Transfer of manualized CBT for social phobia into clinical practice (SOPHO-PRAX) : a study protocol for a cluster-randomized controlled trial

Background: Cognitive-behavioral therapy (CBT) is generally known to be efficacious in the treatment of social phobia when applied in RCT's, namely when the treatment manual is based on the Clark-Wells approach. However, little is known about the efficacy of manualized treatments in routine clinical practice (Phase IV of psychotherapy research). The present study (SOPHO-PRAX) is a continuation of a large multi-centre randomized clinical trial (SOPHO-NET) and analyses the extent to which additional training practitioners in manualized procedures enhances treatment effect. Methods: N = 36 private practitioners will be included in three treatment centres and randomly designated to either training in manualized CBT or no specific training. The treatment effects of the therapies conducted by both groups of therapists will be compared. A total of 162 patients (N = 116 completers; N = 58 per condition) will be enrolled. Liebowitz Social Anxiety Scale (LSAS) will serve as primary outcome measure. Remission from social phobia is defined as LSAS total [less than or equal to] 30 points. Data will be collected at treatment begin, after 8, 15, and 25 sessions (50 mins. each), at treatment completion, as well at 6 and 12 months post-treatment. Discussion: The present CBT trial combines elements of randomized-controlled trials and naturalistic studies in an innovative way. It will directly inform about the incremental effects of procedures established in a controlled trial into clinical practice. Study results are relevant to health care decisions and policy. They may serve to improve quality of treatment, and shorten the timeframe between the development and widespread dissemination of effective methods, thereby reducing health cost expenditures. The results of this study will not only inform about the degree to which the new methods lead to an improvement of treatment course and outcome, but also about whether the effects of routine psychotherapeutic treatment are comparable to those of the controlled, strictly manualized treatments of the SOPHO-NET study. Trial Registration: ClinicalTrials.gov identifier: NCT01388231. This study was funded by the German Federal Ministry of Education and Research (SOPHO-NET: BMBF 01GV0607; SOPHO-PRAX: BMBF 01GV1001)

Pain Intensity Recognition Rates via Biopotential Feature Patterns with Support Vector Machines

Background: The clinically used methods of pain diagnosis do not allow for objective and robust measurement, and physicians must rely on the patient’s report on the pain sensation. Verbal scales, visual analog scales (VAS) or numeric rating scales (NRS) count among the most common tools, which are restricted to patients with normal mental abilities. There also exist instruments for pain assessment in people with verbal and / or cognitive impairments and instruments for pain assessment in people who are sedated and automated ventilated. However, all these diagnostic methods either have limited reliability and validity or are very time-consuming. In contrast, biopotentials can be automatically analyzed with machine learning algorithms to provide a surrogate measure of pain intensity. Methods: In this context, we created a database of biopotentials to advance an automated pain recognition system, determine its theoretical testing quality, and optimize its performance. Eighty-five participants were subjected to painful heat stimuli (baseline, pain threshold, two intermediate thresholds, and pain tolerance threshold) under controlled conditions and the signals of electromyography, skin conductance level, and electrocardiography were collected. A total of 159 features were extracted from the mathematical groupings of amplitude, frequency, stationarity, entropy, linearity, variability, and similarity. Results: We achieved classification rates of 90.94% for baseline vs. pain tolerance threshold and 79.29% for baseline vs. pain threshold. The most selected pain features stemmed from the amplitude and similarity group and were derived from facial electromyography. Conclusion: The machine learning measurement of pain in patients could provide valuable information for a clinical team and thus support the treatment assessment

Why do banks promise to pay par on demand?

We survey the theories of why banks promise to pay par on demand and examine evidence about the conditions under which banks have promised to pay the par value of deposits and banknotes on demand when holding only fractional reserves. The theoretical literature can be broadly divided into four strands: liquidity provision, asymmetric information, legal restrictions, and a medium of exchange. We assume that it is not zero cost to make a promise to redeem a liability at par value on demand. If so, then the conditions in the theories that result in par redemption are possible explanations of why banks promise to pay par on demand. If the explanation based on customers’ demand for liquidity is correct, payment of deposits at par will be promised when banks hold assets that are illiquid in the short run. If the asymmetric-information explanation based on the difficulty of valuing assets is correct, the marketability of banks’ assets determines whether banks promise to pay par. If the legal restrictions explanation of par redemption is correct, banks will not promise to pay par if they are not required to do so. If the transaction explanation is correct, banks will promise to pay par value only if the deposits are used in transactions. After the survey of the theoretical literature, we examine the history of banking in several countries in different eras: fourth-century Athens, medieval Italy, Japan, and free banking and money market mutual funds in the United States. We find that all of the theories can explain some of the observed banking arrangements, and none explain all of them

Transfer of manualized CBT for social phobia into clinical practice (SOPHO-PRAX): a study protocol for a cluster-randomized controlled trial

Abstract Background Cognitive-behavioral therapy (CBT) is generally known to be efficacious in the treatment of social phobia when applied in RCTs, namely when the treatment manual is based on the Clark-Wells approach. However, little is known about the efficacy of manualized treatments in routine clinical practice (Phase IV of psychotherapy research). The present study (SOPHO-PRAX) is a continuation of a large multicenter randomized clinical trial (SOPHO-NET) and analyzes the extent to which additional training practitioners in manualized procedures enhances treatment effect. Methods/design Thirty-six private practitioners will be included in three treatment centers and randomly designated to either training in manualized CBT or no specific training. The treatment effects of the therapies conducted by both groups of therapists will be compared. A total of 162 patients (n = 116 completers; n = 58 per condition) will be enrolled. Liebowitz Social Anxiety Scale (LSAS) will serve as primary outcome measure. Remission from social phobia is defined as LSAS total ≤30 points. Data will be collected at treatment begin, after 8, 15, and 25 sessions (50 min each), at treatment completion, as well at 6 and 12 months post-treatment. Discussion The present CBT trial combines elements of randomized controlled trials and naturalistic studies in an innovative way. It will directly inform about the incremental effects of procedures established in a controlled trial into clinical practice. Study results are relevant to healthcare decisions and policy. They may serve to improve quality of treatment, and shorten the time frame between the development and widespread dissemination of effective methods, thereby reducing health cost expenditure. The results of this study will not only inform about the degree to which the new methods lead to an improvement of treatment course and outcome, but also about whether the effects of routine psychotherapeutic treatment are comparable to those of the controlled, strictly manualized treatments of the SOPHO-NET study. Trial registration ClinicalTrials.gov identifier: NCT01388231. This study was funded by the German Federal Ministry of Education and Research (SOPHO-NET: BMBF 01GV0607; SOPHO-PRAX: BMBF 01GV1001).</p

Top ten importance ranking of selected features.

<p>Top ten importance ranking of selected features.</p

Support Vector Machine hyperplane (<i>H</i>).

<p>Support Vector Machine hyperplane (<i>H</i>).</p

Comparison between accuracy via support vector machine of study Walter et al. [14], without similarity signal feature [red] vs. support vector machine with similarity feature and automated selected feature [green].

<p>Comparison between accuracy via support vector machine of study Walter et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140330#pone.0140330.ref014" target="_blank">14</a>], without similarity signal feature [red] vs. support vector machine with similarity feature and automated selected feature [green].</p

Support Vector Machine learning architecture.

<p>Support Vector Machine learning architecture.</p