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Psychometric Properties of Turkish Version of the particular Dutch Target Burden Stock.
Background There is growing evidence that technology-based interventions (TBIs) are effective for the treatment of depression. As TBIs are gaining acceptance, a question arises whether good therapeutic alliance, considered a key aspect of psychotherapy, can be established without or with minimal face-to-face contact or rather changes if blended concepts are applied. While therapeutic alliance has been studied extensively in the context of face-to-face therapy, only few studies have reviewed evidence on alliance ratings in TBIs. Objective The purpose of this study was to examine therapeutic alliance in technology-based psychological interventions for the treatment of depression. Methods We searched Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, PsycINFO, PSYNDEX, CINAHL, clinical trial registers, and sources of grey literature for randomized controlled trials on TBIs in the treatment of adults with unipolar depression. All publications were selected according to prespecified criteria. Data ut this is based on a small number of studies. Future research needs to determine on what basis therapeutic alliance is formed in settings that do not allow for additional nonverbal cues, perhaps with adapted instruments to measure therapeutic alliance. Trial registration PROSPERO International Prospective Register of Systematic Reviews CRD42016050413; https//www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42016050413). International registered report identifier (irrid) RR2-10.1136/bmjopen-2018-028042.Background As an innovative approach to providing web-based health care services from physical hospitals to patients at a distance, e-hospitals (ie, extended care hospitals through the internet) have been extensively developed in China. This closed health care delivery chain was developed by combining e-hospitals with physical hospitals; treatment begins with web-based consultation and registration, and then, patients are diagnosed and treated in a physical hospital. This approach is promising in its ability to improve accessibility, efficiency, and quality of health care. However, there is limited research on end users' acceptance of e-hospitals and the effectiveness of strategies aimed to prompt the adoption of e-hospitals in China. Objective This study aimed to provide insights regarding the adoption of e-hospitals by investigating patients' willingness to use e-hospitals and analyzing the barriers and facilitators to the adoption of this technology. Methods We used a pretested self-administered questionnaes.Background Social media are an increasingly commonly used platform for delivering health promotion interventions. Although recent research has focused on the effectiveness of social media interventions for health promotion, very little is known about the optimal content within such interventions, and the active ingredients to promote health behavior change using social media are not clear. Identifying which behavior change techniques (BCTs) are reported may help to clarify the content of interventions using a generalizable terminology that may facilitate future intervention development. Objective This study aimed to identify which BCTs are reported in social media interventions for promoting health behavior change in adults. Methods We included 71 studies conducted with adult participants (aged ≥18 years) and for which social media intervention was considered interactive in a Cochrane review of the effectiveness of such interventions. We developed a coding manual informed by the Behavior Change Technique Taxoon how to perform the behavior was most commonly applied in self-directed components of studies, control arms, and individual participant settings. Instruction on how to perform the behavior was also the most frequently reported BCT in both intervention and control arms simultaneously. Instruction on how to perform the behavior, social support (unspecified), self-monitoring of behavior, information about health consequences, and credible source were identified in the top 5 BCTs delivered with the highest intensity. Conclusions This study within a review provides a detailed description of the BCTs and their dose to promote behavior change in web-based, interactive social media interventions. Clarifying active ingredients in social media interventions and the intensity of their delivery may help to develop future interventions that can more clearly build upon the existing evidence.Background Using big data and the theory of cumulative deficits to develop the multimorbidity frailty index (mFI) has become a widely accepted approach in public health and health care services. However, constructing the mFI using the most critical determinants and stratifying different risk groups with dose-response relationships remain major challenges in clinical practice. Objective This study aimed to develop the mFI by using machine learning methods that select variables based on the optimal fitness of the model. In addition, we aimed to further establish 4 entities of risk using a machine learning approach that would achieve the best distinction between groups and demonstrate the dose-response relationship. find more Methods In this study, we used Taiwan's National Health Insurance Research Database to develop a machine learning multimorbidity frailty index (ML-mFI) using the theory of cumulative diseases/deficits of an individual older person. Compared to the conventional mFI, in which the selection of diseases/s selected in the mFI and the ML-mFI. A total of 86,133 subjects aged 65 to 100 years were included in this study and were categorized into 4 groups according to the ML-mFI. Both the Kaplan-Meier survival curves and Cox models showed that the ML-mFI significantly predicted all outcomes of interest, including all-cause mortality, unplanned hospitalizations, and all-cause ICU admissions at 1, 5, and 8 years of follow-up (P less then .01). In particular, a dose-response relationship was revealed between the 4 ML-mFI groups and adverse outcomes. Conclusions The ML-mFI consists of 38 diseases/deficits that can successfully stratify risk groups associated with all-cause mortality, unplanned hospitalizations, and all-cause ICU admissions in older people, which indicates that precise, patient-centered medical care can be a reality in an aging society.
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