Advantages and psychometric validation of proximal intensive assessments of patient-reported outcomes collected in daily life

Abstract

Objectives Ambulatory assessment data collection methods are increasingly used to study behavior, experiences, and patient reported outcomes (PROs) such as emotions, cognitions, and symptoms in clinical samples. Data collected close in time at frequent and fixed intervals can assess PROs that are discrete or changing rapidly and provide information about temporal dynamics or mechanisms of change in clinical samples and individuals, but clinical researchers have not yet routinely and systematically investigated the reliability and validity of such measures or their potential added value over conventional measures. The present study provides a comprehensive, systematic evaluation of the psychometrics of several Proximal Intensive Assessment (PIA) measures in a clinical sample and investigates whether PIA appears to assess meaningful differences in phenomena over time. Methods Data was collected on a variety of psychopathology constructs on handheld devices every 4 hours for 7 days from 62 adults recently exposed to traumatic injury of themselves or a family member. Data was also collected on standard self-report measures of the same constructs at the time of enrollment, one week after enrollment, and two months after injury. Results For all measure scores, results showed good internal consistency across items and within persons over time, provided evidence of convergent, divergent, and construct validity, and showed significant between and within-subject variability. Conclusions Results indicate that PIA measures can provide valid measurement of psychopathology in a clinical sample. PIA may be useful to study mechanisms of change in clinical contexts, identify targets for change, and gauge treatment progress.

A community‐engaged approach to investigate cardiovascular‐associated inflammation among American Indian women: A research protocol

“…Sample items include: “I avoid doing things or being in situations that might remind me of something terrible that happened to me in the past,” and “I have trouble getting to sleep or staying asleep.” The SPTSS score is the total of all item responses; a score of 20 suggests a PTSD diagnosis (Carlson, ). The SPTSS is correlated with other posttraumatic stress measures and has high internal consistency ( α = 0.86; Carlson et al, ).…”

“…The SPTSS score is the total of all item responses; a score of 20 suggests a PTSD diagnosis (Carlson, 2001). The SPTSS is correlated with other posttraumatic stress measures and has high internal consistency (α = 0.86; Carlson et al, 2016).…”

Development and preliminary performance of a risk factor screen to predict posttraumatic psychological disorder after trauma exposure

“…Participants were 147 trauma-exposed adults who were recruited to participate in a study of factors that influence responses to traumatic stress [29] and a study of an intensive ambulatory data collection method [39]. Participants had been treated in a Level I trauma center and hospitalized for a severe injury (54%) or had loved ones treated and hospitalized for life-threatening injuries (46%).…”

The Dynamic Relations Among Peritraumatic Posttraumatic Stress Symptoms: An Experience Sampling Study During Wartime

“…Indeed, processes of suppression (Küpper, Benoit, Dalgleish, & Anderson, 2014), avoidance (Wittekind et al, 2015), and changes in attention and memory processes (Dekel, Solomon, & Ein-Dor, 2016) have certainly biased responses. Furthermore, the variance in threat that can occur between different peritraumatic subperiods (i.e., periods of partial ceasefire, reduced fighting, or fighting in different regions; see Gelkopf et al, 2017) requires a real-time multiple assessment data collection method, such as intensive longitudinal assessments (i.e., experiential sampling methodology; Conner & Mehl, 2015;Myin-Germeys et al, 2009) or proximal intensive assessments (Carlson et al, 2016), to capture its dynamics. Experiential sampling methodology enables the investigation of changes over small time intervals, allows for the exploration of real-time within-subject processes and dynamics, and minimizes retrospective and ecological biases (Trull & Ebner-Priemer, 2013).…”