Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor D.docx

Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor
© Festo Didactic 86368-00 11
Figure 9. Voltage waveform at the output of a PWM inverter
with and without overmodulation
(after filtering).
The Procedure is divided into the following sections:
-frequency
component
in the voltage (unfiltered) at the output of a three-phase PWM
inverter
the frequency on the induction motor speed and
magnetizing
current
Operation at motor nominal frequency. Operation at 4/3 the
motor
nominal frequency. Operation at 1/2 the motor nominal
frequency.

High voltages are present in this laboratory exercise. Do not
make or modify any
banana jack connections with the power on unless otherwise
specified.
Setup and connections
In this part of the exercise, you will set up and connect the
equipment.
1. Refer to the Equipment Utilization Chart in Appendix A to
obtain the list of
equipment required to perform the exercise.
Install the equipment in the Workstation.
Mechanically couple the Four-Pole Squirrel Cage Induction
Motor to the
Four-Quadrant Dynamometer/Power Supply using a timing belt.
PROCEDURE OUTLINE
PROCEDURE
Voltage waveform without overmodulation
Voltage waveform with overmodulation
Sine wave modulating the duty
cycle (with overmodulation)
/2
duty cycle = 100%
/2

duty cycle = 0%
Time
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12 © Festo Didactic 86368-00
2. Make sure that the ac and dc power switches on the Power
Supply are set to
the O (off) position, then connect the Power Supply to a three-
phase
ac power outlet.
Make sure that the main power switch on the Four-Quadrant
Dynamometer/
Power Supply is set to the O (off) position, then connect its
Power Input to an
ac power outlet.

3. Connect the Power Input of the Data Acquisition and Control
Interface to
a 24 V ac power supply.
Connect the Low Power Input of the Chopper/Inverter to the
Power Input of
the Data Acquisition and Control Interface. Turn the 24 V ac
power supply
on.
4. Connect the USB port of the Data Acquisition and Control
Interface to a
USB port of the host computer.
Connect the USB port of the Four-Quadrant
Dynamometer/Power Supply to
a USB port of the host computer.
5. Turn the Four-Quadrant Dynamometer/Power Supply on, then
set the
Operating Mode switch to Dynamometer.
6. Turn the host computer on, then start the LVDAC-EMS
software.
In the LVDAC-EMS Start-Up window, make sure that the Data
Acquisition
and Control Interface and the Four-Quadrant
Dynamometer/Power Supply
are detected. Make sure that the Computer-Based
Instrumentation and
Chopper/Inverter Control functions for the Data Acquisition and
Control
Interface are available. Select the network voltage and
frequency that

correspond to the voltage and frequency of your local ac power
network, then
click the OK button to close the LVDAC-EMS Start-Up
window.
7. Connect the Digital Outputs of the Data Acquisition and
Control
Interface (DACI) to the Switching Control Inputs of the
Chopper/Inverter
using a DB9 connector cable.
On the Chopper/Inverter, set the Dumping switch to the O (off)
position. The
Dumping switch is used to prevent overvoltage on the dc bus of
the
Chopper/Inverter. It is not required in this exercise.
8. Set up the circuit shown in Figure 10. Use the diodes in the
Rectifier and
Filtering Capacitors to implement the three-phase full-wave
rectifier. Use the
Chopper/Inverter to implement the Three-phase inverter.
Connect the
Thermistor Output of the Four-Pole Squirrel Cage Induction
Motor to the
Thermistor Input of the Four-Quadrant Dynamometer/Power
Supply.
Notice that the prefix IGBT
has been left out in this
manual when referring to
the IGBT Chopper/Inverter
module.

Figure 10. Three-phase, variable-frequency induction-motor
drive (with three-phase filter).
Measurement of the rms value of the fundamental-frequency
component in
the voltage (unfiltered) at the output of a three-phase PWM
inverter
In this part of the exercise, you will use the Harmonic Analyzer
in LVDAC-EMS to
measure the rms value of the fundamental-frequency component
in the unfiltered
voltage at the output of a three-phase PWM inverter. You will
also observe that
when the load connected to the three-phase PWM inverter
output is inductive
(like a motor), the current waveform at the output of the three-
phase
PWM inverter remains sinusoidal even with no filter.
a The Four-Quadrant Dynamometer/Power Supply is not used in
this part of the exercise although it is mechanically coupled to
the Four-Pole Squirrel Cage
Induction Motor.
9. In LVDAC-EMS, open the Chopper/Inverter Control window,
then make the
following settings:

-Phase PWM inverter.
-2-
3).
nominal frequency
indicated on the Four-Pole Squirrel Cage Induction Motor front
panel.
Three-phase filter
Three-phase
inverter
Three-phase
rectifier
Three-
phase
induction
machine
Switching control
signals from DACI
Brake
N

2 mH
2 mH
2 mH
5 F 5 F 5 F
5 F
the
modulation index m, i.e., it sets the amplitude of the sine-wave
signal
that modulates the duty cycle of the switching control signals.
When
the Peak Voltage parameter is set to 100%, the amplitude of the
modulating signal is set to obtain a peak output voltage
corresponding to 100% of half the dc bus voltage (100% of /2).
In other words, this sets the value of the modulation index to 1
(m = 1).
Pulse-
Width Modulation.
-Phase PWM Inverter.
10. On the Power Supply, turn the three-phase ac power source

on. The motor
should start to rotate.
11. In LVDAC-EMS, open the Oscilloscope. Use channel 1 to
display the dc bus
voltage (input E1), channels 2 and 3 to display the line voltage
at the output
of the three-phase PWM inverter before and after filtering
(inputs E2 and E3,
respectively), and channel 4 to display the current flowing in
the motor stator
windings (input I1).
Select the Continuous Refresh mode, set the time base to
display only one
complete cycle of the voltage and current waveforms (this
provides more
precision for observing the trains of rectangular bipolar pulses
produced by
the three-phase PWM inverter), and set the trigger controls so
that the
Oscilloscope triggers when the waveform of the motor stator
current passes
through 0 A with a positive slope.
Select convenient vertical scale and position settings to
facilitate observation
of the waveforms.
12. Print or save the waveforms displayed on the Oscilloscope
screen for future
reference. It is suggested that you include these waveforms in
your lab
report.
13. Describe the waveform of the voltage at the output of the

three-phase
PWM inverter before and after filtering.
14. Measure and record the rms value of the line voltage at the
output of the
three-phase PWM inverter before and after filtering.
RMS value of the line voltage at the output of the three-phase
PWM inverter before filtering: V
PWM inverter after filtering: V
15. Are the rms values of the line voltage at the output of the
three-phase
PWM inverter measured before and after filtering using the
Oscilloscope
equal? Explain briefly.
16. Describe the waveform of the current flowing in each stator
winding of the
three-phase motor (input I1).

17. In LVDAC-EMS, open the Harmonic Analyzer window, then
make the
following settings:
nominal
frequency indicated on the Four-Pole Squirrel Cage Induction
Motor
front panel.
r is set to 40.
18. Measure and record the rms value of the fundamental-
frequency
component (1f) in the line voltage at the output of the three-
phase

PWM inverter after filtering (input E3), indicated in the
Harmonic Analyzer
display.
RMS value of the fundamental-frequency component in the line
voltage
at the output of the three-phase PWM inverter after filtering: V
a Compare the rms value of the fundamental-frequency
component in the line voltage at the output of the three-phase
PWM inverter after filtering measured
using the Harmonic Analyzer with the rms value of the line
voltage measured
previously using the Oscilloscope. The values should be
identical because the
voltage waveform is sinusoidal.
19. In the Harmonic Analyzer window, set the Input parameter
to E2 to measure
the line voltage at the output of the three-phase PWM inverter
before filtering.
Measure and record the rms value of the fundamental-frequency
component
in the line voltage at the output of the three-phase PWM
inverter before
filtering (input E2), indicated in the Harmonic Analyzer
display.
voltage
at the output of the three-phase PWM inverter before filtering:
V
a The rms value of the fundamental-frequency component in the
line voltage at the output of the three-phase PWM inverter

before filtering should be slightly
higher than that after filtering because of the voltage drop
across the three-
phase filter.
20. Do your measurements confirm that the rms value of the
fundamental-
frequency component in the line voltage at the outputs of the
three-phase
PWM inverter before filtering can be measured using the
Harmonic
Analyzer? Explain briefly.
21. In the Chopper/Inverter Control window, stop the Three-
Phase
PWM Inverter.
On the Power Supply, turn the three-phase ac power source off.
Remove the three-phase filter from the circuit and disconnect

voltage
input E3. The circuit should be as shown in Figure 11.
Figure 11. Three-phase, variable-frequency induction-motor
drive (without three-phase filter).
22. In the Chopper/Inverter Control window, start the Three-
Phase
PWM Inverter.
On the Power Supply, turn the three-phase ac power source on.
Measure and record the rms value of the fundamental-frequency
component
in the line voltage at the output of the three-phase PWM
inverter indicated in
the Harmonic Analyzer display.
voltage
at the output of the three-phase PWM inverter: V
a Compare the rms value of the fundamental-frequency
component in the line voltage measured without filter to that
measured in step 19 when the filter was in
the circuit.
Three-phase
inverter
Three-phase
full-wave rectifier
Three-

phase
induction
machine
Switching control
signals from DACI
Brake
23. On the Oscilloscope, use channel 1 to display the dc bus
voltage (input E1),
channel 2 to display the voltage at the output of the three-phase
PWM inverter (input E2), and channel 3 to display the current
flowing in one
of the motor stator windings (input I1).
of the waveforms.
screen for future
your lab
report.
25. Explain why the waveform of the motor stator current
displayed on the

Oscilloscope screen is sinusoidal even if the filter is removed.
Overmodulation
In this part of the exercise, you will increase the rms value of
the line voltage at
the output of a three-phase PWM inverter by increasing the
amplitude of the sine
wave that modulates the duty cycle of the switching transistors
of the
PWM inverter to a level that causes overmodulation.
a For the remaining of this exercise, the rms value of the line
voltage at the output of the three-phase PWM inverter always
refers to the rms value of the
fundamental-frequency component in the line voltage (i.e., the
rms value of
the 1f component measured with the Harmonic Analyzer).
26. Compare the rms value of the line voltage at the output of
the three-phase
PWM inverter measured in step 22 with the nominal line voltage
of the
Four-Pole Squirrel Cage Induction Motor (indicated on its front
panel). Does
the maximum line voltage which can be obtained at the outputs
of the three-
phase PWM inverter without overmodulation (i.e., with
modulation index set
to 1) allow the induction motor to be operated at its nominal
voltage?

27. In order to increase the voltage at the output of the three-
phase
PWM inverter (i.e., the voltage applied to the motor windings),
set the Peak
Voltage parameter in the Chopper/Inverter Control window to
117% of the
dc bus voltage/2 (this corresponds to the maximum Peak
Voltage value
available in the Chopper/Inverter Control window). This setting
increases the
amplitude of the sine-wave signal that modulates the duty
cycles of the
switching control signals to a level that causes overmodulation.
28. Using the Harmonic Analyzer, measure and record the rms
value of the line
voltage at the output of the three-phase PWM inverter obtained
when
overmodulation is used.
PWM inverter (with overmodulation): V
29. Is the value measured in the previous step close to the
nominal line voltage
of the Four-Pole Squirrel Cage Induction Motor?

screen for future
your lab
report.
31. Compare the voltage waveforms at the output of the three-
phase
PWM inverter obtained with and without overmodulation (i.e.,
obtained in
step 30 and step 24, respectively). Do you observe that the
voltage
waveform obtained with overmodulation contains less
rectangular pulses,
indicating that overmodulation occurs?
32. Do your observations confirm that overmodulation allows
the rms value of the
maximum voltage at the output of a three-phase PWM inverter
to be
increased?
Phase
PWM Inverter.
Effect of the frequency on the induction motor speed and
magnetizing
current
In this part of the exercise, you will use the same circuit as in

the previous
section (see Figure 11) to observe the effect of the frequency on
the speed and
magnetizing current of the three-phase induction motor.
34. In LVDAC-EMS, open the Four-Quadrant
Dynamometer/Power Supply
window, then make the following settings:
Positive Constant-Torque
Prime
Mover/Brake.
This
allows the torque command of the prime mover/brake to be
controlled manually.
ensuring
that the Four-Pole Squirrel Cage Induction Motor operates with
no
mechanical load.
The first and second
numbers in this parameter specify the number of teeth on the
pulley
of the Four-Quadrant Dynamometer/Power Supply and the
number

of teeth on the pulley of the machine under test, respectively.
The
pulley ratio between the Four-Quadrant Dynamometer/Power
Supply
and the Four-Pole Squirrel Cage Induction Motor is 24:24.
is
required to match the characteristics of the thermistor in the
Four-
Pole Squirrel Cage Induction Motor with the Thermistor Input
in the
Four-Quadrant Dynamometer/Power Supply.
-Torque Primer Mover/Brake by
setting
the Status parameter to Started or by clicking the Start/Stop
button.
de of the meters by clicking
on the
corresponding button.
Operation at motor nominal frequency
35. In the Chopper/Inverter Control window, make the
following settings:
-Phase
PWM inverter.
he Switching Frequency parameter to 2000 Hz.
-2-
3).

nominal
Motor
front panel.
Pulse-
Width Modulation.
-Phase PWM Inverter.
dure
36. The induction motor should rotate at a speed slightly below
the synchronous
speed of the three-phase induction motor. Record the motor
speed of
rotation indicated by the Speed meter in the Four-Quadrant
Dynamometer/Power Supply window in Table 2.
a The synchronous speed of the Four-Pole Squirrel Cage
Induction Motor is 1500 r/min at a local ac power network
frequency of 50 Hz and 1800 r/min at
a local ac power network frequency of 60 Hz.
Table 2. Effect of the frequency on the induction motor speed
and magnetizing current.

Frequency Motor speed of
rotation
(r/min)
DC bus voltage
(V)
RMS value of the
motor magnetizing
current
(A)
Amplitude of the
motor magnetizing
current (peak value)
(A)
Nominal
frequency
4/3 the nominal
frequency
1/2 the nominal
frequency
37. On the Oscilloscope, use channel 1 to display the dc bus

voltage (input E1),
and channel 2 to display the motor stator current (input I1).
of the waveforms.
38. Measure and record the dc bus voltage as well as the rms
value and
amplitude of the motor magnetizing current in Table 2.
Operation at 4/3 the motor nominal frequency
39. In the Chopper/Inverter Control window, gradually increase
the frequency of
the three-phase PWM inverter up to about 4/3 the motor
nominal frequency
while observing the motor speed and magnetizing current.
Measure and record the motor speed, the dc bus voltage, as well
as the
rms value and amplitude of the motor magnetizing current in
Table 2.
40. Gradually decrease the frequency of the three-phase PWM
inverter to the
motor nominal frequency.
The stator current meas-
ured when an induction
motor operates with no
mechanical load is virtually
equal to the motor magnet-
izing current.

41. Compare the motor speed measured at 4/3 the nominal
frequency to that
measured at the motor nominal frequency. Does the motor speed
increase in
direct proportion with the increase in frequency?
42. Compare the motor magnetizing current measured at 4/3 the
motor nominal
frequency to that measured at the motor nominal frequency.
How does the
motor magnetizing current vary when the frequency is
increased? Explain
why.
43. How does the motor magnetizing current variation affect the
maximum
magnetic flux density in the motor?
Operation at 1/2 the motor nominal frequency
In the next steps, the current in the motor stator windings will
exceed the nominal value.

Perform your manipulations rapidly to prevent motor
overheating. Also, make sure that the
motor peak current never exceeds four times the nominal
current shown on the Four-Pole
Squirrel Cage Induction Motor front panel or the motor
temperature never exceeds 80°C
(176°F).
44. Gradually decrease the frequency of the three-phase PWM
inverter down to
about 1/2 the motor nominal frequency while observing the
motor speed and
magnetizing current.
Measure and record the motor speed, the dc bus voltage, as well
as the
rms value and amplitude of the motor magnetizing current in
Table 2.
Phase
PWM Inverter.
On the Power Supply, turn the three-phase ac power source off.
46. Compare the motor speed measured at 1/2 the motor
nominal frequency to
that measured at the motor nominal frequency. Does the motor
speed

decrease in direct proportion with the decrease in frequency?
47. How does the motor magnetizing current vary when the
frequency is
decreased? Explain why.
48. Compare the magnetizing current measured at 1/2 the motor
nominal
frequency to that measured at the motor nominal frequency as
well as to the
motor full-load current rating indicated on the Four-Pole
Squirrel Cage
Induction Motor front panel. What are the consequences of
decreasing the
frequency to 1/2 the motor nominal frequency while keeping the
voltage
constant?
Saturation curves
In this part of the exercise, you will plot the saturation curves
of the three-phase
induction motor at the motor nominal frequency, 2/3 the
nominal frequency,

1/2 the nominal frequency, and 1/3 the nominal frequency.
In the next steps, the current in the motor stator windings will
exceed the nominal value.
Perform your manipulations rapidly to prevent motor
overheating. Also, make sure that the
motor peak current never exceeds four times the nominal
current shown on the Four-Pole
Squirrel Cage Induction Motor front panel or the motor
temperature never exceeds 80°C
(176°F).
49. Remove the timing belt that mechanically couples the Four-
Pole Squirrel
Cage Induction Motor to the Four-Quadrant
Dynamometer/Power Supply.
50. In the Chopper/Inverter Control window, make the
following settings.
-Phase
PWM inverter.
Hz.
Phase Sequence parameter is set to Fwd (1-2-
3).

Motor
front panel.
e-Phase PWM Inverter.
51. On the Power Supply, turn the three-phase ac power source
on.
For each value of the Peak Voltage parameter shown in Table 3,
measure
the rms value of the line voltage applied to the motor stator
windings using
the Harmonic Analyzer and the peak value of the current
flowing in the motor
stator windings (peak magnetizing current) using the
Oscilloscope (use the
horizontal cursors to make the measurement). To obtain optimal
results,
begin your measurements with the highest Peak Voltage setting
as shown in
Table 3.
To reduce the data acquisition time and to prevent the motor
from
overheating, it is strongly recommended to use the Data Table
in
LVDAC-EMS to record the circuit parameters measured.
Table 3. RMS value of the motor stator winding voltage (at the
fundamental frequency) and
peak magnetizing current of the three-phase induction motor at
various frequencies.

Peak voltage
(% of dc
bus/2)
(%)
/ / /
Motor line
voltage
(V)
Peak
magnetizing
current
(A)
Motor line
voltage
(V)
Peak
magnetizing
current
(A)
Motor line
voltage
(V)

Peak
magnetizing
current
(A)
Motor line
voltage
(V)
Peak
magnetizing
current
(A)
117
110
100
90
80
70
60
50
40
30

52. Repeat the previous step at the following three other
frequencies:
2 3⁄ , 1/2 , and 1/3 . As mentioned earlier, make sure
that the motor peak current never exceeds four times the
nominal current
indicated on the Four-Pole Squirrel Cage Induction Motor front
panel or the
temperature never exceeds 80°C (176°F), particularly at 1/2 and
1/3 the
nominal motor operating frequency.
a Use the 40 A terminal of input I1 on the DACI and set the
Range of input I1 to High in the Data Acquisition and Control
Settings window of LVDAC-EMS as
soon as the motor stator current reaches 4 A. Temporarily turn
the ac power
source off before making these changes.
53. Plot on the same graph the saturation curves (rms value of
the motor stator
winding voltage as a function of the peak magnetizing current)
of the Four-
Pole Squirrel Cage Induction Motor at each of the frequencies
in Table 3.
a You will probably not observe any saturation at nominal
frequency because the voltage applied to the motor windings
cannot be increased sufficiently.

54. Do the saturation curves you plotted in the previous step
show that, when the
motor stator winding voltage is increased, the peak magnetizing
current
increases in similar proportions until the voltage reaches a value
at which
saturation in the stator core of the induction motor starts to
occur, causing
the peak magnetizing current to increase much more rapidly
than the motor
stator winding voltage?
55. Do your observations confirm that increasing the frequency
allows higher
voltages to be applied to the motor stator windings before
saturation starts to
occur?
56. Close LVDAC-EMS, turn off all equipment, and remove all
leads and cables.
In this exercise, you learned that a three-phase, variable-
frequency induction-
motor drive consists of a power diode, three-phase full-wave

rectifier and a three-
phase PWM inverter. You learned that varying the operating
frequency of the
PWM inverter varies the frequency of the ac voltage applied to
the induction
motor, and thus, the motor speed. You also learned that varying
the modulation
index of the PWM inverter allows the voltage applied to the
motor stator windings
to be adjusted. You were introduced to the use of
overmodulation in the
PWM inverter to increase the maximum voltage that can be
applied to the motor
stator windings. You saw that for a given frequency, the
maximum flux density
( .) in the stator of the motor is directly proportional to the rms
value of
voltage applied to the motor stator windings. You also saw that
when the voltage
increases and reaches a certain value, saturation occurs in the
stator causing the
motor magnetizing current to start increasing at a rate which
largely exceeds that
of the motor voltage. You learned that for a given voltage, the
maximum flux
density is inversely proportional to the frequency of the ac
voltage applied to the
motor windings, and consequently, the magnetizing current of
the motor
decreases when the frequency increases and vice versa. You
were introduced to
the use of a harmonic analyzer to measure the rms value of the
fundamental-
frequency component of a non-sinusoidal signal (e.g., the
unfiltered voltage at
the output of a three-phase PWM inverter).

1. How can the ac voltage at the output of a three-phase PWM
inverter be
varied?
2. How does the magnetizing current vary when saturation starts
to occur in the
stator of an induction motor?
3. What should be done for an induction motor to be able to
produce the
highest possible torque?
4. How do the maximum flux density ( .) and peak magnetizing
current of an
induction motor vary when the PWM inverter frequency
decreases and the
voltage at the PWM inverter output (motor stator voltage)
remains constant?
CONCLUSION
REVIEW QUESTIONS

5. Explain why the rms value of the fundamental-frequency
component in the
voltage (unfiltered) at the output of a three-phase PWM inverter
cannot be
measured using a conventional voltmeter.
Rates and Predictors of Postpartum Depression by Race
and Ethnicity: Results from the 2004 to 2007 New York City
PRAMS Survey (Pregnancy Risk Assessment Monitoring
System)
Cindy H. Liu • Ed Tronick
Published online: 25 October 2012
� Springer Science+Business Media New York 2012
Abstract The objective of this study was to examine
racial/ethnic disparities in the diagnosis of postpartum
depression (PPD) by: (1) identifying predictors that account

for prevalence rate differences across groups, and (2) com-
paring the strength of predictors across groups. 3,732 White,
African American, Hispanic, and Asian/Pacific Islander
women from the New York City area completed the Preg-
nancy Risk Assessment Monitoring System from 2004 to
2007, a population-based survey that assessed sociodemo-
graphic risk factors, maternal stressors, psycho-education
provided regarding depression, and prenatal and postpartum
depression diagnoses. Sociodemographic and maternal
stressors accounted for increased rates in PPD among Blacks
and Hispanics compared to Whites, whereas Asian/Pacific
Islander women were still 3.2 times more likely to receive a
diagnosis after controlling for these variables. Asian/Pacific
Islanders were more likely to receive a diagnosis after their
providers talked to them about depressed mood, but were less
likely than other groups to have had this conversation. Pre-
natal depression diagnoses increased the likelihood for PPD
diagnoses for women across groups. Gestational diabetes

decreased the likelihood for a PPD diagnosis for African
Americans; a trend was observed in the association between
having given birth to a female infant and increased rates of
PPD diagnosis for Asian/Pacific Islanders and Whites. The
risk factors that account for prevalence rate differences in
postpartum diagnoses depend on the race/ethnic groups
being compared. Prenatal depression is confirmed to be a
major predictor for postpartum depression diagnosis for all
groups studied; however, the associations between other
postpartum depression risk factors and diagnosis vary by
race/ethnic group.
Keywords Postpartum depression � Health status
disparities � Asian Americans � Prenatal depression �
Gestational diabetes
Introduction
Postpartum depression (PPD) is a serious health concern
affecting approximately 13 % of all women [1]. At least
19.2 % of women experience depression within 12 months
after giving birth [2]. The associations between prenatal

depression and PPD depression are well documented [3–5].
Psychosocial factors including high stress, low social sup-
port, and low marital satisfaction are also predictors [4, 5].
Surprisingly little is known about the extent to which
postpartum depression varies by race and ethnicity, given the
effects of culture on the experiences and manifestations of
depression [6, 7]. This dearth of information on postpartum
depression in ethnic minorities is well recognized. In a
published review of maternal depression, the Agency for
Healthcare Research and Quality found ‘‘screening instru-
ments [to be] poorly representative of the U.S. population,’’
and that ‘‘populations [from studies] were overwhelmingly
Caucasian’’ [8]. A review by O’Hara found that meta-anal-
yses on postpartum depression had omitted race and eth-
nicity as risk factors for postpartum depression [4].
Research studies on postpartum depression that have
included ethnic minorities generally compare African
C. H. Liu (&)

Beth Israel Deaconess Medical Center, Harvard Medical School,
75 Fenwood Road, Boston, MA 02115, USA
e-mail: [email protected]
E. Tronick
Child Development Unit, University of Massachusetts,
100 Morrissey Blvd, Boston, MA 02125, USA
e-mail: [email protected]
123
Matern Child Health J (2013) 17:1599–1610
DOI 10.1007/s10995-012-1171-z
Americans and Hispanics with Whites. In these studies,
group differences in prevalence rates have shown to be
inconsistent. Across studies, the rates of postpartum
depression in African American and Hispanic women were
found to be higher [9], lower [10], or no different [11]
compared to Whites. What accounts for observed racial and
ethnic differences in prevalence is unclear. In some studies,
sociodemographic risk variables were associated with

higher levels of depressive symptomatology among Afri-
can Americans, raising the possibility that sociodemo-
graphic variables rather than race and ethnicity account for
different levels of postpartum depression [12–14]. In con-
trast, others have shown greater levels of depressive
symptomatology among African Americans and Hispanics
than Whites, after accounting for sociodemographic factors
[9]. While certain social factors could increase risk, some
factors might buffer against postpartum depression within
groups. For instance, low income foreign-born Hispanic
women with social support exhibited lower rates of post-
partum depression [15], whereas bilingual Hispanic women
were at greater risk than those who spoke only Spanish
[11]. It is possible that factors such as social support or
nativity and its effect on the likelihood of postpartum
depression differ by race/ethnicity because they express
different meanings or incur different implications for each
group. Moreover, stigmas about psychological problems

and help-seeking may have an effect on identifying post-
partum depression, resulting in a subsequent effect on
reported prevalence of postpartum depression rates [6, 16].
Given the mixed picture across groups, this study aimed to
systematically determine the extent to which prevalence
rates across race and ethnicity are explained by factors
associated with postpartum depression.
This study uniquely includes Asian/Pacific Islander
(API) women within the U.S. As the fastest growing ethnic
minority group, over 16 million APIs are estimated to be
living in the U.S [17, 18]. The research on API postpartum
experiences is limited, which is striking given that API
women may hold several risk factors.
If psychiatric history is a major predictor, API women
may be at greatest risk: those between the ages of
15–24 years have the highest rate of depression and su-
icidality compared to any other ethnicity, gender, or age
[19–21]. One study showed APIs to be at lower risk for

postpartum depressive symptoms compared to Whites,
African Americans, and Hispanics [14], while another
study reported a greater percentage of APIs with post-
partum symptoms compared to White Americans [22].
Analyses conducted by the New York City Department of
Health and Mental Hygiene on data from the 2004 to 2007
New York City (NYC) Pregnancy Risk Assessment Mon-
itoring System (PRAMS) revealed a higher rate of PPD
diagnoses among APIs compared to other groups [23–25].
From the most recent sample in 2007, 10.4 % of API
received a PPD diagnosis compared to 1.7 % of non-His-
panic White women [26]. These findings suggest a poten-
tial risk for postpartum depression in APIs.
This study examines racial/ethnic disparities in PPD
diagnosis by identifying predictors accounting for preva-
lence differences. Because previous studies have either
focused mostly on small samples of one group, or did not
examine these risk factors by race/ethnicity, we hypothe-

size that associations of risk factors and PPD differ by race/
ethnic group. The risk factors evaluated were selected
based on the current literature [27–31]. Our study also
sought to explain disparities in PPD rates from a published
report by the NYC Department of Health and Mental
Hygiene. We utilized the study’s comprehensive popula-
tion-based dataset. We also sought to determine the
strength of predictors within each group and differences
across groups. Accordingly, we stratified our analyses by
race/ethnicity. Determining the strength of predictors by
group is essential for identifying individuals most at risk,
and may inform the possible causes of depression for dif-
ferent groups. Unique to this study was the use of diagnosis
as an outcome measure, the inclusion of information on
whether providers talked to women about depressed mood,
and an adequate sample size of APIs. This allowed us to
also examine disparities in psycho-education and diagnosis
across groups.

Methods
Sample
This study used the NYC PRAMS from 2004 to 2007, a
population-based survey administered to postpartum
women from NYC. Coordinated by the Centers for Disease
Control and Prevention and state health departments,
PRAMS’ goal is to monitor maternal behaviors and expe-
riences of women before, during, and after live birth
pregnancies. The dataset was provided by the NYC
Department of Health and Mental Hygiene (DOHMH).
The participants were part of an ongoing population-
based random sampling of NYC live births. NYC mothers
of approximately 180 infants with registered birth certifi-
cates that gave birth during the previous 2–4 months were
contacted for participation monthly. Eighty-three percent
responded by mail and 17 % by phone. The sample was
randomized without replacement and stratified by birth
weight. The final dataset was weighted for stratification,

nonselection, and nonresponse.
According to the DOHMH, a total of 4,813 responses
were received with response rates of at least 70 % from
July to December of 2004, May to December of 2005, and
1600 Matern Child Health J (2013) 17:1599–1610
123
January to December of 2006. A rate of 65 % was achieved
from January to December of 2007. For 2004–2005,
responses were weighted to represent 138,266 live births.
For 2006 and 2007, responses represented 119,079 and
122,222 live births, respectively. Based on the DOHMH
analysis, respondents differed from non-respondents on
some key sociodemographic variables (p .05). APIs
compared to other racial and ethnic groups, younger
women, and women with less education were less likely to
respond to the survey.
Measures
The birth certificate provided information on maternal race/

ethnicity and nativity (i.e., U.S. or non-U.S. born mothers).
Women were classified as Hispanic or non-Hispanic based
on self-report. Non-Hispanic women were categorized in
one of the following groups: White, African American,
Asian/Pacific Islander, and American Indian/Alaskan
Native. Maternal age, nativity (U.S. Born versus Foreign
Born) and education (categorized as: 0–8, 9–11, 12, 13–15,
and [16 years) were based at the time of infant birth from
information in the birth certificate. Mean infant age at the
time of survey completion was 9.7 months; there were no
significant differences in infant age across groups.
The PRAMS survey itself provided information for
remaining variables. To obtain income, women were asked
to indicate ‘‘total household income before taxes in the
12 months before the new baby was born’’ by checking off
one of the following options: $10,000, $10,000–$14,999,
$15,000–$19,999, $20,000–$24,999, $25,000–$34,999,
$35,000–$49,999, $50,000–$74,999, and [$75,000. Stress-
ful events during pregnancy were obtained by ‘‘yes’’ or

‘‘no’’ responses to events that may have occurred during
the last 12 months before the new baby was born. Exam-
ples include ‘‘I moved to a new address,’’ ‘‘I had a lot of
bills to pay,’’ ‘‘I got separated or divorced from my hus-
band or partner,’’ and ‘‘Someone very close to me died.’’
These events were counted and categorized into the fol-
lowing: 0, 1–2, 3–5, and 6–13 events. A ‘‘yes’’ or ‘‘no’’
response was also used to obtain information on following:
gestational diabetes (‘‘High blood sugar (diabetes) that
started during this pregnancy’’), social support from partner
(responses of ‘‘My husband or partner’’ to the question
‘‘During your most recent pregnancy, who would have
helped you if a problem had come up’’), NICU (Neonatal
Intensive Care Unit) (‘‘After your baby was born, was he or
she put in an intensive care unit?’’), unintended pregnancy
(‘‘When you got pregnant with your new baby, were you
trying to get pregnant?’’). The NYC PRAMS included
additional questions related to depression. Mothers were

asked to respond ‘‘yes’’ or ‘‘no’’ regarding prenatal
depression (‘‘At any time during your most recent
pregnancy, did a doctor, nurse, or other health care worker
diagnose you with depression?’’), and discussion about
mood (‘‘At any time during your most recent pregnancy or
after delivery, did a doctor, nurse, or other health care
worker talk with you about ‘‘baby blues’’ or postpartum
depression?’’). In addition, mothers were asked about PPD
diagnosis (‘‘Since your new baby was born, has a doctor,
nurse, or other health care worker diagnosed you with
depression?’’). The response to this item was the outcome
variable used for the analyses in this study.
The language of the survey (English or Spanish version)
was also noted.
Variables
Covariates included maternal age, household income,
maternal education, nativity, and infant age at the time the
mother completed the questionnaire. Variables considered

as potential stressors included: gestational diabetes,
stressful events, social support, NICU, intention for preg-
nancy, and prenatal depression. Discussion about mood
served as an additional predictor of PPD diagnosis.
Responses with missing variables of interest for this
study were eliminated. Variables with less than a 100 %
response rate included household income (86.9 %),
maternal education (99.3 %), maternal age (97.0 %), and
PPD diagnosis (99.4 %) resulting in an unweighted study
sample of 3,732.
Statistical Analyses
To account for the stratified and weighted sample, the data
was analyzed using the complex samples module of SPSS
version 17.0 (SPSS Inc., Chicago, IL). A non-race stratified
model was conducted to determine the likelihood of
receiving a PPD diagnosis for each race/ethnic group with
Whites as the reference group. A series of four logistic
regression models were employed where the variables of

interest (race/ethnicity, sociodemographic, stressors, and
discussion about mood) were sequentially added to the
model, allowing incremental examination of the variables’
effects in identifying factors that explain racial/ethnic
disparities in PPD.
Prevalence estimates within each group were generated
according to predictors. To compare the characteristics of
those with and without PPD and to understand associated
predictors, race-stratified logistic regressions incorporated
all predictors, with sociodemographic variables as covari-
ates. Adjusted odds ratios for each predictor were gener-
ated by race/ethnic group. Note that our models failed to
converge with the inclusion of language, nativity, and
NICU variables because of low cell sizes; thus, these
variables were dropped from our analyses. Unless
Matern Child Health J (2013) 17:1599–1610 1601
123

otherwise noted, all reported proportions represent weigh-
ted averages.
Results
Compared to other groups, API women showed the highest
rate for PPD, followed by Hispanics and African Ameri-
cans. White women had the lowest rate of PPD. The high
rate of a PPD diagnosis among API women is consistent
with previous analyses from this dataset, which utilized a
larger sample size than the dataset here, as this set includes
only women with complete data on the predictor variables.
Other racial/ethnic differences among assessed variables
are presented (Table 1).
A major objective was to determine whether sociode-
mographic variables, stressor variables, and discussion
about mood accounted for PPD differences. In the unad-
justed model, likelihood estimates indicate that API women
were 4.6 times more likely and Hispanic women 2.7 times
more likely than Whites to receive a PPD diagnosis.

African American were 1.7 times more likely to receive the
diagnosis than Whites, although this was not statistically
significant (Table 2). Once sociodemographic factors were
entered, African Americans were no more likely to receive
a diagnosis than Whites. For Hispanics, the greater likeli-
hood for a diagnosis compared to Whites was less pro-
nounced after accounting for sociodemographic factors and
was eliminated with the inclusion of stressors. The diag-
nosis likelihood was slightly reduced for APIs after
accounting for sociodemographic factors, and significantly
reduced with stressor variables, although diagnosis likeli-
hood was still more than double the rate of Whites and
African Americans. In contrast to the other groups, diag-
nosis likelihood for APIs increased to 3.2 times relative to
Whites, after accounting for reports of having discussed
mood with a provider. Prenatal depression was by far the
strongest predictor for all women compared to other
stressors, although women who gave birth to females were

more likely to receive a diagnosis than women with male
infants. Overall, those who had a discussion about mood
were also more likely to receive a diagnosis.
Profiles of women with PPD diagnoses compared to
women without a diagnosis differed by race/ethnicity. The
majority of White women reporting a PPD diagnosis
received a postgraduate education, while API and African
American women with the diagnosis tended to be high
school graduates. Approximately half of the White women
with PPD had household incomes above $75,000 per year.
Among APIs, Hispanics, and African Americans, more
women with PPD had less than $15,000 of household
income per year than those without a diagnosis (Table 3).
With regard to stressors, we found a significantly higher
rate of gestational diabetes among those with PPD than
those without PPD, but only for White women. However,
after controlling for sociodemographic variables through
our race-stratified adjusted model, gestational diabetes did

not significantly predict PPD in White, API, or Hispanic
women (Table 4). In fact, African American women with
gestational diabetes were less likely to receive a diagnosis
of PPD.
Compared to those without PPD, there was a higher
percentage among APIs and Hispanics with the diagnosis
who had an unintended pregnancy. In addition, the
majority of APIs with PPD had a diagnosis of prenatal
depression compared to the other groups. Stressful events
were not associated with greater likelihood for PPD, but
API women who reported having 6–13 stressful events
were significantly more likely to have PPD, a rate that was
statistically significant. The association between prenatal
depression and PPD persisted for all groups, even after
controlling for sociodemographic variables.
Overall, there was a higher rate of women with PPD
who had a discussion about mood with their providers than
women without the diagnosis. However, the association

between PPD and discussion about mood with providers
was specific to only API and African American women in
the adjusted model.
Women from all groups who received a diagnosis of
PPD were more likely to have given birth to females
although the differences were not statistically significant.
However, having a female infant seemed to slightly
increase the likelihood of a PPD diagnosis among White
and API women based on the race-stratified analyses.
Discussion
This study assessed PPD estimates and identified predictors
of PPD as defined by women’s reports of receiving a
diagnosis from a health care provider. We included API
women and used race-stratified analyses, allowing us to
determine whether predictors varied by race/ethnicity.
This study also sought to identify factors that explained
racial/ethnic disparities obtained in a previous analysis of
the dataset by the NYC Department of Health and Mental

Hygiene. As with other studies, we found that sociode-
mographic factors accounted for the higher rates of PPD
among African Americans and Hispanics. Based on such
findings, some have argued for prevention or intervention
programs to provide resources (e.g., financial support,
education) in addressing the racial/ethnic disparities of
PPD for African Americans and Hispanics [12]. However,
unlike other studies that primarily assessed reported
symptoms [9, 12, 14], we used the diagnosis of PPD as the
123
outcome measure. This raises the possibility that sociode-
mographic status accounts for the rates at which one
receives a diagnosis; in our study, African Americans and
Hispanics with lower sociodemographic statuses were less
likely to receive a diagnosis compared to Whites. If race/
ethnic disparities are found among rates of diagnosis, then

the diagnostic process may be another area to target for
improvement among lower sociodemographic status
groups.
Among ethnic minorities in our study, API women were
the most likely to receive a PPD diagnosis, and unlike
African Americans and Hispanics, the likelihood of
receiving a PPD diagnosis for APIs remained significantly
higher even after accounting for other variables (e.g.,
sociodemographic factors). Prenatal depression was asso-
ciated with PPD for all groups in our study, but the like-
lihood was highest for APIs. Although psychiatric history
for depression was not available, the strong association
between prenatal depression and PPD observed among the
API women in our sample adds to the growing concern of
depression experiences and its effects on API women
during motherhood [19–21]. A number of factors specific
to API women’s experiences are potentially associated
with later postpartum mood. The high rate of depression

and suicidal ideation during adolescence and young
adulthood may reflect family and societal pressures faced
by young women to uphold high academic standards and
traditional gender roles [32]. These young women likely
must negotiate their cultural values and beliefs when
assuming a mother’s identity [33, 34]. In addition, the
cultural preference for male infants may affect PPD.
Table 1 Weighted percentage distribution of mothers who
recently
gave birth that completed the NYC PRAMS from 2004 to 2007,
by
characteristic, according to race/ethnicity
White Asian/
Pacific
Islander
Hispanic Black
(n = 1,043) (n = 425) (n = 1,253) (n = 1,027)
Maternal age
20 2.4a 0.9a 9.9b 6.9c
20–34 70.1

a
75.4
b
76.8
b
73.8
a,b
C35 27.5
a
23.7
a,b
13.3
c
19.3
b,d
Maternal education
0–8 1.7
a
2.7
a
11.7
b
1.6
a

9–11 4.2
a
10.7
b
19.6
c
15.8
d
12 22.6
a
26.1
a
34.4
b
32.1
b
13–15 16.2
a
14.7
a
21.1
b
28.1
c

C16 55.4
a
45.8
b
13.2
c
22.4
d
Income
10,000 10.0a 20.4b 40.3c 29.2d
10,000–14,999 6.7
a
15.1
b
14.3
b
10.3
c
15,000–19,999 4.6
a
8.0
b
8.8

b
8.6
c
20,000–24,999 4.7
a
5.8
a
6.8
b
9.2
c
25,000–34,999 6.8
a
5.7
a
9.5
b
13.2
c
35,000–49,999 8.7
a
6.0
a
6.7

a
10.1
a
50,000–74,999 12.1
a
9.9
a
6.3
a
10.2
a
C75,000 46.4
a
29.1
b
7.1
c
9.0
d
Maternal nativity
U.S. born 68.4
a
11.1
b

34.1
c
56.3
d
Non-U.S. born 31.1 88.9 65.6 43.0
Missing data 0.5 0 0.3 0.7
Language of questionnaire
English 99.1
a
99.5
a
51.2
b
98.8
a
Spanish 0 0 48.5 0
Missing data 0.5 0.5 0.3 1.2
NICU
Yes 5.1 5.9 6.4 14.4
No 94.9
a

94.1
a
93.6
a
85.5
b
Don’t know 0 0.1 0 0.1
Gender
Male 49.3
a
52.1
a
51.1
a
52.0
a
Female 50.7 47.9 48.9 48.0
Diabetes
No 92.4 85.1 89.9 89.9
Yes 7.6
a
14.9
b

10.1
c
10.1
c
Stresses
0 45.1
a
49.1
a
31.6
b
26.5
c
1–2 41.8
a
38.7
a
41.5
a
42.8
a
3–5 12.1
a

11.3
a
23.3
b
25.2
b
6–13 1.1
a
0.8
a
3.6
b
5.5
c
Social support
No 90.4 90.8 76.9 75.2
Yes 9.6
a
9.2
a
23.1
b
24.8
b

Table 1 continued
White Asian/
Pacific
Islander
Hispanic Black
(n = 1,043) (n = 425) (n = 1,253) (n = 1,027)
Intention for pregnancy
No 30.9
a
35.1
a
59.0
b
66.5
c
Yes 69.1 64.9 41.0 33.5
Prenatal depression diagnosis
No 97.2 87.6 92.4 94.5
Yes 2.8
a

12.4
b
7.6
c
5.5
d
Discussion about mood
No 46.0 61.4 42.7 39.3
Yes 54.0
a
38.6
b
57.3
a,c
60.7
c
Postpartum depression diagnosis
No 97.4 89.3 93.6 96.3
Yes 2.6
a
10.7
b
6.4

c
3.7
a
Lower case superscripts that differ across each row represent
statistically
different values across racial/ethnic groups. Conversely, groups
within a
row that share the same superscript demonstrate no statistically
significant
difference in values within p .05
123
Table 2 Logistic regression models of race/ethnicity, other
sociodemographic factors, stressors, and discussion of mood
with provider, with
adjusted odds of postpartum depression diagnosis
Model 1 Model 2 Model 3 Model 4
OR CI OR CI OR CI OR CI
Race
White 1.0 1.0 1.0 1.0
Asian/Pacific Islander 4.6*** 2.6–8.2 4.0*** 2.2–7.2 2.7** 1.4–

4.9 3.2*** 1.7–6.0
Hispanic 2.7*** 1.7–4.5 1.8* 1.0–3.1 1.5 0.9–2.7 1.5 0.9–2.7
Black 1.7
�
1.0–3.0 1.2 0.6–2.2 0.9 0.5–1.8 0.9 0.4–1.8
Maternal age
20 1.0
20–34 0.5 0.3–1.1 0.5 0.2–1.1 0.5 0.2–1.2
C35 0.7 0.3–1.6 0.7 0.3–1.7 0.7 0.3–1.9
Maternal education
0–8 1 1 1
9–11 0.8 0.3–1.9 1.2 0.4–3.2 1.1 0.4–3.0
12 1.0 0.5–2.1 1.6 0.7–4.1 1.6 0.7–4.0
13–15 1.1 0.5–2.5 1.6 0.6–4.2 1.6 0.6–4.3
C16 0.8 0.4–1.8 1.5 0.6–4.0 1.6 0.6–4.2
Income
10,000 1.0 1.0 1.0
10,000–14,999 1.2 0.7–2.1 1.5* 0.8–2.8 1.5* 0.8–2.8
15,000–19,999 0.8* 0.3–1.6 1.1 0.5–2.4 1.0 0.5–2.2

20,000–24,999 0.5 0.2–1.2 0.6 0.3–1.4 0.6 0.2–1.3
25,000–34,999 0.6 0.3–1.3 0.7 0.3–1.7 0.7 0.3–1.6
35,000–49,999 0.3 0.1–0.7 0.3 0.1–0.9 0.3 0.1–0.8
50,000–74,999 0.4 0.2–0.9 0.5 0.2–1.3 0.5 0.2–1.3
C75,000 0.5 0.3–1.0 0.7 0.3–1.5 0.7 0.3–1.4
Gender
Male 1.0 1.0
Female 1.6* 1.1–2.4 1.7* 1.1–2.5
Diabetes
No 1.0 1.0
Yes 0.8 0.4–1.5 0.8 0.4–1.6
Stresses
0 1.0 1.0
1–2 0.8 0.5–1.3 0.8 0.5–1.3
3–5 1.0 0.6–1.8 1.0 0.6–1.8
6–18 1.8
�
0.7–4.9 2.0
�

0.8–5.1
Social support
No 1.0 1.0
Yes 1.1 0.7–1.9 1.2 0.7–2.0
No 1.0 1.0
Yes 1.2 0.8–1.8 1.2 0.8–1.8
No 1.0 1.0
Yes 17.3*** 10.9–27.5 15.0*** 9.4–23.8
No 1.0
Yes 2.6*** 1.6–4.1
�
p 0.1; * p .05; ** p .01; *** p .001
123

Table 3 Weighted percentage of mothers who completed the
NYC PRAMS from 2004 to 2007, by characteristic according to
race/ethnicity and
postpartum depression diagnosis
White Asian/Pacific Islander Hispanic Black
No PPD PPD No PPD PPD No PPD PPD No PPD PPD
(n = 1,010) (n = 33) (n = 383) (n = 42) (n = 1,162) (n = 91) (n =
979) (n = 48)
Maternal age
20 2.3 5.9 1 0 9.6 13.4 6.2 25.2***
20–34 70.4 62 74.1 86.1
�
77.7 63.7** 74.2 63.4
�
C35 27.4 32.1 24.9 13.9 12.6 22.9** 19.6 11.5
Maternal education
0–8 1.7 0 2.4 4.9 11.5 15.8 1.7 0.7
9–11 4 9 10.6 11.4 19.3 23 15.9 12.5
12 22.9 12.1 23 52.8*** 34.9 25.9
�
31.4 49.3***
13–15 16.5 5.3 14.5 16.5 20.7 27.7 28.2 26.2

C16 54.9 73.6
�
49.5 14.3*** 13.6 7.6 22.8 11.4
Income
10,000 9.9 15.4 18.7 34.2* 40.1 44.2 28.6 46.9***
10,000–14,999 6.7 4.7 14.8 18.2 13.5 27*** 9.8 21.8***
15,000–19,999 4.8 0 7.3 13.5 9.1 5.3 8.4 14.9*
20,000–24,999 4.8 0 6 4.5 6.7 8.5 9.6 0.6**
25,000–34,999 7 0.5 4.6 15.3 9.7 5.8 13.5 4.8*
35,000–49,999 8.5 15.1 6.7 0.3 7.1 0.5* 10.5 1.7**
50,000–74,999 12.1 11.5 10.7 3.6 6.5 4.4 10.6 0.5**
C75,000 46.2 52.7 31.3 10.3** 7.3 4.3 9 8.8
Maternal nativity
U.S. born 68.4 66.7 12.3 100*** 35.0 23.1* 56.4 54.2
Non-U.S. born 31.1 30.3 87.7 0 64.7 76.9 42.9 45.8
Missing data 0.5 0.3 0 0 0.3 0 0.7 0
Language of questionnaire
English 99.2 97.0 99.5 100 50 46.2 98.8 100
Spanish 0 0 0.1 0 50 53.8 1.1 0

Missing data 0.8 3.0 0.4 0 0 0 0.1 0
NICU
No 94.9 94.2 93.6 98.1 93.7 91.9 85.7 82.3
Yes 5.1 5.8 6.3 1.9 6.3 8.1 14.3 17.3
Don’t know 0 0 0.1 0 0 0 0.1 0.5
Gender
Male 49.5 40.2 53.2 43 51.7 43.8 52.3 44.7
Female 50.5 59.8 46.8 57 48.3 56.2 47.7 55.3
Diabetes
No 92.5 90 85.6 81.4 90.2 86.2 89.6 98.9
Yes 7.5 10* 14.4 18.6 9.8 13.8 10.4 1.1
Stresses
0 45.6 26.4* 47.6 61.2 31.6 30.8 27 12.5**
1–2 41.4 54.3 39.9 28.8 42.6 25 42.9 39.4
3–5 12 14.2 12.1 5.4 22.7 32.1 24.6 41.2
6–13 1 5.1* 0.4 4.5** 3 12.1 5.4 6.9
Social support
No 9.4 15.3 8.6 13.7 22.7 28.3 24.6 31.4

Yes 90.6 84.7 91.4 86.3 77.3 71.7 75.4 68.6
123
Chinese women with a female infant were more likely to
experience PPD [35, 36]. In another study on Indian
women, having a female infant increased the effects of
other risk factors [37]. Recent findings have also demon-
strated a greater likelihood for Asian women to develop
gestational diabetes, which is associated with PPD [38–40].
Other explanations for Asian American depression in the
literature range from biological [41] to social [42]. Toge-
ther, these explanations may represent a general vulnera-
bility for depression generalizing to API women’s
depressed mood during the postpartum period. Future
studies in PPD research may want to specifically examine
the association between psychiatric history and PPD by

race/ethnicity to determine if psychiatric history predicts
PPD more strongly in API women.
Furthermore, discussing depressed mood with providers
increased the likelihood for women to receive a diagnosis.
This was especially true for APIs where the likelihood of
receiving a diagnosis was 3.2 times more than White
women after our analyses considered such discussions as a
factor. These high rates could reflect the quality of the
diagnostic processes that take place between API women
and their providers. The use of a diagnostic criterion by the
NYC PRAMS to assess PPD is unlike other prevalence
studies that typically use structured assessments for PPD
(e.g., a single question on depressive mood during preg-
nancy, multiple items covering symptomatology, etc.) [9,
12–14]. APIs tend to endorse somatic experiences rather
than psychological symptoms [43, 44]. Conversations with
a provider could increase sensitivity during the assessment,
thus facilitating a positive diagnosis. Increased research on

the diagnostic process within a health care setting would
greatly enhance understanding of how dialogues between
provider and patient result in diagnoses. In particular,
future research should consider differences in the charac-
teristics of providers and clinics among those who did and
did not receive a PPD diagnosis, and the nature of the
actual exchanges occurring between providers and patients.
It was particularly striking that approximately half of the
providers did not talk to women about PPD. Racial/ethnic
disparities were also found when assessing these rates.
While the majority of African American, Hispanic, and
White women reported having had a conversation with
their providers, only 38.6 % of API women in our study
reported this. Given that Asians tend to minimize their
psychological distress [6, 16], providers may not realize
distress nor recognize the need to bring up depressed mood.
APIs who had a conversation were 9.1 times more likely to
receive a diagnosis than APIs without, regardless of their

sociodemographic background. Thus, although APIs were
the group most likely to benefit from information about
depressed mood, they were the least likely to be provided
with it. Additionally, African Americans showed the
highest rate of having been presented with information
about mood compared to the other groups; those with a
conversation were 5.8 times more likely to receive a
diagnosis.
Altogether, and of greatest concern were the low rates of
assessment for all groups, and especially for APIs. Our
findings suggest that the information presented by a pro-
vider has powerful implications for determining diagnosis,
especially for APIs and African Americans. This finding
has implications for studies obtaining prevalence rates
without considering racial/ethnic disparities within the
screening or diagnostic process. Differences in prevalence
rates may be attributed to the lack of medical information
and treatment opportunities available to certain groups.

Our inclusion of known predictors for PPD in race-
stratified analyses allowed us to compare the strength of
stressors across groups. Most of the group differences in
the predicted likelihood for PPD were not statistically
significant suggesting few group differences in the
Table 3 continued
No PPD PPD No PPD PPD No PPD PPD No PPD PPD
(n = 1,010) (n = 33) (n = 383) (n = 42) (n = 1,162) (n = 91) (n =
979) (n = 48)
No 31.1 26.3 33.4 49.2 58.2 69.8 66.4 67.6
Yes 68.9 73.7 66.6 50.8* 41.8 30.2** 33.6 32.4
No 98 67.1 93.8 35.8 95 54.4 95.4 71
Yes 2 32.9*** 6.2 64.2*** 5 45.6*** 4.6 29***
No 46.5 26.1 66.2 21.1 43.5 30.5 40.5 8.6
Yes 53.5 73.9* 33.8 78.9*** 56.5 69.5** 59.5 91.4***

�
p 0.1; * p .05; ** p .01; *** p .001
123
association between stressors and PPD. Furthermore,
stressful events were not associated with a greater likeli-
hood at a statistical level, with the exception of API
women; those who reported 6-13 stressful events were
significantly more likely to receive a diagnosis. The
explanation may reside in the distribution of reported
stressful events for APIs; compared to other groups, the
majority of API women reported zero stressful events. As
such, the few APIs who disclosed high numbers of stressful
events may have been the most likely to receive a diag-
nosis. APIs may still minimize their experience of stress
despite being asked to state the occurrence of stressful
events given their tendency to minimize psychological
problems in general [6, 16]. Providers may want to inquire

further about actual events and how it affects their API
patients both psychologically and physically.
A number of associations between stressors and PPD
require clarification through further research. There was a
trend for increased PPD rates among API and White
women who gave birth to female infants. Few studies have
included infant gender in PPD studies within the U.S.;
those that have find no association [45, 46]. Given these
studies’ small samples (n 200), any effects may have
been too small to detect. One study did find increased self-
esteem in mothers of male infants, although this association
was mediated by paternal support [47]. The statistical trend
in our data may indicate actual preferences for infant
gender, but it could also reflect other factors moderated by
infant gender. Our findings demonstrate the need to include
infant gender in future studies and to identify mechanisms
that explain this association.
In addition, we did not find a general link between

gestational diabetes and PPD, despite a previous study’s
results [39]. When examining groups separately, APIs in
our study were more likely to have gestational diabetes;
however, this did not predict PPD. Instead, we found a
decrease in the likelihood for PPD diagnoses among
African Americans with gestational diabetes. There is
evidence to suggest that African Americans may be less
inclined to disclose symptoms even though providers speak
Table 4 Race/ethnicity stratified logistic regression showing
adjusted odds of postpartum depression diagnosis per predictor
by race/ethnic
group
OR CI OR CI OR CI OR CI
Gender
Male 1.0 1.0 1.0 1.0
Female 2.2
�
0.9–5.8 2.6
�
0.9–7.2 1.5 0.8–2.7 1.5 0.5–4.1

Diabetes
No 1.0 1.0 1.0 1.0
Yes 1.0 0.3–3.8 0.8 0.2–3.7 1.4 0.6–3.3 0.1** 0.0–0.5
Stresses
0 1.0 1.0 1.0 1.0
1–2 2.3
�
0.8–6.7 0.8 0.3–2.2 0.4* 0.2–0.9 1.8 0.3–10.2
3–5 1.2 0.2–8.5 0.1 0.0–1.1 0.8
�
0.4–1.8 3.1 0.4–21.7
6–13 5.2 0.9–30.8 2.7* 0.5–15.8 2.5 0.7–9.7 1.5 0.1–15.6
Social support
No 1.0 1.0 1.0 1.0
Yes 1.6 0.4–6.0 1.9 0.4–9.5 0.9 0.4–1.7 1.5 0.5–4.6
No 1.0 1.0 1.0 1.0
Yes 0.9 0.3–3.3 2.2 0.8–6.4 1.4 0.7–2.6 0.7 0.2–1.9

No 1.0 1.0 1.0 1.0
Yes 29.4*** 8.5–101.4 52.1*** 16.4–166.0 15.3*** 7.6–30.9
8.1*** 2.9–22.8
No 1.0 1.0 1.0 1.0
Yes 1.7 0.6–4.8 9.1** 2.5–33.4 1.3 0.7–2.6 5.8** 2.1–15.9
Only adjusted odds ratios are presented because race/ethnic
stratified analyses did not converge when including unadjusted
factors in the model.
This was due to zero to small sample sizes in race 9
sociodemographic contingency tables
�
p 0.1; * p .05; ** p .01; *** p .001
123
with them about prenatal depression and PPD at a higher
rate [48]. Mistrust and perceived discrimination within the
medical care setting may prevent disclosure about depres-
sion [22, 49]. In particular, some studies have found that

among those with depressive mood accompanied with
diabetes, African Americans were less likely to be recog-
nized as depressed and to receive depression treatment
[49–51]. Given our initial findings, the association between
gestational diabetes and PPD may not be generalizable,
although further research is needed to fully understand the
relationship. Studies that do not stratify by race may
overlook differences in the effect of gestational diabetes on
depression by race/ethnicity.
Interpretation of results should be made with caution in
light of our limitations. As with any self-report, inaccura-
cies in this data are possible given recall problems. In
addition, prenatal and PPD diagnoses were used in our
study. It would have been far preferable to obtain corrob-
orating information from medical records; however, this
information was unavailable within this survey study. It is
possible that providers employed different standards for
diagnoses, which may be reflected in this data, for instance,

the consideration of ‘‘baby blues’’ or the inclusion of dif-
ferent methods to assess depression (e.g., questionnaire,
verbal report). Furthermore, these diagnoses may not nec-
essarily reflect actual depression rates, but as discussed,
may be more of a reflection of provider sensitivity to
detecting symptoms in a particular group. Finally, the race/
ethnic categories are a proxy for a culture, and are com-
prised of heterogeneous subgroups. For instance, the
unique experiences of Chinese, Japanese or Filipino groups
may have been overlooked since they were combined into
one race/ethnic category.
Conclusion
Our results highlight racial/ethnic disparities in PPD and its
diagnosis, inviting a more nuanced approach in the con-
sideration of PPD risk factors. Although we relied on broad
race/ethnic categories, these findings demonstrate at a basic
level, the possibility of differential effects in the risk fac-
tors associated with PPD. Explanations for racial/ethnic

disparities in diagnosis compared to Whites differ by group
and are not necessarily due to sociodemographic status or
stress, factors that usually explain racial/ethnic disparities.
While prenatal depression seems to be a major risk factor
for PPD across all groups, the extent to which a factor is a
‘‘risk’’ for a particular racial/ethnic group needs to be
evaluated. These associations point to the possibility of
group-specific mechanisms leading to a PPD diagnosis.
Universal postpartum depression screening as a single
approach may not be adequate given the role that provider-
patient interactions might have as suggested by these study
findings. Rather, this study broadly reveals a need to con-
sider the diagnostic process between provider-patient by
race/ethnicity to better understand the source of treatment
disparities.
Acknowledgments The authors would like to acknowledge the
NYC Department of Health and Mental Hygiene Bureau of
Maternal,
Infant and Reproductive Health PRAMS Team, Bureau of Vital

Statistics, and the CDC PRAMS Team, Program Services and
Development Branch, Division of Reproductive Health. Support
during the preparation of this manuscript was provided through
a
grant from the Sackler Foundation for Psychobiological
Research and
through the Stuart T. Hauser Clinical Research Training
Fellowship
(2T32MH016259-30).
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123
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Rates and Predictors of Postpartum Depression by Race and
Ethnicity: Results from the 2004 to 2007 New York City
PRAMS Survey (Pregnancy Risk Assessment Monitoring
System)AbstractIntroductionMethodsSampleMeasuresVariables
Statistical
AnalysesResultsDiscussionConclusionAcknowledgmentsReferen
ces
CLINICAL ISSUES
The effect of nurse–patient interaction on anxiety and
depression in
cognitively intact nursing home patients
Gørill Haugan, Siw T Innstrand and Unni K Moksnes
Aims and objectives. To test the effects of nurse–patient
interaction on anxiety and depression among cognitively intact
nursing home patients.
Background. Depression is considered the most frequent mental

disorder among the older population. Specifically, the
depression rate among nursing home patients is three to four
times higher than among community-dwelling older people,
and a large overlap of anxiety is found. Therefore, identifying
nursing strategies to prevent and decrease anxiety and depres-
sion is of great importance for nursing home patients’ well-
being. Nurse–patient interaction is described as a fundamental
resource for meaning in life, dignity and thriving among nursing
home patients.
Design. The study employed a cross-sectional design. The data
were collected in 2008 and 2009 in 44 different nursing
homes from 250 nursing home patients who met the inclusion
criteria.
Methods. A sample of 202 cognitively intact nursing home
patients responded to the Nurse–Patient Interaction Scale and
the Hospital Anxiety and Depression Scale. A structural
equation model of the hypothesised relationships was tested by
means of LISREL 8.8 (Scientific Software International Inc.,
Lincolnwood, IL, USA).
Results. The SEM model tested demonstrated significant direct
relationships and total effects of nurse–patient interaction on
depression and a mediated influence on anxiety.
Conclusion. Nurse–patient interaction influences depression, as
well as anxiety, mediated by depression. Hence, nurse–

patient interaction might be an important resource in relation to
patients’ mental health.
Relevance to clinical practice. Nurse–patient interaction is an
essential factor of quality of care, perceived by long-term nurs-
ing home patients. Facilitating nurses’ communicating and
interactive skills and competence might prevent and decrease
depression and anxiety among cognitively intact nursing home
patients.
Key words: anxiety, depression, nurse–patient interaction,
nursing home, structural equation model analysis
Accepted for publication: 11 September 2012
Introduction
With advances in medical technology and improvement in the
living standard globally, the life expectancy of people is
increasing worldwide. The document An Aging World (US
Census Bureau 2009) highlights a huge shift to an older popu-
lation and its consequences. Within this shift, the most rapidly
growing segment is people over 80 years old: by 2050, the per-
centage of those 80 and older would be 31%, up from 18% in
1988 (OECD 1988). These perspectives have given rise to the

notions of the ‘third’ (65–80 years old) and the ‘fourth age’
(over 80 years old) in the lifespan developmental literature
(Baltes & Smith 2003). These notions are also referred to as
the ‘young old’ and the ‘old old’ (Kirkevold 2010).
Authors: Gørill Haugan, PhD, RN, Associate Professor, Faculty
of
Nursing, Research Centre for Health Promotion and Resources,
Sør-Trøndelag University College, HIST, Trondheim; Siw T
Innstrand, PhD, Associate Professor, Research Centre for Health
Promotion and Resources Norwegian University of Science and
Technology, NTNU, Trondheim; Unni K Moksnes, PhD, RN,
Associate Professor, Faculty of Nursing, Research Centre for
Health Promotion and Resources, Sør-Trøndelag University
College, HIST, Trondheim, Norway
Correspondence: Gørill Haugan, Associate Professor, Research
Centre for Health Promotion and Resources, HIST/NTNU,
NTNU,
SVT/ISH, 7491 Trondheim, Norway. Telephone:
+47 73 55 29 27.E-mail: [email protected]
© 2013 Blackwell Publishing Ltd

2192 Journal of Clinical Nursing, 22, 2192–2205, doi:
10.1111/jocn.12072
For many of those in the fourth age, issues such as physi-
cal illness and approaching mortality decimates their func-
tioning and subsequently lead to the need for nursing home
(NH) care. A larger proportion of older people will live for
shorter or longer time in a NH at the end of life. This
group will increase in accordance with the growing popula-
tion older than 65, and in particular for individuals older
than 80 years. Currently, 1�4 million older adults in the
USA live in long-term care settings, and this number is
expected to almost double by 2050 (Zeller & Lamb 2011).
In Norway, life expectancy by 2050 is 90�2 years for men
and 93�4 years for women (Statistics of Norway 2010).
Depression is one of the most prevalent mental health
problems facing European citizens today (COM 2005);
and, the World Health Organization (WHO 2001) has esti-
mated that by 2020, depression is expected to be the high-
est ranking cause of disease in the developed world.

Moreover, depression is described to be one of the most
frequent mental disorders in the older population and is
particularly common among individuals living in long-term
care facilities (Choi et al. 2008, Karakaya et al. 2009,
Lattanzio et al. 2009, Drageset et al. 2011, Phillips et al.
2011). A linear increase in prevalence of depression with
increasing age is described (Stordal et al. 2003); the three
strongest explanatory factors on the age effect of depression
are impairment, diagnosis and somatic symptoms, respec-
tively (Stordal et al. 2001, 2003). Worse general medical
health is seen as the strongest factor associated with depres-
sion among NH patients (Djernes 2006, Barca et al. 2009).
A review that included 36 studies from various countries,
reported a prevalence rate for major depression ranging
from 6–26% and from 11–50% for minor depression.
However, the prevalence rate for depressive symptoms ran-
ged from 36–49% (Jongenelis et al. 2003). Twice as many
women are likely to be affected by depression than men

(Kohen 2006), and older people lacking social and emo-
tional support tend to be more depressed (Grav et al.
2012). A qualitative study on successful adjustment among
women in later life identified three main areas as being the
main obstacles for many; these were depression, maintain-
ing intimacy through friends and family and managing the
change process associated with older age (Traynor 2005).
Significantly more hopelessness, helplessness and depres-
sion are found among patients in NHs compared with those
living in the community (Ron 2004). Jongenelis et al.
(2004) found that depression was three to four times higher
in NH patients than in community-dwelling adults. Moving
to a NH results from numerous losses, illnesses, disabilities,
loss of functions and social relations, and approaching mor-
tality, all of which increases an individual’s vulnerability
and distress; in particular, loneliness and depression are iden-
tified as risks to the well-being of older people (Routasalo
et al. 2006, Savikko 2008, Drageset et al. 2012). The NH

life is institutionalised, representing loss of social relation-
ships, privacy, self-determination and connectedness.
Because NH patients are characterised by high age, frailty,
mortality, disability, powerlessness, dependency and vulner-
ability, they are more likely to become depressed. A recent
literature review showed several studies reporting prevalence
of depression in NHs ranging from 24–82% (Drageset et al.
2011). Also, with a persistence rate of more than 50% of
depressed patients still depressed after 6–12 months, the
course of major depression and significant depressive symp-
toms in NH patients tend to be chronic (Rozzini et al.
1996, Smalbrugge et al. 2006a).
Moreover, studies in NHs report a large co-occurrence of
depression and anxiety (Beekman et al. 2000, Kessler et al.
2003, Smalbrugge et al. 2005, Van der Weele et al. 2009,
Byrne & Pachana 2010). A recent review concerning anxi-
ety and depression reports a paucity of findings on anxiety
in older people (Byrne & Pachana 2010). Hence, more

research is urgently required into anxiety disorders in older
people, as these are highly prevalent and associated with
considerable disease burden (ibid.).
Depression and anxiety in NH patients are associated
with negative outcomes such as poor functioning in
activities of daily living and impaired quality of life (QoL)
(Smalbrugge et al. 2006b, Diefenbach et al. 2011, Drageset
et al. 2011), substantial caregiver burden and worsened
medical outcomes (Bell & Goss 2001, Koenig & Blazer
2004, Sherwood et al. 2005), increased risk of hospital
admission (Miu & Chan 2011), a risk of increased demen-
tia (Devanand et al. 1996) and a higher mortality rate
(Watson et al. 2003, Ahto et al. 2007). Accordingly, efforts
to prevent and decrease depression and anxiety are of great
importance for NH patients’ QoL.
Social support and relations to significant others are
found to be a vital resource for QoL and thriving among
NH patients (Bergland & Kirkevold 2005, 2006, Drageset

et al. 2009a, Tsai et al. 2010, Tsai & Tsai 2011), as well
as the nurse–patient relationship (Haugan Hovdenes 2002,
Cox & Bottoms 2004, Franklin et al. 2006, Medvene &
Lann-Wolcott 2010, Burack et al. 2012). The perspective
of promoting health and well-being is fundamental in nurs-
ing and a major nursing concern in long-term care (Nakrem
et al. 2011, Drageset et al. 2009b). However, low rates of
recognition of depression by staff nurses is found (Bagley
et al. 2000, Volkers et al. 2004).
Through the last decades, the importance of establishing
the nurse–patient relationship as an integral component of
Journal of Clinical Nursing, 22, 2192–2205 2193
Clinical issues Nurse-patient-interaction, depression, and
anxiety
nursing practice has been well documented (Nåden &
Eriksson 2004, Arman 2007, Carpiac-Claver & Levy-
Storms 2007, Granados Gámez 2009, Rchaidia et al. 2009,

Fakhr-Movahedi et al. 2011). Excellent nursing care is
characterised by a holistic view with inherent human values
and moral; thus, excluding the patient as a unique human
being should be regarded as noncaring and amoral practice
(Haugan Hovdenes 2002, Nåden & Eriksson 2004, Aust-
gard 2008, Watson 2008). NH patients are in general
extremely vulnerable and hence the nurse–patient relation-
ship and the nurse–patient interaction are critical to their
experience of dignity, self-respect, sense of self-worth and
well-being (Dwyer et al. 2008, Harrefors et al. 2009,
Heliker 2009). NH patient receiving self-worth therapy
showed statistically significantly reduced depressive symp-
toms relative to control groups members 2 months after
receiving the intervention (Tsai et al. 2008). Self-worth
therapy comprised establishment of a therapeutic relation-
ship offering feedback and focusing the patient’s dignity,
emotional and mental well-being (ibid.).
Caring nurses engage in person-to-person relationships

with the NH patients as unique persons. Good nursing care
is defined by the nurses’ way of being present together with
the patient while performing nursing activities, in which
attitudes and competence are inseparately connected. ‘Pres-
ence’, ‘connectedness’ and ‘trust’ are described as funda-
mental cores of holistic nursing care (McGilton & Boscart
2007, Potter & Frisch 2007, Carter 2009) in the context of
the nurse–patient relationship in which the nurse–patient
interaction is taking place. Trust is seen as a confident
expectation that the nurses can be relied upon to act with
good will and to secure what is best for the individuals
residing in the NH. Hence, trust is the core moral ingredi-
ent in nurse–patient relationships; even more basic than
duties of beneficence, respect, veracity, and autonomy
(Carter 2009).
Caring is a context-specific interpersonal process that is
characterised by expert nursing practice, interpersonal sen-
sitivity, and intimate relationships (Finfgeld-Connett 2008)

which increases patient’s well-being (Nakrem et al. 2011,
Hollinger-Samson & Pearson 2000, Cowling et al. 2008,
Rchaidia et al. 2009, Reed 2009). The relationship between
NH staff attention and NH patients’ affect and activity par-
ticipation have been assessed among depressed NH
patients, showing that positive staff engagement was signifi-
cantly related to patients’ interest, activity participating,
and pleasure (Meeks & Looney 2011). These results suggest
that staff behaviour and engagement could be a reasonable
target for interventions to increase positive affect among
NH patients (ibid.).
In summary, the literature suggests depression as a com-
mon mental disorder among older people characterised by
high age, impairment, and somatic symptoms. In addition,
a large overlap of anxiety is reported. The patients’ sense
of loss of independency and privacy, feelings of isolation
and loneliness, and lack of meaningful activities are risk
factors for depression in NH patients. Nurse–patient inter-

action might be a resource for preventing and decreasing
depression among NH patients. To the authors’ knowl-
edge, previous research has not examined these relation-
ships in NHs by means of structural equation modelling
(SEM).
Aims
The main aim of this study was to investigate the relation-
ships between nurse–patient interaction, anxiety and
depression among cognitively intact NH patients by means
of SEM. Based on the theoretical and empirical knowledge
of depression, anxiety and nurse–patient interaction our
research question was: ‘Does the nurse–patient interaction
affect anxiety and depression in cognitively intact NH
patients?’ The following hypotheses were formulated:
� Hypothesis 1 (H1): nurse–patient interaction positively
affects anxiety.
� Hypothesis 2 (H2): nurse–patient interaction positively
affects depression.
� Hypothesis 3 (H3): depression negatively affects anxiety.

Methods
Design and ethical considerations
The study employed a cross-sectional design. The data was
collected in 2008 and 2009 in 44 different NHs from 250
NH patients who met the inclusion criteria: (1) local
authority’s decision of long-term NH care; (2) residential
time six months or longer; (3) informed consent compe-
tency recognised by responsible doctor and nurse; and (4)
capable of being interviewed. Two counties comprising in
total 48 municipalities in central Norway were selected,
from which 25 (at random) were invited to contribute in
this study. In total, 20 municipalities were partaken. Then,
all the NHs in each of the 20 municipalities was asked to
participate. A total of 44 NHs took part in the study. To
include as many participants from rural and central NHs,
respectively, the NHs was one by one invited to participate,
until the minimum of n = 200 was reached. The NH
patients were approached by a head nurse they knew

well. The nurse presented them with oral and written
2194 Journal of Clinical Nursing, 22, 2192–2205
G Haugan et al.
information about their rights as participants and their
right to withdraw at any time. Each participant provided
informed consent. Because this population has problems
completing a questionnaire independently, three trained
researchers conducted one-on-one interviews in the patient’s
room in the actual NH. Researchers with identical profes-
sional background were selected (RN, MA, trained and
experienced in communication with older people, as well as
teaching gerontology at an advanced level) and trained to
conduct the interviews as identically as possible. Inter-rater
reliability was assessed by comparing mean scores between
interviewers by means of Bonferroni-corrected one-way
ANOVAs. No statistically significant differences were found
that were not accounted for by known differences between

the areas in which the interviewers operated.
The questionnaires relevant for the present study were part
of a questionnaire comprising 130 items. The interviews
lasted from 45–120 minutes due to the individual partici-
pant’s tempo, form of the day, and need for breaks. Inter-
viewers held a large-print copy of questions and possible
responses in front of the participants to avoid misunder-
standings. Approval by the Norwegian Social Science Data
Services was obtained for a licence to maintain a register
containing personal data (Ref. no. 16443) and likewise we
attained approval from The Regional Committee for
Medical and Health Research Ethics in Central Norway
(Ref. no. 4.2007.645) as well as the directory of the 44 NHs.
Participants
The total sample comprised 202 (80�8%) of 250 long-term
NH patients representing 44 NHs. Long-term NH care was
defined as 24-hour care; short-term care patients, rehabilita-
tions patients, and cognitively impaired patients were not

included. Participants’ age was 65–104, with a mean of
86 years (SD = 7�65). The sample comprised 146 women
(72�3%) and 56 men (27�7%), where the mean age was
87�3 years for women and 82 years for men. A total of 38
(19%) were married/cohabitating, 135 (67%) were widows/
widowers, 11 (5�5%) were divorced, and 18 (19%) were
single. Duration of time of NH residence when interviewed
was at mean 2�6 years for both sexes (range 0�5–13 years);
117 were in rural NHs, while 85 were in urban NHs. In
all, 26�1% showed mild to moderate depression, only one
woman scored >15 indicating severe depression, 70�4%
was not depressed, and nearly 88% had no anxiety disor-
der. Missing data was low in frequency and was handled
by means of the listwise procedure; for the nurse–patient
interaction 4�0% and for anxiety and depression 5�0% had
some missing data.
Measures
The Nurse–Patient Interaction Scale (NPIS) was developed
to identify important characteristics of NH patients’ experi-
ences of the nurse–patient interaction. The NPIS comprises
14 items identifying essential relational qualities stressed in
the nursing literature (Watson 1988, Martinsen 1993,

Eriksson 1995a,b, Nåden & Eriksson 2004, Nåden &
Sæteren 2006, Levy-Malmberg et al. 2008). Examples of
NPIS-items include ‘Having trust and confidence in the staff
nurses’; ‘The nurses take me seriously’, ‘Interaction with
nurses makes me feel good’ as well as experiences of being
respected and recognised as a person, being listened to and
feel included in decisions. The items were developed to
measure the NH patients’ ability to derive a sense of well-
being and meaningfulness through the nurse–patient inter-
action (Haugan Hovdenes 1998, 2002, Hollinger-Samson
& Pearson 2000, Finch 2006, Rchaidia et al. 2009). The
NPIS has shown good psychometric properties with good
content validity and reliability among NH patients;
(Haugan et al. 2012). The NPIS is a 10-points scale from 1
(not at all)–10 (very much); higher numbers indicating
better nurse–patient interaction (Appendix 1). Cronbach’s
Table 1 Means (M), standard deviations (SD), Cronbach’s
alpha, and correlation coefficients for the study variables

Exercise 1 – Three-Phase, Variable-Frequency Induction-Motor D.docx

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