Category: Statistics

JMIR Form Res. 2023 Jan 27;7:e37293. doi: 10.2196/37293.

ABSTRACT

BACKGROUND: A growing number of studies highlight the importance of emotion regulation in the treatment and recovery of individuals with psychosis and concomitant disorders such as substance use disorder (SUD), for whom access to integrated dual-disorder treatments is particularly difficult. In this context, dedicated smartphone apps may be useful tools to provide immediate support to individuals in need. However, few studies to date have focused on the development and assessment of apps aimed at promoting emotional regulation for people with psychosis.

OBJECTIVE: The aim of this study was to evaluate the feasibility, acceptability, and potential clinical impact of a dedicated app (ChillTime) for individuals with psychotic disorders and concurrent SUD. The app design process followed recommendations for reducing cognitive effort on a mobile app. A total of 20 coping strategies regrouped in four categories (behavioral, emotional, cognitive, spiritual) were included in the app.

METHODS: This open pilot study followed a pre-post design. After the initial assessment, researchers asked participants to use the app as part of their treatment over a 30-day period. Feasibility was determined by the frequency of use of the app and measured using the number of completed strategies. Acceptability was determined by measuring ease of use, ease of learning, satisfaction, and perceived utility at the end of the 30-day study period based on responses to satisfaction questionnaires. Clinical scales measuring emotion regulation, substance use (ie, type of substance, amount taken, and frequency of use), and various psychiatric symptoms were administered at the beginning and end of the 30-day period.

RESULTS: A total of 13 participants were recruited from two first-episode psychosis clinics in Montreal, Quebec, Canada. All participants were symptomatically stable, were between 18 and 35 years of age (mostly men; 70% of the sample), and had a schizophrenia spectrum disorder with a comorbid substance use diagnosis. A total of 11 participants completed the study (attrition<20%). Approximately half of the participants used the tool at least 33% of the days (11-21 days). Cognitive and emotion-focused techniques were rated the highest in terms of usefulness and were the most frequently used. The majority of participants gave positive answers about the ease of use and the ease of learning the tool. A nonsignificant association of ChillTime use with negative symptoms and drug use was observed. No other statistically significant changes were observed.

CONCLUSIONS: The ChillTime app showed good feasibility (approximately half of the participants used the tool at least 33% of the days) and acceptability among people with schizophrenia spectrum disorder and SUD. Trends suggesting a potential impact on certain clinical outcomes will need to be replicated in larger-sample studies before any conclusion can be drawn.

PMID:36705963 | DOI:10.2196/37293

JMIR Aging. 2023 Jan 27;6:e36663. doi: 10.2196/36663.

ABSTRACT

BACKGROUND: Assessing cognitive constructs affected by Alzheimer disease, such as processing speed (PS), is important to screen for potential disease and allow for early detection. Digital PS assessments have been developed to provide widespread, efficient cognitive testing, but all have been validated only based on the correlation between test scores. Best statistical practices dictate that concurrent validity should be assessed for agreement or equivalence rather than using correlation alone.

OBJECTIVE: This study aimed to assess the concurrent validity of a novel digital PS assessment against a gold-standard measure of PS.

METHODS: Adults aged 45-75 years (n=191) participated in this study. Participants completed the novel digital digit-symbol substitution test (DDSST) and the Repeatable Battery for the Assessment of Neuropsychological Status coding test (RBANS-C). The correlation between the test scores was determined using a Pearson product-moment correlation, and a difference in mean test scores between tests was checked for using a 2-tailed dependent samples t test. Data were analyzed for agreement between the 2 tests using Bland-Altman limits of agreement and equivalency using a two one-sided t tests (TOST) approach.

RESULTS: A significant moderate, positive correlation was found between DDSST and RBANS-C scores (r=.577; P<.001), and no difference in mean scores was detected between the tests (P=.93). Bias was nearly zero (0.04). Scores between the tests were found to display adequate agreement with 90% of score differences falling between -22.66 and 22.75 (90% limits of agreement=-22.91 to 22.99), and the scores were equivalent (P=.049).

CONCLUSIONS: Analyses indicate that the DDSST is a valid digital assessment of PS. The DDSST appears to be a suitable option for widespread, immediate, and efficient PS testing.

TRIAL REGISTRATION: ClinicalTrials.gov NCT04559789; https://clinicaltrials.gov/ct2/show/NCT04559789.

PMID:36705951 | DOI:10.2196/36663

JMIR Mhealth Uhealth. 2023 Jan 27;11:e41660. doi: 10.2196/41660.

ABSTRACT

BACKGROUND: A growing body of evidence shows that financial incentives can effectively reinforce individuals’ positive behavior change and improve compliance with health intervention programs. A critical factor in the design of incentive-based interventions is to set a proper incentive magnitude. However, it is highly challenging to determine such magnitudes as the effects of incentive magnitude depend on personal attitudes and contexts.

OBJECTIVE: This study aimed to illustrate loss-framed adaptive microcontingency management (L-AMCM) and the lessons learned from a feasibility study. L-AMCM discourages an individual’s adverse health behaviors by deducting particular expenses from a regularly assigned budget, where expenses are adaptively estimated based on the individual’s previous responses to varying expenses and contexts.

METHODS: We developed a mobile health intervention app for preventing prolonged sedentary lifestyles. This app delivered a behavioral mission (ie, suggesting taking an active break for a while) with an incentive bid when 50 minutes of uninterrupted sedentary behavior happened. Participants were assigned to either the fixed (ie, deducting the monotonous expense for each mission failure) or adaptive (ie, deducting varying expenses estimated by the L-AMCM for each mission failure) incentive group. The intervention lasted 3 weeks.

RESULTS: We recruited 41 participants (n=15, 37% women; fixed incentive group: n=20, 49% of participants; adaptive incentive group: n=21, 51% of participants) whose mean age was 24.0 (SD 3.8; range 19-34) years. Mission success rates did not show statistically significant differences by group (P=.54; fixed incentive group mean 0.66, SD 0.24; adaptive incentive group mean 0.61, SD 0.22). The follow-up analysis of the adaptive incentive group revealed that the influence of incentive magnitudes on mission success was not statistically significant (P=.18; odds ratio 0.98, 95% CI 0.95-1.01). On the basis of the qualitative interviews, such results were possibly because the participants had sufficient intrinsic motivation and less sensitivity to incentive magnitudes.

CONCLUSIONS: Although our L-AMCM did not significantly affect users’ mission success rate, this study configures a pioneering work toward adaptively estimating incentives by considering user behaviors and contexts through leveraging mobile sensing and machine learning. We hope that this study inspires researchers to develop incentive-based interventions.

PMID:36705949 | DOI:10.2196/41660

J Physician Assist Educ. 2023 Jan 27. doi: 10.1097/JPA.0000000000000476. Online ahead of print.

ABSTRACT

INTRODUCTION: The purpose of this study was to investigate clinical faculty perceptions of online learning in health professions education.

METHODS: Clinical faculty members from various health professions programs in New York were surveyed to determine whether there was a relationship between clinical faculty members’ attitudes toward online learning competencies and their ability to teach online. Additionally, this study explored what type of impact years of teaching experience and online training had on clinical faculty perceptions of online learning.

RESULTS: The study received 60 responses from clinical faculty, most of whom were teaching on Long Island, New York. Although the findings were not significant for most variables, a t-test demonstrated a significant statistical difference between online training and faculty perceptions of online learning. When clinical faculty completed online training, they had a more positive attitude toward online learning. The study also found that clinical faculty members’ attitudes toward technology in online learning positively influenced their ability to troubleshoot technical issues in online environments. Years of online teaching experience did not affect how clinical faculty perceived online learning; however, clinical faculty with 1-5 years and 6-10 years of teaching experience rated their attitudes and abilities the lowest out of all the groups.

DISCUSSION: While there appears to be a correlation between faculty perceptions of online learning and online training, more research is needed to objectively determine which specific trainings would be most advantageous.

PMID:36705927 | DOI:10.1097/JPA.0000000000000476

JAMA Netw Open. 2023 Jan 3;6(1):e2253301. doi: 10.1001/jamanetworkopen.2022.53301.

ABSTRACT

IMPORTANCE: Randomized clinical trials (RCTs) on COVID-19 are increasingly being posted as preprints before publication in a scientific, peer-reviewed journal.

OBJECTIVE: To assess time to journal publication for COVID-19 RCT preprints and to compare differences between pairs of preprints and corresponding journal articles.

EVIDENCE REVIEW: This systematic review used a meta-epidemiologic approach to conduct a literature search using the World Health Organization COVID-19 database and Embase to identify preprints published between January 1 and December 31, 2021. This review included RCTs with human participants and research questions regarding the treatment or prevention of COVID-19. For each preprint, a literature search was done to locate the corresponding journal article. Two independent reviewers read the full text, extracted data, and assessed risk of bias using the Cochrane Risk of Bias 2 tool. Time to publication was analyzed using a Cox proportional hazards regression model. Differences between preprint and journal article pairs in terms of outcomes, analyses, results, or conclusions were described. Statistical analysis was performed on October 17, 2022.

FINDINGS: This study included 152 preprints. As of October 1, 2022, 119 of 152 preprints (78.3%) had been published in journals. The median time to publication was 186 days (range, 17-407 days). In a multivariable model, larger sample size and low risk of bias were associated with journal publication. With a sample size of less than 200 as the reference, sample sizes of 201 to 1000 and greater than 1000 had hazard ratios (HRs) of 1.23 (95% CI, 0.80-1.91) and 2.19 (95% CI, 1.36-3.53) for publication, respectively. With high risk of bias as the reference, medium-risk articles with some concerns for bias had an HR of 1.77 (95% CI, 1.02-3.09); those with a low risk of bias had an HR of 3.01 (95% CI, 1.71-5.30). Of the 119 published preprints, there were differences in terms of outcomes, analyses, results, or conclusions in 65 studies (54.6%). The main conclusion in the preprint contradicted the conclusion in the journal article for 2 studies (1.7%).

CONCLUSIONS AND RELEVANCE: These findings suggest that there is a substantial time lag from preprint posting to journal publication. Preprints with smaller sample sizes and high risk of bias were less likely to be published. Finally, although differences in terms of outcomes, analyses, results, or conclusions were observed for preprint and journal article pairs in most studies, the main conclusion remained consistent for the majority of studies.

PMID:36705921 | DOI:10.1001/jamanetworkopen.2022.53301

Methods Mol Biol. 2023;2586:233-249. doi: 10.1007/978-1-0716-2768-6_14.

ABSTRACT

Understanding the three-dimensional structure of an RNA molecule is often essential to understanding its function. Sampling algorithms and energy functions for RNA structure prediction are improving, due to the increasing diversity of structural data available for training statistical potentials and testing structural data, along with a steady supply of blind challenges through the RNA-Puzzles initiative. The recent FARFAR2 algorithm enables near-native structure predictions on fairly complex RNA structures, including automated selection of final candidate models and estimation of model accuracy. Here, we describe the use of a publicly available webserver for RNA modeling for realistic scenarios using FARFAR2, available at https://rosie.rosettacommons.org/farfar2 . We walk through two cases in some detail: a simple model pseudoknot from the frameshifting element of beet western yellows virus modeled using the “basic interface” to the webserver and a replication of RNA-Puzzle 20, a metagenomic twister sister ribozyme, using the “advanced interface.” We also describe example runs of FARFAR2 modeling including two kinds of experimental data: a c-di-GMP riboswitch modeled with low-resolution restraints from MOHCA-seq experiments and a tandem GA motif modeled with ¹H NMR chemical shifts.

PMID:36705908 | DOI:10.1007/978-1-0716-2768-6_14

Methods Mol Biol. 2023;2586:107-120. doi: 10.1007/978-1-0716-2768-6_7.

ABSTRACT

A point mutation in coding RNA can cause not only an amino acid substitution but also a dynamic change of RNA secondary structure, leading to a dysfunctional RNA. Although in silico structure prediction has been used to detect structure-disrupting point mutations known as riboSNitches, exhaustive simulation of long RNAs is needed to detect a significant enrichment or depletion of riboSNitches in functional RNAs. Here, we have developed a novel algorithm Radiam (RNA secondary structure Analysis with Deletion, Insertion, And substitution Mutations) for a comprehensive riboSNitch analysis of long RNAs. Radiam is based on the ParasoR framework, which efficiently computes local RNA secondary structures for long RNAs. ParasoR can compute a variety of structure scores over globally consistent structures with maximal span constraints for the base pair distance. Using the reusable structure database made by ParasoR, Radiam performs an efficient recomputation of the secondary structures for mutated sequences. An exhaustive simulation of Radiam is expected to find reliable riboSNitch candidates on long RNAs by evaluating their statistical significance in terms of the change of local structure stability.

PMID:36705901 | DOI:10.1007/978-1-0716-2768-6_7

Methods Mol Biol. 2023;2586:35-48. doi: 10.1007/978-1-0716-2768-6_3.

ABSTRACT

The information of RNA secondary structure has been widely applied to the inference of RNA function. However, a classical prediction method is not feasible to long RNAs such as mRNA due to the problems of computational time and numerical errors. To overcome those problems, sliding window methods have been applied while their results are not directly comparable to global RNA structure prediction. In this chapter, we introduce ParasoR, a method designed for parallel computation of genome-wide RNA secondary structures. To enable genome-wide prediction, ParasoR distributes dynamic programming (DP) matrices required for structure prediction to multiple computational nodes. Using the database of not the original DP variable but the ratio of variables, ParasoR can locally compute the structure scores such as stem probability or accessibility on demand. A comprehensive analysis of local secondary structures by ParasoR is expected to be a promising way to detect the statistical constraints on long RNAs.

PMID:36705897 | DOI:10.1007/978-1-0716-2768-6_3

Arch Osteoporos. 2023 Jan 27;18(1):27. doi: 10.1007/s11657-023-01213-1.

ABSTRACT

The current study shows that patients aged 50 or more who have sustained an osteoporotic fracture have a significant risk of suffering a new fracture. Refracture risk is also increased when anatomic site of both index and subsequent fracture are the same.

PURPOSE: The purpose of this study is to describe the profile of a patient sustaining a fragility fracture, the influence of the initial or index fracture on subsequent fracture risk and the role that anatomic site of index and subsequent fracture play on fracture risk.

METHODS: In this retrospective observational cohort study, individuals aged ≥ 50 years who sustained at least one clinical fragility fracture were identified from the public health service register between January 1, 2014, and December 31, 2015. Two separate analysis cohorts were identified. Group 1 (index FF) included patients that sustained at least one clinical fragility fracture during the study period. Group 2 (subsequent FF) included those patients from group 1 who sustained at least one clinical subsequent fracture during the following 2 years after index fracture.

RESULTS: A total of 11,986 fractures constituted group 1 (index FF), and 792 constituted group 2 (subsequent FF). The incidence of subsequent fractures was 6.61%, with a major percentage of them (36.99%) identified within the first 6 months following index FF. Hip was the most frequent site for index (30.09%) and subsequent fracture (34.85%). We found an increased risk mainly when anatomic site of index and subsequent FF are the same.

CONCLUSIONS: Sustaining a subsequent fracture after an index fracture is a common event in the population over age 50, more commonly occurring within 6 months of index fracture. Analysis of fracture site correlation shows that refracture risk is increased mainly when index and subsequent fracture site are the same.

PMID:36705860 | DOI:10.1007/s11657-023-01213-1

J Med Syst. 2023 Jan 27;47(1):14. doi: 10.1007/s10916-023-01910-x.

ABSTRACT

The study aims to (1) investigate current levels of patient acceptance of mHealth in Germany; (2) determine the influencing factors of patients’ intention to use, and (3) test the influence of prescription and reimbursement status on patient acceptance. Online survey with 1349 participants, of which 1051 were complete and included for statistical analysis, from a broad cross-section of the German population, addressing both users of mobile health (mHealth) applications and people without prior experience. SEM modeling based on a combination of two theoretical frameworks: the extended Unified Theory of Acceptance and Use of Technology and Health Protective Behavior Theories were used to assess acceptance. Users of mHealth in Germany are mostly patients between the ages of 30 – 50 with mental health or endocrine conditions. General willingness to use mHealth apps / DiGAs (mHealth apps fully reimbursed by social health insurance) is high at 76%, especially if they are governmentally certified, however only 27% of respondents were willing to pay out of pocket. With the exception of a spike in performance expectancy and data security, DiGAs lack a clear differentiation from mHealth apps. Perceived self-efficacy and performance expectancy are significant predictors of willingness to use digital health interventions; with age, attitude, and e-literacy being key demographic predictors. A key takeaway for regulators, providers of mHealth apps/ DiGAs, and other stakeholders involved in mHealth adoption is the importance of addressing negative beliefs early on, targeted communication around effortless usage of mHealth services across age groups and demographics, and focus on highlighting expected benefits of mHealth app/ DiGA usage.

PMID:36705853 | DOI:10.1007/s10916-023-01910-x