating depressive symptom severity were developed-one combining features from smartphone and wearable sensors, one including only features from the smartphones, and one including features from wrist sensors-and evaluated in two different scenarios. Correlations between the models' estimate of HDRS scores and clinician-rated HDRS ranged from moderate to high (0.46 [CI: 0.42, 0.74] to 0.7 [CI: 0.66, 0.74]) and had moderate accuracy with Mean Absolute Error ranging between 3.88 ± 0.18 and 4.74 ± 1.24. The time-split scenario of the model including only features from the smartphones performed the best. The ten most predictive features in the model combining physiological and mobile features were related to mobile phone engagement, activity level, skin conductance, and heart rate variability. Conclusion: Monitoring of MDD patients through smartphones and wrist sensors following a clinician-rated HDRS assessment is feasible and may provide an estimate of changes in depressive symptom severity. Future studies should further examine the best features to estimate depressive symptoms and strategies to further enhance accuracy.