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

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

Internists‘ career choice towards primary care: a cross-sectional survey

BACKGROUND: Swiss primary care (PC) is facing workforce shortage. Up to 2011 this workforce was supplied by two board certifications: general medicine and internal medicine. To strengthen them against subspecialties, they were unified into one: general internal medicine. However, since unification general practitioners' career options are no longer restrained by early commitment to PC. This may lead to a decrease of future primary care physicians (PCPs). METHODS: To gain insights in timing and factors influencing career choice of internists, we addressed a cross sectional survey to all board certified internists in the years 2000-2010 (n = 1462). Main measures were: final career choice (PCPs, hospital internists or subspecialists), timing and factors influencing career choice, and attractiveness of PCP career during medical school and residency. RESULTS: Response rate was 53.2%, 44.8% were female and median age was 45 years old. Final career choice was PCP for 39.1% of participants, 15.0% chose to become hospital internists, 41.8% became subspecialists and 4.0% other. Timing of career choice significantly differed between groups. Most of the subspecialists have chosen their career during residency (65.3%), while only 21.9% of the PCPs chose during residency. Work experience in an academic hospital was negatively associated with becoming PCP (P < 0.001). Family influence on career choice was more frequently reported among PCPs and chiefs' influence more reported among non-PCPs (P < 0.001). Fifty-nine percent of the participants considered a career as PCP to be attractive during medical school, this proportion decreased over time. CONCLUSIONS: Timing of career choice of PCPs and subspecialists strongly differed. PCPs opted late for their career and potentially modifiable external factors seem to contribute to their decision. This stresses the importance of fostering attractiveness of PC during medical school as well as during and after residency and of tailored residency positions for future PCPs in the hospital-dominated new general internal medicine training