| Keyword search (4,163 papers available) | ![]() |
"Methods" Keyword-tagged Publications:
| Title | Authors | PubMed ID | |
|---|---|---|---|
| 1 | Establishing work productivity loss norms: Absenteeism and presenteeism in a Canadian working population | Zhang W; Qian H; L' Heureux J; Johns G; Koehoorn M; Woodcock S; | 41469277 JMSB |
| 2 | Perceptions et attitudes des personnes âgées souffrant d insomnie par rapport aux médicaments et aux produits de santé naturels | Nguyen PV; Dang-Vu T; Forest G; Saidi L; Desmarais P; | 40968485 CONCORDIA |
| 3 | A portrait of online gambling: a look at a transformation amid a pandemic | Kairouz S; Savard AC; Murch WS; Dixon MR; Martin NB; Brodeur M; Dauphinais S; Ferland F; Hamel D; Dufour M; French M; Monson E; Van Mourik V; Morvannou A; | 40770758 CONCORDIA |
| 4 | Advancements in Magnetorheological Foams: Composition, Fabrication, AI-Driven Enhancements and Emerging Applications | Khodaverdi H; Sedaghati R; | 40732777 ENCS |
| 5 | Exploring interaction paradigms for segmenting medical images in virtual reality | Jones Z; Drouin S; Kersten-Oertel M; | 40402355 ENCS |
| 6 | Facebook recruitment: understanding research relations Prior to data collection | Young K; Browne K; | 39877298 CONCORDIA |
| 7 | Infants' Social Evaluation of Helpers and Hinderers: A Large-Scale, Multi-Lab, Coordinated Replication Study | Lucca K; Yuen F; Wang Y; Alessandroni N; Allison O; Alvarez M; Axelsson EL; Baumer J; Baumgartner HA; Bertels J; Bhavsar M; Byers-Heinlein K; Capelier-Mourguy A; Chijiiwa H; Chin CS; Christner N; Cirelli LK; Corbit J; Daum MM; Doan T; Dresel M; Exner A; Fei W; Forbes SH; Franchin L; Frank MC; Geraci A; Giraud M; Gornik ME; Wiesmann CG; Grossmann T; Hadley IM; Havron N; Henderson AME; Matzner EH; Immel BA; Jankiewicz G; Jedryczka W; Kanakogi Y; Kominsky JF; Lew-Williams C; Liberman Z; Liu L; Liu Y; Loeffler MT; Martin A; Mayor J; Meng X; Misiak M; Moreau D; Nencheva ML; Oña LS; Otálora Y; Paulus M; Pepe B; Pickron CB; Powell LJ; Proft M; Quinn AA; Rakoczy H; Reschke PJ; Roth-Hanania R; Rothmaler K; Schlegelmilch K; Schlingloff-Nemecz L; Schmuckler MA; Schuwerk T; Seehagen S; Sen HH; Shainy MR; Silvestri V; Soderstrom M; Sommerville J; Song HJ; Sorokowski P; Stutz SE; Su Y; Taborda-Osorio H; Tan AWM; Tatone D; Taylor-Partridge T; Tsang CKA; Urbanek A; Uzefovsky F; Visser I; Wertz AE; Williams M; Wolsey K; Wong TT; Woodward AM; Wu Y; Zeng Z; Zimmer L; Hamlin JK; | 39600132 PSYCHOLOGY |
| 8 | Searching and reporting in Campbell Collaboration systematic reviews: A systematic assessment of current methods | Young S; MacDonald H; Louden D; Ellis UM; Premji Z; Rogers M; Bethel A; Pickup D; | 39176233 CONCORDIA |
| 9 | Measuring what matters to older persons for active living: part I content development for the OPAL measure across four countries | Mayo NE; Auais M; Barclay R; Branin J; Dawes H; Korfage IJ; Sawchuk K; Tal E; White CL; Ayoubi Z; Chowdhury F; Henderson J; Mansoubi M; Mate KKV; Nadea L; Rodriguez S; Kuspinar A; | 38967870 BIOLOGY |
| 10 | Evaluation of the effectiveness of a Strengths-Based Nursing and Healthcare Leadership program aimed at building leadership capacity: A concurrent mixed-methods study | Lavoie-Tremblay M; Boies K; Clausen C; Frechette J; Manning K; Gelsomini C; Cyr G; Lavigne G; Gottlieb B; Gottlieb LN; | 38746801 JMSB |
| 11 | Identifying priority questions regarding rapid systematic reviews' methods: protocol for an eDelphi study | Vieira AM; Szczepanik G; de Waure C; Tricco AC; Oliver S; Stojanovic J; Ribeiro PAB; Pollock D; Akl EA; Lavis J; Kuchenmuller T; Bragge P; Langer L; Bacon S; | 37419644 HKAP |
| 12 | How to present work productivity loss results from clinical trials for patients and caregivers? A mixed methods approach | L' Heureux J; McTaggart-Cowan H; Johns G; Chen L; Steiner T; Tocher P; Sun H; Zhang W; | 37276772 JMSB |
| 13 | Barriers and facilitators to diet, physical activity and lifestyle behavior intervention adherence: a qualitative systematic review of the literature | Alysha L Deslippe | 36782207 PERFORM |
| 14 | Pan-Canadian caregiver experiences in accessing government disability programs: A mixed methods study | Finlay B; Wittevrongel K; Materula D; Hébert ML; O' Grady K; Lach LM; Nicholas D; Zwicker JD; | 36621140 CONCORDIA |
| 15 | Double-Bind of Recruitment of Older Adults Into Studies of Successful Aging via Assistive Information and Communication Technologies: Mapping Review | Khalili-Mahani N; Sawchuk K; | 36563033 CONCORDIA |
| 16 | Toward a digital citizen lab for capturing data about alternative ways of self-managing chronic pain: An attitudinal user study | Khalili-Mahani N; Woods S; Holowka EM; Pahayahay A; Roy M; | 36188996 PERFORM |
| 17 | Young women's engagement with gambling: A critical qualitative inquiry of risk conceptualisations and motivations to gamble | McCarthy S; Thomas S; Pitt H; Marko S; Randle M; Cowlishaw S; Kairouz S; Daube M; | 36002940 SOCANTH |
| 18 | Comparison of different severe obesity definitions in predicting future cardiometabolic risk in a longitudinal cohort of children | Kakinami L; Smyrnova A; Paradis G; Tremblay A; Henderson M; | 35705336 PERFORM |
| 19 | A Proposed Multi-Criteria Optimization Approach to Enhance Clinical Outcomes Evaluation for Diabetes Care: A Commentary | Wan TTH; Matthews S; Luh H; Zeng Y; Wang Z; Yang L; | 35372638 ENCS |
| 20 | Simple, Economical Methods for the Culture of Green Algae for Energy Harvesting from Photosynthesis in a Microfluidic Environment | Kuruvinashetti K; Rahimi S; Pakkiriswami S; Packirisamy M; | 34898042 ENCS |
| 21 | Data-driven methods distort optimal cutoffs and accuracy estimates of depression screening tools: a simulation study using individual participant data | Bhandari PM; Levis B; Neupane D; Patten SB; Shrier I; Thombs BD; Benedetti A; | 33838273 CONCORDIA |
| 22 | Overestimation of Postpartum Depression Prevalence Based on a 5-item Version of the EPDS: Systematic Review and Individual Participant Data Meta-analysis | Thombs BD; Levis B; Lyubenova A; Neupane D; Negeri Z; Wu Y; Sun Y; He C; Krishnan A; Vigod SN; Bhandari PM; Imran M; Rice DB; Azar M; Chiovitti MJ; Saadat N; Riehm KE; Boruff JT; Cuijpers P; Gilbody S; Ioannidis JPA; Kloda LA; Patten SB; Shrier I; Ziegelstein RC; Comeau L; Mitchell ND; Tonelli M; Barnes J; Beck CT; Bindt C; Figueiredo B; Helle N; Howard LM; Kohlhoff J; Kozinszky Z; Leonardou AA; Radoš SN; Quispel C; Rochat TJ; Stein A; Stewart RC; Tadinac M; Tandon SD; Tendais I; Töreki A; Tran TD; Trevillion K; Turner K; Vega-Dienstmaier JM; Benedetti A; | 33104415 LIBRARY |
| 23 | A threshold LC-MS/MS method for 92 analytes in oral fluid collected with the Quantisal® device | Desharnais B; Lajoie MJ; Laquerre J; Mireault P; Skinner CD; | 33035929 CHEMBIOCHEM |
| 24 | Group sample sizes in nonregulated health care intervention trials described as randomized controlled trials were overly similar | Thombs BD; Levis AW; Azar M; Saadat N; Riehm KE; Sanchez TA; Chiovitti MJ; Rice DB; Levis B; Fedoruk C; Lyubenova A; Malo Vázquez de Lara AL; Kloda LA; Benedetti A; Shrier I; Platt RW; Kimmelman J; | 31866472 LIBRARY |
| Title: | Comparison of different severe obesity definitions in predicting future cardiometabolic risk in a longitudinal cohort of children | ||||
| Authors: | Kakinami L, Smyrnova A, Paradis G, Tremblay A, Henderson M | ||||
| Link: | pubmed.ncbi.nlm.nih.gov/35705336/ | ||||
| DOI: | 10.1136/bmjopen-2021-058857 | ||||
| Publication: | BMJ open | ||||
| Keywords: | community child health; epidemiology; paediatrics; public health; statistics and research methods; | ||||
| PMID: | 35705336 | Category: | Date Added: | 2022-06-16 | |
| Dept Affiliation: |
PERFORM
1 PERFORM Centre, Concordia University, Montreal, Québec, Canada lisa.kakinami@concordia.ca. 2 Department of Mathematics and Statistics, Concordia University, Montreal, Québec, Canada. 3 Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Québec, Canada. 4 Département de kinésiologie, Université Laval, Quebec City, Quebec, Canada. 5 Department of Pediatrics, Université de Montréal, Montreal, Quebec, Canada. 6 Research Center of CHU Sainte Justine, Université de Montréal, Montreal, Quebec, Canada. |
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Description: |
Objectives: Severe obesity (SO) prevalence varies between reference curve-based definitions (WHO: =99th percentile, Centers for Disease Control and Prevention (CDC): >1.2×95th percentile). Whether SO definitions differentially predict cardiometabolic disease risk is critical for proper clinical care and management but is unknown. Design: Prospective cohort study SETTING: SO definitions were applied at baseline (2005-2008, M<sub>age</sub>=9.6 years, n=548), and outcomes (fasting lipids, glucose, homoeostatic model assessment (HOMA-IR) and blood pressure) were assessed at first follow-up (F1: 2008-2011, M<sub>age</sub>=11.6 years) and second follow-up (2015-2017, M<sub>age</sub>=16.8 years) of the Quebec Adipose and Lifestyle Investigation in Youth cohort in Montreal, Quebec. Participants: Respondents were youth who had at least one biological parent with obesity. Primary outcome measures: Unfavourable cardiometabolic levels of fasting blood glucose (=6.1 mmol/L), insulin resistance (HOMA-IR index =2.0), high-density lipoprotein <1.03 mmol/L, low-density lipoprotein =2.6 mmol/L and triglycerides <underline>></underline>1.24 mmol/L. Unfavourable blood pressure was defined as =90th percentile for age-adjusted, sex-adjusted and height-adjusted systolic or diastolic blood pressure. Analysis: Area under the receiver operating characteristic curve (AUC) and McFadden psuedo R<sup>2</sup> for predicting F1 or F2 unfavourable cardiometabolic levels from baseline SO definitions were calculated. Agreement was assessed with kappas. Results: Baseline SO prevalence differed (WHO: 18%, CDC: 6.7%). AUCs ranged from 0.52 to 0.77, with fair agreement (kappa=37%-55%). WHO-SO AUCs for detecting unfavourable HOMA-IR (AUC>0.67) and high-density lipoprotein (AUC>0.59) at F1 were statistically superior than CDC-SO (AUC>0.59 and 0.53, respectively; p<0.05). Only HOMA-IR and the presence of more than three risk factors had acceptable model fit. WHO-SO was not more predictive than WHO-obesity, but CDC-SO was statistically inferior to CDC-obesity. Conclusion: WHO-SO is statistically superior at predicting cardiometabolic risk than CDC-SO. However, as most AUCs were generally uninformative, and obesity definitions were the same if not better than SO, the improvement may not be clinically meaningful. |



