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.
Keywords: Neuropsychiatry; behavioural theory; mental health; self-management; systematic review; telehealth; teleneurology.