The efficacy of psychological interventions for infertile patients: a meta-analysis examining mental health and pregnancy rate

BACKGROUND Psychological interventions for infertile patients seek to improve mental health and increase pregnancy rates. The aim of the present meta-analysis was to examine if psychological interventions improve mental health and pregnancy rate among infertile patients. Thus, controlled studies were pooled investigating psychological interventions following the introduction of assisted reproductive treatments (ART). METHODS The databases of Medline, PsycINFO, PSYNDEX, Web of Science and the Cochrane Library were searched to identify relevant articles published between 1978 and 2007 (384 articles). Included were prospective intervention studies on infertile patients (women and men) receiving psychological interventions independent of actual medical treatment. The outcome measures were mental health and pregnancy rate. A total of 21 controlled studies were ultimately included in a meta-analysis comparing the efficacy of psychological interventions. Effect sizes (ES) were calculated for psychological measures and risk ratios (RR) for pregnancy rate. RESULTS The findings from controlled studies indicated no significant effect for psychological interventions regarding mental health (depression: ES 0.02, 99% CI: −0.19, 0.24; anxiety: ES 0.16, 99% CI: −0.10, 0.42; mental distress: ES 0.08, 99% CI: −0.10, 0.51). Nevertheless, there was evidence for the positive impact of psychological interventions on pregnancy rates (RR 1.42, 99% CI: 1.02, 1.96). Concerning pregnancy rates, significant effects for psychological interventions were only found for couples not receiving ART. CONCLUSIONS Despite the absence of clinical effects on mental health measures, psychological interventions were found to improve some patients' chances of becoming pregnant. Psychological interventions represent an attractive treatment option, in particular, for infertile patients who are not receiving medical treatmen

"It's like a glimpse into the future": Exploring the Role of Blood Glucose Prediction Technologies for Type 1 Diabetes Self-Management

Self-management of type 1 diabetes (T1D) involves multiple factors, frequent anticipation of changes in blood glucose, and complex decision-making. ML-based blood glucose predictions (BGP) may be valuable in supporting T1D management. However, it may be difficult for people with T1D to integrate BGP into their decision-making due to prediction uncertainty and interpretation. In this study, we investigate the lived experience of people with T1D focusing on their needs and expectations in using apps that provide BGP. We designed MOON-T1D, an app that shows simulated BGP and conducted a five-day study using the Experience Sampling Method coupled with semi-structured interviews with 15 individuals with T1D who used MOON-T1D. A reflexive thematic analysis of our data revealed implications for the design and use of BGP, including the complex role of emotions and trust surrounding predictions, and ways in which BGP may ease or complicate T1D management

Comparability of Patients in Trials of eHealth and Face-to-Face Psychotherapeutic Interventions for Depression: Meta-synthesis.

Background: Depressive disorders (DDs) are a public health problem. Face-to-face psychotherapeutic interventions are a first-line option for their treatment in adults. There is a growing interest in eHealth interventions to maximize accessibility for effective treatments. Thus, the number of randomized controlled trials (RCTs) of eHealth psychotherapeutic interventions has increased, and these interventions are being offered to patients. However, it is unknown whether patients with DDs differ in internet-based and face-to-face intervention trials. This information is essential to gain knowledge about eHealth trials’ external validity. Objective: We aimed to compare the baseline characteristics of patients with DDs included in the RCTs of eHealth and face-to-face psychotherapeutic interventions with a cognitive component. Methods: In this meta-epidemiological study, we searched 5 databases between 1990 and November 2017 (MEDLINE, Embase, PsycINFO, Google Scholar, and the database of Cuijpers et al). We included RCTs of psychotherapeutic interventions with a cognitive component (eg, cognitive therapy, cognitive behavioral therapy [CBT], or interpersonal therapy) delivered face-to-face or via the internet to adults with DDs. Each included study had a matching study for predefined criteria to allow a valid comparison of characteristics and was classified as a face-to-face (CBT) or eHealth (internet CBT) intervention trial. Two authors selected the studies, extracted data, and resolved disagreements by discussion. We tested whether predefined baseline characteristics differed in face-to-face and internet-based trials using a mixed-effects model and testing for differences with z tests (statistical significance set at .05). For continuous outcomes, we also estimated the difference in means between subgroups with 95% CI. Results: We included 58 RCTs (29 matching pairs) with 3846 participants (female: n=2803, 72.9%) and mean ages ranging from 20-74 years. White participants were the most frequent (from 63.6% to 100%). Other socioeconomic characteristics were poorly described. The participants presented DDs of different severity measured with heterogeneous instruments. Internet CBT trials had a longer depression duration at baseline (7.19 years higher, CI 95% 2.53-11.84; 10.0 vs 2.8 years; P=.002), but the proportion of patients with previous depression treatment was lower (24.8% vs 42%; P=.04). Subgroup analyses found no evidence of differences for the remaining baseline characteristics: age, gender, education, living area, depression severity, history of depression, actual antidepressant medication, actual physical comorbidity, actual mental comorbidity, study dropout, quality of life, having children, family status, and employment. We could not compare proficiency with computers due to the insufficient number of studies. Conclusions: The baseline characteristics of patients with DDs included in the RCTs of eHealth and face-to-face psychotherapeutic interventions are generally similar. However, patients in eHealth trials had a longer duration of depression, and a lower proportion had received previous depression treatment, which might indicate that eHealth trials attract patients who postpone earlier treatment attempts.post-print289 K

Comparability of Patients in Trials of E-Health and Face-To-Face Psychotherapeutic Interventions for Depression: a Meta-Synthesis

Background: Depressive disorders are a public health problem. Face-to-face psychotherapeutic interventions are considered to be a first-line option for their treatment in adults. There is a growing interest in eHealth interventions to maximize accessibility for effective treatments. Thus, the number of randomized controlled trials (RCTs) of eHealth psychotherapeutic interventions has increased, and these interventions are often being offered to patients. However, it is unknown whether patients with depressive disorders differ in internet and face-to-face intervention trials. This information is essential to gain knowledge about the external validity of eHealth trials. Objective: To compare the baseline characteristics of patients with depressive disorder in eHealth and face-to-face psychotherapeutic intervention RCTs. Methods: Meta-epidemiological study. We searched five databases between 1990 and November 2017 (MEDLINE, EMBASE, PsycINFO, Google Scholar, Cuijpers’s database). We included RCTs of psychotherapeutic interventions with a cognitive component (such as cognitive therapy, cognitive–behavioral therapy, or interpersonal therapy) delivered face-to-face or via the internet to adults with a depressive disorder. Each included study had a matching study for predefined criteria to allow a valid comparison of baseline characteristics. Each study was classified as a face-to-face (CBT) or eHealth (iCBT) intervention trial. Two authors selected the studies, extracted data, and resolved disagreements by discussion. We tested whether predefined baseline characteristics differed in face-to-face and internet-based trials by using a mixed-effects model and testing for differences with a Z-test (statistical significance threshold set at 0.05). For continuous outcomes, we also estimated the difference in means between subgroups along with the 95% CI. Results: We included 58 RCTs (29 matching pairs) with 3,655 participants (71.5% females) with a mean age from 20 to 74 years. Caucasian participants were the most frequently reported. Other socioeconomic characteristics were poorly reported. The participants presented different depressive disorders measured with heterogeneous instruments. iCBT trials had a longer mean duration of depression at baseline (7.19 years higher; CI 95% 2.53 to 11.84; 10.0 versus 2.8 years, P=.002), but the proportion of patients with previous depression treatment was lower (24.8% versus 42.0%, P=.035). The subgroup analyses found no evidence of differences for the remaining baseline characteristics: age, gender, education, living area, depression severity, history of depression, actual antidepressant medication, actual physical comorbidity, actual mental comorbidity, study drop-out, quality of life, having children, family status and employment. We could not compare proficiency with computers due to the insufficient number of studies reporting this information. Conclusions: Our study found that the baseline characteristics of patients with depressive disorders included in RCTs of eHealth and face-to-face psychotherapeutic interventions are generally similar. However, patients in eHealth trials had a longer duration of depression, and a lower proportion had received previous depression treatment. This might indicate that eHealth trials attract patients who postpone earlier treatment attempts