Digital Shared Decision-Making Interventions in Mental Healthcare: A Systematic Review and Meta-Analysis

Abstract

Background: Shared decision-making (SDM) in mental healthcare has received increased attention as a process to reinforce person-centered care. With the rapid development of digital health technology, researchers investigate how digital interventions may be utilized to support SDM. Despite the promise of digital interventions to support SDM, the effect of these in mental healthcare has not been evaluated before. Thus, this paper aims to assess the effect of SDM interventions complimented by digital technology in mental healthcare. Objective: The objective of this review was to systematically examine the effectiveness of digital SDM interventions on patient outcomes as investigated in randomized trials. Methods: We performed a systematic review and meta-analysis of randomized controlled trials on digital SDM interventions for people with a mental health condition. We searched for relevant studies in MEDLINE, PsycINFO, EMBASE, CINAHL, and the Cochrane Central Register of Controlled Trials. The search strategy included terms relating to SDM, digital systems, mental health conditions, and study type. The primary outcome was patient activation or indices of the same (e.g., empowerment and self-efficacy), adherence to treatment, hospital admissions, severity of symptoms, and level of functioning. Secondary outcomes were satisfaction, decisional conflict, working alliance, usage, and adherence of medicine; and adverse events were defined as harms or side effects. Results: Sixteen studies met the inclusion criteria with outcome data from 2,400 participants. Digital SDM interventions had a moderate positive effect as compared with a control condition on patient activation [standardized mean difference (SMD) = 0.56, CI: 0.10, 1.01, p = 0.02], a small effect on general symptoms (SMD = -0.17, CI: -0.31, -0.03, p = 0.02), and working alliance (SMD = 0.21, CI: 0.02, 0.41, p = 0.03) and for improving decisional conflict (SMD = -0.37, CI: -0.70, -0.05, p = 0.02). No effect was found on self-efficacy, other types of mental health symptoms, adverse events, or patient satisfaction. A total of 39 outcomes were narratively synthesized with results either favoring the intervention group or showing no significant differences between groups. Studies were generally assessed to have unclear or high risk of bias, and outcomes had a Grading of Recommendations Assessment, Development and Evaluation (GRADE) rating of low- or very low-quality evidence. Conclusions: Digital interventions to support SDM may be a promising tool in mental healthcare; but with the limited quality of research, we have little confidence in the estimates of effect. More quality research is needed to further assess the effectiveness of digital means to support SDM but also to determine which digital intervention features are most effective to support SDM. Systematic Review Registration: PROSPERO, identifier CRD42020148132.

Keywords: digital health (eHealth); mental health; patient activation; shared decision-making; systematic review and meta-analysis.

Figure 1

PRISMA flow diagram (19).

Figure 2

Risk of bias of the 16 trials.

Figure 3

Forest plot for patient activation and indices of the same.

Figure 4

Forest plots for symptoms.

Figure 5

Forest plot for adverse events defined as harms or side effects.

Figure 6

Forest plot for patient satisfaction by duration.

Figure 7

Forest plot for patient satisfaction by type of intervention.

Figure 8

Forest plot for patient satisfaction by diagnosis.

Figure 9

Forest plot for working alliance by duration perceived by the patient.

Figure 10

Forest plot for working alliance by type of intervention perceived by the patient.

Figure 11

Forest plot for working alliance by diagnosis perceived by the patient.

Figure 12

Forest plot for decisional conflict by diagnosis.