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Intervention | cognitive distortions | DISEASE, SESSION, EVENTS, MUSCLE RELAXATION | The researcher conducted the group counseling in 8 sessions of 60–90 min weekly with 5–7 women in each group in a quiet room. All health and safety protocols were observed to prevent the spread of COVID-19. Online counseling was also available for whom wanted to participate due to pandemic conditions; it was provided in eight calls of 45 min weekly. Additionally, the counseling was conducted in the native language. The content of the sessions included mood screening, introducing cognitive-behavioral patterns, practicing skills, challenging thoughts, generalizing and maintaining, following up, and evaluating after the intervention. The content of the sessions was as follows:Session 1: The researcher attempted to establish a proper relationship with the participants. She explained the number and duration of each session, group rules, problem identification, cognitive-behavioral patterns, problem description, the concept of stress and its effects, objectives, and receiving feedback from counseling sessions.Session 2: Mood checking, presenting the progressive muscle relaxation and its practice, homework assignment: planning for progressive muscle relaxation twice a day, and receiving feedback.Session 3: Reviewing the cognitive-behavioral pattern according to the problem, mood checking, asking the participants to explain their issues, introduction of imagery practice, homework assignment: progressive muscle relaxation, and imagery practice.Session 4: Mood checking, reviewing the imagery practice, introduction of the first three columns of the thought record sheet (situation – automatic thoughts - emotions and mood) and the concept of hot thoughts, practicing the sheet using one of the events of last week, homework assignment: imagery practice, and receiving feedback.Session 5: Mood checking, discussing the treatment process, reviewing the homework, introducing cognitive distortions, completing the three columns of thought record sheet (situation - automatic thoughts - emotions and mood), identification and challenging hot thoughts, homework assignment: completing the three columns of thought record sheet, and receiving feedback.Session 6: Mood checking, reviewing the homework, presenting thought challenging and the seven-column thought record sheet (situation – automatic thoughts - emotions and mood - confirming evidence - rejecting evidence - alternative thinking - re-evaluating), completing the columns during the session, introducing the concept of challenging hot thoughts, homework assignment: relaxation techniques, and receiving feedback.Session 7: Mood checking, reviewing the homework, completing the seven-column thought record sheet, homework assignment: completing the seven-column thought record sheet, and receiving feedback.Session 8: Mood checking, reviewing the homework and treatment process (cognitive behavioral techniques), prevention, introducing self-management sessions, homework assignment: self-management practice.The validity of the intervention program (CBT sessions) was reviewed and approved by the research consultant professor (third author as a psychologist) and the reviewer of the research project (another psychologist). The control group received routine drug treatments related to the disease. After the intervention, STAI, BDI, and PCOSQ questionnaires were completed again by both groups. | PMC10174601 |
Data collection tools | Socio-demographics and obstetric characteristics questionnaire, STAI, BDI, and PCOSQ were used for data collection before and after the intervention. | PMC10174601 | ||
Socio-demographics and obstetric characteristics questionnaire | PCOS, infertility | DISEASE | It included questions about the age, ethnicity, marital status, childbearing, the level of education and occupation of the couples, average length of the menstrual cycle, average days of menstruation, menstrual flow volume, income sufficiency, the impact of stress on life, disease symptoms, first supporter, sexual satisfaction, infertility and evaluations in this field, duration of pregnancy attempt, PCOS duration, and the treatments used. Content and face validity were used to determine the validity of this questionnaire. It was provided to the faculty members of Tabriz University of Medical Sciences; adjustments were made based on their feedback. | PMC10174601 |
Spielberger state-trait anxiety inventory (STAI) | anxiety | The concepts of state and trait anxiety were first presented by Cattell and then in further detail by Spielberger (1970). STAI has been used widely in clinical research. It includes separate self-assessment scales to measure state and trait anxiety. Scores 20–31 indicate mild anxiety, 32–42 moderate to low anxiety, 43–53 moderate anxiety, 54–64 moderately severe anxiety, and 65–75 severe anxiety. The Persian version of this tool had good validity and reliability in the research of Mehram (1373) and Panahi (1372) [ | PMC10174601 | |
Beck Depression Inventory (BDI) | depressive symptoms | BDI is a self-assessment questionnaire that measures the severity of depressive symptoms. It includes 21 items, each containing four options, scored on a scale of 0 to 3. A higher score indicates the severity of the symptoms. The minimum and maximum scores are 0 and 63, respectively. BDI is designed for people aged 13 and over [ | PMC10174601 | |
Quality of life questionnaire for women with polycystic ovary syndrome (PCOSQ) | PCOS, infertility, hirsutism | MENSTRUAL PROBLEMS, HIRSUTISM | PCOSQ was developed by Cronin et al. (1998) to measure the quality of life of women with PCOS. It consists of 26 items that evaluate five domains: emotions (items 2, 4, 6, 11, 14, 17, 18, 20), hirsutism (items 1, 9, 15, 16, 26), weight (items 3, 10, 12, 22, 24), infertility problems (items 5, 13, 23, 25), and menstrual problems (items 7, 8, 19, 21) [In this study, the reliability of questionnaires was confirmed by testing on 20 people and determining internal consistency. Cronbach’s alpha coefficients were 0.894, 0.772, and 0.832 for STAI, BDI and PCOSQ. | PMC10174601 |
Statistical analysis | depression, anxiety | Statistical analysis was performed using SPSS version 24. The normality of the quantitative data was assessed using the Kolmogorov-Smirnov (K-S) test. Independent t tests, chi-square, chi-square by trend and Fisher’s exact tests were used to assess the homogeneity of the study groups. To compare the groups in terms of mean scores of anxiety, depression, and quality of life, independent t-test was performed before the intervention and ANCOVA test after the intervention by adjusting baseline values. All analyses were performed based on Intention-to-Treat by including all randomized patients in the analysis, regardless of what intervention they received. P < 0.05 was considered significant. | PMC10174601 | |
Discussion | obesity, cognitive skills, Anxiety, cognitive distortions, anxiety, Nobakht, CDI, PCOS, infertility, depressive, depression, Depression, stress reduction | OBESITY, OBESE, MENSTRUAL PROBLEMS, DISORDERS | The results showed that CBT was effective in reducing depression and anxiety and promoting the quality of life of women with the PCOS.CBT significantly reduced the mean depression score in this study. In a research by Rofey et al. (2009), women with PCOS, depression, and obesity participated in eight weekly CBT sessions focusing on lifestyle changes, medical history, and psychological education. The SECA (a calibrated weighing scale) and CDI (Children’s Depression Inventory) scales were used to measure changes in weight and depression. Follow-ups showed a significant decrease in mean weight and depression score after the intervention [CBT significantly reduced the mean anxiety score in this study. Nobakht et al. (2018) investigated the effect of cognitive-behavioral counseling on anxiety in women with HIV. Six weekly counseling sessions were conducted based on a review of mood, awareness, self-image, and experiences, along with questions about the desire to change lifestyles, stress reduction strategies, mental focus, and relaxation. The Depression, Anxiety and Stress Scale (DASS-21) was used to measure anxiety. The results showed a significant reduction in anxiety scores and improvements in patient mood [In this study, CBT significantly improved the mean score of quality of life in domains of menstrual problems, weight, infertility, and emotional problems. Cooney et al. (2018) studied the effect of CBT on obese women with PCOS and depressive symptoms. The counseling was provided in 8 sessions of 30 min weekly. PCOSQ scale was used to measure the quality of life before and after the intervention. The content of the sessions was based on planning and practicing cognitive skills such as identifying automatic thoughts and cognitive distortions. The participants in the counseling group reported a significant decrease in weight; the quality of life score was increased compared to the lifestyle change group [In recent years, psychological problems caused by PCOS has been drawn much attention from researchers worldwide. In addition to being a fertility and beauty issue for women, it can lead to psychological disorders such as depression and anxiety [ | PMC10174601 |
Strengths and limitations | Observing all the principles of clinical trials, including random allocation and allocation concealment, was among the strengths of our study. Content design and consulting intervention were based on the cultural and moral values of the region. There was no drop-out from the study; all the participants were analyzed. Due to the nature of the intervention, blinding the participants, researcher, and outcome assessor was impossible. | PMC10174601 | ||
Acknowledgements | The authors appreciate the assistance and cooperation of the participants of this study. | PMC10174601 | ||
Authors’ contributions | MM | SM involved in the conception and design, acquisition of data and drafting the manuscript. PY (corresponding author) involved in the conception and design, acquisition of data, analysis of the data, interpretation of data and writing this manuscript. MF and MM involved in the conception and design, interpretation of the data and revising this manuscript. All authors gave their final approval of this version to be published. | PMC10174601 | |
Funding | This research is supported by Tabriz University of Medical Sciences. The funding source had no involvement in design of the study, data collection, data analysis, etc. | PMC10174601 | ||
Data availability | The datasets generated and/or analyzed during the current study are not publicly available due to limitations of ethical approval involving the patient data and anonymity but are available from the corresponding author on reasonable request. | PMC10174601 | ||
Declarations | PMC10174601 | |||
Ethics approval and consent to participate | This research has been approved by the Ethics Committee of the Tabriz University of Medical Sciences, Tabriz, Iran (code number: IR.TBZED.REC.1400.229). All participants were ensured about the matter of confidentiality and signed the informed written consent form. All methods were performed in accordance with the Declaration of Helsinki. | PMC10174601 | ||
Consent for publication | Not applicable. | PMC10174601 | ||
Competing interests | The authors declare that they have no competing interests. | PMC10174601 | ||
Abbreviations | polycystic ovary syndromestandard, Depression, Anxiety | Cognitive-Behavioral TherapyAnalysis of Covariance95% confidence intervalIranian Registry of Clinical TrialsSpielberger State-Trait Anxiety InventoryBeck Depression InventoryQuality of Life Questionnaire for women with polycystic ovary syndromestandard deviation | PMC10174601 | |
References | PMC10174601 | |||
Background | adiposity | ADIPOSITY | Long periods of uninterrupted sitting, i.e., sedentary bouts, and their relationship with adverse health outcomes have moved into focus of public health recommendations. However, evidence on associations between sedentary bouts and adiposity markers is limited. Our aim was to investigate associations of the daily number of sedentary bouts with waist circumference (WC) and body mass index (BMI) in a sample of middle-aged to older adults. | PMC10007749 |
Methods | cardiovascular disease | CARDIOVASCULAR DISEASE | In this cross-sectional study, data were collected from three different studies that took place in the area of Greifswald, Northern Germany, between 2012 and 2018. In total, 460 adults from the general population aged 40 to 75 years and without known cardiovascular disease wore tri-axial accelerometers (ActiGraph Model GT3X+, Pensacola, FL) on the hip for seven consecutive days. A wear time of ≥ 10 h on ≥ 4 days was required for analyses. WC (cm) and BMI (kg m | PMC10007749 |
Results | Participants (66% females) were on average 57.1 (standard deviation, SD 8.5) years old and 36% had a school education >10 years. The mean number of sedentary bouts per day was 95.1 (SD 25.0) for 1-to-10-minute bouts, 13.3 (SD 3.4) for >10-to-30-minute bouts and 3.5 (SD 1.9) for >30-minute bouts. Mean WC was 91.1 cm (SD 12.3) and mean BMI was 26.9 kg m | PMC10007749 | ||
Conclusion | adiposity | ADIPOSITY | The findings provide some evidence on favourable associations of short sedentary bouts as well as unfavourable associations of long sedentary bouts with adiposity markers. Our results may contribute to a growing body of literature that can help to define public health recommendations for interrupting prolonged sedentary periods. | PMC10007749 |
Trial registration | Study 1: German Clinical Trials Register (DRKS00010996); study 2: ClinicalTrials.gov (NCT02990039); study 3: ClinicalTrials.gov (NCT03539237). | PMC10007749 | ||
Supplementary Information | The online version contains supplementary material available at 10.1186/s12889-023-15304-8. | PMC10007749 | ||
Keywords | Open Access funding enabled and organized by Projekt DEAL. | PMC10007749 | ||
Introduction | adiposity | ADIPOSITY, CARDIOVASCULAR DISEASE | Higher amounts of sedentary behaviour have been found to be associated with a range of health risks including incidence of cardiovascular disease [In addition to limiting total sedentary time, several countries e.g., Australia [Sedentary bouts and breaks have been operationalized using various minimum durations with the study that first introduced the concept classifying each ≥ 1 min interruption after ≥ 1 min of sedentary time as a break [One issue that has been discussed in the literature is, whether benefits of sedentary breaks simply reflect favourable effects of higher amounts of physical activity that is performed during those breaks [Among the variety of cardio-metabolic biomarkers, WC and BMI are easy to assess and with results straightforward to communicate to the public. To our best knowledge, this is the first study among German adults that investigated associations between sedentary behaviour patterns and cardio-metabolic biomarkers. Thus, the aim of our study was to investigate associations of short sedentary bouts with a length of 1 to 10 min, moderate sedentary bouts with a length of >10 to 30 min, and long sedentary bouts with a length of >30 min with two indicators of adiposity, i.e., WC and BMI, accounting for accelerometer-based time-use compositions (i.e., total sedentary time and physical activity). | PMC10007749 |
Methods | PMC10007749 | |||
Participants and procedure | EVENTS, DIABETES MELLITUS, MYOCARDIAL INFARCTION, STROKE | We combined socio-demographic, anthropometric, and accelerometer data from apparently healthy adults, collected in three previous studies. All studies took place in the area of Greifswald in Northern Germany between 2012 and 2018. Detailed description of the design and sampling procedures for each study are reported elsewhere [Data from participants were included in the current analyses if (i) socio-demographic, anthropometric, and accelerometer data were complete, (ii) participants had no history of cardiovascular events (myocardial infarction, stroke) or vascular intervention, no diabetes mellitus, and a BMI ≤ 35 kg m | PMC10007749 | |
Measures | PMC10007749 | |||
Waist circumference and body mass index | WC (cm) and BMI (kg m | PMC10007749 | ||
Accelerometer-based measures | Physical activity and sedentary time were obtained using tri-axial ActiGraph Model GT3X + accelerometers (Pensacola, FL) worn on the right hip attached to an elastic belt for seven consecutive days. Participants were instructed to wear the accelerometer during waking hours and to put it off for water-based activities such as morning hygiene or swimming. Using ActiLife version 6.13.3 (ActiGraph, Pensacola, FL), the accelerometers were initialized at a sampling rate of 100 Hz (study 1 and 2) or 30 Hz (study 3) and raw data were integrated into 10 s epochs. Data from the vertical axis were used. ActiGraph accelerometers provide counts as the output metric. To identify accelerometer wear time as well as time spent in different intensities of physical activity, intensity cut points were applied according to Troiano and colleagues [
A sedentary bout ended when sedentary time was interrupted for ≥ 1 min in which the accelerometer count rose up to or above 100 counts/minute. The mean daily number of bouts with a length of 1 to 10 min, >10 to 30 min, and >30 min was analysed. | PMC10007749 | ||
Covariates | Sex, age, school education (< 10 years, 10 years, >10 years), employment (employed, unemployed or retired), and current smoking (yes, no) were obtained by a self-administered questionnaire. Variables related to data collection included study (study 1, study 2, study 3) and season of data collection (spring or summer, autumn or winter). | PMC10007749 | ||
Statistical analysis | REGRESSIONS | Multilevel mixed-effects linear regressions were used to examine the associations of sedentary bouts with WC and BMI including study as a higher-level group variable. Models were estimated using the | PMC10007749 | |
Results | PMC10007749 | |||
Sample characteristics | Characteristics of the total sample (N = 460) and separately for women (66%) and men are described in Table
Sample characteristics (N = 460)Data are presented as mean ± standard deviation for continuous variables and as the number of participants (%) for categorical variables. Presented p-values for comparisons between women and men are based on t-test for continuous variables and chi-square test for categorical variables | PMC10007749 | ||
Discussion | obesity, adiposity | OBESITY, ADIPOSITY, CARDIOVASCULAR DISEASE | In this observational study using combined data from three different studies, we examined cross-sectional associations of short (1 to 10 min), moderate (>10 to 30 min), and long (>30 min) sedentary bouts with WC and BMI in subjects without prevalent cardiovascular disease. Our data revealed three main findings: first, there was a statistically significant inverse relationship of the daily number of short sedentary bouts with BMI but not with WC. Second, the daily number of moderate sedentary bouts was not related to WC or BMI. Third, the daily number of long sedentary bouts was significantly associated with a higher WC but not with BMI.A number of studies investigated associations between sedentary behaviour patterns and obesity metrics [In our study, associations of short sedentary bouts were only statistically significant for BMI but not for WC. However, associations of quartiles of short bouts showed that compared to those with the lowest number of bouts (i.e., first quartile) individuals with the highest number of bouts (i.e., third and fourth quartile) had not only a significantly lower BMI, but also a significantly lower WC. Thus, the relationship with WC may be curvilinear (Fig. Our results provide some evidence for beneficial associations of short sedentary bouts and for unfavourable associations of long sedentary bouts with adiposity markers. These findings suggest that obesity-related risk factors might be improved if sitting time is frequently interrupted and if sitting periods that last longer than 30 min are avoided. It has been discussed in the literature, whether benefits of sedentary breaks solely stem from favourable effects of higher amounts of physical activity [ | PMC10007749 |
Strengths and limitations | obesity | OBESITY, ADIPOSITY | This study investigated sedentary behaviour patterns measured by device in a moderate-sized sample of middle-aged to older adults in Germany. Results add data to the literature on associations between uninterrupted sitting time and adiposity markers. We used short, moderate, and long sedentary bouts as measures of sitting patterns and we addressed WC and BMI as generally acknowledged health risk factors to enable inferring a straightforward public health message. Furthermore, we adjusted our analyses for the composition of accelerometer-based time use to draw conclusions on the benefit of interrupting sedentary time in addition to total sedentary time and physical activity levels.Some limitations of this study should be considered. First, our findings may not be generalizable to the whole general population. Similar to other accelerometer studies, the proportion of non-participants was high and selection bias of highly motivated and physically active individuals is likely. Second, hip-worn accelerometers used in this study assess sedentary time from data indicating a lack of movement (< 100 counts/minute) compared to more movement (≥ 100 counts/minute). As other stationary behaviour such as standing may be captured below this threshold, sitting data might be biased [Despite these limitations, our finding that sedentary bouts of short, moderate, and long length were differently associated with obesity indicators among 460 individuals deserves further research. If the relationships revealed in this study are found in larger samples, other populations, and within prospective longitudinal studies that allow for inferences on causality, recommendations on sedentary behaviour should explicitly address interruptions of prolonged sitting. | PMC10007749 |
Conclusion | In a sample of 460 apparently healthy middle-aged to older adults, the daily number of sedentary bouts lasting 1 to 10 min was significantly associated with lower BMI but not with WC and the number of bouts lasting >30 min was significantly associated with higher WC but not with BMI. These relationships persisted independent of time spent in sedentary behaviour, LPA and MVPA. Besides limiting total sedentary time or increasing physical activity, frequent interruptions of sedentary time might improve obesity-related risk factors. Thus, the results of this study to some extent support sedentary behaviour guidelines that promote regular interruptions of sitting. | PMC10007749 | ||
Acknowledgements | Not applicable. | PMC10007749 | ||
Author contributions | RECRUITMENT | LV, AU, and SU planned und designed the study. LV, MD, and SU managed participants’ recruitment and data collection. LV, AU, DG, and SU analysed and interpreted the data. LV drafted the manuscript. SU supervised the writing and AU, SG, DG, LJ, MD, NvdB, and UJ provided additional input. All authors read, critically revised, and approved the final version of the manuscript. | PMC10007749 | |
Funding | This study was supported and funded by grants from the Federal Ministry of Education and Research as part of the German Centre for Cardiovascular Research, DZHK, grant number 81/Z540100152 (original studies 1 and 2) and grant number D347000002 (original study 3). The DZHK had no direct role in the development of methodology, the acquisition, analysis, and interpretation of data or in writing the manuscript.Open Access funding enabled and organized by Projekt DEAL. | PMC10007749 | ||
Data availability | The datasets generated and/or analysed during the current study are not publicly available due to restrictions associated with anonymity of participants but are available from the corresponding author on reasonable request. The data is shared with researchers who submit a methodologically sound proposal to achieve the aims of the approved proposal. Requests in this regard should be directed to the corresponding author to gain access. Requestors must sign a data access agreement ensuring data usage in compliance with the statement given in the informed consent procedure and with the German data protection law, that the data will not be transferred to others, and that the data will be deleted after the intended analysis has been completed. | PMC10007749 | ||
Declarations | PMC10007749 | |||
Ethics approval and consent to participate | All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Further, this study was conducted in accordance with the current guidelines of good clinical practice, and in line with CONSORT guidelines for non-pharmacological studies. The ethics committee of the University Medicine Greifswald approved the study protocols of all included studies (study 1: number BB 64/07; study 2: number BB 002/15a; study 3: number BB 076/18). Written informed consent was obtained before inclusion of study participants. | PMC10007749 | ||
Consent for publication | Not applicable. | PMC10007749 | ||
Competing interests | The authors declare that there is no conflict of interest. | PMC10007749 | ||
Abbreviations | Body mass indexCompositional Data AnalysisLight physical activityModerate-to-vigorous physical activityStandard deviationWaist circumference | PMC10007749 | ||
References | PMC10007749 | |||
Key Points | PMC10203888 | |||
Question | DISORDERS | Do existing screening tools, such as the Screening to Brief Intervention (S2BI), Brief Screener for Tobacco, Alcohol, and Drugs (BSTAD), and Tobacco, Alcohol, Prescription Medication, and Other Substances (TAPS), accurately identify substance use disorders among youths? | PMC10203888 | |
Findings | DISORDERS | In this cross-sectional study of 798 adolescents, high agreement was observed between results for the S2BI, BSTAD, and TAPS tools and for the criterion standard measure (a brief electronic assessment battery and a research assistant–administered diagnostic interview). Area under the curve values were near or equal to 1 for nicotine, alcohol, and cannabis use disorders for each of the 3 screening tools. | PMC10203888 | |
Meaning | DISORDERS | These findings suggest that brief screening tools that measure past-year frequency of use effectively identify substance use disorders among youths.This cross-sectional study evaluates the psychometric properties of 3 brief screening tools that measure past-year frequency of nicotine/tobacco, alcohol, cannabis, and other drug use and their efficacy in identifying substance use disorders among youths. | PMC10203888 | |
Importance | SUDs | DISORDERS | Efficient screening tools that effectively identify substance use disorders (SUDs) among youths are needed. | PMC10203888 |
Objective | To evaluate the psychometric properties of 3 brief substance use screening tools (Screening to Brief Intervention [S2BI]; Brief Screener for Tobacco, Alcohol, and Drugs [BSTAD]; and Tobacco, Alcohol, Prescription Medication, and Other Substances [TAPS]) with adolescents aged 12 to 17 years. | PMC10203888 | ||
Design, Setting, and Participants | This cross-sectional validation study was conducted from July 1, 2020, to February 28, 2022. Participants aged 12 to 17 years were recruited virtually and in person from 3 health care settings in Massachusetts: (1) an outpatient adolescent SUD treatment program at a pediatric hospital, (2) an adolescent medicine program at a community pediatric practice affiliated with an academic institution, and (3) 1 of 28 participating pediatric primary care practices. Participants were randomly assigned to complete 1 of the 3 electronic screening tools via self-administration, followed by a brief electronic assessment battery and a research assistant–administered diagnostic interview as the criterion standard measure for | PMC10203888 | ||
Main Outcomes and Measures | The main outcome was a | PMC10203888 | ||
Results | DISORDERS | This study included 798 adolescents, with a mean (SD) age of 14.6 (1.6) years. The majority of participants identified as female (415 [52.0%]) and were White (524 [65.7%]). High agreement between screening results and the criterion standard measure was observed, with area under the curve values ranging from 0.89 to 1 for nicotine, alcohol, and cannabis use disorders for each of the 3 screening tools. | PMC10203888 | |
Conclusions and Relevance | SUDs | These findings suggest that screening tools that use questions on past-year frequency of use are effective for identifying adolescents with SUDs. Future work could examine whether these tools have differing properties when used with different groups of adolescents in different settings. | PMC10203888 | |
Introduction | Adolescent substance use is an important and modifiable behavioral health concern for youths. A burgeoning evidence base is demonstrating that intervention in pediatric primary care can improve outcomes. | PMC10203888 | ||
Methods | This cross-sectional validation study was conducted with approval and a waiver of parental consent granted by the Boston Children’s Hospital Institutional Review Board. Parental consent was waived in this low-risk study because previous work has demonstrated that a requirement for parental consent biases the sample to a lower-risk population. The study followed the Standards for Reporting of Diagnostic Accuracy ( | PMC10203888 | ||
Participant Recruitment | RA | RECRUITMENT, RECRUITMENT | This study was conducted between July 1, 2020, and February 28, 2022. Due to restrictions on medical care facilities in response to the COVID-19 pandemic, the study was conducted entirely virtually from July 1, 2020, through February 28, 2021; we used a hybrid model from March 1, 2021, through February 28, 2022.Participants aged 12 to 17 years were recruited virtually and in person from 3 health care settings in Massachusetts: (1) an outpatient adolescent SUD treatment program at a pediatric hospital, (2) an adolescent medicine program at a community pediatric practice affiliated with an academic institution, and (3) 1 of 28 participating pediatric primary care practices. The enrollment of younger adolescents (aged 12-13 years) was capped at 200 because substance use is less common in this age group.Recruitment strategies evolved in response to restrictions resulting from the COVID-19 pandemic. We used a combination of virtual and in-person strategies, including direct referral from a primary care physician during a virtual health care visit, patient portal messaging, posters and postcards with links to study information, telephone calls made to patients and parents, and in-person recruitment at the study sites. Each recruitment strategy is described in detail in eFigure 1 in We defined “attempted to contact” as any patient who (1) entered a videoconference room, (2) provided contact information (including emailed interest or called or texted the study phone number), (3) received a call from a research assistant (RA), (4) received study information in a videoconference chat during an appointment, or (5) was scheduled for an in-person appointment on a day when a study RA was present in the clinic. Of the 2840 potential participants whom we attempted to contact, approximately 1411 had spoken directly with an RA and were considered invited to participate in the study; the remaining 1429 could not be reached ( | PMC10203888 |
Exclusion Criteria | RECRUITMENT | Patients were excluded if they were unable to understand English, were physically or emotionally unwell at the time of recruitment, had been admitted to a residential or inpatient SUD treatment program within the past 12 months (because we believed this might affect their response to screening questions), were currently in the custody of the Massachusetts Department of Youth Services or the Department of Children and Families, were living with a foster family, or were pregnant or parenting. Patients at primary care sites who were currently enrolled in an SUD treatment program at the time of recruitment were also excluded. Based on these criteria, 236 of the 1047 patients (22.5%) were ineligible; the remaining 811 patients provided assent and were enrolled in this study. However, 13 screening results were later linked to 5 single individuals and thus eliminated from the overall sample. Of the 798 randomized participants, 12 (1.5%) did not complete the study, leaving a total of 786 who completed all study assessments ( | PMC10203888 | |
Screening Tools and Assessment Battery | depression, RA | ABUSE | Participants were randomized to receive 1 of 3 screening tools—the S2BI (n = 256), BSTAD (n = 267), or TAPS (n = 275)—using a randomization scheme balanced for age (12-13 years vs 14-17 years), sex (male vs female), and clinical setting to ensure baseline comparability of the cohorts assigned to the 3 tools. Screening tool assignments were randomly generated and assigned using a permuted block design with blocks of varying sizes within strata. We administered 1 tool to each participant to avoid the response to an initial screen affecting the response to a subsequent one.Participants self-administered both the screening tool electronically according to their randomization group and a brief electronic assessment battery. An RA then administered a modified version of the World Mental Health Composite International Diagnostic Interview Substance Abuse Module (WMH-CIDI-SAM).The self-administered assessment battery included 7 demographic items (age, sex, gender, race, ethnicity, number of parents or caregivers at home, and highest level of education completed by parents or caregivers), screens for depression (2-question Patient Health Questionnaire [PHQ-2] | PMC10203888 |
Statistical Analysis | DISORDERS | All 798 participants in the modified intention-to-treat (mITT) population were considered in the primary descriptive analysis but had to have both screening tool question(s) and a criterion standard measure for a substance (tobacco/nicotine, alcohol, and cannabis) to be used in calculating the psychometric properties. Details of randomization, screening, and criterion measure responses for tobacco/nicotine, alcohol, or cannabis use disorders are summarized in STARD flow charts in eFigures 2, 3, and 4 in Baseline characteristics were described and compared by study site, using analysis of variance or the Kruskal-Wallis test for continuous variables and the Fisher exact or χAnalyses were conducted using SAS, version 9.4 (SAS Institute Inc). Statistical significance was set at | PMC10203888 | |
Results | PMC10203888 | |||
Participant Description | This cross-sectional study comprised 798 participants, with a mean (SD) age of 14.6 (1.6) years ( | PMC10203888 | ||
Sociodemographics and Health Characteristics by Study Site | anxiety disorder, Anxiety, anxiety, depressive disorder, ADD, attention-deficit disorder, attention-deficit/hyperactivity disorder, ADHD, depression | DISORDER, DISORDER | Abbreviations: ADD, attention-deficit disorder; ADHD, attention-deficit/hyperactivity disorder; GAD, Generalized Anxiety Disorder Scale; PHQ, Patient Health Questionnaire; SUD, substance use disorder.Unless indicated otherwise, values are presented as No. (%) of participants.Study sites comprised the following, all in Massachusetts: (1) an outpatient adolescent SUD treatment program at a pediatric hospital, (2) an adolescent medicine program at a community pediatric practice affiliated with an academic institution, and (3) 28 participating pediatric primary care practices.A total of 798 participants were enrolled but 12 terminated participation early.Gender, race, and ethnicity were self-reported.This category was created because the samples were small and includes American Indian or Alaska Native or Native Hawaiian or other Pacific Islander.Participants were asked: “Of the parent(s)/caregiver(s) who live with you at home, what is the highest level of education he/she has completed?”Responses included (1) grade 12 or less or high-school graduate or (2) general educational development test, high-school equivalency test, some college, associate degree, or technical school training.This category includes an undergraduate (bachelor) degree or graduate or greater (master, doctorate, etc) degree.The GAD-2 uses the first 2 questions of the 7-item GAD scale. Scores range from 0 to 6, with higher scores indicating greater likelihood of generalized anxiety; scores of 3 or greater suggest that generalized anxiety disorder is likely.The PHQ-2 uses the first 2 questions of the 9-item PHQ. Scores range from 0 to 6, with higher scores indicating greater likelihood of depression; scores of 3 or greater suggest that a major depressive disorder is likely.Participants were asked: “Has a doctor or health care provider ever told you that you have ADD or ADHD? In the past 12 months, have you been prescribed medication for ADD or ADHD?”The majority of participants (662 [83.0%]) lived in a household with 2 or more caregivers and reported the highest level of caregiver education as a bachelor degree or higher as (564 [70.7%]). There were 134 participants (16.8%) and 105 participants (13.2%) with a score of 3 or greater on the GAD-2 and the PHQ-2, respectively; 149 participants (18.7%) had either been diagnosed with ADHD or ADD by a health care clinician or had received a prescription medication to treat ADHD or ADD.Sociodemographic characteristics and mental health status of participants differed substantially among the 3 sites, reflecting the differences in patient populations ( | PMC10203888 |
Substance Use Disclosure by Screening Tool | Among the participants recruited from adolescent medicine or primary care, 20 (8.3%), 5 (2.0%), and 11 (4.2%; | PMC10203888 | ||
Subgroup Analyses of Disclosure of Any Past 12-Month Substance Use Among Primary Care and Adolescent Medicine Patient Groups by Screening Tool | Abbreviations: BSTAD, Brief Screener for Tobacco, Alcohol, and Drugs; NA, not applicable; S2BI, Screening to Brief Intervention; TAPS, Tobacco, Alcohol, Prescription Medication, and Other Substances.Unless indicated otherwise, values are presented as No. (%) of participants.Among the 242 participants randomized to S2BI screening, 2 did not receive it. The S2BI assesses past 12-month use of tobacco, alcohol, cannabis, prescription medication, e-cigarette, illegal drugs, inhalants, and herbs or synthetic drugs.Among the 253 participants randomized to BSTAD screening, 1 did not receive it. The BSTAD assesses past 12-month use of nicotine/tobacco, alcohol, cannabis, prescription medications, and illegal drugs.Among the 262 participants randomized to TAPS screening, 2 did not complete it. The TAPS screening assesses past 12-month use of tobacco, alcohol, illegal drugs (including cannabis), and prescription medications.A question on cannabis use in the past 12 months was included in the aggregated “illegal drugs” question in the TAPS. No tests were performed for these items due to lack of comparability. Column percentages are shown. χIn a subgroup analysis, we assessed the prevalence of disclosure of any past 12-month substance use, using the total subgroup of 752 participants from adolescent medicine and primary care who completed 1 of the 3 screening tools (S2BI, BSTAD, and TAPS). Since the rates of self-disclosure of past 12-month substance use were near universal among patients in the SUD treatment program, they were excluded from this exploratory analysis ( | PMC10203888 | ||
Screening Tool Performance for Identifying SUDs | PMC10203888 | |||
Tobacco/Nicotine | DISORDERS, SENSITIVITY | Sensitivity of the S2BI, BSTAD, and TAPS for identifying tobacco/nicotine use disorders at the specified cutoffs was 0.89 (95% CI, 0.52-1.00), 1.00 (95% CI, 0.77-1.00), and 0.63 (95% CI, 0.24-0.91), respectively ( | PMC10203888 | |
Performance of the S2BI, BSTAD, and TAPS Screening Tools vs the WMH-CIDI-SAM for Identifying Substance Use Disorders (SUDs) in the Modified Intention-to-Treat Population | ABUSE | Abbreviations: BSTAD, Brief Screener for Tobacco, Alcohol, and Drugs; LR, likelihood ratio; NA, not appliable; NPV, negative predictive value; PPV, positive predictive value; S2BI, Screening to Brief Intervention; TAPS, Tobacco, Alcohol, Prescription Medication, and Other Substances; WMH-CIDI-SAM, World Mental Health Composite International Diagnostic Interview Substance Abuse Module.Estimates and CIs not able to be calculated due to 0 counts or division by 0 are denoted with NA. | PMC10203888 | |
Alcohol | DISORDERS, SENSITIVITY | Sensitivity of the S2BI, BSTAD, and TAPS for identifying alcohol use disorders at the specified cutoffs was 0.50 (95% CI, 0.07-0.93), 1.00 (95% CI, 0.48-1.00), and 0.78 (95% CI, 0.40-0.97), respectively ( | PMC10203888 | |
Cannabis | DISORDERS, SENSITIVITY | Sensitivity of the S2BI, BSTAD, and TAPS for identifying cannabis use disorders at the specified cutoffs was 0.92 (95% CI, 0.64-1.00), 0.89 (95% CI, 0.67-0.99), and 0.75 (95% CI, 0.43-0.95), respectively ( | PMC10203888 | |
Discussion | SUDs | DISORDERS | In this cross-sectional study, we observed that the S2BI, BSTAD, and TAPS screening tools all had adequate psychometric properties for identifying tobacco/nicotine, alcohol, and cannabis use disorders among adolescents at the recommended cut points. Point estimates of sensitivity and specificity were generally high, and there were no notable differences in performance across the 3 tools for any measure or any substance. Our findings confirm that brief screening tools that ask about the past-year frequency of use are useful for identifying adolescents with SUDs.The TAPS, which comprises past-use frequency questions combined with questions about problems, contains more questions and takes longer to administer than either the S2BI or BSTAD, yet the results of this study suggest that the psychometric properties were similar. Because our findings suggest that the extra questions did not appear to improve performance, we recommend the shorter tools for widespread implementation in pediatric care settings. However, our findings also suggest that the TAPS may be useful in settings that provide care for patients in both pediatric and adult age ranges in which a single tool may simplify clinical protocols.Rates of substance use disclosure and prescription medication use were higher among participants screened with the S2BI compared with the other tools. This finding may be of importance because previous studies have found lower rates of substance use disclosure in clinical samples compared with research trials. | PMC10203888 |
Limitations | Several limitations of this cross-sectional study should be noted. Participants were heterogeneous regarding gender, race, and ethnicity, and our practices were spread across the state of Massachusetts, including in urban, suburban, and rural settings. Nonetheless, participants were predominantly White, non-Hispanic, and from higher-income backgrounds recruited entirely from Massachusetts, which may limit generalizability. Furthermore, we note that participants were recruited during the COVID-19 pandemic, which may have affected the results. However, our findings were similar to studies with these tools conducted before 2020, including a study conducted in a different geographic region. | PMC10203888 | ||
Conclusions | SUDs | The findings of this study suggest that the S2BI, BSTAD, and TAPS tools all have adequate psychometric properties for screening adolescents for SUDs in general primary care settings and can be recommended for substance use screening of adolescent patients. Future work could examine whether these tools have differing properties when used with different groups of adolescents in different settings. | PMC10203888 | |
Abstract | PMC10766121 | |||
Background | AKI, acute kidney injury | Persistent acute kidney injury (AKI) after cardiac surgery is not uncommon and linked to poor outcomes. | PMC10766121 | |
Hypothesis | AKI | The purpose was to develop a model for predicting postoperative persistent AKI in patients with normal baseline renal function who experienced AKI after cardiac surgery. | PMC10766121 | |
Methods | AKI | REGRESSION | Data from 5368 patients with normal renal function at baseline who experienced AKI after cardiopulmonary bypass cardiac surgery in our hospital were retrospectively evaluated. Among them, 3768 patients were randomly assigned to develop the model, while the remaining patients were used to validate the model. The new model was developed using logistic regression with variables selected using least absolute shrinkage and selection operator regression. | PMC10766121 |
Results | AKI, diabetes | HYPERTENSION, CORONARY HEART DISEASE, DIABETES | The incidence of persistent AKI was 50.6% in the development group. Nine variables were selected for the model, including age, hypertension, diabetes, coronary heart disease, cardiopulmonary bypass time, AKI stage at initial diagnosis after cardiac surgery, postoperative serum magnesium level of <0.8 mmol/L, postoperative duration of mechanical ventilation, and postoperative intra‐aortic balloon pump use. The model's performance was good in the validation group. The area under the receiver operating characteristic curve was 0.761 (95% confidence interval: 0.737–0.784). Observations and predictions from the model agreed well in the calibration plot. The model was also clinically useful based on decision curve analysis. | PMC10766121 |
Conclusions | AKI, acute kidney injury, postoperative AKI | It is feasible by using the model to identify persistent AKI after cardiac surgery in patients with normal baseline renal function who experienced postoperative AKI, which may aid in patient stratification and individualized precision treatment strategy.Among 5368 patients with baseline normal renal function and acute kidney injury (AKI) after cardiac surgery, 3768 patients were randomly used for model development and the rest for validation. We developed a model for predicting postoperative persistent AKI. The model's performance was good in the validation group.
| PMC10766121 | |
INTRODUCTION | AKI, Acute kidney injury | ACUTE KIDNEY INJURY, COMPLICATION | Acute kidney injury (AKI) is a serious and major complication of cardiac surgery.Novel biomarkers may aid in identifying persistent AKI.At present, several risk prediction models for persistent AKI have been introduced. | PMC10766121 |
METHODS | PMC10766121 | |||
Population and study protocol | AKI | Due to the nature of the present single‐center, retrospectively designed study, the need for signed informed consent from participants was waived. Data were analyzed anonymously. The Ethics Committee of Guangdong Provincial People's Hospital approved the study according to the Declaration of Helsinki (No. GDREC2018416H).The data from patients with normal renal function at baseline who experienced AKI after cardiac surgery requiring cardiopulmonary bypass between January 1, 2006 and December 31, 2018 at the Guangdong Provincial People's Hospital (a tertiary teaching hospital) were retrospectively analyzed. There were the following exclusion criteria: renal replacement therapy before surgery; history of unilateral nephrectomy; cardiac transplantation; emergency surgery; critical status before surgery; participants hospitalized for a maximum of 2 days after the AKI episode; and serum creatinine values were not available within 1 week after the AKI episode. The estimated glomerular filtration rate (eGFR) greater than or equal to 60 mL/min × 1.72 m | PMC10766121 | |
Data collection and definition | AKI, coronary atherosclerotic heart disease, postoperative AKI, diabetes | HYPERTENSION, DIABETES | The data for patients were extracted from the electronic medical record system of our hospital. Based on previous literature review and clinical expertise, the following candidate predictors were considered: demographic characteristics, comorbidities (hypertension, diabetes, and coronary atherosclerotic heart disease), history of heart surgery, baseline serum creatinine level, preoperative left ventricular ejection fraction (LVEF), cardiac surgery type, cardiopulmonary bypass time, AKI stage at the initial diagnosis of AKI after cardiac surgery, postoperative medication use, laboratory test results after surgery, duration of postoperative mechanical ventilation, postoperative intra‐aortic balloon pump (IABP) use, and reoperation status. AKI stage at the initial diagnosis of AKI after cardiac surgery was classified by the KDIGO criteria upon first AKI diagnosis after surgery. Postoperative variable data, such as postoperative medication use, laboratory test results after surgery, and reoperation status, were collected from the time after cardiac surgery to the time at first diagnosis of postoperative AKI. If more than one laboratory test result was available after surgery, the latest result before the time of the initial diagnosis of AKI after cardiac surgery was analyzed. Previous cardiac surgery was defined as the cardiac surgery was underwent before January 1, 2006. | PMC10766121 |
Outcome | AKI | The outcome of interest was persistent AKI. It was defined according to the consensus report for the ADQI, where the duration of AKI defined by the KDIGO criteria was longer than 2 days. | PMC10766121 | |
Sample size | EVENTS | The sample size for developing a predictive model was determined by events per variable. Empirically, a minimum of 10 events per variable is generally needed. | PMC10766121 | |
Statistical analysis | Patients were randomly assigned to the development and validation groups at a ratio of 7:3. The clinical expertise and boxplot plots were used to check for extreme values in the continuous variables. Then, continuous variables (preoperative LVEF, baseline eGFR, cardiopulmonary bypass time, postoperative hemoglobin level, postoperative white blood cell count, postoperative serum magnesium level, postoperative carbon dioxide combining power, and postoperative serum uric acid level) were winsorized at 1% and 99% to reduce the effect of the extreme values. Multiple imputation by chained equations with 20 iterations was used to estimate the missing data, and the results were pooled according to the Rubin's rule. All variables, including the outcome variable, were included in the imputation model, which was recommended for handling missing data.Restricted cubic splines were used to analyze the potential nonlinear association between continuous variables and the outcome risk.The final model was validated in the development and validation groups, respectively. The performance of the model was assessed by its discrimination, calibration, and clinical utility. The area under the receiver operating characteristic curve (AUC) was used to evaluate the discrimination. In general, the AUC > 0.7 indicated a good discrimination. The calibration of the model was assessed by the calibration curve and examined utilizing the Spiegelhalter All analyses results for developing and validating the model were in accordance with the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guidelines. R software (version 4.2.1; | PMC10766121 | ||
RESULTS | AKI, congenital heart disease, acute kidney injury, SE | REGRESSION, HYPERTENSION, DIABETES MELLITUS | A total of 6106 patients were identified, and 5368 were ultimately included in the present study. The data for 3768 patients were randomly assigned to develop the model, while the data for the remaining 1600 patients were used for validating the model. A summary of the screening results is presented (Supporting Information: Figure Baseline characteristics.
Abbreviations: AKI, acute kidney injury; CABG, coronary artery bypass grafting; CHD, congenital heart disease; COPatients were divided into two groups according to whether persistent AKI occurred. All predictors were compared in the development group. Some variables, such as male gender, hypertension, and postoperative duration of mechanical ventilation, may be potential risk factors for persistent AKI (Supporting Information: Table Multivariate logistic regression analysis of variables for predicting persistent AKI after cardiac surgery.
Abbreviations: AKI, acute kidney injury; CI, confidence interval; CPB, cardiopulmonary bypass; IABP, intra‐aortic balloon pump; MV, mechanical ventilation; OR, odds ratio; SE, standard error.Nomogram for predicting persistent AKI after cardiac surgery. AKI, acute kidney injury; CHD, congenital heart disease; CPB, cardiopulmonary bypass; DM, diabetes mellitus; IABP, intra‐aortic balloon pump; MV, mechanical ventilation.The discrimination and calibration of the model in the development group (Figure Model performance was evaluated using receiver‐operating characteristic and calibration curves. (A) AUC for the model in the development group showing a mean AUC of 0.752 (95% CI: 0.736–0.767). (B) Calibration curve for the new model in the development group. (C) AUC for the model in the validation group showing a mean AUC of 0.761 (95% CI: 0.737–0.784). (D) Calibration curve for the new model in the validation group. Calibration plots show the relationship between the predicted values and actual incidence of persistent AKI. Solid and dotted lines are closely matched, which indicates a more accurate prediction model. AKI, acute kidney injury; AUC, area under the receiver operating characteristic curve.Clinical values for the prediction model with decision curve analyses (DCA). Threshold probability was represented versus the net benefit. Dashed and solid black lines indicated the hypothesis of “all patients” and “no patients with persistent AKI,” respectively. As the curve on the DCA graph approaches the top, the model diagnosis value increases. AKI, acute kidney injury. | PMC10766121 |
DISCUSSION | AKI, proteinuria, chronic kidney disease, diabetes | HYPERTENSION, CORONARY HEART DISEASE, DIABETES | A model for predicting persistent AKI after cardiac surgery in patients with normal baseline renal function who experienced AKI after cardiac surgery was developed using routinely collected clinical data. The model was also evaluated based on its discrimination, calibration, and clinical utility in the validation group. To the best of our knowledge, this is the first study to develop and validate a model for predicting persistent AKI after cardiac surgery. The new model may aid the reevaluation and clinical management of patients with AKI after cardiac surgery to reduce the chance of chronic kidney disease and improve prognosis.Some scholars call for clinicians to pay more attention to persistent AKI, since it is more consistent with what clinicians understand as the true “clinical AKI”.Limited data on the rate of persistent AKI after cardiac surgery were reported in previous studies. The occurrence rate of persistent AKI fluctuated from 10.4% to 71.4% in limited number of previous studies due to different diagnostic criteria and study populations.Several predictors, such as age, hypertension, diabetes, coronary heart disease, postoperative IABP use, and AKI stage at the initial diagnosis of AKI, were risk factors for persistent AKI in previous reports.Admittedly, the present study had some limitations. First, even though the sample size was relatively large, the model needs to be verified at other independent centers since it was a single‐center study. Second, data were missing for some variables. Multiple imputation was used to minimize the effect of missing data on model performance as recommended by previous reports. Finally, data for some variables, such as proteinuria and hemodynamic variables before or at the initial diagnosis of AKI after cardiac surgery, were not obtained due to the retrospective nature of the study. The influence of these variables on the model's performance was unknown. For the same reason, aortic cross‐clamp time, surgical anesthesia duration, and intraoperative use of red blood cells and platelets were not analyzed. Fortunately, the model developed using the available data showed a good performance. | PMC10766121 |
CONCLUSION | AKI, kidney injury, postoperative AKI | In the present study, a model for predicting persistent postoperative AKI in patients with normal baseline renal function who experienced AKI after cardiac surgery was developed and validated. This model can identify population at high risk of persistent AKI after cardiac surgery early, facilitate the re‐evaluation of medical regimen to avoid further damage after kidney injury, and provide individualized treatment strategies. In the future, efforts will be made to integrate the model into the existing electronic medical records system to make its clinical use more convenient. | PMC10766121 | |
AUTHOR CONTRIBUTIONS | PMC10766121 | |||
CONFLICT OF INTEREST STATEMENT | The authors declare no conflict of interest | PMC10766121 | ||
Supporting information | Supporting information.Click here for additional data file. | PMC10766121 | ||
ACKNOWLEDGMENTS | The authors extend gratitude to all patients included in the study and are thankful for the help from the information section staff. This study was funded by the National Natural Science Foundation of China (Grant No. 82070742) and the Science and Technology Project of Administration of Traditional Chinese Medicine of Wuxi City (Grant No. ZYYB39). | PMC10766121 | ||
DATA AVAILABILITY STATEMENT | The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. The data are not publicly available due to privacy or ethical restrictions. | PMC10766121 | ||
REFERENCES | PMC10766121 |
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