The effectiveness of various computer-based interventions for patients with chronic pain or functional somatic syndromes: A systematic review and meta-analysis.

Computer-based interventions target improvement of physical and emotional functioning in patients with chronic pain and functional somatic syndromes. However, it is unclear to what extent which interventions work and for whom. This systematic review and meta-analysis (registered at PROSPERO, 2016: CRD42016050839) assesses efficacy relative to passive and active control conditions, and explores patient and intervention factors. Controlled studies were identified from MEDLINE, EMBASE, PsychInfo, Web of Science, and Cochrane Library. Pooled standardized mean differences by comparison type, and somatic symptom, health-related quality of life, functional interference, catastrophizing, and depression outcomes were calculated at post-treatment and at 6 or more months follow-up. Risk of bias was assessed. Sub-group analyses were performed by patient and intervention characteristics when heterogeneous outcomes were observed. Maximally, 30 out of 46 eligible studies and 3,387 participants were included per meta-analysis. Mostly, internet-based cognitive behavioral therapies were identified. Significantly higher patient reported outcomes were found in comparisons with passive control groups (standardized mean differences ranged between -.41 and -.18), but not in comparisons with active control groups (SMD = -.26 - -.14). For some outcomes, significant heterogeneity related to patient and intervention characteristics. To conclude, there is a minority of good quality evidence for small positive average effects of computer-based (cognitive) behavior change interventions, similar to traditional modes. These effects may be sustainable. Indications were found as of which interventions work better or more consistently across outcomes for which patients. Future process analyses are recommended in the aim of better understanding individual chances of clinically relevant outcomes

Developing mHealth to the context and valuation of injured patients and professionals in hospital trauma care: Qualitative and quantitative formative evaluations

Background: Trauma care faces challenges to innovating their services, such as with mobile health (mHealth) app, to improve the quality of care and patients’ health experience. Systematic needs inquiries and collaborations with professional and patient end users are highly recommended to develop and prepare future implementations of such innovations. Objective: This study aimed to develop a trauma mHealth app for patient information and support in accordance with the Center for eHealth Research and Disease Management road map and describe experiences of unmet information and support needs among injured patients with trauma, barriers to and facilitators of the provision of information and support among trauma care professionals, and drivers of value of an mHealth app in patients with trauma and trauma care professionals. Methods: Formative evaluations were conducted using quantitative and qualitative methods. Ten semistructured interviews with patients with trauma and a focus group with 4 trauma care professionals were conducted for contextual inquiry and value specification. User requirements and value drivers were applied in prototyping. Furthermore, a complementary quantitative discrete choice experiment (DCE) was conducted with 109 Dutch trauma surgeons, which enabled triangulation on value specification results. In the DCE, preferences were stated for hypothetical mHealth products with various attributes. Panel data from the DCE were analyzed using conditional and mixed logit models. Results: Patients disclosed a need for more psychosocial support and easy access to more extensive information on their injury, its consequences, and future prospects. Health care professionals designated workload as an essential issue; a digital solution should not require additional time. The conditional logit model of DCE results suggested that access to patient app data through electronic medical record integration (odds ratio [OR] 3.3, 95% CI 2.55-4.34; P<.001) or a web viewer (OR 2.3, 95% CI 1.64-3.31; P<.001) was considered the most important for an mHealth solution by surgeons, followed by the inclusion of periodic self-measurements (OR 2, 95% CI 1.64-2.46; P<.001), the local adjustment of patient information (OR 1.8, 95% CI 1.42-2.33; P<.001), local hospital identification (OR 1.7, 95% CI 1.31-2.10; P<.001), complication detection (OR 1.5, 95% CI 1.21-1.84; P<.001), and the personalization of rehabilitation through artificial intelligence (OR 1.4, 95% CI 1.13-1.62; P=.001). Conclusions: In the context of trauma care, end users have many requirements for an mHealth solution that addresses psychosocial functioning; dependable information; and, possibly, a prediction of how a patient’s recovery trajectory is evolving. A structured development approach provided insights into value drivers and facilitated mHealth prototype enhancement. The findings imply that iterative development should move on from simple and easily implementable mHealth solutions to those that are suitable for broader innovations of care pathways that most—but plausibly not yet all—end users in trauma care will value. This study could inspire the trauma care community

PRISMA flow-diagram of studies.

<p>Abbreviations and symbols: k = number of studies, <i>n</i> = number of study participants, OC = outcome, SS = Somatic Symptoms, HRQOL = Health Related Quality Of Life, FI = Functional Interference, CAT = Catastrophizing, DEP = Depression.</p

Numbers of studies in which behavioral change techniques were identified by comparison.

<p>Numbers of studies in which behavioral change techniques were identified by comparison.</p

Risk of bias assessment by the 7 key categories of the Cochrane risk of bias tool.

<p>Risk of bias assessment by the 7 key categories of the Cochrane risk of bias tool.</p

Funnel plot for symptom severity scores post treatment by various types of control groups.

<p><i>SE</i> = Standard Error, <i>SMD</i> = Standardized Mean Difference. Comments: The meta-analysis presented here included the results for active comparisons (not the passive ones) from Trompetter et al. (2015) and Dear et al. (2015) to avoid double entries. Online discussion was facilitated for control group participants while being on a waiting list for receiving the experimental CBI.</p

Funnel plot for symptom severity scores post treatment by various patient conditions.

<p><i>SE</i> = Standard Error, <i>SMD</i> = Standardized Mean Difference.</p

Overview of meta-analyses results for direct effects based on all eligible studies with relevant data.

<p>Overview of meta-analyses results for direct effects based on all eligible studies with relevant data.</p