Standalone smartphone apps for mental health—a systematic review and meta-analysis

While smartphone usage is ubiquitous, and the app market for smartphone apps targeted at mental health is growing rapidly, the evidence of standalone apps for treating mental health symptoms is still unclear. This meta-analysis investigated the efficacy of standalone smartphone apps for mental health. A comprehensive literature search was conducted in February 2018 on randomized controlled trials investigating the effects of standalone apps for mental health in adults with heightened symptom severity, compared to a control group. A random-effects model was employed. When insufficient comparisons were available, data was presented in a narrative synthesis. Outcomes included assessments of mental health disorder symptom severity specifically targeted at by the app. In total, 5945 records were identified and 165 full-text articles were screened for inclusion by two independent researchers. Nineteen trials with 3681 participants were included in the analysis: depression (k = 6), anxiety (k = 4), substance use (k = 5), self-injurious thoughts and behaviors (k = 4), PTSD (k = 2), and sleep problems (k = 2). Effects on depression (Hedges’ g = 0.33, 95%CI 0.10–0.57, P = 0.005, NNT = 5.43, I2 = 59%) and on smoking behavior (g = 0.39, 95%CI 0.21–0.57, NNT = 4.59, P ≤ 0.001, I2 = 0%) were significant. No significant pooled effects were found for anxiety, suicidal ideation, self-injury, or alcohol use (g = −0.14 to 0.18). Effect sizes for single trials ranged from g = −0.05 to 0.14 for PTSD and g = 0.72 to 0.84 for insomnia. Although some trials showed potential of apps targeting mental health symptoms, using smartphone apps as standalone psychological interventions cannot be recommended based on the current level of evidence

Additive effects of adjunctive app-based interventions for mental disorders - A systematic review and meta-analysis of randomised controlled trials

Background: It is uncertain whether app-based interventions add value to existing mental health care. Objective: To examine the incremental effects of app-based interventions when used as adjunct to mental health interventions. Methods: We searched PubMed, PsycINFO, Scopus, Web of Science, and Cochrane Library databases on September 15th, 2023, for randomised controlled trials (RCTs) on mental health interventions with an adjunct app-based intervention compared to the same intervention-only arm for adults with mental disorders or respective clinically relevant symptomatology. We conducted meta-analyses on symptoms of different mental disorders at postintervention. PROSPERO, CRD42018098545. Results: We identified 46 RCTs (4869 participants). Thirty-two adjunctive app-based interventions passively or actively monitored symptoms and behaviour, and in 13 interventions, the monitored data were sent to a therapist. We found additive effects on symptoms of depression (g = 0.17; 95 % CI 0.02 to 0.33; k = 7 comparisons), anxiety (g = 0.80; 95 % CI 0.06 to 1.54; k = 3), mania (g = 0.2; 95 % CI 0.02 to 0.38; k = 4), smoking cessation (g = 0.43; 95 % CI 0.29 to 0.58; k = 10), and alcohol use (g = 0.23; 95 % CI 0.08 to 0.39; k = 7). No significant effects were found on symptoms of depression within a bipolar disorder (g = -0.07; 95 % CI -0.37 to 0.23, k = 4) and eating disorders (g = -0.02; 95 % CI -0.44 to 0.4, k = 3). Studies on depression, mania, smoking, and alcohol use had a low heterogeneity between the trials. For other mental disorders, only single studies were identified. Only ten studies had a low risk of bias, and 25 studies reported insufficient statistical power. Discussion: App-based interventions may be used to enhance mental health interventions to further reduce symptoms of depression, anxiety, mania, smoking, and alcohol use. However, the effects were small, except for anxiety, and limited due to study quality. Further high-quality research with larger sample sizes is warranted to better understand how app-based interventions can be most effectively combined with established interventions to improve outcomes

The DEAR experiment—first results on kaonic hydrogen

The goal of the DEAR (DAΦNE exotic atom research) experiment is the precise determination of the isospin dependent antikaon–nucleon scattering lengths. The experiment accurately measures the Kα line shift and broadening, due to the strong interaction, in kaonic hydrogen and, for the first time, in kaonic deuterium. A precision measurement of kaonic hydrogen tests chiral symmetry breaking in systems with strangeness. An initial analysis of the DEAR experiment yields a shift ε1s=−195±45 eV and a width Γ1s=250±125 eV, which is more precise than the previous kaonic X-ray experiment KpX at KEK, and allows for the first time to disentangle the full pattern of the kaonic hydrogen K-series line Kα, Kβ and Kγ

Measurement of the kaonic Hydrogen X-ray spectrum

The DEAR (DAΦNE exotic atom research) experiment measured the energy of x rays emitted in the transitions to the ground state of kaonic hydrogen. The measured values for the shift ε and the width Γ of the 1s state due to the K⁻p strong interaction are ε1s=-193±37 (stat) ±6 (syst) eV and Γ1s=249±111 (stat) ±30 (syst) eV, the most precise values yet obtained. The pattern of the kaonic hydrogen K-series lines, Kα, Kβ, and Kγ, was disentangled for the first time

New analysis method for CCD X-ray data

The analysis method developed for kaonic nitrogen X-ray data obtained at the DAΦNE electron–positron collider of Frascati National Laboratories using Charge-Coupled Devices (CCDs) in the DEAR experimental setup is described. Background events could be highly rejected by this analysis procedure. Three sequential X-ray lines from kaonic nitrogen transitions, showing good energy resolution, could be clearly identified, and the yields measured for the first time

Kaonic nitrogen X-ray transition yields in a gaseous target

The first measurement of the yields of three kaonic nitrogen X-ray transitions, using the DEAR (DAΦNE Exotic Atom Research) setup at the DAΦNE collider of Frascati, is reported. The yields are 41.5±8.7(stat.)±4.1(sys.)% for the n=7→6 transition, 55.0±3.9(stat.)±5.5(sys.)% for the n=6→5 transition and 57.4±15.2(stat.)±5.7(sys.)% for the n=5→4 transition at a density ρ=3.4ρNTP. By using the experimental yields in an atomic cascade calculation, a 1 to 3% K-shell electron population in the n=6 level was deduced

First results of the “Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS)"

Purpose Knowledge regarding patients' clinical condition at severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection is sparse. Data in the international, multicenter Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS) cohort study may enhance the understanding of COVID-19. Methods Sociodemographic and clinical characteristics of SARS-CoV-2-infected patients, enrolled in the LEOSS cohort study between March 16, 2020, and May 14, 2020, were analyzed. Associations between baseline characteristics and clinical stages at diagnosis (uncomplicated vs. complicated) were assessed using logistic regression models. Results We included 2155 patients, 59.7% (1,287/2,155) were male; the most common age category was 66-85 years (39.6%; 500/2,155). The primary COVID-19 diagnosis was made in 35.0% (755/2,155) during complicated clinical stages. A significant univariate association between age; sex; body mass index; smoking; diabetes; cardiovascular, pulmonary, neurological, and kidney diseases; ACE inhibitor therapy; statin intake and an increased risk for complicated clinical stages of COVID-19 at diagnosis was found. Multivariable analysis revealed that advanced age [46-65 years: adjusted odds ratio (aOR): 1.73, 95% CI 1.25-2.42,p = 0.001; 66-85 years: aOR 1.93, 95% CI 1.36-2.74,p 85 years: aOR 2.38, 95% CI 1.49-3.81,p < 0.001 vs. individuals aged 26-45 years], male sex (aOR 1.23, 95% CI 1.01-1.50,p = 0.040), cardiovascular disease (aOR 1.37, 95% CI 1.09-1.72,p = 0.007), and diabetes (aOR 1.33, 95% CI 1.04-1.69,p = 0.023) were associated with complicated stages of COVID-19 at diagnosis. Conclusion The LEOSS cohort identified age, cardiovascular disease, diabetes and male sex as risk factors for complicated disease stages at SARS-CoV-2 diagnosis, thus confirming previous data. Further data regarding outcomes of the natural course of COVID-19 and the influence of treatment are required

First results of the Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS)

Purpose Knowledge regarding patients' clinical condition at severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection is sparse. Data in the international, multicenter Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS) cohort study may enhance the understanding of COVID-19. Methods Sociodemographic and clinical characteristics of SARS-CoV-2-infected patients, enrolled in the LEOSS cohort study between March 16, 2020, and May 14, 2020, were analyzed. Associations between baseline characteristics and clinical stages at diagnosis (uncomplicated vs. complicated) were assessed using logistic regression models. Results We included 2155 patients, 59.7% (1,287/2,155) were male; the most common age category was 66-85 years (39.6%; 500/2,155). The primary COVID-19 diagnosis was made in 35.0% (755/2,155) during complicated clinical stages. A significant univariate association between age; sex; body mass index; smoking; diabetes; cardiovascular, pulmonary, neurological, and kidney diseases; ACE inhibitor therapy; statin intake and an increased risk for complicated clinical stages of COVID-19 at diagnosis was found. Multivariable analysis revealed that advanced age [46-65 years: adjusted odds ratio (aOR): 1.73, 95% CI 1.25-2.42,p = 0.001; 66-85 years: aOR 1.93, 95% CI 1.36-2.74,p 85 years: aOR 2.38, 95% CI 1.49-3.81,p < 0.001 vs. individuals aged 26-45 years], male sex (aOR 1.23, 95% CI 1.01-1.50,p = 0.040), cardiovascular disease (aOR 1.37, 95% CI 1.09-1.72,p = 0.007), and diabetes (aOR 1.33, 95% CI 1.04-1.69,p = 0.023) were associated with complicated stages of COVID-19 at diagnosis. Conclusion The LEOSS cohort identified age, cardiovascular disease, diabetes and male sex as risk factors for complicated disease stages at SARS-CoV-2 diagnosis, thus confirming previous data. Further data regarding outcomes of the natural course of COVID-19 and the influence of treatment are required

Prediction of COVID-19 deterioration in high-risk patients at diagnosis: an early warning score for advanced COVID-19 developed by machine learning

Purpose!#!While more advanced COVID-19 necessitates medical interventions and hospitalization, patients with mild COVID-19 do not require this. Identifying patients at risk of progressing to advanced COVID-19 might guide treatment decisions, particularly for better prioritizing patients in need for hospitalization.!##!Methods!#!We developed a machine learning-based predictor for deriving a clinical score identifying patients with asymptomatic/mild COVID-19 at risk of progressing to advanced COVID-19. Clinical data from SARS-CoV-2 positive patients from the multicenter Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) were used for discovery (2020-03-16 to 2020-07-14) and validation (data from 2020-07-15 to 2021-02-16).!##!Results!#!The LEOSS dataset contains 473 baseline patient parameters measured at the first patient contact. After training the predictor model on a training dataset comprising 1233 patients, 20 of the 473 parameters were selected for the predictor model. From the predictor model, we delineated a composite predictive score (SACOV-19, Score for the prediction of an Advanced stage of COVID-19) with eleven variables. In the validation cohort (n = 2264 patients), we observed good prediction performance with an area under the curve (AUC) of 0.73 ± 0.01. Besides temperature, age, body mass index and smoking habit, variables indicating pulmonary involvement (respiration rate, oxygen saturation, dyspnea), inflammation (CRP, LDH, lymphocyte counts), and acute kidney injury at diagnosis were identified. For better interpretability, the predictor was translated into a web interface.!##!Conclusion!#!We present a machine learning-based predictor model and a clinical score for identifying patients at risk of developing advanced COVID-19

Clinical course and predictive risk factors for fatal outcome of SARS-CoV-2 infection in patients with chronic kidney disease

Purpose!#!The ongoing pandemic caused by the novel severe acute respiratory coronavirus 2 (SARS-CoV-2) has stressed health systems worldwide. Patients with chronic kidney disease (CKD) seem to be more prone to a severe course of coronavirus disease (COVID-19) due to comorbidities and an altered immune system. The study's aim was to identify factors predicting mortality among SARS-CoV-2-infected patients with CKD.!##!Methods!#!We analyzed 2817 SARS-CoV-2-infected patients enrolled in the Lean European Open Survey on SARS-CoV-2-infected patients and identified 426 patients with pre-existing CKD. Group comparisons were performed via Chi-squared test. Using univariate and multivariable logistic regression, predictive factors for mortality were identified.!##!Results!#!Comparative analyses to patients without CKD revealed a higher mortality (140/426, 32.9% versus 354/2391, 14.8%). Higher age could be confirmed as a demographic predictor for mortality in CKD patients (&amp;gt; 85 years compared to 15-65 years, adjusted odds ratio (aOR) 6.49, 95% CI 1.27-33.20, p = 0.025). We further identified markedly elevated lactate dehydrogenase (&amp;gt; 2 × upper limit of normal, aOR 23.21, 95% CI 3.66-147.11, p &amp;lt; 0.001), thrombocytopenia (&amp;lt; 120,000/µl, aOR 11.66, 95% CI 2.49-54.70, p = 0.002), anemia (Hb &amp;lt; 10 g/dl, aOR 3.21, 95% CI 1.17-8.82, p = 0.024), and C-reactive protein (≥ 30 mg/l, aOR 3.44, 95% CI 1.13-10.45, p = 0.029) as predictors, while renal replacement therapy was not related to mortality (aOR 1.15, 95% CI 0.68-1.93, p = 0.611).!##!Conclusion!#!The identified predictors include routinely measured and universally available parameters. Their assessment might facilitate risk stratification in this highly vulnerable cohort as early as at initial medical evaluation for SARS-CoV-2