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Fall Risk and Cognition Assessments

Fall Risk and Cognition Assessments

CASE STUDY

Fall Risk and Cognition Assessments

 

Mrs. L is an 89-year-old widow who lives independently in her home. She drives, and she is an avid quilter. Her daughter lives within 2 miles and checks in daily with Mrs. L by phone or in person. Mrs. L has been admitted to your hospital with the diagnosis of R/O pneumonia.

 

Her past medical problems include the following:

 

· Bilateral hip replacements

· Detached retina × 2 (right eye)

· Osteoarthritis

· Depression

· Orthostatic hypotension

· Falls at home × 1

· Urinary frequency

· Insomnia (sleeps about 3 hours per night)

·

Mrs. L takes no medication, “not even an aspirin.”

 

Mrs. L uses her call button frequently to request assistance to the bathroom. Upon rounding, her nurse found Mrs. L on the floor, having crawled out of her bed with the side rails up. She was assessed and was found to have no injuries. Her gown was wet with urine. When asked to describe what happened, Mrs. L stated the following: “I called for help to the bathroom, and no one came. So rather than wetting the bed, I managed to crawl over the side rails. I slipped on the floor trying to get to the bathroom.”

 

1. What are Mrs. L’s known risk factors (in the hospital) for falling?

· Environmental:

 

 

· Medical conditions:

 

 

· Unsafe behaviors:

 

 

2. Using the Hendrich II Fall Risk Model (in Doc Sharing), determine Mrs. L’s fall risk score (attached below).

 

3. Mrs. L states she is “plagued by insomnia.” Using the Pittsburgh Sleep Quality Index (attached below) measure Mrs. L’s quality and patterns of sleep. Could Mrs. L’s sleeping pattern contribute to her risk for falls? What actions will you take based on your analysis?

 

4. Mrs. L is asked to complete the “Clock Drawing” portion of the Mini-Cog (attached below). She draws the clock showing 3 PM. Her drawing is missing the numbers 3, 4, and 5. One clock hand is pointing at 9 and the other is pointing at 12. For the “Three Word Recall” portion, Mrs. L remembers two words. What is her Mini-Cog score?

 

5. Having assessed Mrs. L’s fall risk and her cognitive state, develop three safety goals for each of the following:

 

· Environmental and equipment

 

 

· Gait and mobility

 

 

· New medications

 

 

· Anxiety, depression, and unsafe behavior

 

6. Write a 1 paragraph summary describing Mrs. L’s risk for falls in your own words.

 

 

 

 

 

 

 

FILL OUT ASSESSMENTS BELOW — COMPLETE EVERYTHING HIGHLIGHTED YELLOW TO THE BEST OF YOUR ABILITY FROM THE CASE STUDY INFORMATION.

 

( general assessment series Best Practices in Nursing Care to Older Adults From The Hartford Institute for Geriatric Nursing, New York University, College of Nursing )

 

 

 

 

 

 

 

Issue Number 8, Revised 2016

Editor-in-Chief: Sherry A. Greenberg, PhD, RN, GNP-BC New York University Rory Meyers College of Nursing

 

Fall Risk Assessment for Older Adults: The Hendrich II Fall Risk ModelTM

By: Ann Hendrich, PhD, RN, FAAN

Patient Safety Organization (PSO); Ascension Health

 

WHY: Falls among older adults, unlike other ages tend to occur from multifactorial etiology such as acute1,2 and chronic3,4 illness, medications5 , as a prodrome to other diseases 6 , or as idiopathic phenomena. Because the rate of falling increases proportionally with increased number of pre-existing conditions and

risk factors7, fall risk assessment is a useful guideline for practitioners. One must also determine the underlying etiology of why a fall occurred with a comprehensive post-fall assessment. 8Fall risk assessment and post-fall assessment are two interrelated but distinct approaches to fall evaluation, both recommended by national professional organizations.9

 

Fall assessment tools have often been used only on admission or infrequently during the course of an illness or in the primary care health management of an individual. Repeated assessments, yearly, and with patient status changes, will increase the reliability of assessment and help predict a change in condition signaling fall risk.

 

BEST PRACTICE APPROACH: In acute care, a best practice approach incorporates use of the Hendrich II Fall Risk ModelTM, which is quick to administer and provides a determination of risk for falling based on gender, mental and emotional status, symptoms of dizziness, and known categories of medications increasing risk.10 This tool screens for fall risk and is integral in a post-fall assessment for the secondary prevention of falls.

TARGET POPULATION: The Hendrich II Fall Risk ModelTM is intended to be used in the adult acute care, ambulatory, assisted living, long-term care, and population health settings to identify adults at risk for falls and to align interventions that will reduce the risk factor’s presence whenever possible.

 

VALIDITY AND RELIABILITY: The Hendrich II Fall Risk ModelTM was originally validated in a large case control study in an acute care tertiary facility with skilled nursing, behavioral health, and rehabilitation populations. The risk factors in the model had a statistically significant relationship with patient falls (Odds Ratio 10.12-1.00, .01 > p <.0001). Content validity was established through an exhaustive literature review, accepted nursing nomenclature, and the extensive experience of the principal investigators in this area.11

The instrument is sensitive (74.9%) and specific (73.9%), with inter-rater reliability measuring 100% agreement.11 Numerous national and international published and unpublished studies and presentations have tested the Hendrich II Fall Risk ModelTM in diverse settings. For example, the Model has demonstrated high sensitivity and specificity for fall risk prediction in general acute-care patients and, recently, in psychiatric patients, suggesting utility in this patient population.11,12

 

Further, the Model has been used successfully in multiple international studies. For example, the Model has been translated into Portuguese and evaluated in inpatient settings in Portugal.13 The authors of this study reported a sensitivity of 93.2% at admission and 75.7% at discharge, with positive and negative predictive values of 17.2% and 97.3%, respectively. The Model has also been adapted for use in Italian geriatric acute care settings, showing high specificity, sensitivity, and inter-rater reliability in one study.14 A comparison of the Hendrich II ModelTM to other fall risk models in the acute care setting in Australia found similar, strong sensitivity compared to other models, but acceptable specificity only with the Hendrich II ModelTM.15 Recently, a study from Lebanon reported higher sensitivity with the Hendrich II Model™ when compared to the Morse Fall Scale for fall prediction in 1815 inpatients.16 Finally, the Model was translated into Chinese and evaluated in elderly inpatients at a hospital in Peking, China.17 The Chinese version of the Model demonstrated excellent repeatability, inter-rater reliability, content validity, and, most importantly, high sensitivity (72%) and specificity (69%) for fall risk prediction.

 

STRENGTHS AND LIMITATIONS: The major strengths of the Hendrich II Fall Risk ModelTM are its brevity, the inclusion of “risky” medication categories, and its focus on interventions for specific areas of risk, rather than on a single, summed general risk score. Categories of medications increasing fall risk, as well as adverse side effects from medications leading to falls are built into this tool. Further, with permission, the Hendrich II Fall Risk ModelTM can be inserted into existing electronic health platforms, documentation forms, or used as a single document. It has been built into electronic health records with targeted interventions that prompt and alert the caregiver to modify and/or reduce specific risk factors present.11

 

FOLLOW-UP: Fall risk warrants thorough assessment as well as prompt intervention and treatment. The Hendrich II Fall Risk ModelTM may be used to monitor fall risk over time, minimally yearly, and with patient status changes in all clinical settings. Post-fall assessments area also critical for an evidenced- based approach to fall risk factor reduction.

 

REFERENCES:

Best practice information on care of older adults: www.ConsultGeri.org.

Gangavati, A., Hajjar, I., Quach, L., Jones, R.N., Kiely, D.K., Gagnon, P., & Lipsitz, L.A. (2011). Hypertension, orthostatic hypotension, and the risk of falls in a community-dwelling elderly population: The maintenance of balance, independent living, intellect, and zest in the elderly of Boston study. JAGS, 59(3), 383-389.

Sachpekidis, V., Vogiatzis, I., Dadous, G., Kanonidis, I., Papadopoulos, C., & Sakadamis, G. (2009). Carotid sinus hypersensitivity is common in patients presenting with hip fracture and unexplained falls. Pacing and Clinical Electrophysiology, 32(9), 1184-1190.

Stolze, H., Klebe, S., Zechlin, C., Baecker, C., Friege, L., & Deuschl, G. (2004). Falls in frequent neurological diseases-prevalence, risk factors and etiology. Journal of Neurology, 251(1), 79-84.

Roig, M., Eng, J.J., MacIntyre, D.L., Road, J.D., FitzGerald, J.M., Burns, J., & Reid, W.D. (2011). Falls in people with chronic obstructive pulmonary disease: An observational cohort study. Respiratory Medicine, 105(3), 461-469.

Cashin, R.P., & Yang, M. (2011). Medications prescribed and occurrence of falls in general medicine inpatients. The Canadian Journal of Hospital Pharmacy, 64(5), 321-326.

Miceli. D.L., Waxman, H., Cavalieri, T., & Lage, S. (1994). Prodromal falls among older nursing home residents. Applied Nursing Research, 7(1), 18-27.

Tinetti, M.E., Williams, T.S., & Mayewski, R. (1986). Fall risk index for elderly patients based on number of chronic disabilities. American Journal of Medicine, 80(3), 429-434.

Gray-Miceli, D., Johnson, J, & Strumpf, N. (2005). A step-wise approach to a comprehensive post-fall assessment. Annals of Long-Term Care, 13(12), 16-24.

Permission is hereby granted to reproduce, post, download, and/or distribute, this material in its entirety only for not-for-profit educational purposes only, provided that The Hartford Institute for Geriatric Nursing, New York University, Rory Meyers College of Nursing is cited as the source. This material may be downloaded and/or distributed in

electronic format, including PDA format. Available on the internet at www.hign.org and/or www.ConsultGeri.org. E-mail notification of usage to: hartford.ign@nyu.edu.

Panel on Prevention of Falls in Older Persons. American Geriatrics Society, British Geriatrics Society, & American Academy of Orthopaedic Surgeons Panel on Falls Prevention. (2011). Summary of the Updated American Geriatrics Society/British Geriatrics Society clinical practice guideline for prevention of falls in older persons. JAGS, 59(1), 148-157.

Hendrich, A.L. Bender, P.S. & Nyhuis, A. (2003). Validation of the Hendrich II Fall Risk Model: A large concurrent case/control study of hospitalized patients. Applied Nursing Research, 16(1), 9-21.

Hendrich, A., Nyhuuis, A., Kippenbrock, T., & Soga, M.E. (1995). Hospital falls: Development of a predictive model for clinical practice. Applied Nursing Research, 8(3), 129-139.

Van Dyke, D., Singley, B., Speroni, K. G., & Daniel, M. G. (2014). Evaluation of fall risk assessment tools for psychiatric patient fall prevention: a comparative study. Journal of Psychosocial Nursing and Mental Health Services, 52(12), 30-35.

Caldevilla, M.N., Costa, M.A., Teles, P., & Ferreira, P.M. (2012). Evaluation and cross-cultural adaptation of the Hendrich II Fall Risk Model to Portuguese. Scandinavian Journal of Caring Sciences. doi: 10.1111/j.1471-6712.2012.01031.x

Ivziku, D, Matarese, M., & Pedone, C. (2011). Predictive validity of the Hendrich Fall Risk Model II in an acute geriatric unit. International Journal of Nursing Studies, 48(4), 468-474.

Kim, E.A., Mordiffi, S.Z., Bee, W.H., Devi, K., & Evans, D. (2007). Evaluation of three fall-risk assessment tools in an acute care setting. Journal of Advanced Nursing, 60(4), 427- 435.

Nassar, N., Helou, N., & Madi, C. (2014). Predicting falls using two instruments (the Hendrich Fall Risk Model and the Morse Fall Scale) in an acute care setting in Lebanon. [Evaluation Studies]. Journal of Clinical Nursing, 23(11-12), 1620-1629.

Zhang, C., Wu, X., Lin, S., Jia, Z., & Cao, J. (2015). Evaluation of Reliability and Validity of the Hendrich II Fall Risk Model in a Chinese Hospital Population. PLoS One, 10(11), e0142395.

 

Hendrich II Fall Risk Model ™
RISK FACTOR RISK POINTS SCORE
Confusion/Disorientation/Impulsivity 4  
Symptomatic Depression 2  
Altered Elimination 1  
Dizziness/Vertigo 1  
Gender (Male) 1  
Any Administered Antiepileptics (anticonvulsants):

(Carbamazepine, Divalproex Sodium, Ethotoin, Ethosuximide, Felbamate, Fosphenytoin, Gabapentin, Lamotrigine, Mephenytoin, Methsuximide, Phenobarbital, Phenytoin, Primidone, Topiramate, Trimethadi- one, Valproic Acid)1

 

 

2

 
Any Administered Benzodiazepines:2

(Alprazolam, Chloridiazepoxide, Clonazepam, Clorazepate Dipotassium, Diazepam, Flurazepam, Halazepam3, Lorazepam, Midazolam, Oxazepam, Temazepam, Triazolam)

 

1

 
Get-Up-and-Go Test: “Rising from a Chair”

If unable to assess, monitor for change in activity level, assess other risk factors, document both on patient chart with date and time.

Ability to rise in single movement – No loss of balance with steps 0  
Pushes up, successful in one attempt 1  
Multiple attempts but successful 3  
Unable to rise without assistance during test

If unable to assess, document this on the patient chart with the date and time.

 

4

 
(A score of 5 or greater = High Risk) TOTAL SCORE  
© 2013 AHI of Indiana, Inc. All rights reserved. United States Patent No. 7,282,031 and U.S. Patent No. 7,682,308.

 

Reproduction of copyright and patented materials without authorization is a violation of federal law.

 

 

 

On-going Medication Review Updates:

 

Levetiracetam (Keppra) was not assessed during the original research conducted to create the Hendrich Fall Risk Model. As an antiepileptic, levetiracetam does have a side effect of somnolence and dizziness which contributes to its fall risk and should be scored (effective June 2010).

 

The study did not include the effect of benzodiazepine-like drugs since they were not on the market at the time. However, due to their similarity in drug structure, mechanism of action and drug effects, they should also be scored (effective January 2010).

 

Halazepam was included in the study but is no longer available in the United States (effective June 2010).

 

© 2012 AHI of Indiana, Inc. All Rights Reserved. Upright Fall Prevention Program

The Hendrich II Fall Risk ModelTM and all related materials may be used and reproduced only under license from AHI of Indiana, Inc. www.ahiofindiana.com. The Hartford Institute would like to acknowledge the original author of this Try This:®, Deanna Gray-Miceli, DNSc, APRN, BC, FAANP

 

 

general assessment series

 

Best Practices in Nursing Care to Older Adults

A series provided by The Hartford Institute for Geriatric Nursing, New York University, College of Nursing

EMAIL hartford.ign@nyu.edu HARTFORD INSTITUTE WEBSITE www.hartfordign.org

CLINICAL NURSING WEBSITE www.ConsultGeriRN.org

 

 

Instructions for Administration & Scoring

ID: Date:

 

Step 1: Three Word Registration

Look directly at person and say, “Please listen carefully. I am going to say three words that I want you to repeat back to me now and try to remember. The words are [select a list of words from the versions below]. Please say them for me now.” If the person is unable to repeat the words after three attempts, move on to Step 2 (clock drawing).

 

The following and other word lists have been used in one or more clinical studies.1-3 For repeated administrations, use of an alternative word list is recommended.

 

 

( Mini-Cog™ © S. Borson. All rights reserved. Reprinted with permission of the author solely for clinical and educational purposes. May not be modified or used for commercial, marketing, or research purposes without permission of the author (soob@uw.edu). v. 01.19.16 )

Version 1 Banana Sunrise Chair

Version 2 Leader Season Table

Version 3 Village Kitchen Baby

Version 4 River Nation Finger

Version 5 Captain Garden Picture

Version 6 Daughter Heaven Mountain

 

( Mini-Cog™ )

Step 2: Clock Drawing

Say: “Next, I want you to draw a clock for me. First, put in all of the numbers where they go.” When that is completed, say: “Now, set the hands to 10 past 11.”

 

Use preprinted circle (see next page) for this exercise. Repeat instructions as needed as this is not a memory test. Move to Step 3 if the clock is not complete within three minutes.

 

Step 3: Three Word Recall

Ask the person to recall the three words you stated in Step 1. Say: “What were the three words I asked you to remember?” Record the word list version number and the person’s answers below.

 

Word List Version: Person’s Answers:

 

Scoring

 

 

Word Recall:

 

(0-3 points)

 

1 point for each word spontaneously recalled without cueing.

 

 

Clock Draw:

 

 

(0 or 2 points)

Normal clock = 2 points. A normal clock has all numbers placed in the correct sequence and approximately correct position (e.g., 12, 3, 6 and 9 are in anchor positions) with no missing or duplicate numbers. Hands are pointing to the 11 and 2 (11:10). Hand length is not scored.

Inability or refusal to draw a clock (abnormal) = 0 points.

 

 

Total Score:

 

 

(0-5 points)

Total score = Word Recall score + Clock Draw score.

 

A cut point of <3 on the Mini-Cog™ has been validated for dementia screening, but many individuals with clinically meaningful cognitive impairment will score higher. When greater sensitivity is desired, a cut point of <4 is recommended as it may indicate a need for further evaluation of cognitive status.

 

 

( Clock Drawing )

ID: Date:

 

 

 

USE DRAW FUNCTION TO FILL THIS OUT OR MAY PRINT AND UPLOAD.

 

 

 

 

 

 

 

References

 

1. Borson S, Scanlan JM, Chen PJ et al. The Mini-Cog as a screen for dementia: Validation in a population-based sample. J Am Geriatr Soc 2003;51:1451–1454.

2. Borson S, Scanlan JM, Watanabe J et al. Improving identification of cognitive impairment in primary care. Int J Geriatr Psychiatry 2006;21: 349–355.

3. Lessig M, Scanlan J et al. Time that tells: Critical clock-drawing errors for dementia screening. Int Psychogeriatr. 2008 June; 20(3): 459–470.

4. Tsoi K, Chan J et al. Cognitive tests to detect dementia: A systematic review and meta-analysis. JAMA Intern Med. 2015; E1-E9.

5. McCarten J, Anderson P et al. Screening for cognitive impairment in an elderly veteran population: Acceptability and results using different versions of the Mini-Cog. J Am Geriatr Soc 2011; 59: 309-213.

6. McCarten J, Anderson P et al. Finding dementia in primary care: The results of a clinical demonstration project. J Am Geriatr Soc 2012; 60: 210-217.

7. Scanlan J & Borson S. The Mini-Cog: Receiver operating characteristics with the expert and naive raters. Int J Geriatr Psychiatry 2001; 16: 216-222.

 

( general assessment series Best Practices in Nursing Care to Older Adults From The Hartford Institute for Geriatric Nursing, New York University, College of Nursing )

 

 

 

 

 

 

 

 

Issue Number 6.1, Revised 2012

Series Editor: Marie Boltz, PhD, GNP-BC

Series Co-Editor: Sherry A. Greenberg, MSN, GNP-BC New York University College of Nursing

 

 

The Pittsburgh Sleep Quality Index (PSQI)

By: Carole Smyth MSN, APRN, BC, ANP/GNP, Montefiore Medical Center

 

WHY: Sleep is an important aspect of maintaining the body’s circadian rhythm. Inadequate sleep contributes to heart disease, diabetes, depression, falls, accidents, impaired cognition, and a poor quality of life. While normal aging changes interfere with the quality of sleep, other disease conditions and medications used by older adults compromise sleep patterns. A nursing assessment of sleep begins with a comprehensive assessment of sleep quality and sleep patterns. The nurse may be able to improve the sleep problem immediately with interventions or work with the health care team to assess the sleep issue in greater depth.

 

BEST TOOL: The Pittsburgh Sleep Quality Index (PSQI) is an effective instrument used to measure the quality and patterns of sleep in the older adult. It differentiates “poor” from “good” sleep by measuring seven domains: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction over the last month. The client self-rates each of these seven areas of sleep. Scoring of the answers is based on a 0 to 3 scale, whereby 3 reflects the negative extreme on the Likert Scale.

 

A global sum of “5” or greater indicates a “poor” sleeper. Although there are several questions that request the evaluation of the client’s bedmate or roommate, these are not scored, nor reflected in the attached instrument. An update to the scoring: if 5J is not complete or the value is missing, it now counts as a “0”. More information on administration and scoring is available at the University of Pittsburgh, Sleep Medicine Institute, Pittsburgh Sleep Quality Index (PSQI) website at http://www.sleep.pitt.edu/content.asp?id=1484&subid=2316.

 

TARGET POPULATION: The PSQI can be used for both an initial assessment and ongoing comparative measurements with older adults across the health care continuum.

 

VALIDITY AND RELIABILITY: The PSQI has internal consistency and a reliability coefficient (Cronbach’s alpha) of 0.83 for its seven compo- nents. Numerous studies using the PSQI in a variety of older adult populations internationally have supported high validity and reliability.

 

STRENGTHS AND LIMITATIONS: The PSQI is a subjective measure of sleep. Self-reporting by clients though empowering, may can reflect inaccurate information if the client has difficulty understanding what is written or cannot see or physically write out responses. The scale has been translated into over 56 languages. For those with visual impairments, the nurse can read the PSQI as written to the client.

 

MORE ON THE TOPIC:

Best practice information on care of older adults: www.ConsultGeriRN.org.

University of Pittsburgh, Sleep Medicine Institute, Pittsburgh Sleep Quality Index (PSQI).

Available at http://www.sleep.pitt.edu/content.asp?id=1484&subid=2316.

Alessi, C.A., Martin, J.L., Webber, A.P., Alam, T., Littner, M.R., Harker, J.O., & Josephson, K.R. (2008). More daytime sleeping predicts less functional recovery among older people undergoing inpatient post-acute rehabilitation. Sleep 31(9), 1291-1300.

Buysse, D.J., Reynolds III, C.F., Monk, T.H., Berman, S.R., & Kupfer, D.J. (1989). The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research. Journal of Psychiatric Research, 28(2), 193-213.

Carney, S., Koetters, T., Cho, M., West, C., Paul, S.M. , Dunn, L., Aouizerat, B.E., Dodd, M., Cooper, B., Lee, K. Wara, W., Swift, P.,

& Miaskowski, C. (2011). Differences in sleep disturbance parameters between oncology outpatients and their family caregivers.

Journal of Clinical Oncology, 29(8), 1001-1006.

Taibi, D.M., Vitiello M.V. (2011). A pilot study of gentle yoga for sleep disturbance in women with osteoarthritis. Sleep Med, 12(5), 512-517. Neale, A., Hwalek, M., Scott, R., Sengstock, M., & Stahl, C. (1991). Validation of the Hwalek-Sengstock elder abuse screening test.

Journal of Applied Gerontology, 10(4), 406-418.

 

Permission is hereby granted to reproduce, post, download, and/or distribute, this material in its entirety only for not-for-profit educational purposes only, provided that

The Hartford Institute for Geriatric Nursing, New York University, College of Nursing is cited as the source. This material may be downloaded and/or distributed in electronic format, including PDA format. Available on the internet at www.hartfordign.og and/or www.ConsultGeriRN.org. E-mail notification of usage to: hartford.ign@nyu.edu.

 

 

The Pittsburgh Sleep Quality Index (PSQI)

 

Instructions: The following questions relate to your usual sleep habits during the past month only. Your answers should indicate the most accurate reply for the majority of days and nights in the past month. Please answer all questions. During the past month,

 

When have you usually gone to bed?

How long (in minutes) has it taken you to fall asleep each night?

When have you usually gotten up in the morning?

How many hours of actual sleep do you get at night? (This may be different than the number of hours you spend in bed)

 

5. During the past month, how often have you had trouble sleeping because you… Not during the past month (0) Less than once a week (1) Once or twice a week (2) Three or more times week (3)
a. Cannot get to sleep within 30 minutes        
b. Wake up in the middle of the night or early morning        
c. Have to get up to use the bathroom        
d. Cannot breathe comfortably        
e. Cough or snore loudly        
f. Feel too cold        
g. Feel too hot        
h. Have bad dreams        
i. Have pain        
j. Other reason(s), please describe, including how often you have had trouble sleeping because of this reason(s):        
6. During the past month, how often have you taken medicine (prescribed or “over the counter”) to help you sleep?        
7. During the past month, how often have you had trouble staying awake while driving, eating meals, or engaging in social activity?        
8. During the past month, how much of a problem has it been for you to keep up enthusiasm to get things done?        
  Very good (0) Fairly good (1) Fairly bad (2) Very bad (3)
9. During the past month, how would you rate your sleep quality overall?        

 

 

Component 1 #9 Score C1

Component 2 #2 Score (15min=0; 16-30 min=1; 31-60 min=2, >60 min=3) + #5a Score

(if sum is equal 0=0; 1-2=1; 3-4=2; 5-6=3) C2

Component 3 #4 Score (>7=0; 6-7=1; 5-6=2; <5=3) C3

Component 4 (total # of hours asleep)/ (total # of hours in bed) x 100

>85%=0, 75%-84%=1, 65%-74%=2, <65%=3 C4

Component 5 Sum of Scores #5b to #5j (0=0; 1-9=1; 10-18=2; 19-27=3) C5

Component 6 #6 Score C6

Component 7 #7 Score + #8 Score (0=0; 1-2=1; 3-4=2; 5-6=3) C7

 

Add the seven component scores together Global PSQI Score

 

Buysse, D.J., Reynolds III, C.F., Monk, T.H., Berman, S.R., & Kupfer, D.J. (1989). The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research. Journal of Psychiatric Research, 28(2), 193-213.

Reprinted with permission from copyright holder for educational purposes per the University of Pittsburgh, Sleep Medicine Institute, Pittsburgh Sleep Quality Index (PSQI) website at http://www.sleep.pitt.edu/content.asp?id=1484&subid=2316.

 

 

 

general assessment series

 

Best Practices in Nursing Care to Older Adults

A series provided by The Hartford Institute for Geriatric Nursing, New York University, College of Nursing

EMAIL hartford.ign@nyu.edu HARTFORD INSTITUTE WEBSITE www.hartfordign.org

CLINICAL NURSING WEBSITE www.ConsultGeriRN.org

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