This systematic review examined the reliability and validity of the Morisky Medication Adherence Scale-8 (MMAS-8), which has been widely used to assess patient medication adherence in clinical research and medical practice. Of 418 studies identified through searching 4 electronic databases, we finally analyzed 28 studies meeting the selection criteria of this study regarding the reliability and validity of MMAS-8 including sensitivity and specificity. Meta-analysis for Cronbach’s α, intraclass correlation coefficient (ICC), sensitivity and specificity to detect a patient with nonadherence to medication were performed. The pooled estimates for Cronbach’s α and ICC were calculated using the random-effects weighted T transformation. A bivariate random-effects model was used to estimate pooled sensitivity and specificity. The pooled Cronbach's α estimate for type 2 diabetes group in 7 studies and osteoporosis group in 3 studies were 0.67 (95% Confidence Interval(CI), 0.65 to 0.69) and 0.77 (95% CI, 0.72 to 0.83), respectively. With regard to test-retest, the pooled ICC for type 2 diabetes group in 3 studies and osteoporosis group in 2 studies were 0.81 (95% CI, 0.75 to 0.85) and 0.80 (95% CI, 0.74 to 0.85). For a cut-off value of 6, the pooled sensitivity and specificity in 12 studies were 0.43 (95% CI, 0.33 to 0.53) and 0.73 (95% CI, 0.68 to 0.78), respectively. The MMAS-8 had acceptable internal consistency and reproducibility in a few diseases like type 2 diabetes. Using the cut-off value of 6, criterion validity was not enough good to validly screen a patient with nonadherence to medication. However, this study did not calculated a pooled estimate for criterion validity using the higher values than 6 as a cut-off value since most of included individual studies did not report criterion validity based on those values.

This article performs a systemic review of psychometric properties of Internet Addiction Test (IAT)—the most widely used tool for assessing Internet addiction in clinic and research field. Studies measuring psychometric properties of IAT (original version) were searched through MEDLINE, The Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycINFO, and Embase. A total of 25 studies including 18,421 subjects were reviewed in our study. Based on meta-analysis for internal consistency, the pooled Cronbach's alpha coefficient from college/university students with a single department subgroup was 0.90 (95percent confidence interval [CI], 0.89–0.91), and that from middle-/high-school students (older than 15 years) subgroup was 0.93 (95 percent CI, 0.92–0.93). According to test–retest analysis, the pooled Spearman's correlation coefficient from college/university students with a single department subgroup was high at 0.83 (95 percent CI, 0.81–0.85), along with low publication bias. Convergent validity showed correlation coefficients of 0.62–0.84, as compared with major tools. For construct validity, the number of factors is believed to be 1–2, only considering studies that followed the guidelines. IAT appears to have acceptable internal consistency, test–retest reliability, and convergent validity in specific groups. To verify these values, well-designed evidence-based studies assessing psychometric properties of IAT across diverse populations are warranted.

Background: In the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, their application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder (ASD). However, given their complexity and potential clinical implications, there is an ongoing need for further research on their accuracy. Objective: This study aimed to perform a systematic review and meta-analysis to summarize the available evidence for the accuracy of machine learning algorithms in diagnosing ASD. Methods: The following databases were searched on November 28, 2018: MEDLINE, EMBASE, CINAHL Complete (with Open Dissertations), PsycINFO, and Institute of Electrical and Electronics Engineers Xplore Digital Library. Studies that used a machine learning algorithm partially or fully for distinguishing individuals with ASD from control subjects and provided accuracy measures were included in our analysis. The bivariate random effects model was applied to the pooled data in a meta-analysis. A subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false-negative, and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw Summary Receiver Operating Characteristics curves, and obtain the area under the curve (AUC) and partial AUC (pAUC). Results: A total of 43 studies were included for the final analysis, of which a meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural magnetic resonance imaging (sMRI) subgroup meta-analysis (12 samples with 1776 participants) showed a sensitivity of 0.83 (95% CI 0.76-0.89), a specificity of 0.84 (95% CI 0.74-0.91), and AUC/pAUC of 0.90/0.83. A functional magnetic resonance imaging/deep neural network subgroup meta-analysis (5 samples with 1345 participants) showed a sensitivity of 0.69 (95% CI 0.62-0.75), specificity of 0.66 (95% CI 0.61-0.70), and AUC/pAUC of 0.71/0.67. Conclusions: The accuracy of machine learning algorithms for diagnosis of ASD was considered acceptable by few accuracy measures only in cases of sMRI use; however, given the many limitations indicated in our study, further well-designed studies are warranted to extend the potential use of machine learning algorithms to clinical settings. Trial Registration: PROSPERO CRD42018117779; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=117779

The optimal timing of enteral nutrition (EN) in sepsis patients is controversial among societal guidelines. We aimed to evaluate the evidence of early EN’s impact on critically ill sepsis patients’ clinical outcomes. We searched the MEDLINE, Embase, CINAHL, Cochrane Library, ClinicalTrials.gov, and ICTRP databases on 10 March 2023. We included studies published after 2004 that compared early EN versus delayed EN in sepsis patients. We included randomized controlled trials (RCTs), non-RCTs, cohort studies, and case–control studies. Forest plots were used to summarize risk ratios (RRs), including mortality and mean difference (MD) of continuous variables such as intensive care unit (ICU) length of stay and ventilator-free days. We identified 11 eligible studies with sample sizes ranging from 31 to 2410. The RR of short-term mortality from three RCTs was insignificant, and the MD of ICU length of stay from two RCTs was −2.91 and −1.00 days (95% confidence interval [CI], −5.53 to −0.29 and −1.68 to −0.32). Although the RR of intestinal-related complications from one RCT was 3.82 (95% CI, 1.43 to 10.19), indicating a significantly higher risk for the early EN group than the control group, intestinal-related complications of EN reported in five studies were inconclusive. This systematic review did not find significant benefits of early EN on mortality in sepsis patients. Evidence, however, is weak due to inconsistent definitions, heterogeneity, risk of bias, and poor methodology in the existing studies.

Objective The current study was performed to assess the evidence for effects of therapeutic intervention with mobile device applications (apps) for individuals with autism spectrum disorder (ASD). Design The main methodology of the current study was systematic review with meta-analysis. Setting Only randomised controlled trials (RCTs) for mobile device apps for individuals with ASD were considered for review in the current study. Patients The target population was individuals clinically diagnosed with ASD. Interventions Applications that are operable on a smart (mobile) device and interactive with users. Main outcome measures The main outcomes were based on standardised mean differences in pretrial and post-trial scales in each control and intervention group. Results Out of a total of 1100 studies (after duplicate removal), 7 RCTs were selected for final analysis. Of the seven studies, two RCTs were further analysed for effects based on the visual and fine motor subscales of the Mullen Scales of Early Learning, which favoured the intervention groups (standardised mean difference (SMD)=0.41, 95% CI 0.03 to 0.80; SMD=0.41, 95% CI 0.03 to 0.80), without either having any heterogeneity (p>0.1) or publication bias. Conclusions Although it is still early to draw a conclusion, available studies are showing promise for use of mobile device apps for treatment of individuals with ASD. More well-designed and large-scale studies focused on improving behavioural symptoms of ASD are warranted. PROSPERO registration number CRD42019128362.