Objective The objective of this systematic review is to reevaluate and update the Integrative Model of Pediatric Medical Traumatic Stress (PMTS; Kazak et al., 2006), which provides a conceptual framework for traumatic stress responses across pediatric illnesses and injuries. Methods Using established systematic review guidelines, we searched PsycINFO, Cumulative Index to Nursing and Allied Health Literature, and PubMed (producing 216 PMTS papers published since 2005), extracted findings for review, and organized and interpreted findings within the Integrative Model framework. Results Recent PMTS research has included additional pediatric populations, used advanced longitudinal modeling techniques, clarified relations between parent and child PMTS, and considered effects of PMTS on health outcomes. Results support and extend the model’s five assumptions, and suggest a sixth assumption related to health outcomes and PMTS. Conclusions Based on new evidence, the renamed Integrative Trajectory Model includes phases corresponding with medical events, adds family-centered trajectories, reaffirms a competency-based framework, and suggests updated assessment and intervention implications.

Digital stress is believed to play a role in the association between social media use and psychosocial outcomes. However, the literature is limited by a lack of measures that conform to published theoretical models of the construct. The present investigation details the development of a new multidimensional measure of digital stress. Based on an earlier conceptualization of Digital Stress (Steele et al., 2020), Study 1 identified items from extant measures of digital stress, conducted a qualitative review of the literature to compose new items, and conducted focus groups with young adults and adolescents (N = 23) to improve item wording and interpretation. Study 2 conducted an exploratory factor analysis (EFA) of items with a young adult sample (N = 247) collected online, yielding support for four hypothesized factors (i.e., availability stress, approval anxiety, fear of missing out [FoMO], and connection overload) plus one unanticipated factor (i.e., online vigilance). In Study 3, college students (N = 174) completed paper-and-pencil surveys, and EFA results showed a similar structure as detected in Study 2. In Study 4, confirmatory factor analysis examining the five-factor model was conducted on data from adolescents (N = 163) and college students (N = 152). These procedures yielded 24 items measuring 5 components of digital stress: availability stress, approval anxiety, FoMO, connection overload, and online vigilance. Associations between digital stress and psychosocial distress and functioning are reported to demonstrate convergent and divergent validity. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

This systematic review provides a synthesis of randomized-controlled trials of technology-focused family interventions for children with chronic illnesses and their families, focused on describing the current state of the literature and generating recommendations for future family systems work in the field of eHealth/mHealth. Twenty-three studies met inclusion criteria and were included in the systematic review. No identified studies featured an mHealth delivery modality. Relevant data were extracted and studies were assessed for quality. There was great variability with regard to intervention factors (e.g., format) and family-centered intervention targets. There is some evidence that eHealth interventions may lead to improvements in particular domains of family functioning (e.g., family conflict) for some groups of participants. However, mixed results and selection of numerous different intervention targets by investigators make it challenging to draw summative conclusions about the overall effectiveness of family systems eHealth interventions. Future research should move beyond feasibility/acceptability studies and examine family-centered processes as primary outcomes. Future research should also consider novel intervention formats to determine whether intervention effects are more robust for certain groups (e.g., individuals who prefer electronic intervention delivery to in-person intervention).