Background:Tobacco consumption is a leading cause of death and disease, killing >8 million people each year. Smoking cessation significantly reduces the risk of developing smoking-related diseases. Although combined treatment for addiction is promising, evidence of its effectiveness is still emerging. Currently, there is no published research comparing the effectiveness of blended smoking cessation treatments (BSCTs) with face-to-face (F2F) treatments, where web-based components replace 50% of the F2F components in blended treatment.Objective:The primary objective of this 2-arm noninferiority randomized controlled trial was to determine whether a BSCT is noninferior to an F2F treatment with identical ingredients in achieving abstinence rates.Methods:This study included 344 individuals who smoke (at least 1 cigarette per day) attending an outpatient smoking cessation clinic in the Netherlands. The participants received either a blended 50% F2F and 50% web-based BSCT or only F2F treatment with similar content and intensity. The primary outcome measure was cotinine-validated abstinence rates from all smoking products at 3 and 15 months after treatment initiation. Additional measures included carbon monoxide–validated point prevalence abstinence; self-reported point prevalence abstinence; and self-reported continuous abstinence rates at 3, 6, 9, and 15 months after treatment initiation.Results:None of the 13 outcomes showed statistically confirmed noninferiority of the BSCT, whereas 4 outcomes showed significantly (P<.001) inferior abstinence rates of the BSCT: cotinine-validated point prevalence abstinence rate at 3 months (difference 12.7, 95% CI 6.2-19.4), self-reported point prevalence abstinence rate at 6 months (difference 19.3, 95% CI 11.5-27.0) and at 15 months (difference 11.7, 95% CI 5.8-17.9), and self-reported continuous abstinence rate at 6 months (difference 13.8, 95% CI 6.8-20.8). The remaining 9 outcomes, including the cotinine-validated point prevalence abstinence rate at 15 months, were inconclusive.Conclusions:In this high-intensity outpatient smoking cessation trial, the blended mode was predominantly less effective than the traditional F2F mode. The results contradict the widely assumed potential benefits of blended treatment and suggest that further research is needed to identify the critical factors in the design of blended interventions.Trial Registration:Netherlands Trial Register 27150; https://onderzoekmetmensen.nl/nl/trial/27150
Objective:Acknowledging study limitations in a scientific publication is a crucial element in scientific transparency and progress. However, limitation reporting is often inadequate. Natural language processing (NLP) methods could support automated reporting checks, improving research transparency. In this study, our objective was to develop a dataset and NLP methods to detect and categorize self-acknowledged limitations (e.g., sample size, blinding) reported in randomized controlled trial (RCT) publications.Methods:We created a data model of limitation types in RCT studies and annotated a corpus of 200 full-text RCT publications using this data model. We fine-tuned BERT-based sentence classification models to recognize the limitation sentences and their types. To address the small size of the annotated corpus, we experimented with data augmentation approaches, including Easy Data Augmentation (EDA) and Prompt-Based Data Augmentation (PromDA). We applied the best-performing model to a set of about 12K RCT publications to characterize self-acknowledged limitations at larger scale.Results:Our data model consists of 15 categories and 24 sub-categories (e.g., Population and its sub-category DiagnosticCriteria). We annotated 1090 instances of limitation types in 952 sentences (4.8 limitation sentences and 5.5 limitation types per article). A fine-tuned PubMedBERT model for limitation sentence classification improved upon our earlier model by about 1.5 absolute percentage points in F1 score (0.821 vs. 0.8) with statistical significance (). Our best-performing limitation type classification model, PubMedBERT fine-tuning with PromDA (Output View), achieved an F1 score of 0.7, improving upon the vanilla PubMedBERT model by 2.7 percentage points, with statistical significance ().Conclusion:The model could support automated screening tools which can be used by journals to draw the authors’ attention to reporting issues. Automatic extraction of limitations from RCT publications could benefit peer review and evidence synthesis, and support advanced methods to search and aggregate the evidence from the clinical trial literature.
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Abstract Background: Antipsychotic-induced Weight Gain (AiWG) is a debilitating and common adverse effect of antipsychotics. AiWG negatively impacts life expectancy, quality of life, treatment adherence, likelihood of developing type-2 diabetes and readmission. Treatment of AiWG is currently challenging, and there is no consensus on the optimal management strategy. In this study, we aim to evaluate the use of metformin for the treatment of AiWG by comparing metformin with placebo in those receiving treatment as usual, which includes a lifestyle intervention. Methods: In this randomized, double-blind, multicenter, placebo-controlled, pragmatic trial with a follow-up of 52 weeks, we aim to include 256 overweight participants (Body Mass Index (BMI) > 25 kg/m2) of at least 16years of age. Patients are eligible if they have been diagnosed with schizophrenia spectrum disorder and if they have been using an antipsychotic for at least three months. Participants will be randomized with a 1:1 allocation to placebo or metformin, and will be treated for a total of 26 weeks. Metformin will be started at 500 mg b.i.d. and escalated to 1000 mg b.i.d. 2 weeks thereafter (up to a maximum of 2000mg daily). In addition, all participants will undergo a lifestyle intervention as part of the usual treatment consisting of a combination of an exercise program and dietary consultations. The primary outcome measure is difference in body weight as a continuous trait between the two arms from treatment inception until 26 weeks of treatment, compared to baseline. Secondary outcome measures include: 1) Any element of metabolic syndrome (MetS); 2) Response, defined as ≥5% body weight loss at 26 weeks relative to treatment inception; 3) Quality of life; 4) General mental and physical health; and 5) Cost-effectiveness. Finally, we aim to assess whether genetic liability to BMI and MetS may help estimate the amount of weight reduction following initiation of metformin treatment. Discussion: The pragmatic design of the current trial allows for a comparison of the efficacy and safety of metformin in combination with a lifestyle intervention in the treatment of AiWG, facilitating the development of guidelines on the interventions for this major health problem.
Een goede voorbereiding is het halve werk, ook voor patiënten op de wachtlijst voor een chirurgische ingreep. We onderzoeken hoe de e-health-applicatie 'Beter Voorbereid' mensen helpt om sterker aan de start van een operatie te staan en zo sneller te herstellen.Doel De e-health-applicatie Beter Voorbereid helpt patiënten om zich voor te bereiden op hun operatie. Ons onderzoek richt zich op de effectiviteit, verbetering en implementatie van de app met vier werkpakketten: Het optimaliseren van de app op basis van ervaringen van patiënten en zorgverleners. Het vormen van een netwerk van eerste- en tweedelijns zorgverleners die zorg voorafgaande aan een operatie bieden. Het onderzoeken van de effectiviteit van de app. Het opstellen van een implementatieplan, inclusief businessmodellen, van de app Beter Voorbereid. Resultaten Dit onderzoek loopt momenteel. Na afronding vind je hier een samenvatting van de resultaten. Lees meer over dit project op beter-voorbereid.nl. Looptijd 01 november 2018 - 01 januari 2023 Aanpak Binnen het Amsterdam UMC, locatie VUmc en het UMC Utrecht is een pilot gehouden naar de studieprocedures en bruikbaarheid van de applicatie. Naar aanleiding van deze resultaten worden verbeteringen doorgevoerd in de procedures van het onderzoek en de inhoud van de applicatie. Vervolgens zal in vier ziekenhuizen een groot Randomized Controlled Trial (RCT) starten naar de effectiviteit van de applicatie. Naast het Amsterdam UMC en UMC Utrecht zal dit ook plaatsvinden bij het Dijklander ziekenhuis in Hoorn. In deze periode zal ook het onderzoek naar de bredere implementatie van de app 'Beter Voorbereid' worden uitgevoerd.
Een goede voorbereiding is het halve werk, ook voor patiënten op de wachtlijst voor een chirurgische ingreep. We onderzoeken hoe de e-health-applicatie 'Beter Voorbereid' mensen helpt om sterker aan de start van een operatie te staan en zo sneller te herstellen.Doel De e-health-applicatie Beter Voorbereid helpt patiënten om zich voor te bereiden op hun operatie. Ons onderzoek richt zich op de effectiviteit, verbetering en implementatie van de app met vier werkpakketten: Het optimaliseren van de app op basis van ervaringen van patiënten en zorgverleners. Het vormen van een netwerk van eerste- en tweedelijns zorgverleners die zorg voorafgaande aan een operatie bieden. Het onderzoeken van de effectiviteit van de app. Het opstellen van een implementatieplan, inclusief businessmodellen, van de app Beter Voorbereid. Resultaten Dit onderzoek loopt momenteel. Na afronding vind je hier een samenvatting van de resultaten. Lees meer over dit project op beter-voorbereid.nl. Looptijd 01 november 2018 - 01 januari 2023 Aanpak Binnen het Amsterdam UMC, locatie VUmc en het UMC Utrecht is een pilot gehouden naar de studieprocedures en bruikbaarheid van de applicatie. Naar aanleiding van deze resultaten worden verbeteringen doorgevoerd in de procedures van het onderzoek en de inhoud van de applicatie. Vervolgens zal in vier ziekenhuizen een groot Randomized Controlled Trial (RCT) starten naar de effectiviteit van de applicatie. Naast het Amsterdam UMC en UMC Utrecht zal dit ook plaatsvinden bij het Dijklander ziekenhuis in Hoorn. In deze periode zal ook het onderzoek naar de bredere implementatie van de app 'Beter Voorbereid' worden uitgevoerd.
Effectiveness of Supported Education for students with mental health problems, an experimental study.The onset of mental health problems generally occurs between the ages of 16 and 23 – the years in which young people follow postsecondary education, which is a major channel in ourso ciety to prepare for a career and enhance life goals. Several studies have shown that students with mental health problems have a higher chance of early school leaving. Supported Education services have been developed to support students with mental health to remain at school. The current project aims to study the effect of an individually tailored Supported Education intervention on educational and mental health outcomes of students with mental health problems at a university of applied sciences and a community college. To that end, a mixed methods design will be used. This design combines quantitative research (Randomized Controlled Trial) with qualitative research (focus groups, monitoring, interviews). 100 students recruited from the two educational institutes will be randomly allocated to either the intervention or control group.
