N O N – E X P E R I M E N T A L S T U D Y D E S I G N S
P A R T 2 : C O H O R T S T U D Y D E S I G N
6310-WEEK 4
MAIN NON-EXPERIMENTAL STUDY
DESIGNS
• Cross-sectional study design
• Cohort study design
• Case-control study design
COHORT STUDY DESIGN
• Cohort: a group of subjects followed over time
• Cohort design: A non-experimental design in which a defined
group of people (a cohort) is followed over time to study
outcomes for subsets of the cohorts
• Data is collected at baseline to assess exposure/characteristic
• Data is collected again at later point(s) in time to examine the
development of a disease or condition
• Time frame: longitudinal
• Advantages:
• Allows calculation of incidence (number of new cases of a condition
occurring over time)
• Establishes the time sequence of variable ïƒ strengthens the process of
inferring the causal basis of an association
• Types
• Prospective
• Retrospective
• Multiple cohort
COHORT STUDY DESIGN
Over time
Baseline
Gather data at: Defined
Population
Exposed
Disease No Disease
Not
Exposed
Disease No Disease
STEPS IN A PROSPECTIVE
COHORT STUDY
• Define selection criteria and recruit sample from the
population (cohort).
• At baseline, measure predictor variables and, if
appropriate, baseline level of outcome variable(s).
• Follow cohort over time, minimizing loss to follow-up.
• Measure outcome variable(s) at follow-up.
STEPS IN A RETROSPECTIVE
COHORT STUDY
• Identify an existing cohort that has some predictor
information already recorded
• Assess loss to follow-up that has occurred
• Measure outcome variable(s) that have already
occurred.
MULTIPLE-COHORT DESIGN
• Two or more separate samples: one with exposure to a
potential risk factor (predictor) and one or more with no
exposure.
• Next steps: measure other predictors; follow up; assess
outcomes
• Note that a double-cohort design is different from the
use of two samples in a case-control design
• Double-cohort: two groups chosen based on level of predictor
• Case-control: two groups chosen based on presence or
absence of an outcome
• Strengths: Feasible approach to study rare exposures to
environmental and occupational hazards
• Weaknesses: Confounding since the cohorts are
assembled from separate populations.
STATISTICAL MEASURES
IN COHORT DESIGNS
• Cohort study results are usually reported in
measures that reflect the concept of being at risk.*
• Risk
• Odds
•
© 2018 American Journal of Preventive Medicine. Pu
reserved.
RESEARCH ARTICLE
From the 1Cleve
Reserve Univers
Research, Medici
ter for Behavior
Cleveland Clinic,
Address corr
Clinic Main Cam
44195. E-mail: ch
0749-3797/$3
https://doi.org
blished by Elsevier Inc. All
Associations Between Unhealthy Weight-Loss
Strategies and Depressive Symptoms
D1X XAlexander Chaitoff, D2X XMPH,1 D3X XCarol Swetlik, D4X XBA,1 D5X XCatherine Ituarte, D6X XBS,1 D7X XElizabeth Pfoh, D8X XPhD, MPH,2
D9X XLing-Ling Lee, D10X XBS,1 D11X XLeslie J. Heinberg, D12X XPhD,3 D13X XMichael B. Rothberg, D14X XMD, MPH2
Introduction: There appears to be a link between weight loss and improved mental health, but less
is known about how using unhealthy weight-loss strategies impacts the odds of reporting
depression.
Methods: This study includes respondents from the National Health and Nutrition Examination
Survey from 2005 to 2014 who attempted to lose weight over the past year. Analysis occurred in
2017. Multivariable logistic regression was used to describe associations between all weight-loss
strategies, including those grouped as unhealthy (smoking, vomiting, laxatives, skipping meals, and
using diet pills), and the adjusted odds of depression (Patient Health Questionnaire−9 score ≥10).
The model was then stratified by BMI, sex, race, and antidepressant use to compare the effect of
using at least one unhealthy weight-loss strategy and depression within certain populations.
Results: The sample included 6,765 respondents (weighted n=59.2 million, 95% CI=55.5, 62.9 mil-
lion). Of these respondents, 18.0% (n=1,270) reported using at least one unhealthy weight-loss
strategy. In unadjusted analysis, unhealthy weight-loss strategies were generally associated with
higher incidence and odds of reporting depression. In multivariable analysis, using at least one
unhealthy weight-loss strategy was significantly associated with odds of reporting depression
(AOR=1.47, 95% CI=1.14, 1.91, p<0.01). When the model was stratified, this effect was statistically
significant among respondents with class I or II obsesity (AOR=2.20, 95% CI=1.56, 3.10, p<0.01);
female respondents (AOR=1.46, 95% CI=1.06, 2.00, p=0.02); and respondents who did not use an
antidepressant (AOR=1.57, 95% CI=1.14, 2.15, p=0.01).
Conclusions: Unhealthy weight-loss strategies are associated with increased odds of depression.
This may inform screening practices and public health messaging.
Am J Prev Med 2019;56(2):241−250. © 2018 American Journal of Preventive Medicine. Published by Elsevier
Inc. All rights reserved.
INTRODUCTION
land Clinic Lerner College of Medicine, Case Western
ity, Cleveland, Ohio; 2Center for Value-Based Care
ne Institute, Cleveland Clinic, Cleveland, Ohio; and 3Cen-
al Health, Department of Psychiatry and Psychology,
Cleveland, Ohio
e
PREVENTING CHRONIC DISEASE
P U B L I C H E A L T H R E S E A R C H , P R A C T I C E , A N D P O L I C Y
Volume 13, E181 DECEMBER 2016
ORIGINAL RESEARCH
Association Between Sitting Time and
Cardiometabolic Risk Factors After
Adjustment for Cardiorespiratory Fitness,
Cooper Center Longitudinal Study,
2010–2013
Carolyn E. Barlow, PhD1,2; Kerem Shuval, PhD3; Bijal A. Balasubramanian, MBBS, PhD2,4;
Darla E. Kendzor, PhD5,6; Nina B. Radford, MD7; Laura F. DeFina, MD1;
Kelley Pettee Gabriel, PhD8
Suggested citation for this article: Barlow CE, Shuval K,
Balasubramanian BA, Kendzor DE, Radford NB, DeFina LF, et
al. Association Between Sitting Time and Cardiometabolic Risk
Factors After Adjustment for Cardiorespiratory Fitness, Cooper
Center Longitudinal Study, 2010–2013. Prev Chronic Dis 2016;
13:160263. DOI: https://doi.org/10.5888/pcd13.160263.
PEER REVIEWED
Abstract
Introduction
Objective estimates, based on waist-worn accelerometers, indicate
that adults spend over half their day (55%) in sedentary behaviors.
Our study examined the association between sitting time and car-
diometabolic risk factors after adjustment for cardiorespiratory fit-
ness (CRF).
Methods
A cross-sectional analysis was conducted with 4,486 men and
1,845 women who reported daily estimated sitting time, had meas-
ures for adiposity, blood lipids, glucose, and blood pressure, and
underwent maximal stress testing. We used a modeling strategy
using logistic regression analysis to assess CRF as a potential ef-
fect modifier and to control for potential confounding effects of
CRF.
Results
Men who sat almost all of the time (about 100%) were more likely
to be obese whether defined by waist girth (OR, 2.61; 95% CI,
1.25–5.47) or percentage of body fat (OR, 3.33; 95% CI,
1.35–8.20) than were men who sat almost none of the time (about
0%). Sitting time was not significantly associated with other cardi-
ometabolic risk factors after adjustment for CRF level. For wo-
men, no significant associations between sitting time and cardi-
ometabolic risk factors were observed after adjustment for CRF
and other covariates.
Conclusion
As health professionals struggle to find ways to combat obesity
and its health effects, reducing sitting time can be an initial step in
a total physical activity plan that includes strategies to reduce
sedentary time through increases in physical activity among men.
In addition, further research is needed to elucidate the relation-
ships between sitting time and CRF for women as well as the un-
derlying mechanisms involved in these relationships.
Introduction
Prolonged sitting time characterizes the dai
Association of Daily Step Count and Intensity With Incident
Dementia in 78 430 Adults Living in the UK
Borja del Pozo Cruz, PhD; Matthew Ahmadi, PhD; Sharon L. Naismith, PhD; Emmanuel Stamatakis, PhD
IMPORTANCE Step-based recommendations may be appropriate for dementia-prevention
guidelines. However, the association of step count and intensity with dementia incidence is
unknown.
OBJECTIVE To examine the dose-response association between daily step count and intensity
and incidence of all-cause dementia among adults in the UK.
DESIGN, SETTING, AND PARTICIPANTS UK Biobank prospective population-based cohort study
(February 2013 to December 2015) with 6.9 years of follow-up (data analysis conducted May
2022). A total of 78 430 of 103 684 eligible adults aged 40 to 79 years with valid wrist
accelerometer data were included. Registry-based dementia was ascertained through
October 2021.
EXPOSURES Accelerometer-derived daily step count, incidental steps (less than 40 steps per
minute), purposeful steps (40 steps per minute or more), and peak 30-minute cadence (ie,
mean steps per minute recorded for the 30 highest, not necessarily consecutive, minutes in a
day).
MAIN OUTCOMES AND MEASURES Incident dementia (fatal and nonfatal), obtained through
linkage with inpatient hospitalization or primary care records or recorded as the underlying or
contributory cause of death in death registers. Spline Cox regressions were used to assess
dose-response associations.
RESULTS The study monitored 78 430 adults (mean [SD] age, 61.1 [7.9] years; 35 040 [44.7%]
male and 43 390 [55.3%] female; 881 [1.1%] were Asian, 641 [0.8%] were Black, 427 [0.5%]
were of mixed race, 75 852 [96.7%] were White, and 629 [0.8%] were of another,
unspecified race) over a median (IQR) follow-up of 6.9 (6.4-7.5) years, 866 of whom
developed dementia (mean [SD] age, 68.3 [5.6] years; 480 [55.4%] male and 386 [54.6%]
female; 5 [0.6%] Asian, 6 [0.7%] Black, 4 [0.4%] mixed race, 821 [97.6%] White, and 6
[0.7%] other). Analyses revealed nonlinear associations between daily steps. The optimal
dose (ie, exposure value at which the maximum risk reduction was observed) was 9826 steps
(hazard ratio [HR], 0.49; 95% CI, 0.39-0.62) and the minimal dose (ie, exposure value at
which the risk reduction was 50% of the observed maximum risk reduction) was 3826 steps
(HR, 0.75; 95% CI, 0.67-0.83). The incidental cadence optimal dose was 3677 steps (HR,
0.58; 95% CI, 0.44-0.72); purposeful cadence optimal dose was 6315 steps (HR, 0.43; 95%
CI, 0.32-0.58); and peak 30-minute cadence optimal dose was 112 steps per minute (HR,
0.38; 95% CI, 0.24-0.60).
CONCLUSIONS AND RELEVANCE In this cohort study, a higher number of steps was associated
with lower risk of all-cause dementia. The findings suggest that a dose of just under 10 000
steps per day may be optimally associated with a lower risk of dementia. Steps performed a
Template for Article Critique Reports
Week 4 – Assignment 4a – Barlow
Part Question Answer Points
Title Title of the article, journal name, your
name
Title (1): Association Between Sitting Time and Cardiometabolic Risk
Factors After Adjustment for Cardiorespiratory Fitness, Cooper Center
Longitudinal Study, 2010–2013
Journal (1): Preventing Chronic Disease
Your name (1) 3
Purpose/Research
problem
What is the purpose of the study? Is it
clearly identified? Is the research problem
important?
Primary goal
To examine the association between sitting time and cardiometabolic
risk factors after adjustment for cardiorespiratory fitness (CRF) (3)
Secondary goals
– To examine whether CRF confounded or modified the associations
between sitting time and cardiometabolic risk factors (1)
– To explore whether the role of CRF differed by sex (1)
Yes the purpose is clearly identified (0.25 bonus).
Yes, this is an important research problem given the high prevalence of
extended sitting times in our society (deskwork, TVs, computers, video
games, etc.) (0.25 bonus) 5
Identify the dependent variable(s) Cardiometabolic risk factors 3
Identify the independent variable(s) Sitting time 3
Literature review Are the cited sources relevant to the
study?
Yes.
3
Does the literature review offer a balanced
critical analysis of the literature?
Yes.
3
Are the cited studies recent? Yes. All are all from the past 10 years. 3
Theoretical
framework
Has a conceptual or theoretical framework
been identified?
No theoretical framework was identified.
3
If yes, is the framework adequately
described?
Not applicable.
3
Design and
procedures
Identify the study design used in this
study?
Cross sectional
5
Is the study design appropriate to answer
the research question?
Yes, given that the objective is to assess the association and not the
causality. 3
What type of sampling design was used? Not clearly indicated but it seems like a convenience sample (no
indication of random selection of participants or consecutive sampling) 5
Was the sample size justified on the basis
of a power analysis or other rationale?
No, sample size was not justified on the basis of a power analysis.
5
Are the incl
N O N – E X P E R I M E N T A L S T U D Y D E S I G N S
P A R T 1 : C R O S S – S E C T I O N A L S T U D Y D E S I G N
6310-WEEK 4
LEARNING OBJECTIVES
• Describe different types of non- experimental study
designs
• Identify the strengths and weaknesses of non-
experimental study designs
• Critically appraise the strength of evidence in non-
experimental study designs
• Utilize major national health surveys to extract local
and regional data
NON-EXPERIMENTAL STUDY DESIGNS
• Non-experimental or observational study design:
Research design in which the investigators simply
observe subjects without making any interventions.
• Goals
• Descriptive: examine the distribution of predictors and
outcomes in a population
• Analytic: examine associations between predictor and
outcome variables
MAIN NON-EXPERIMENTAL STUDY
DESIGNS
• Cross-sectional study design
• Cohort study design
• Case control study design
• Other types
• Case reports
• Case series
• Natural history studies
• Ecological studies
CROSS-SECTIONAL STUDY
DESIGN
CROSS-SECTIONAL STUDY DESIGN
• Time frame:
• All measurements for each subject are taken at the same
time.
• No follow-up period.
• Goals
• Describe variables and their distribution patterns within a
sample.
• Estimate prevalence (the proportion who have a disease or
condition at one point in time)
• Examine associations but not the direction of the
relationship.
CROSS-SECTIONAL STUDY DESIGN
Defined
Population
Exposed
Have Disease
Exposed
Do not Have
Disease
Not Exposed
Have Disease
Not Exposed
Do not Have
Disease
Collect data on exposure and disease/condition
Four groups are possible
STATISTICAL MEASURES
IN CROSS-SECTIONAL DESIGNS
• Prevalence: the proportion who have a disease or
condition at one point in time
• Prevalent cases are existing cases at a point in time.
This is in contrast to incident cases which are new
cases over a period of time.
• Prevalence = #of people with health outcome
# of people in study population
• Example: Using self-reported information from a
sample of U.S. adults in 2017, the Centers for Disease
Control and Prevention estimates obesity
prevalence in Texas at 33%.
• For obesity prevalence by state, click here.
ADVANTAGES OF CROSS-SECTIONAL
STUDIES
N O N – E X P E R I M E N T A L S T U D Y D E S I G N S
P A R T 3 : C A S E – C O N T R O L S T U D Y D E S I G N
6310-WEEK 4
MAIN NON-EXPERIMENTAL STUDY
DESIGNS
• Cross-sectional study design
• Cohort study design
• Case-control study design
CASE-CONTROL RESEARCH DESIGN
• A non-experimental research design involving the
comparison of a “case†(person with disease/condition
of interest) and a “matched control†(similar person
without the condition).
• Retrospective study design: A group of subjects with the
outcome (cases) and another without the outcome
(controls) are identified. The investigator then works
backward to find differences in predictor variables that
may be associated with the outcome.
• Advantages:
• Inexpensive and efficient for studying rare diseases/conditions
CASE CONTROL STUDY DESIGN
Cases
Disease
Exposed Not
Exposed
Controls
No
Disease
Exposed Not
Exposed
STEPS: CASE CONTROL STUDIES
• Develop a research question
• Select a sample from a population of people with the
outcome of interest or disease (cases)
• Select a sample from a population at risk without the
outcome of interest or disease (controls)
• Measure predictor variables
• Note that the use of two samples in a case-control
design is different from a double-cohort design
• Double-cohort: two groups chosen based on level of predictor
• Case-control: two groups chosen based on presence or
absence of an outcome
STATISTICAL MEASURES
IN CASE-CONTROL DESIGNS
• Odds ratio (OR) is a measure of association between an
exposure and an outcome. The OR represents the odds
that an outcome will occur given a particular exposure,
compared to the odds of the outcome occurring in the
absence of that exposure.
• OR is used to determine whether an exposure is a risk
factor for an outcome, and to compare the magnitude
of various risk factors for that outcome.
• OR=1 Exposure does not affect odds of outcome
• OR>1 Exposure associated with higher odds of outcome
• OR<1 Exposure associated with lower odds of outcome
Source: Szumilas M. Explaining Odds Ratios. Journal of the Canadian Academy of
Child and Adolescent Psychiatry. 2010;19(3):227-229.
STRENGTHS OF CASE CONTROL
STUDIES
• Efficient for rare diseases and those with long latent
periods between exposure and disease
• Inexpensive
• Small sample size
• Ability to examine a large number of predictor
variables
• Short duration
WEAKNESSES OF CASE-CONTROL
STUDIES
• One o
Step-by-step guide to critiquing
research. Part 1: quantitative research
Michael Coughlan, Patricia Cronin, Frances Ryan
Abstract
When caring for patients it is essential that nurses are using the
current best practice. To determine what this is, nurses must be able
to read research critically. But for many qualified and student nurses
the terminology used in research can be difficult to understand
thus making critical reading even more daunting. It is imperative
in nursing that care has its foundations in sound research and it is
essential that all nurses have the ability to critically appraise research
to identify what is best practice. This article is a step-by step-approach
to critiquing quantitative research to help nurses demystify the
process and decode the terminology.
Key words: Quantitative research â– Review process â– Research
methodologies
or many qualified nurses and nursing students
research is research, and it is often quite difficult
to grasp what others are referring to when they
discuss the limitations and or strengths within
a research study. Research texts and journals refer to
critiquing the literature, critical analysis, reviewing the
literature, evaluation and appraisal of the literature which
are in essence the same thing (Bassett and Bassett, 2003).
Terminology in research can be confusing for the novice
research reader where a term like ‘random’ refers to an
organized manner of selecting items or participants, and the
word ‘significance’ is applied to a degree of chance. Thus
the aim of this article is to take a step-by-step approach to
critiquing research in an attempt to help nurses demystify
the process and decode the terminology.
When caring for patients it is essential that nurses are
using the current best practice. To determine what this is
nurses must be able to read research. The adage ‘All that
glitters is not gold’ is also true in research. Not all research
is of the same quality or of a high standard and therefore
nurses should not simply take research at face value simply
because it has been published (Cullum and Droogan, 1999;
Polit and Beck, 2006). Critiquing is a systematic method of
Michael Coughlan, Patricia Cronin and Frances Ryan are Lecturers,
School of Nursing and Midwifery, University of Dublin, Trinity
College, Dublin
Accepted for publication: March 2007
appraising the strengths and limitations of a piece of research
in order to determine its credibility and/ or its applicability
to practice (Valente, 2003). Seeking only limitations in a
study is criticism and critiquing and criticism are not the
same (Burns and Grove, 1997). A critique is an impersonal
evaluation of the strengths and limitations of the research
being reviewed and should not be seen as a disparagement
of the researchers ability. Neither should it be rega
© 2018 American Journal of Preventive Medicine. Pu
reserved.
RESEARCH ARTICLE
From the 1Cleve
Reserve Univers
Research, Medici
ter for Behavior
Cleveland Clinic,
Address corr
Clinic Main Cam
44195. E-mail: ch
0749-3797/$3
https://doi.org
blished by Elsevier Inc. All
Associations Between Unhealthy Weight-Loss
Strategies and Depressive Symptoms
D1X XAlexander Chaitoff, D2X XMPH,1 D3X XCarol Swetlik, D4X XBA,1 D5X XCatherine Ituarte, D6X XBS,1 D7X XElizabeth Pfoh, D8X XPhD, MPH,2
D9X XLing-Ling Lee, D10X XBS,1 D11X XLeslie J. Heinberg, D12X XPhD,3 D13X XMichael B. Rothberg, D14X XMD, MPH2
Introduction: There appears to be a link between weight loss and improved mental health, but less
is known about how using unhealthy weight-loss strategies impacts the odds of reporting
depression.
Methods: This study includes respondents from the National Health and Nutrition Examination
Survey from 2005 to 2014 who attempted to lose weight over the past year. Analysis occurred in
2017. Multivariable logistic regression was used to describe associations between all weight-loss
strategies, including those grouped as unhealthy (smoking, vomiting, laxatives, skipping meals, and
using diet pills), and the adjusted odds of depression (Patient Health Questionnaire−9 score ≥10).
The model was then stratified by BMI, sex, race, and antidepressant use to compare the effect of
using at least one unhealthy weight-loss strategy and depression within certain populations.
Results: The sample included 6,765 respondents (weighted n=59.2 million, 95% CI=55.5, 62.9 mil-
lion). Of these respondents, 18.0% (n=1,270) reported using at least one unhealthy weight-loss
strategy. In unadjusted analysis, unhealthy weight-loss strategies were generally associated with
higher incidence and odds of reporting depression. In multivariable analysis, using at least one
unhealthy weight-loss strategy was significantly associated with odds of reporting depression
(AOR=1.47, 95% CI=1.14, 1.91, p<0.01). When the model was stratified, this effect was statistically
significant among respondents with class I or II obsesity (AOR=2.20, 95% CI=1.56, 3.10, p<0.01);
female respondents (AOR=1.46, 95% CI=1.06, 2.00, p=0.02); and respondents who did not use an
antidepressant (AOR=1.57, 95% CI=1.14, 2.15, p=0.01).
Conclusions: Unhealthy weight-loss strategies are associated with increased odds of depression.
This may inform screening practices and public health messaging.
Am J Prev Med 2019;56(2):241−250. © 2018 American Journal of Preventive Medicine. Published by Elsevier
Inc. All rights reserved.
INTRODUCTION
land Clinic Lerner College of Medicine, Case Western
ity, Cleveland, Ohio; 2Center for Value-Based Care
ne Institute, Cleveland Clinic, Cleveland, Ohio; and 3Cen-
al Health, Department of Psychiatry and Psychology,
Cleveland, Ohio
e
|
