BackgroundMost research examining birth history (i.e. related birth complications) and developmental milestone achievement follow outcomes for infants at-risk with very specific birth weight categories and gestational age classifications. The purpose of this study was to examine how birth weight relates to infants’ birth histories and developmental milestone achievement when they fall into a variety of birth weight and gestational age categories.MethodsIn the current study, we examined birth histories and onset ages for developmental milestones by analyzing a convenience sample of anonymous existing data from 663 developmental histories completed by parents at the time of an initial evaluation at a pediatric outpatient occupational therapy clinic. Infants fell into 3 birth weight categories; low birth weight (LBW), normal birth weight (NBW), and high birth weight (HBW) and 3 gestational age classifications considered with birth weight; small for gestational age (SGA), appropriate for gestational age (AGA), and large for gestational age (LGA).ResultsNBW, AGA, and SGA infants with related birth complications had lower birth weights than infants without birth complications. Larger birth weights were associated with earlier ages for independent sitting for HBW infants, earlier ages for eating solids for NBW infants, and earlier walking onsets for LBW and NBW infants. Higher birth weights were also linked with rolling at a younger age for LGA infants, earlier walking and speaking words for AGA infants, and sooner independent sitting for SGA and AGA infants.ConclusionsOur findings suggest that birth weight and gestational age categories provide unique insights into infants’ birth history and developmental milestone achievement.
Background Digital therapeutics, such as interventions provided via smartphones or the internet, have been proposed as promising solutions to support self-management in persons with chronic conditions. However, the evidence supporting self-management interventions through technology in stroke is scarce, and the intervention development processes are often not well described, creating challenges in explaining why and how the intervention would work. Objective This study describes a specific use case of using intervention mapping (IM) and the taxonomy of behavior change techniques (BCTs) in designing a digital intervention to manage chronic symptoms and support daily life participation in people after stroke. IM is an implementation science framework used to bridge the gap between theories and practice to ensure that the intervention can be implemented in real-world settings. The taxonomy of BCTs consists of a set of active ingredients designed to change self-management behaviors. Methods We used the first 4 steps of the IM process to develop a technology-supported self-management intervention, interactive Self-Management Augmented by Rehabilitation Technologies (iSMART), adapted from a face-to-face stroke-focused psychoeducation program. Planning group members were involved in adapting the intervention. They also completed 3 implementation measures to assess the acceptability, appropriateness, and feasibility of iSMART. Results In step 1, we completed a needs assessment consisting of assembling a planning group to codevelop the intervention, conducting telephone surveys of people after stroke (n=125) to identify service needs, and performing a systematic review of randomized controlled trials to examine evidence of the effectiveness of digital self-management interventions to improve patient outcomes. We identified activity scheduling, symptom management, stroke prevention, access to care resources, and cognitive enhancement training as key service needs after a stroke. The review suggested that digital self-management interventions, especially those using cognitive behavioral theory, effectively reduce depression, anxiety, and fatigue and enhance self-efficacy in neurological disorders. Step 2 identified key determinants, objectives, and strategies for self-management in iSMART, including knowledge, behavioral regulation, skills, self-efficacy, motivation, negative and positive affect, and social and environmental support. In step 3, we generated the intervention components underpinned by appropriate BCTs. In step 4, we developed iSMART with the planning group members. Especially, iSMART simplified the original psychoeducation program and added 2 new components: SMS text messaging and behavioral coaching, intending to increase the uptake by people after stroke. iSMART was found to be acceptable (mean score 4.63, SD 0.38 out of 5), appropriate (mean score 4.63, SD 0.38 out of 5), and feasible (mean score 4.58, SD 0.34 out of 5). Conclusions We describe a detailed example of using IM and the taxonomy of BCTs for designing and developing a digital intervention to support people after stroke in managing chronic symptoms and maintaining active participation in daily life.
Introduction The impact of depressed mood in daily life is difficult to investigate using traditional retrospective assessments, given daily or even within-day mood fluctuations in various contexts. This study aimed to use a smartphone-based ambulatory assessment to examine real-time relationships between depressed mood and functional behaviors among individuals with stroke. Methods A total of 202 participants with mild-to-moderate stroke (90% ischemic, 45% female, 44% Black) completed an ecological momentary assessment five times per day for 2 weeks by reporting their depressed mood and functional behaviors regarding where, with whom, and what activity was spent. Results Participants spent 28% of their wake-up time participating in passive leisure activities but spent the least time in physical (4%) and vocational (9%) activities. Depressed mood was concurrently lower when participants engaged in social activities (β = −0.023 ± 0.011) and instrumental activities of daily living (β = −0.061 ± 0.013); spent time with family members (β = −0.061 ± 0.014), spouses (β = −0.043, ± 0.016), friends (β = −0.094, ± 0.021), and coworkers (β = −0.050 ± 0.021); and were located in restaurants (β = −0.068 ± 0.029), and homes of family members (β = −0.039 ± 0.020) or friends (β = −0.069 ± 0.031). Greater depressed mood was associated with worse ratings in satisfaction, performance, and engagement of activities in concurrent (βs = −0.036 ± 0.003, −0.053 ± 0.003, −0.044 ± 0.003) and time-lagged models (βs = −0.011 ± 0.004, −0.012 ± 0.004, −0.013 ± 0.004). Discussion Smartphone-based ambulatory assessment can elucidate functional behaviors and associated mood after stroke. Findings support behavioral activation treatments to schedule social and instrumental activities for stroke survivors to reduce their depressed mood.
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