Power analysis for idiographic (within-subject) clinical trials: Implications for treatments of rare conditions and precision medicine.

Power analysis informs a priori planning of behavioral and medical research, including for randomized clinical trials that are nomothetic (i.e., studies designed to infer results to the general population based on interindividual variabilities). Far fewer investigations and resources are available for power analysis of clinical trials that follow an idiographic approach, which emphasizes intraindividual variabilities between baseline (control) phase versus one or more treatment phases. We tested the impact on statistical power to detect treatment outcomes of four idiographic trial design factors that are under researchers' control, assuming a multiple baseline design: sample size, number of observations per participant, proportion of observations in the baseline phase, and competing statistical models (i.e., hierarchical modeling versus piecewise regression). We also tested the impact of four factors that are largely outside of researchers' control: population size, proportion of intraindividual variability due to residual error, treatment effect size, and form of outcomes during the treatment phase (phase jump versus gradual change). Monte Carlo simulations using all combinations of the factors were sampled with replacement from finite populations of 200, 1750, and 3500 participants. Analyses characterized the unique relative impact of each factor individually and all two-factor combinations, holding all others constant. Each factor impacted power, with the greatest impact being from larger treatment effect sizes, followed respectively by more observations per participant, larger samples, less residual variance, and the unexpected improvement in power associated with assigning closer to 50% of observations to the baseline phase. This study's techniques and R package better enable a priori rigorous design of idiographic clinical trials for rare diseases, precision medicine, and other small-sample studies.

Adapting the multilevel model for estimation of the reliable change index (RCI) with multiple timepoints and multiple sources of error.

OBJECTIVE: One of the primary tools in the assessment of individual-level patient outcomes is Jacobson and Truax, (1991's) Reliable Change Index (RCI). Recent efforts to optimize the RCI have revolved around three issues: (a) extending the RCI beyond two timepoints, (b) estimating the RCI using scale scores from item response theory or factor analysis and (c) estimation of person- and time-specific standard errors of measurement. METHOD: We present an adaptation of a two-stage procedure, a measurement error-corrected multilevel model, as a tool for RCI estimation (with accompanying Statistical Analysis System syntax). Using DASS-21 data from a community-based mental health center (N = 379), we illustrate the potential for the model as unifying framework for simultaneously addressing all three limitations in modeling individual-level RCI estimates. RESULTS: Compared to the optimal-fitting RCI model (moderated nonlinear factor analysis scoring with measurement error correction), an RCI model that uses DASS-21 total scores produced errors in RCI inferences in 50.8% of patients; this was largely driven by overestimation of the proportion of patients with statistically significant improvement. CONCLUSION: Estimation of the RCI can now be enhanced by the use of latent variables, person- and time-specific measurement errors, and multiple timepoints.

Use of Mobile Technology Paired with Heart Rate Monitor to Remotely Quantify Behavioral Health Markers among Military Reservists and First Responders.

INTRODUCTION: Heart rate variability (HRV) is a biological marker that reflects an individual's autonomic nervous system regulation. Psychological resilience is an individual's ability to recover from an adverse event and return to physiological homeostasis and mental well-being, indicated by higher resting HRV. The Biofeedback Assisted Resilience Training (BART) study evaluates a resilience-building intervention, with or without HRV biofeedback. This article evaluates the feasibility of remote psychophysiological research by validating the HRV data collected. MATERIALS AND METHODS: The BART platform consists of a mobile health application (BART app) paired to a wearable heart rate monitor. The BART app is installed on the participant's personal phone/tablet to track and collect self-report psychological and physiological data. The platform collects raw heart rate data and processes HRV to server as online biofeedback. The raw data is processed offline to derive HRV for statistical analysis. The following HRV parameters are validated: inter-beat interval, respiratory sinus arrhythmia, low-frequency HRV, biofeedback HRV, and heart period. Bland-Altman and scatter plots are used to compare and contrast online and offline HRV measures. Repeated-measures ANOVA are used to compared means across tasks during the stress (rest, stress, and recovery) and training (rest and paced breathing) sessions in order to validate autonomic nervous system changes to physiological challenges. RESULTS: The analyses included 245 participants. Bland-Altman plots showed excellent agreement and minimal bias between online and offline unedited inter-beat interval data during the stress session. RMANOVA during the training session indicated a significant strong effect on biofeedback HRV, F(11,390) = 967.96, P < .01. During the stress session, RMANOVA showed significant strong effect on respiratory sinus arrhythmia and low-frequency HRV, and a significant but weak effect on heart period. CONCLUSIONS: The BART digital health platform supports remote behavioral and physiological data collection, intervention delivery, and online HRV biofeedback.

Mental health, stress, and resilience correlates of heart rate variability among military reservists, guardsmen, and first responders.

The use of heart rate variability (HRV) for monitoring stress has been growing in the behavioral health literature, especially in the areas of posttraumatic stress disorder, stress reactivity, and resilience. Few studies, however, have included general populations under workplace conditions. This study evaluates whether military and other first responders show lower HRV during stress than at baseline and greater post stress rebound, controlling for a myriad of potential confounders. A convenience sample of Reserves, National Guard, veteran, fire, and police personnel provided HRV and self-reported questionnaire responses before, during, and after a cognitive-stressor task with a smart phone application. Timing of HRV application; mental and physical health scores; coping and posttraumatic growth indicators, including being open to new possibilities; and emotional support were predictors of trajectories of the HRV response to stress. Findings from this exploratory study emphasize the strong link between stress and relaxation breathing in both respiratory sinus arrhythmia and low frequency heart rate variability and the need for controlling potential covariates for understanding the relationship between HRV and the stress response and providing a basis for hypothesis driven research.

Biofeedback-Assisted Resilience Training for Traumatic and Operational Stress: Preliminary Analysis of a Self-Delivered Digital Health Methodology.

BACKGROUND: Psychological resilience is critical to minimize the health effects of traumatic events. Trauma may induce a chronic state of hyperarousal, resulting in problems such as anxiety, insomnia, or posttraumatic stress disorder. Mind-body practices, such as relaxation breathing and mindfulness meditation, help to reduce arousal and may reduce the likelihood of such psychological distress. To better understand resilience-building practices, we are conducting the Biofeedback-Assisted Resilience Training (BART) study to evaluate whether the practice of slow, paced breathing with or without heart rate variability biofeedback can be effectively learned via a smartphone app to enhance psychological resilience. OBJECTIVE: Our objective was to conduct a limited, interim review of user interactions and study data on use of the BART resilience training app and demonstrate analyses of real-time sensor-streaming data. METHODS: We developed the BART app to provide paced breathing resilience training, with or without heart rate variability biofeedback, via a self-managed 6-week protocol. The app receives streaming data from a Bluetooth-linked heart rate sensor and displays heart rate variability biofeedback to indicate movement between calmer and stressful states. To evaluate the app, a population of military personnel, veterans, and civilian first responders used the app for 6 weeks of resilience training. We analyzed app usage and heart rate variability measures during rest, cognitive stress, and paced breathing. Currently released for the BART research study, the BART app is being used to collect self-reported survey and heart rate sensor data for comparative evaluation of paced breathing relaxation training with and without heart rate variability biofeedback. RESULTS: To date, we have analyzed the results of 328 participants who began using the BART app for 6 weeks of stress relaxation training via a self-managed protocol. Of these, 207 (63.1%) followed the app-directed procedures and completed the training regimen. Our review of adherence to protocol and app-calculated heart rate variability measures indicated that the BART app acquired high-quality data for evaluating self-managed stress relaxation training programs. CONCLUSIONS: The BART app acquired high-quality data for studying changes in psychophysiological stress according to mind-body activity states, including conditions of rest, cognitive stress, and slow, paced breathing.