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.
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
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.
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.