RESUMEN
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 (ß = -0.023 ± 0.011) and instrumental activities of daily living (ß = -0.061 ± 0.013); spent time with family members (ß = -0.061 ± 0.014), spouses (ß = -0.043, ± 0.016), friends (ß = -0.094, ± 0.021), and coworkers (ß = -0.050 ± 0.021); and were located in restaurants (ß = -0.068 ± 0.029), and homes of family members (ß = -0.039 ± 0.020) or friends (ß = -0.069 ± 0.031). Greater depressed mood was associated with worse ratings in satisfaction, performance, and engagement of activities in concurrent (ßs = -0.036 ± 0.003, -0.053 ± 0.003, -0.044 ± 0.003) and time-lagged models (ßs = -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.
RESUMEN
INTRODUCTION: This study aimed to (a) review what theories have been applied to the development of digital self-management interventions for people with neurological disorders; (b) examine their effectiveness to improve depression, anxiety, fatigue and self-efficacy; and (c) identify the optimal mode of intervention delivery. METHODS: Electronic databases (SCOPUS, MEDLINE, EMBASE, CINAHL, Cochrane Library and Clinicaltrials.gov) were searched. Two investigators independently screened studies and extracted data. Study quality and use of theory were also assessed. RESULTS: A total of 944 studies were screened, and 16 randomised controlled trials were included. Theory-based digital self-management interventions were effective in reducing depression (standardised mean difference (SMD) = -0.77, 95% confidence interval (CI) -1.04 to -0.49), anxiety (SMD = -0.88, 95% CI -1.54 to -0.21) and fatigue (SMD = -0.62, 95% CI -0.96 to -0.27) and in enhancing self-efficacy (SMD = 1.15, 95% CI 0.11-2.18). Cognitive-behavioural theory (CBT)-based interventions were effective in reducing depression (SMD = -0.81, 95% CI -1.22 to -0.39), anxiety (SMD = -1.15, 95% CI -1.85 to -0.44) and fatigue (SMD = -0.75, 95% CI -0.97 to -0.54) and in improving self-efficacy (SMD = 0.84, 95% CI 0.63-1.05), whereas social cognitive theory (SCT)-based interventions were effective in reducing depression (SMD = -0.73, 95% CI -1.17 to -0.28). Partially digital interventions were more effective than fully digital interventions. DISCUSSION: Our findings support the use of theory to guide the development of digital self-management interventions to increase intervention effectiveness. In particular, CBT-based interventions have a positive impact on depression, anxiety, fatigue and self-efficacy, whereas SCT-based interventions have a positive impact on depression.