Measuring Knowledge of Disease Management in Adolescents with Cystic Fibrosis: Initial Psychometric Evaluation

Abstract

There are no updated, valid knowledge measures for adolescents with cystic fibrosis (CF). This study assessed the psychometric properties of the Knowledge of Disease Management-CF (KDM-CF). Participants (n = 266 age 11–20 years; 50% female) completed the KDM-CF. Items were examined based on floor/ceiling thresholds, clinical relevance, and correlation with its subscale. Item difficulty and discrimination, subscale structure, reliability, and convergent and discriminant validity were also examined. Two subscales were identified, Self-Management (12 items, M = 67.58, SD = 22.44) and Treatment Information (11 items, M = 65.69, SD = 22.22), in addition to Total Knowledge (M = 66.67, SD = 18.56). The KDM-CF demonstrated good subscale structure and internal consistency (α = 0.68–0.78). Convergent validity was found for age (older; β = 2.836–3.593); discriminant validity was found for gender (females had higher scores; F(1) = 4.945–6.47). The KDM-CF demonstrated adequate reliability and validity, good scale structure, good item difficulty and discrimination, and revealed knowledge deficits. The KDM-CF can be used to identify and remediate knowledge gaps in adolescents with CF.

Introduction

Pediatric patients with cystic fibrosis (CF) have significant gaps in their knowledge of disease management (KDM).^1,2 In a prior study measuring knowledge among patients ages 6–13 and their parents, children answered only 55% of the questions correctly, and parents answered 68% correctly.¹ Deficits in disease-specific knowledge have been associated with worse adherence to clinic attendance, and deficits in knowledge of prescribed treatment plan have been associated with greater disease severity.^3,4 Using a valid measure, CF care teams could target their education and teaching time to KDM deficits and evaluate adolescents' readiness to assume responsibility for disease management and transition to adult care.²

To our knowledge, there is currently only 1 CF knowledge measure for adults (CF Knowledge Test),⁵ 1 measure for adolescents (KDM-CF),² and 1 for caregivers (KDM-CF-parent [P]).⁶ The adult measure has 49 questions, which were derived from a literature review of previously validated questionnaires and interviews with CF clinicians. It focuses on the clinical manifestations of CF and its etiology (e.g., What is the basic defect?) with few questions targeting management of the disease. Higher scores were associated with more education and being female.

In contrast, the KDM-CF, initially created in 2000 (originally named the CFKQ) and revised in 2012, focuses squarely on applied information and self-management. This measure was designed for adolescents, ages 11–20, with a 5th grade reading level. Because the treatment regimen and CF care guidelines change frequently, knowledge measures must regularly be updated. This study examined the updated version of the KDM-CF for adolescents, as well as its association with a variety of factors.

Although recent studies on KDM in adolescents with CF is limited to specific concerns (e.g., reproduction), in adults, being female and more educated was associated with higher knowledge scores.⁵ Psychometric analyses of the KDM-CF-P identified that more maternal education and private insurance were associated with more knowledge of CF.⁶ Despite the lack of a direct relationship, KDM is an important component of adherence. Given the low rates of adherence documented in CF across the lifespan, including a decrease in adherence during adolescence,⁷ remediating knowledge gaps may be one easy step to improve adherence. To identify and remediate these knowledge gaps, however, we need a valid measure that is up to date.

The purpose of this study was to evaluate the item difficulty and discrimination, subscale structure, and reliability and validity of the KDM-CF measure for adolescents with CF. We evaluated convergent validity using age and measures of health [i.e., forced expiratory volume in one second percent (FEV₁%) predicted, body mass index (BMI) percentile] and discriminant validity using gender and markers of socioeconomic status (SES; mother's and father's education, income, and insurance). We expected that younger adolescents and those with worse disease severity would have lower KDM scores. We also expected that males and adolescents whose parents had less education, lower family income, and public insurance would have lower KDM scores.

Methods

We evaluated the psychometric properties of the KDM-CF in a large, national sample of adolescents participating in iCARE (I Change Adherence & Raise Expectations) clustered randomized trial.^2,8 Data for this study came from the 9 waitlist control group sites before their receipt of educational remediation. The revised measure was administered at the 12-month visit. Of the 307 adolescents randomized to the waitlist control group, data were available for 266 adolescents.

Participants were enrolled from October, 2009 through February, 2011. Eligible participants were 11–20 years of age at enrollment, attended an accredited CF Center in the United States, English speaking, and had a verified diagnosis of CF. Additionally, patients were prescribed at least 1 pulmonary medication (i.e., azithromycin, hypertonic saline, dornase alfa, or inhaled tobramycin), and consented to provide data to the Cystic Fibrosis Foundation (CFF) Registry. This was intended to be a representative sample. Thus, participants were not recruited based on their rate of adherence. Patients were not eligible if they were planning to change care teams in the next 2 years, seen at a satellite clinic, or on a lung transplant list. Participants were given $50 gift cards for completing the assessment procedures. For inclusion in these analyses, eligible participants had completed the KDM-CF at the 12-month iCARE study visit. No other additional inclusion or exclusion criteria were used. The Institutional Review Boards at the participating centers approved the study. Written consent and assent, when appropriate, was obtained from all participants.

Measure development

Items for the KDM-CF were based on a modification of the cystic fibrosis knowledge questionnaire (CFKQ), developed by Quittner et al.^1,9 The CFKQ items were derived from existing literature, knowledge materials created by the CFF, and previous studies assessing deficits in KDM.^10,11 Experts in the field consulted on the appropriateness and accuracy of the questions and answers.⁹

Both the original and modified versions were written to minimize medical jargon and facilitate good comprehension. The CFKQ was modified before beginning the iCARE study to update questions about new treatment and care guidelines. At that time, the measure was renamed, the KDM-CF. During the iCARE study, the KDM-CF was given at baseline, then modified due to significant ceiling effects on several items. Revisions included rephrasing of questions and answers, item deletions, and additions. The version tested in these analyses had 35 items. Questions were presented in a multiple-choice format. All questions had only one correct response and were analyzed as dichotomous indicators (i.e., right, wrong).

Demographic and medical information

Demographic information (age, gender, parent education, marital status, and type of insurance) was obtained through questionnaire. Father's and mother's education were coded as ordinal categories: Some high school or less, High school diploma/General Education Diploma, Vocational school, Some college, College degree (e.g., BA, BS), and Graduate or professional degree. Income was coded into $20,000 increments from <$20,000 to >$100,000. Primary health insurance was coded as Public (Medicare/Medicaid) or Private.

Medical information (height, weight, pulmonary function testing results) were obtained from the CFF Patient Registry. Clinic encounter data, including date of visit, height, weight and FEV₁, need for pancreatic enzymes, dates of hospitalizations, and dates of intravenously (IV) administered antibiotic use, were abstracted from the CF Registry. A pulmonary exacerbation was defined as having a course of IV antibiotics. The Global Lung Function Initiative (GLI)-2012 reference equations (GLI) were used to calculate FEV₁% predicted.¹² Centers for Disease Control and Prevention growth charts were used to calculate BMI percentile.

Analytic approach

Item reduction and subscale reliability

Items were evaluated for floor and ceiling effects, with at least 20% of the sample answering the question correctly or incorrectly.¹³ Once subscales were established, items were also deleted based on insufficient loadings. A correlation coefficient threshold of ≥0.40 was used.

Item response theory parameters

Item difficulty and item discrimination parameters were calculated based on Item response theory (IRT) methodology. The 2PL and 3PL models were examined and compared. After model comparison, item difficulty (typical range = −1, 1) and item discrimination (typical range = −3, 3) were examined. Because the measure is intended for use with adolescents, items with difficulty below the lower bound of the typical range (−1) were retained, whereas items with difficulty above the upper bound of the typical range¹ were deleted, unless all authors agreed on its clinical utility.

Subscale development

Exploratory Factor Analysis (EFA) was used to determine the number of factors by reviewing model fit, eigenvalues, scree plots, and observed variance. Confirmatory Factor Analyses (CFA) were then used to investigate structure and loadings on each subscale and the total scale. Model fit for the EFA and CFA models were determined by the chi-square test of model fit, the comparative fit index (CFI), the root mean square error of approximation (RMSEA), and the weighted root mean residual (WRMR). A model had good fit based on a nonsignificant chi-square test of model fit (P > 0.05), a CFI >0.95, a RMSEA <0.06, and a WRMR <0.90.¹⁴ Individual item loadings were determined to be sufficient if ≥0.40. Internal consistency with values >0.70, measured by Cronbach's alpha, was considered acceptable. Additionally, split-half reliability was examined using random halves and evaluated by the Spearman–Brown coefficient.

Subscale and measure validation

Convergent validity was examined with univariate regression by regressing both KDM-CF subscale scores and the Total score on age, FEV₁% predicted, and BMI. We hypothesized that those who were younger and had worse disease severity would have worse KDM-CF scores. Discriminant validity was examined by comparing group differences in KDM-CF subscales and Total scores by gender. One-way analysis of variance (ANOVA) was used to analyze these differences.

Differences in knowledge by SES markers were evaluated based on father's education, mother's education, income, and primary health insurance coverage. These were analyzed using one-way ANOVAs using subscale scores and Total score.

Results

Demographic and medical information

The mean age of the 266 adolescents was 15.61 years (SD = 2.66), ranging from 11 to 22 years, and 50.2% were female. Sixty-three percent of the sample had lung function in the mild range (defined as 70% or greater FEV₁% predicted), about 20% had lung function in the moderate range (FEV₁% predicted = 40–69), about 5% had lung function in the severe range (FEV₁% predicted <40), and about 11% were missing. The average BMI was about 50% and about 12% of the sample met criteria for nutritional failure (BMI percentile <10th percentile). Most parents had attended some college or beyond and ∼23% of adolescents were on Medicaid/Medicare (Table 1).

Table 1.

Sample Demographics and Markers of Disease Severity

Characteristic		% Missing
Female, %	50.0	—
Age at 12-month visit (years) (SD) [range]	15.61 (2.66) [11–22]	—
FEV₁% predicted (SD) [range]	81.08 (22.51) [20.51–112.48]	10.0
BMI% (SD) [range]	48.08 (25.87) [0–98.10]	10.0
Nutritional failure, %	11.5	10.4
Race, %		2.9
Caucasian	94.1
Black	3.3
Asian	1.3
Ethnicity, %
Hispanic	3.6	6.5
Parental marital status, %		—
Single/never married	18.6
Married	50.8
Divorced	10.4
Widowed	3.6
Separated/remarried/with a partner	3.6
Father's education, %		6.1
Some high school or less	5.9
High school diploma/GED	22.1
Vocational school	7.5
Some college	12.4
College degree (e.g., BA, BS)	18.9
Graduate or professional degree	12.7
Mother's education, %		6.5
Some high school or less	4.6
High school diploma/GED	20.7
Vocational school	5.7
Some college	26.8
College degree (e.g., BA, BS)	31.4
Graduate or professional degree	10.7
Family income (yearly), %		9.0
Under $20,000	11.4
$20–39,000	11.4
$40–59,999	10.7
$60–79,999	11.7
$80–99,999	8.5
Over $100,000	29.0
Insurance, %		4.3
Medicaid/Medicare	22.8
Private	54.1

BMI, body mass index; FEV₁, forced expiratory volume in one second; GED, General Education Diploma.

Item reduction

Five items were deleted for being above the ceiling threshold and 1 item was deleted for being below the floor threshold (Table 2). One additional item was on the threshold, was reviewed, and determined to be unrelated to day-to-day care and, thus, was also deleted. Three additional items fell just outside of these thresholds, but were retained due to their significant clinical importance.

Table 2.

Subscale Structure, Item Loadings, and Deletion Decisions

Question		% Correct	Self-management loading	Treatment information loading
1. PFTs are a fancy name for tests that:		60	0.56	−0.56
a) Show how your lungs are working
b) Only need to be done once in a while
c) Depend on your effort in blowing
d) Answers a and c
4. Which of the following is a sign that your body is losing too much salt?		67	0.53	−0.24
a) Weakness
b) Fever
c) Muscle cramps
d) Abdominal pain
e) Answers b and c
f) All of the above
9. When you exercise, you should:		76	0.89	0.00
a) Eat more salt
b) Pay attention to your breathing
c) Sit down frequently and rest
d) Drink more water
e) Answers a and d
13. If your body is not digesting fat from the foods you eat, your stools may:		78	0.93	−0.21
a) Float
b) Change color
c) Sink
d) Smell bad
e) Not change
f) Answers a, b, and d
14. To make the most out of your clinic visit, you should:	^****	86	0.91	−0.03
a) Write down your questions before going to your clinic visit
b) Ask your CF Team to write down changes in your treatment plan
c) Keep a journal or write down changes in your health
d) Ask questions if you do not understand something
e) Answers a and d
f) All of the above
18. Stress, lots of homework, or problems with a friend can affect your:		73	0.81	−0.21
a) Eating
b) Mood
c) Health
d) Answers b and c
e) All of the above
21. Foods that contain the most energy/calories are:	^****	21	0.45	−0.40
a) Fats
b) Carbohydrates
c) Proteins
26. Teens with CF should eat:		70	0.68	0.02
a) Up to twice as much food as teens without CF
b) The same amount of food as teens without CF
c) More fat than teens without CF
d) Less milk and cheese than teens without CF
e) Answers a and c
27. When you feel stressed, it may help to:		77	0.94	−0.11
a) Ignore it
b) Act out (yelling, hitting)
c) Do something fun
d) Talk to someone
e) Answers a and b
f) Answers c and d
31. Being open and talkative with your CF Team is important because:		70	0.69	−0.01
a) You know the most about your body
b) They are nosy about your life
c) The team needs to get information from you
d) It helps your clinic visits go faster
e) Answers a, c, and d
f) All of the above
33. To keep from getting new CF bacteria in your lungs, you should:		62	0.75	0.05
a) Not hang out with others who have CF
b) Cough into tissues and throw them away
c) Stay home as much as possible
d) Answers a and b
e) All of the above
34. Teens with CF need to eat more because:		73	0.90	−0.15
a) Some energy is lost when food is not properly digested
b) Their metabolism is faster
c) They have to fight off infections
d) Their appetites are naturally bigger
e) Answers a, b, and c
2. Most of the food you eat is absorbed in the:	^****	43	0.24	0.18
a) Stomach
b) Intestines
c) Liver
7. Inhaled antibiotics are generally most effective if done:		55	0.48	0.33
a) Before airway clearance
b) After airway clearance
c) In the morning
8. Stomach cramps and gas can be signs of hunger and:		75	0.61	0.29
a) Overeating
b) A lack of vitamins
c) Poorly digested fat
d) Do not need treatment
16. How can you decrease the number of lung infections you get?	^****	81	0.88	0.06
a) Stay away from people who are sick
b) Try to cough less
c) Keep up with your airway clearance
d) Answers a and c
e) All of the above
19. Exercise can replace regular airway clearance:		79	0.80	0.21
a) True
b) False
20. Changes in your mucus, cough, or energy levels:		64	0.25	0.42
a) Should be watched until the next clinic visit
b) Can mean you are getting an infection
c) Show that you need to eat more
23. Your CF Team will check your blood glucose:		69	0.12	0.72
a) To see if you are eating enough
b) Because people with CF have a higher chance of developing diabetes
c) To see if you are doing your treatments
d) To see if you need more vitamins
e) All of the above
24. Timing is everything; what is the right order to take the following treatments?		53	0.27	0.66
a) Airway clearance, inhaled antibiotics, inhaled short-acting bronchodilators
b) Inhaled short-acting bronchodilators, airway clearance, inhaled antibiotics
c) Inhaled antibiotics, inhaled short-acting bronchodilators, airway clearance
29. With CF, coughing:	^****	86	0.75	0.23
a) Should be controlled with cough medicines
b) Can make others sick
c) Makes it harder to fight infections
d) Helps you spit mucus out
30. One way to add calories to scrambled eggs is to:		73	0.57	0.43
a) Mix them with 2% milk instead of whole milk
b) Add grated cheese
c) Add salt and pepper
d) Add vegetables
35. People with CF take vitamins A, D, E, and K because these vitamins:		43	0.02	0.75
a) Can be poorly absorbed from food
b) Need extra water to be absorbed
c) Are not found in foods people normally eat
d) Answers a and b
3. Undigested food will pass through your digestive system if you take:	^***	79	0.18	0.19
a) Too many enzymes
b) Too few enzymes
c) The right amount of enzymes
d) Enzymes just before you eat
5. People with CF should eat _____ snack(s) per day.	^***	45	0.31	−0.02
a) 1
b) 2
c) 3
d) More than 4
6. Airway clearance:	^**	58	0.50	0.13
a) Keeps your body from producing mucus
b) Helps prevent lung infections
c) Should be done only when you are sick
d) Can be combined with any inhaled treatment
10. The CF bacteria in your lungs will always be killed with IV antibiotics:	^*	85	0.57	0.23
a) True
b) False
11. If you cough up a small amount of blood on a Friday night, what should you do?	^**	73	0.70	0.01
a) Do your nebulized treatments and airway clearance
b) Wait and see if it happens again
c) Contact the CF doctor on call
d) Call the CF clinic to make an appointment
12. Bronchodilators are used to:	^*	89	0.81	0.18
a) Open your airways
b) Reduce mucus
c) Prevent cough
d) Clear up a stuffy nose
15. Which of the following prevents lung damage?	^**	71	0.69	0.00
a) Hypertonic saline
b) Enzymes
c) Airway clearance
d) Exercise
e) Eating more calories
17. Your CF Team is always talking about your BMI percentile, which:	^*	18	0.72	−0.42
a) Should be the same as your age
b) Comes from measuring your weight
c) Should be at or above the 50th percentile for your age
d) Should be between the 10th and 25th percentile for your age
e) Answers b and c
22. Inhaled medications like Pulmozyme^® and hypertonic saline:	^***	66	0.35	0.24
a) Treat bacteria in the lungs
b) Help remove mucus from the lungs
c) Improve appetite
d) Can replace airway clearance
25. When you might have a pulmonary exacerbation, you should:	^*	82	0.73	0.08
a) Wait a week and see if you get better
b) Take extra vitamins
c) Call your CF Team when you develop a fever
d) Call your CF Team right away
28. It is best to take enzymes:	^*	95	0.88	0.18
a) As soon as you remember them
b) At the beginning of a meal or snack
c) During a meal or snack
d) Soon after eating
32. Enzymes should be kept in a cool, dark place.	^*	80	0.75	0.07
a) True
b) False

No questions were deleted due to difficulty/discrimination.

Bold indicates designated scale.

Question was deleted due to floor/ceiling effect.

Question was deleted due to lack of conceptual fit with either subscale.

***

Question was deleted due to insufficient loading (i.e., the extent to which a factor explains a variable).

****

Question was retained due to clinical importance.

BMI, body mass index; CF, cystic fibrosis; IV, intravenous; PFT, pulmonary function test.

The EFA, run on half of the sample, which was randomly selected (n = 133), indicated a 2-factor structure, as evidenced by a nonsignificant chi-square (χ² = 545.19, P = 0.27, CFI = 0.97, RMSEA = 0.02, standardized root mean square residual = 0.18) and eigenvalues were used to determine question organization onto the subscales. Next, items were examined for their loading. Three items were deleted due to insufficient loading (<0.40). Finally, after the establishment of the subscales, each item was reviewed for conceptual fit with its subscales. Three items were determined to not conceptually fit with either subscale and were deleted. This process left a remaining total of 23 items.

Internal consistency for the Self-Management subscale (0.74), the Treatment Information subscale (0.68), and the Total measure (0.78) were calculated. Split-half reliability was calculated for the Self-Management subscale (0.73), Treatment Information subscale (0.65), and the Total measure (0.76).

IRT analyses

There was a significant difference between the Bayesian Information Criteria (BIC) of the 3PL model (BIC = 3,515.78) and the 2PL model (BIC =3,449.35), demonstrating that the 2PL was the more parsimonious model and, thus, was retained. With the exception of one, all of the items demonstrated discrimination within the expected range (−3, 3) and no item had both low difficulty and low discrimination (Table 3). One item (question 24) demonstrated low discrimination (0.13) and extremely high difficulty (10.82). This item was discussed extensively with the research team and was determined to be a concept that had high clinical utility and was frequently misunderstood; thus, it was retained.

Table 3.

Item Response Theory Discrimination and Difficulty

Subscale	Item discrimination	Item difficulty
Item number
Self-management
Q1	0.65	−0.66
Q4	0.87	−0.91
Q9	1.55	−1.02
Q13	1.48	−1.18
Q14	1.94	−1.45
Q18	1.46	−0.93
Q21	0.13	10.82
Q26	1.14	−0.92
Q27	2.48	−0.89
Q31	0.98	−1.00
Q33	1.11	−0.53
Q34	1.82	−0.83
Treatment information
Q2	0.80	0.39
Q7	1.00	−0.26
Q8	0.77	−1.63
Q16	1.01	−1.72
Q19	1.33	−1.33
Q20	0.79	−0.82
Q23	1.50	−0.74
Q24	1.10	−0.15
Q29	1.01	−2.10
Q30	1.63	−0.91
Q35	1.13	0.30

Measure descriptives

All scores are presented as percent correct with possible scores ranging from 0% to 100%. Participants had an average score on the Self-Management subscale of 67.58, (SD = 22.44), M = 65.69 (SD = 22.22) on the Treatment Information subscale, and M = 66.67 (SD = 18.56) on the Total measure. Two participants scored 0 (0.7%) on the Self-Management subscale, whereas no participants scored 0 on the Treatment Information subscale or the Total measure. Seven participants scored 100 (2.6%) on the Self-Management subscale, 17 participants scored 100 (6.4%) on the Treatment Information subscale, and no participant scored 100 on the Total measure.

The EFA, discussed previously, indicated a 2-factor structure. The 2 subscales derived in these analyses were examined and found to have conceptual groupings. The identified subscales were “Self-Management” (12 items) and “Treatment Information” (11 items; Table 2). Separate CFAs were performed on the other half of the sample (n = 133) for each of the subscales and the overall measure. Both the Treatment Information and Self-Management subscales demonstrated good model fit, evidenced by a nonsignificant chi-square. The Total measure indicated moderately good fit on the basis of a nonsignificant chi-square, and indicated good fit on the basis of other fit indices: χ² = 180.78, P = 0.05, CFI (0.98), the RMSEA (0.03), and the WRMR (0.84).

Subscale and measure validity

Self-Management subscale

Older child age (F(1, 264) = 34.067, β = 2.836, P < 0.001) and being female (F(1, 264) = 6.47, P = 0.012) were associated with greater KDM on the Self-Management subscale. No associations were found between KDM and FEV₁% predicted or BMI percentile (Table 4).

Table 4.

Mean Scores on Subscales and Total Measure by Validity Factors

Variable, M (SD)	Treatment information subscale	Self-management subscale	Total score
Gender	P = 0.280	P = 0.012	P = 0.027
Male	64.19 (24.15)	64.06 (23.84)	64.12 (20.45)
Female	67.14 (20.14)	70.99 (20.51)	69.15 (16.21)
Family income	P = 0.311	P = 0.001	P = 0.015
Under $20,000	58.29 (23.59)	52.45 (25.58)	55.24 (21.98)
$20–39,000	61.88 (20.11)	57.80 (22.97)	59.75 (16.95)
$40–59,999	62.46 (22.60)	72.58 (18.90)	67.74 (17.53)
$60–79,999	72.99 (19.83)	65.93 (26.93)	69.31 (20.28)
$80–99,999	67.83 (22.30)	70.19 (16.01)	69.06 (14.09)
Over $100,000	67.19 (22.41)	73.48 (19.00)	70.47 (16.88)
Insurance	P = 0.001	P = 0.001	P = 0.001
Medicaid/Medicare	57.48 (22.79)	56.28 (24.87)	56.86 (18.82)
Private	68.35 (21.31)	71.03 (20.40)	69.75 (17.35)
Mother's education	P = 0.404	P = 0.025	P = 0.041
Some high school or less	60.33 (24.15)	50.76 (33.22)	55.33 (24.14)
High school diploma/GED	63.10 (24.82)	61.44 (24.77)	62.23 (20.23)
Vocational school	70.13 (25.57)	67.26 (15.14)	68.63 (16.63)
Some college	64.05 (20.07)	66.29 (24.13)	65.22 (19.22)
College degree (e.g., BA, BS)	69.58 (21.87)	72.01 (19.60)	70.85 (17.29)
Graduate or professional degree	62.66 (21.19)	72.62 (16.01)	67.86 (13.97)
Father's education	P = 0.293	P = 0.111	P = 0.063
Some high school or less	61.36 (22.64)	63.02 (28.54)	62.23 (22.27)
High school diploma/GED	61.02 (22.31)	63.51 (22.73)	62.32 (18.31)
Vocational school	67.27 (25.95)	69.58 (20.28)	68.48 (20.92)
Some college	67.81 (21.17)	73.65 (18.58)	70.86 (15.54)
College degree (e.g., BA, BS)	68.10 (22.87)	69.55 (22.92)	68.85 (19.83)
Graduate or professional degree	70.33 (20.57)	73.68 (16.73)	72.08 (14.64)

GED, General Education Diploma.

There were significant differences for mother's education (F(7, 259) = 2.341, P = 0.025), family income (F(10, 262) = 3.038, P = 0.001), and health insurance type (F(1, 256) = 22.498, P < 0.001). Children of mothers with a higher level of education, higher family income, and private insurance scored higher on the Self-Management subscale. No significant associations were seen based on father's education.

Treatment Information subscale

Older child age was associated with greater KDM (F(1, 264) = 60.79, β = 3.593, P < 0.001). No associations were found between KDM and gender, FEV₁% predicted, or BMI percentile.

Significant differences were also seen based on health insurance type (F(1, 256) = 12.135, P = 0.001), with individuals with private insurance performing better than those with public insurance. No significant differences were seen based on father's education, mother's education, or family income.

Total measure

Older child age (F(1, 264) = 71.215, β = 3.198, P < 0.001) and being female (F(1, 264) = 4.945, P = 0.027) were associated with greater KDM on the Total measure. No associations were found between KDM and FEV₁% predicted or BMI percentile.

There were significant differences by mother's education (F(5, 242) = 2.35, P = 0.041), family income (F(10, 262) = 2.265, P = 0.015), and health insurance type (F(2, 256) = 13.183, P < 0.001). Those with higher income and private insurance scored higher. No significant differences were seen based on father's education level.

Discussion

This study examined the psychometric properties of the KDM-CF for adolescents. The KDM-CF Total measure, Self-Management, and Treatment Information subscales demonstrated acceptable reliability, overall. The EFA identified a 2-factor structure, which was then confirmed by the CFA. IRT analyses demonstrated that, overall, the items had good item discrimination and difficulty. Item reduction, which occurred as needed, reduced the overall number of items; the measure is now short enough (23-items) to minimize patient burden and increase the feasibility of clinic-based administration. While participants in this study completed the longer, 35-item version, time for completion of the current version is estimated at 10–15 min.

As hypothesized, associations were found with gender, age, and SES markers. In line with previous research, we found that being female, older, having higher family income and maternal education, and private insurance is associated with more KDM. The association with age is intuitive. As adolescents get older, they acquire more knowledge about their disease and take on more responsibility for their care. The association with SES markers also makes sense; families with higher income and private insurance are likely to have more resources at their disposal, which facilitate greater levels of knowledge. Parents with more education are likely to ask more questions, seek out information, and have access to more resources, which then facilitates the learning process in their children. It is not clear why females have consistently been found to evidence higher knowledge scores, but this finding is in line with previous research and warrants future exploration as to the underlying mechanism.

No association was found between KDM and health outcomes, suggesting that KDM does not directly affect these health indices. Instead, this relationship is most likely mediated by other factors, such as adherence, access to resources, and understanding of the patient's specific treatments.

Use of the KDM-CF could serve many purposes. For example, it can be used annually to identify gaps in knowledge which different members of the multidisciplinary team can address. It can also be used when preparing an adolescent for transition to adult care, to ensure they have basic and accurate information to guide management of their disease. This would best be accomplished by administering the measure and remediating gaps in knowledge annually rather than requiring a specific cutoff to be reached before transition.

The results of this study are likely generalizable to the broader population of adolescents because of our large sample size and enrollment at centers across the United States. Additionally, a high level of diversity in SES was also demonstrated with a wide spread level of education, income, and private versus public insurance.

Limitations and future directions

This study had some limitations. First, the KDM-CF was not compared with other self-reported measures of knowledge, which is a core strategy for evaluating validity. This is partly due to the paucity of knowledge measures for adolescents with CF. This was a substudy within a larger clinical trial, which precluded adding several additional measures. Additionally, this measure was not compared with the patients' level of adherence to their treatment regimen. For example, if a patient is pancreatic insufficient, their knowledge of taking enzymes may be more directly related to their adherence.

As with all knowledge measures, updates will be needed. For example, at the time of our study initiation, few of our adolescents were on CFTR modulators and this would be an important item to add in terms of taking the medication with a fat source.¹⁵

Conclusions

Given that management of CF is both complex and time consuming, ensuring adolescents have the practical knowledge needed to carry out their daily treatments is essential. Thus, a validated measure of KDM is important for improving clinical care. The revised KDM-CF provides a valid, reliable measure to assess KDM in adolescents with CF. Results indicated that younger age and lower SES were associated with less KDM. Although we believe that the extent of knowledge should be evaluated and remediated each year for all children/adolescents with CF each year, it may be important to direct more time and attention to adolescents with these known risks. Despite a lack of direct association with health outcomes, adherence to the CF treatment regimen is not possible without sufficient knowledge. Thus, identifying and remedying gaps in KDM are important aspects of clinical care and future research.

Footnotes

Acknowledgments

The authors would like to thank the Cystic Fibrosis Foundation for the use of CF Foundation Patient Registry data to conduct this study. Additionally, they would like to thank the patients, care providers, and clinic coordinators at CF Centers throughout the United States for their contributions to the CF Foundation Patient Registry. This study was funded by The Cystic Fibrosis Foundation and Genentech, Inc. [grant number RIEKER08AO], & Novartis Pharmaceuticals Corp. [grant number P065223578]. Analyses and article preparation was performed at the University of Miami.

Author Disclosure Statement

No competing financial interests exist.

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