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

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.

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Digital Interventions for Screening and Treating Common Mental Disorders or Symptoms of Common Mental Illness in Adults: Systematic Review and Meta-analysis (Preprint)

10.2196/preprints.20581 ◽

2020 ◽

Author(s):

Jacqueline Sin ◽

Gian Galeazzi ◽

Elicia McGregor ◽

Jennifer Collom ◽

Anna Taylor ◽

...

Keyword(s):

Mental Health ◽

Systematic Review ◽

Common Mental Disorders ◽

Meta Analysis ◽

Self Care ◽

Well Being ◽

Controlled Trials ◽

Web Based ◽

Randomized Controlled ◽

Digital Interventions

BACKGROUND Digital interventions targeting common mental disorders (CMDs) or symptoms of CMDs are growing rapidly and gaining popularity, probably in response to the increased prevalence of CMDs and better awareness of early help-seeking and self-care. However, no previous systematic reviews that focus on these novel interventions were found. OBJECTIVE This systematic review aims to scope entirely web-based interventions that provided screening and signposting for treatment, including self-management strategies, for people with CMDs or subthreshold symptoms. In addition, a meta-analysis was conducted to evaluate the effectiveness of these interventions for mental well-being and mental health outcomes. METHODS Ten electronic databases including MEDLINE, PsycINFO, and EMBASE were searched from January 1, 1999, to early April 2020. We included randomized controlled trials (RCTs) that evaluated a digital intervention (1) targeting adults with symptoms of CMDs, (2) providing both screening and signposting to other resources including self-care, and (3) delivered entirely through the internet. Intervention characteristics including target population, platform used, key design features, and outcome measure results were extracted and compared. Trial outcome results were included in a meta-analysis on the effectiveness of users’ well-being and mental health outcomes. We also rated the meta-analysis results with the Grading of Recommendations, Assessment, Development, and Evaluations approach to establish the quality of the evidence. RESULTS The electronic searches yielded 21 papers describing 16 discrete digital interventions. These interventions were investigated in 19 unique trials including 1 (5%) health economic study. Most studies were conducted in Australia and North America. The targeted populations varied from the general population to allied health professionals. All interventions offered algorithm-driven screening with measures to assess symptom levels and to assign treatment options including automatic web-based psychoeducation, self-care strategies, and signposting to existing services. A meta-analysis of usable trial data showed that digital interventions improved well-being (3 randomized controlled trials [RCTs]; n=1307; standardized mean difference [SMD] 0.40; 95% CI 0.29 to 0.51; I2=28%; fixed effect), symptoms of mental illness (6 RCTs; n=992; SMD −0.29; 95% CI −0.49 to −0.09; I2=51%; random effects), and work and social functioning (3 RCTs; n=795; SMD −0.16; 95% CI −0.30 to −0.02; I2=0%; fixed effect) compared with waitlist or attention control. However, some follow-up data failed to show any sustained effects beyond the post intervention time point. Data on mechanisms of change and cost-effectiveness were also lacking, precluding further analysis. CONCLUSIONS Digital mental health interventions to assess and signpost people experiencing symptoms of CMDs appear to be acceptable to a sufficient number of people and appear to have enough evidence for effectiveness to warrant further study. We recommend that future studies incorporate economic analysis and process evaluation to assess the mechanisms of action and cost-effectiveness to aid scaling of the implementation.

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Digital Interventions for Screening and Treating Common Mental Disorders or Symptoms of Common Mental Illness in Adults: Systematic Review and Meta-analysis

Journal of Medical Internet Research ◽

10.2196/20581 ◽

2020 ◽

Vol 22 (9) ◽

pp. e20581

Author(s):

Jacqueline Sin ◽

Gian Galeazzi ◽

Elicia McGregor ◽

Jennifer Collom ◽

Anna Taylor ◽

...

Keyword(s):

Mental Health ◽

Systematic Review ◽

Common Mental Disorders ◽

Meta Analysis ◽

Self Care ◽

Well Being ◽

Controlled Trials ◽

Web Based ◽

Randomized Controlled ◽

Digital Interventions

Background Digital interventions targeting common mental disorders (CMDs) or symptoms of CMDs are growing rapidly and gaining popularity, probably in response to the increased prevalence of CMDs and better awareness of early help-seeking and self-care. However, no previous systematic reviews that focus on these novel interventions were found. Objective This systematic review aims to scope entirely web-based interventions that provided screening and signposting for treatment, including self-management strategies, for people with CMDs or subthreshold symptoms. In addition, a meta-analysis was conducted to evaluate the effectiveness of these interventions for mental well-being and mental health outcomes. Methods Ten electronic databases including MEDLINE, PsycINFO, and EMBASE were searched from January 1, 1999, to early April 2020. We included randomized controlled trials (RCTs) that evaluated a digital intervention (1) targeting adults with symptoms of CMDs, (2) providing both screening and signposting to other resources including self-care, and (3) delivered entirely through the internet. Intervention characteristics including target population, platform used, key design features, and outcome measure results were extracted and compared. Trial outcome results were included in a meta-analysis on the effectiveness of users’ well-being and mental health outcomes. We also rated the meta-analysis results with the Grading of Recommendations, Assessment, Development, and Evaluations approach to establish the quality of the evidence. Results The electronic searches yielded 21 papers describing 16 discrete digital interventions. These interventions were investigated in 19 unique trials including 1 (5%) health economic study. Most studies were conducted in Australia and North America. The targeted populations varied from the general population to allied health professionals. All interventions offered algorithm-driven screening with measures to assess symptom levels and to assign treatment options including automatic web-based psychoeducation, self-care strategies, and signposting to existing services. A meta-analysis of usable trial data showed that digital interventions improved well-being (3 randomized controlled trials [RCTs]; n=1307; standardized mean difference [SMD] 0.40; 95% CI 0.29 to 0.51; I2=28%; fixed effect), symptoms of mental illness (6 RCTs; n=992; SMD −0.29; 95% CI −0.49 to −0.09; I2=51%; random effects), and work and social functioning (3 RCTs; n=795; SMD −0.16; 95% CI −0.30 to −0.02; I2=0%; fixed effect) compared with waitlist or attention control. However, some follow-up data failed to show any sustained effects beyond the post intervention time point. Data on mechanisms of change and cost-effectiveness were also lacking, precluding further analysis. Conclusions Digital mental health interventions to assess and signpost people experiencing symptoms of CMDs appear to be acceptable to a sufficient number of people and appear to have enough evidence for effectiveness to warrant further study. We recommend that future studies incorporate economic analysis and process evaluation to assess the mechanisms of action and cost-effectiveness to aid scaling of the implementation.

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Shared Decision Making in Surgery: A Meta-Analysis and Full Systematic Review

International Journal of Whole Person Care ◽

10.26443/ijwpc.v7i1.242 ◽

2020 ◽

Vol 7 (1) ◽

pp. 59

Author(s):

Kacper Niburski ◽

Elena Guadagno ◽

Dan Poenaru

Keyword(s):

Systematic Review ◽

Decision Making ◽

Shared Decision Making ◽

Meta Analysis ◽

Decisional Conflict ◽

Shared Decision ◽

Cross Sectional ◽

Medical Librarian ◽

Meta Analyses ◽

The Impact

Shared decision-making (SDM), the process where physician and patient reach an agreed-upon choice by understanding the values, concerns, and preferences inherent within each treatment option available, has been increasingly implemented in clinical practice to better health care outcomes. Despite the proven efficacy of SDM to provide better patient-guided care in medicine, its use in surgery has not been studied widely. A search strategy was developed with a medical librarian. It included nine databases from inception until December 2018. After a 2-person title and abstract screen, full-text publications were analyzed in detail. A meta-analysis was done to quantify the impact of SDM in surgical specialties. In total 5,596 studies were retrieved. After duplicates were removed, titles and abstracts were screened, and p-values were recorded, 140 (45 RCTs and 95 cross-sectional studies) were used for the systematic review and 42 for the meta-analyses. Most of the studies noted decreased intervention rate (8 of 14), decisional conflict (13 of 16), and decisional regret (3 of 3), and an increased decisional satisfaction (9 of 12), knowledge (19 of 20), SDM preference (6 of 8), and physician trust (3 of 4) when using SDM. Time increase per patient encounter was inconclusive. The meta-analysis showed that despite high heterogeneity, the results were significant. Far from obviating surgical immediacy, these results suggest that SDM is vital for the best indicators of care. With decreased conflict and anxiety, increasing knowledge and satisfaction, and creating a more whole, trusting relationship, SDM appears to be beneficial in surgery.

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The effects of vitamin D supplementation on mental health, and biomarkers of inflammation and oxidative stress in patients with psychiatric disorders: A systematic review and meta-analysis of randomized controlled trials

Progress in Neuro-Psychopharmacology and Biological Psychiatry ◽

10.1016/j.pnpbp.2019.109651 ◽

2019 ◽

Vol 94 ◽

pp. 109651 ◽

Cited By ~ 14

Author(s):

Hamidreza Jamilian ◽

Elaheh Amirani ◽

Alireza Milajerdi ◽

Fariba Kolahdooz ◽

Hamed Mirzaei ◽

...

Keyword(s):

Oxidative Stress ◽

Mental Health ◽

Systematic Review ◽

Vitamin D ◽

Randomized Controlled Trials ◽

Meta Analysis ◽

Vitamin D Supplementation ◽

Controlled Trials ◽

Randomized Controlled ◽

And Oxidative Stress

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Suicide prevention using self-guided digital interventions: a systematic review and meta-analysis of randomised controlled trials

The Lancet Digital Health ◽

10.1016/s2589-7500(19)30199-2 ◽

2020 ◽

Vol 2 (1) ◽

pp. e25-e36 ◽

Cited By ~ 14

Author(s):

Michelle Torok ◽

Jin Han ◽

Simon Baker ◽

Aliza Werner-Seidler ◽

Iana Wong ◽

...

Keyword(s):

Systematic Review ◽

Suicide Prevention ◽

Randomised Controlled Trials ◽

Meta Analysis ◽

Controlled Trials ◽

Digital Interventions ◽

Randomised Controlled

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A feasibility trial of Power Up: A smartphone app to support patient activation and shared decision making for mental health in young people (Preprint)

10.2196/preprints.11677 ◽

2018 ◽

Author(s):

Julian Edbrooke-Childs ◽

Chloe Edridge ◽

Phoebe Averill ◽

Louise Delane ◽

Michael P Craven ◽

...

Keyword(s):

Mental Health ◽

Decision Making ◽

Young People ◽

Shared Decision Making ◽

Randomized Control Trial ◽

Patient Activation ◽

Shared Decision ◽

Cluster Randomized Control Trial ◽

Randomized Control ◽

Cluster Randomized

BACKGROUND Digital tools have the potential to support patient activation and shared decision making in the face of increasing levels of mental health problems in young people. There is a need for feasibility trials of digital interventions to determine the usage and acceptability of interventions. In addition, there is a need to determine the ability to recruit and retain research participants to plan rigorous effectiveness trials and therefore, develop evidence-based recommendations for practice. OBJECTIVE To determine the feasibility of undertaking a cluster randomized control trial to test the effectiveness of a smartphone app, Power Up, co-designed with young people to support patient activation and shared decision making for mental health. METHODS Overall, 270 young people were screened for participation and 53% (N = 142) were recruited and completed baseline measures across eight specialist child mental health services (n = 62, mean (SD) age = 14.66 (1.99) years, 52% female) and two mainstream secondary schools (n = 80; mean (SD) age = 16.88 (0.68) years, 46% female). Young people received Power Up in addition to management as usual or received management as usual only. Post-trial interviews were conducted with 11 young people from the intervention arms (specialist services n = 6; schools n = 5). RESULTS Usage data showed that there were an estimated 50 (out of 64) users of Power Up in the intervention arms. Findings from the interviews indicated that young people found Power Up to be acceptable. Young people reported: 1) their motivation for use of Power Up, 2) the impact of use, and 3) barriers to use. Out of the 142 recruited participants, 45% (64/142) completed follow up measures, and the approaches to increase retention agreed by the steering group are discussed. CONCLUSIONS The findings of the present research indicate that the app is acceptable and it is feasible to examine the effectiveness of Power Up in a prospective cluster randomized control trial. CLINICALTRIAL ISRCTN: ISRCTN77194423, ClinicalTrials.gov NCT02552797

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