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Keywords:

  • Child;
  • obesity;
  • parent;
  • psychometric;
  • self-efficacy

Abstract

  1. Top of page
  2. Abstract
  3. METHODS AND PROCEDURES
  4. RESULTS
  5. DISCUSSION
  6. FUTURE RESEARCH AND IMPLICATIONS
  7. CONCLUSION
  8. How might this information affect nursing practice?
  9. Acknowledgements
  10. References

Purpose.  The purpose of this study was to develop and test a questionnaire to assess parental self-efficacy for enacting healthy diet and physical activity behaviors in their 6- to 11-year-old children.

Design and Methods.  A 35-item questionnaire was developed and tested with 146 U.S. parents.

Results.  Participant responses resulted in a 34-item questionnaire with two subscales (dietary behaviors and physical activity behaviors), which were valid and reliable in the study sample.

Practice Implications.  This new measure will serve as a tool for the assessment of parental self-efficacy for enacting healthy lifestyles in their children 6–11 years old.

The problem of childhood overweight and obesity has reached epidemic proportions in the United States. The consequences of obesity are well known, with effects that are physical, psychosocial, and financial (Hodges, 2003; Tershakovec, 2004; Wang & Dietz, 2002). Childhood is an important period for the prevention of overweight and obesity, as many diet and physical activity behaviors are learned during this time and carried on into adulthood (Jenkins & Horner, 2005; Trudeau, Laurencelle, & Shephard, 2004). Parents play a key role in the learning and development of behavior patterns in children, acting as role models for their children and mediators of the household environment and should thus be targeted for intervention (Hodges, 2003; McCaffree, 2003; Ornelas, Perreira, & Ayala, 2007). In particular, targeting parents of children 6–11 years old is critical as preadolescent children are more reliant upon their parents than older children for food choices available at home and when dining out (Baranowski, Cullen, & Baranowski, 1999). As Kelder, Perry, Klepp, and Lytle (1994, p. 1121) stated, “. . . early consolidation and tracking of physical activity [and] food preference . . . implies that interventions should begin prior to sixth grade, before behavioral patterns are resistant to change.”

The U.S. Department of Agriculture (USDA) provides Americans with guidelines for a healthy lifestyle via the MyPyramid Food Guidance System (Pyramid; USDA, 2008a). The Pyramid, since its original release in 1992, is one of the most well-known and utilized healthy lifestyle guides of all time (Britten, Haven, & Davis, 2006; Goldberg et al., 2004; Nestle, 1998). Despite being recognized by more than two thirds of U.S. adults (Nestle, 1998), many Americans do not use the guidelines in their daily lives (Britten et al., 2006; Goldberg et al., 2004), and they state that they do not know how, nor do they possess the belief in their own ability or self-efficacy, to apply the recommendations (Britten et al., 2006). In fact, findings have long shown that knowledge of healthy diet and physical activity behaviors do not translate into healthier behavior (Povey, Conner, Sparks, James, & Shepherd, 1998; Stevenson, Doherty, Barnett, Muldoon, & Trew, 2007). According to Bandura (1997), people are more likely to perform a behavior if they possess confidence in their ability to perform that behavior, achieve a positive outcome, and overcome barriers. This confidence, or self-efficacy, is the moderator between knowing how to perform a behavior and actually engaging in that behavior. Parents are often well informed and possess knowledge of healthy diet and physical activity recommendations, yet state they have difficulty and lack self-efficacy for translating that knowledge into their family lifestyle (Hart, Herriot, Bishop, & Truby, 2003; Hesketh, Waters, Green, Salmon, & Williams, 2005).

Thus, it is evident that interventions need to focus upon increasing parental self-efficacy to engender a family ethos espousing healthy diet and physical activity for their children. To determine the effect of a self-efficacy intervention, there must be a means to measure change or improvement in the self-efficacy beliefs of the parent and how that may change across time. However, extensive review of the literature shows a lack of instruments to measure this phenomenon. Therefore, the purpose of this study was to develop and test a questionnaire that assesses parental self-efficacy beliefs to engender a family ethos espousing healthy diet and physical activity for their children ages 6–11 years.

METHODS AND PROCEDURES

  1. Top of page
  2. Abstract
  3. METHODS AND PROCEDURES
  4. RESULTS
  5. DISCUSSION
  6. FUTURE RESEARCH AND IMPLICATIONS
  7. CONCLUSION
  8. How might this information affect nursing practice?
  9. Acknowledgements
  10. References

Sample

The target population for this study was U.S. parents of children 6–11 years old. Eligibility requirements were (a) parent of a child 6–11 years old, (b) able to read and write in English, and (c) available computer with Internet access. A convenience sample with recruitment via the Internet was used to identify a sample of parents with children in that age group to which research findings may apply (Wyatt, 2000). Recruitment via the Internet included postings to numerous parenting discussion groups and websites, such as http://www.parents.com. The postings contained a brief introduction to the study and its purpose, as well as a link to, or URL address for, the questionnaire. Additional recruiting methods included sending e-mails to several parental, professional, and healthcare organizational membership lists, posting fliers at several local pediatrician and pediatric dentist's offices, and postings to an Internet-based social networking site (Facebook©). Word-of-mouth also aided recruitment because eligible participants could easily e-mail and forward information about the study to other eligible individuals within their personal network. Finally, a small incentive, a $5 electronic gift card (e-gift card) to a national retail store chain, was offered for each completion of the questionnaire. The use of incentives may increase response rates in Internet-based surveys (Heerwegh, 2006). If the incentive was desired, the participants were asked to enter a valid e-mail address where they wished to receive this incentive.

An initial sample of 15 participants was recruited to pilot test and refine the questionnaire (Wilson, 2002). Following this pilot test, a separate sample of 145 participants was recruited to fully test the questionnaire. A sample size of 130 was suggested for a confidence interval of .10, with α= .05 and an expected reliability coefficient of .70 (Streiner & Norman, 2003, p. 151). An additional 15 participants were oversampled to compensate for refusals, incomplete data, and attrition (Oman, Krugman, & Fink, 2003). The final sample consisted of 146 participants. The participants were mostly female (88%) and primarily non-Hispanic or Latino ethnicity (91%) and Caucasian race (82%). Most participants were married (84%), employed full-time (64%), and well educated (97%), with at least some college education. Total annual household income varied, but most participants (53%) came from households earning more than $75,000 annually. Demographic data are presented in Table 1.

Table 1. Demographics
Categoryn%
Gender14599.3
 Male1611
 Female12988.4
Race14599.3
 White11981.5
 Black1611.0
 Asian42.7
 More than one race64.1
Ethnicity14498.6
 Not Hispanic or Latino13391.1
 Hispanic or Latino117.5
Marital status146100
 Single, never married74.8
 Living with partner, not married32.1
 Married12384.2
 Separated1.7
 Divorced96.2
 Widowed32.1
Highest education level146100
 High school or equivalent53.4
 Some college2315.8
 Associate's degree2013.7
 Bachelor's degree4732.2
 Master's degree3624.7
 Doctoral degree1510.3
Work status146100
 Full time9363.7
 Part time2617.8
 Full-time homemaker106.8
 College/university student64.1
 Self-employed74.8
 Retired1.7
 Not employed32.1
Total annual household income14297.3
 < $25,00053.4
 $25,000–49,9992013.7
 $50,000–74,9994027.4
 $75,000–99,9992517.1
 ≥ $100,0005235.6

A subsample of 25 participants completed the questionnaire again in 5–10 days to evaluate test–retest reliability. This timeframe was considered long enough to ensure that participants would not recall previous responses, yet short enough that their self-efficacy would not have changed (Streiner & Norman, 2003). Participants were not able to print or save their previous answers and were not given the opportunity to view their previous responses. Willing participants were asked to enter a valid e-mail address where they wished to receive a reminder e-mail and link to the questionnaire sent.

Data collection

The University of Central Florida Institutional Review Board approved the conduct of this study. Because this study was conducted via the Internet and no identifying information was required from participants, a waiver of documentation of consent was requested, and granted, for this study. As such, the informed consent statement, appearing prior to the questionnaire, included the statement that “completion of this questionnaire implies consent to participate in this study” (Eysenbach & Wyatt, 2002). All participants who completed the questionnaire did so anonymously in an encrypted environment via SurveyMonkey© (http://www.surveymokey.com), a secure Internet survey design and response collection website. The study was made available for participants for a period of 4 months, from August to November 2008. All e-mail addresses provided to receive the incentive were kept separate from all other data (Nosek, Banaji, & Greenwald, 2002). All data were stored on a password-enabled flash drive stored in a locked drawer when not in use, and only the investigator had access to the drawer.

Measures

The questionnaire to assess parental self-efficacy to engender a family ethos for healthy diet and physical activity (Table 2) was developed using the USDA Pyramid guidelines for healthy diet and physical activity behaviors for children (USDA, 2008b) as well as outcome expectancies and environmental factors identified during the literature review. This questionnaire consisted of 35 questions covering two domains: diet and physical activity. A composite score was derived from summated scores on the total questionnaire, as were diet and physical activity subscale scores.

Table 2. Parental Self-efficacy QuestionnaireThumbnail image of

The questionnaire was sent to eight content experts: four nurse researchers with experience in one or more content areas: obesity research, clinical obesity care, self-efficacy theory, or psychometrics; three dieticians; and one physician with childhood obesity clinical and research experience. These experts were asked to evaluate the questionnaire for face validity and to rate each item on a 4-point scale from totally irrelevant (1) to extremely relevant (4) for content validity assessment (DeVon et al., 2007; Lynn, 1986; Streiner & Norman, 2003; Waltz, Strickland, & Lenz, 2005). The plan for evaluating experts' ratings was to either rewrite or remove items ranked less than 3 by more than one content expert. However, none of the content experts ranked any of the items less than 3. The content validity index (CVI) of the questionnaire was .97, with an average rating of 3.41 for the items on the 4-point scale (DeVon et al., 2007; Lynn, 1986). Thus, the CVI was adequate, and content validity of the questionnaire was deemed acceptable. All content experts also noted that the questionnaire appeared to be measuring what it purported to measure (face validity).

Subsequently, the questionnaire was pilot tested with 15 participants from the target sample. The questionnaire asked respondents to rate their confidence in their ability to perform certain tasks related to healthy diet and physical activity in their children. They rated their confidence on an 11-point scale, from “not at all confident” (0) to “mostly or totally confident” (10), derivative of a 100-point scale (0–100) recommended by Bandura when constructing self-efficacy scales (Bandura, 2006). The internal reliability (Cronbach's alpha) calculated for data from the pilot sample was satisfactory (.95), so no revision was necessary for use with the larger study sample. Additionally, participants did not express any difficulty with either comprehension of questionnaire items or completion of the questionnaire. Finally, no issues with the use of SurveyMonkey© arose in the collection or download of data from the website.

No identifying data were required as a part of the questionnaire. In order to characterize the sample, sociodemographic data were collected and included age, race, ethnicity, gender, marital status, highest educational level achieved, work status, household income, zip code of primary residence, parental contact, and number of children, with their ages, height, and weight.

Two existing surveys were used to estimate concurrent validity. Because no existing surveys to measure parental self-efficacy for enacting healthy diet or physical activity in their children were located in the literature, questionnaires regarding self-efficacy of the parents for their own diet and physical activity behaviors were selected. These were chosen because data have shown that parental behaviors and self-efficacy beliefs were related to similar behaviors in their children (Bois, Sarrazin, Brustad, Trouilloud, & Cury, 2005; DiLorenzo, Stucky-Ropp, Vander Wal, & Gotham, 1998; Moore et al., 1991; Oliveria et al., 1992). Therefore, it was expected that if parents had higher self-efficacy beliefs for their own eating and physical activity behaviors, they would have higher self-efficacy beliefs in their ability to provide the same environment for their children. Two surveys frequently used in obesity research (Folta et al., 2009; Hagler, Norman, Radick, Calfas, & Sallis, 2005; Ievers-Landis et al., 2003; Nothwehr & Peterson, 2005; Nothwehr & Stump, 2002; Resnicow, McCarty, & Baranowski, 2003; Resnicow et al., 2001; Walker, Pullen, Hertzog, Boeckner, & Hageman, 2006; White et al., 2004; Zabinski et al., 2006), the Self-Efficacy for Exercise Behaviors Scale (SEB-Ex) and Self-Efficacy for Eating Behaviors Scale (SEB-Eat), were used (Sallis, Pinski, Grossman, Patterson, & Nader, 1988). Both the SEB-Ex and SEB-Eat asked individuals to rate their confidence in their ability to motivate themselves to do certain activities consistently for at least six months. The 5-point Likert-type scale of each survey ranged from 1 (I know I cannot) to 5 (I know I can). The SEB-Ex consists of 12 items on two subscales, “resisting relapse” and “making time for exercise,” which each showed a satisfactory internal consistency (α= .85 and α= .83, respectively). Test–retest reliability for both subscales was r= .68, p < .001, after 1–2 weeks. The SEB-Eat consisted of 61 items on five factors: resisting relapse, reducing calories, reducing salt, reducing fat, and behavioral skills. All of the SEB-Eat subscales demonstrated satisfactory internal consistency (α= .85–.93). Test–retest reliabilities of the five subscales ranged from r= .43 to r= .6, after 1 or 2 weeks.

Data analysis

All data from the questionnaire responses were downloaded directly from the SurveyMonkey© website. Once data were checked for completeness, all analyses were completed using SPSS version 15.0 (SPSS, Inc., Chicago, IL, USA). Responses from the questionnaire were summed to create a total parental self-efficacy score. Subscales for healthy diet and physical activity self-efficacy were summed to create subscale scores.

The determination of the factors present within the 35 items was conducted using maximum likelihood factor analysis. Three criteria were used to determine the number of factors to rotate: the a priori hypothesis that the measure had two dimensions, the screen test and the interpretability of the factor solution. Item analysis was performed by calculating the correlation of each item with its own subscale (with the item removed) and with the other subscales using a Bonferroni correction. Thus, a p value of less than .005 was required for significance. Concurrent validity was assessed by computing Pearson's correlation coefficients between the new questionnaire total scores with the SEB-Ex and SEB-Eat total and subscale scores. Pearson's correlation coefficients were also computed between the dietary behaviors (DB) subscale scores and SEB-EAT total and subscale scores. Finally, the correlation between the physical activity behaviors (PAB) subscale scores and the SEB-Ex total and subscales scores were calculated.

Demographic data were descriptively analyzed. Internal consistency reliability was assessed by computing Cronbach's alpha for each factor derived from the exploratory factor analysis and for the total score. Test–retest reliability was examined in a subsample of the total participant sample willing to complete the questionnaire a second time, within 5–10 days. Test–retest reliability was assessed by computing the Pearson correlation coefficients for each individual item and the total scores.

RESULTS

  1. Top of page
  2. Abstract
  3. METHODS AND PROCEDURES
  4. RESULTS
  5. DISCUSSION
  6. FUTURE RESEARCH AND IMPLICATIONS
  7. CONCLUSION
  8. How might this information affect nursing practice?
  9. Acknowledgements
  10. References

Demographics analyses

Demographic characteristics, summarized earlier, can be found in Table 1. Correlations between demographic groups, such as race or income level, on questionnaire responses or scores did not reveal any significant results. Participants were primarily from the Southeastern United States (84%), although there were responses from the Northeastern (6%), Midwestern (7%), Southern (2%), and Western (1%) United States.

Construct validity

Factor analysis.  Two factors were rotated using a varimax rotation. The rotated solution yielded two interpretable factors, DB and PAB. The item means, standard deviations, and inter-item correlation matrix were examined (Table available from author by request). On the 11-point scale, where 0 =“not at all confident” to 11 =“mostly or totally confident,” the means ranged from 5.27 (item 23) to 9.18 (item 14). Examination of the correlation matrix indicated that all items correlated ≥ .30 with at least three other items in the matrix (range 3–30). Nineteen of the 35 items (54%) had 11 or more shared correlations that exceeded .30. Four items (28–31) had inter-item correlations exceeding .80, suggesting multicollinearity of the items. The items were retained for further analysis at this time. Bartlett's test of sphericity was significant (χ2= 3480.996, p < .01) and the KMO statistic (.87) is considered “meritorious” according to Kaiser's (1974) criteria. Dietary behaviors accounted for 25.3% of the item variance, and PAB accounted for 16.8% of the item variance. The screen plot confirmed the initial hypothesis of bidimensionality.

Examination of the rotated factor pattern matrix (Table 3) revealed that all but one item loaded ≥ .35 onto its hypothesized factor. Item 33 loaded more strongly onto the DB factor, contrary to the a priori belief that it would be related to physical activity. However, this item did not load very strongly onto either factor, with factor loadings of .37 and .35 on the DB and PAB factors, respectively. Therefore, this item was removed from the questionnaire and excluded from further analysis.

Table 3. Rotated Factor Pattern Matrix for the 35-Item Parental Self-Efficacy Questionnaire: Maximum Likelihood Factoring With Varimax Rotation
Items (how confident are you that . . .)Factors
Dietary behaviorsPhysical activity behaviorsMSD
  1. Note: The questionnaire was developed by the author. SD, standard deviation.

Dietary behaviors (DB) items
Q16Your child eats very few saturated fats or trans fats?.73.116.362.70
Q24Your child chooses healthy foods at a sit-down restaurant?.69.176.262.81
Q26Your child chooses healthy foods when eating with friends?.69.155.342.74
Q15Your child eats very few solid fats and foods that contain these?.67−.027.112.68
Q17Your child eats foods with low sodium content or added sodium?.66.166.272.64
Q7Your child eats 2 servings of whole fruit or 100% pure fruit juice every day?.65.297.462.95
Q3Your child eats at least 2 servings of vegetables every day?.64.115.843.25
Q6Your child eats a variety of vegetables?.64.156.213.19
Q8The juice your child drinks contains 100% fruit juice?.63.107.523.21
Q18Your child eats very few foods with added sugar?.63.265.432.83
Q19Your child drinks very few drinks with added sugar?.60.307.143.14
Q23Your child chooses healthy foods at a fast-food restaurant?.58.105.272.99
Q25Your child chooses healthy foods at school?.58.296.362.85
Q13The meats or poultry your child eats are low-fat or lean?.57.217.582.55
Q2At least half of your child's total grain servings each day are whole grains?.56.316.012.89
Q21Your child drinks mostly water or fat-free milk and not fruit juice, soda, or sports drinks?.54.227.232.82
Q27There are limited unhealthy snacks in your home for snacks or meals?.54.237.522.73
Q20The cereals that your child eats are unsweetened?.50.104.973.23
Q4Your child will eat vegetables, even if they do not enjoy the taste?.47.174.863.33
Q14If cooking with oils, you use vegetable oils?.47.119.181.73
Q9The juice your child drinks is limited to one small glass ( cup) per day?.45.186.463.18
Q5Your child eats only 3 servings of starchy vegetables each week?.44.245.563.07
Q1Your child eats only 3 servings of grains every day?.41.316.402.82
Q11Your child eats at least 2 servings of milk or an equivalent dairy product every day?.40.087.333.35
Q12Your child eats 2 servings of meat, beans or eggs every day?.40.287.672.53
Q22You eat meals together as a family?.36.068.222.47
Q10Your child eats at least 2 servings of milk or an equivalent dairy product every day?.35.208.422.29
Physical activity behaviors (PAB) items
Q30Your child is physically active, even if you have excessive demands at work?.17.938.092.30
Q31Your child is physically active, even if there are no gyms, parks, or playgrounds nearby?.16.928.022.27
Q28Your child plays outside or is active in sports for a total of at least 60 min on most days of the week?.10.878.512.24
Q29Your child is physically active, even if the weather is bad?.22.827.582.60
Q35Your child is physically active, even if they have homework?.21.798.102.19
Q32Your child is physically active, even if you are concerned about safety?.23.737.692.38
Q34Your child is physically active when with friends?.20.668.302.06
Q33You can limit your child's screen time to no more than 2 hr per day?.37.357.862.60

Item analysis.  In support of the questionnaire's validity, items were more highly correlated with their own subscale than with the other subscale, with one exception: question 33. Items on the DB subscale correlated more strongly (.31–.70) with other items on the DB subscale versus items on the PAB subscale (.12–.43). Other than question 33, all items on the PAB subscale (.67–.90) correlated more strongly with other items on the same scale versus items on the DB subscale (.36–.44).

Concurrent validity.  Correlations between the questionnaire total scores and the SEB-Eat (.51) and SEB-Ex (.35) total scores were both significant (p < .01). Total score on the questionnaire also significantly (p < .01) correlated with subscale scores of the five SEB-Eat (.32–.48) and the two SEB-Ex (.32 and .34) subscales. The DB subscale scores significantly (p < .01) correlated with all SEB-Eat subscales (.38–.50) and the SEB-Eat total score (.55). The PAB subscale correlations were all less than .06 and not significant with the SEB-Ex total and two subscale scores.

Internal consistency reliability

Cronbach's alpha coefficients were computed for the original 35 items, for the 34 items that were retained after item number 33 was dropped during data analysis, and for the two subscales (DB and PAB). The coefficient alpha for the initial 35-item scale was .94 and remained at .94 after removal of question number 33, “How confident are you that you can limit your child's screen time (i.e., TV, video games, computer) to no more than 2 hr per day?” The DB subscale had an alpha of .93, which did not change with removal of question 33. The PAB subscale had an alpha of .92. However, when question 33 was removed, the alpha increased to .94.

Test–retest reliability

The subsample of 25 participants used to evaluate test–retest reliability all completed the parental self-efficacy questionnaire a second time between 5 and 10 days after their initial completion. All item and score (total and subscale scores) correlations between participants' responses at times 1 and 2 were significant at p < .05. Item responses between questionnaire administrations correlated significantly for both the DB (.50–.95, p < .05) and PAB (.53–.92, p < .01) subscales. Total questionnaire (.94), DB (.89), and PAB (.93) scores between times 1 and 2 were also significantly (p < .001) correlated.

DISCUSSION

  1. Top of page
  2. Abstract
  3. METHODS AND PROCEDURES
  4. RESULTS
  5. DISCUSSION
  6. FUTURE RESEARCH AND IMPLICATIONS
  7. CONCLUSION
  8. How might this information affect nursing practice?
  9. Acknowledgements
  10. References

This study describes the initial development and psychometric testing of a new measure of parental self-efficacy for enacting healthy lifestyles in their children. Evaluation of responses from 146 parents of children 6–11 years old resulted in the removal of one item, resulting in a 34-item questionnaire clustered into dietary and physical activity behavior subscales and a total parental self-efficacy score.

Findings suggest that the questionnaire has promise for future use. Measures of validity used in this study suggest the instrument may be a valid measure of the constructs desired. The initial evaluation of content and face validity by eight content experts suggested that the questionnaire, as designed, appeared to measure what it purported andcontained the necessary items to measure these constructs.

Results of the factor analysis suggested two factors, DB and PAB, as was intended during item development. Each factor did have more than four factor loadings above .60, supporting the reliability of each factor. However, question 33, “How confident are you that you can limit your child's screen time (i.e., TV, video games, computer) to no more than 2 hr per day?” did not load primarily onto either factor (diet or physical activity), despite being conceptually generated as a physical activity item. Perhaps the specific item as an outlier should attempt to better convey that limiting screen time has long been related with increasing physical activity time (Anderson, Economos, & Must, 2008; Boone, Gordon-Larsen, Adair, & Popkin, 2007). At this time, this item was removed from the questionnaire for further analysis. The remaining 34 items, however, all associated fittingly with their conceptually appropriate subscale. Item analysis further supported the two-factor structure and placement of items on each factor. The inclusion of 27 items on the DB factor also warrants further refinement of the questionnaire to either include fewer items or additional factors.

Although examination of the inter-item correlation matrix did show correlations exceeding .80 for items 28–31, these items were retained for further analysis and kept in the final questionnaire despite possible multicollinearity. These items are closely related but concerned with different barriers found in the review of the literature. The items are concerned with the barriers of time (item 28), weather (item 29), work demands (item 30), and resources (item 31). Because these barriers are each frequently and distinctly identified, the items have been retained, despite their similarities. In addition, the CVI of .97 and item scores all above 3 (on a 4-point scale) supported their inclusion.

Evaluation of the concurrent validity was conducted using the SEB-Eat and SEB-Ex scales. It was hypothesized that the SEB-Eat and SEB-Ex scores, on which the participants rate their self-efficacy for their own healthy behaviors, would correlate with the scores on the parental self-efficacy questionnaire. These scales were selected because previous research suggested that parental behaviors often correlate with those of their children. Results of the analyses confirmed this. The questionnaire total scores significantly correlated with both the SEB-Eat and SEB-Ex total scores. However, the moderate correlations (.51 and .35, respectively) support the notion that the questionnaire is, in fact, measuring a new concept.

Of interest is the strength of the correlation between questionnaire scores and SEB-Eat and SEB-Ex scores. The questionnaire total score correlated more strongly with the SEB-Eat (.51) than the SEB-Ex (.35). This is possibly because physical activity within a household is generally not as consistent across the family members as is dietary intake. In general, the parental figures in a household decide what foods are purchased in a store or restaurant or prepared for meals, especially for this age group. In addition, one would expect that dietary choices within a household are mostly consistent among family members, as meals are generally prepared for a group rather than individuals, thus increasing the likelihood that parents and their children are essentially eating the same food items.

Conversely, parents' perception of their own ability to be physically active is not as strongly related to their belief in their ability to get their children to be physically active. This author hypothesizes many parents may sacrifice their own time and physical activity in order to ensure that their children are physically active. For example, a parent might enroll a child in an activity or sport, but then must commit to providing transportation and time to the child's activity, rather than his or her own. This notion is further supported by the lack of significant correlation between DB subscale scores and SEB-Ex total and subscale scores.

Initial reliability estimates in this sample population were satisfactory. The total scale score and DB and PAB subscale scores demonstrated internal consistency and the test–retest reliabilities for total scale, and DB and PAB subscale scores were also satisfactory.

The main limitation of this study was the sample recruited. Primarily there was a lack of diversity in the sample, especially in race, ethnicity, socioeconomic status, and educational level. This was a concern when designing the study and may be attributed to the study being conducted on the Internet. The Internet was used to conduct the study even with the knowledge that many people do not have computer and Internet access or computer literacy (Eysenbach & Wyatt, 2002; Fricker & Schonlau, 2002). A more diverse population sample was expected as recent data suggested that there were more than 200 million Internet users, approximately 70.2% of the total U.S. population (“United States of America: Internet usage and broadband usage report,” 2007). A larger number of participants in demographic subgroups, such as African Americans, Hispanics, or low SES, more than were anticipated. Although these demographic subgroups have historically been underrepresented in Internet studies because of lack of access or computer literacy, these disparities are lessening (Fricker & Schonlau, 2002). This was not demonstrated in this study.

As a result, the homogeneity of the sample made analysis of difference between various demographic groups difficult, as the number of minority participants was too small to identify between-groups differences. Given the results of this study, further testing of this questionnaire with a more racially and ethnically diverse sample of parents is warranted.

Additionally, the sample recruited for this study was unrestricted, although limited by inclusion criteria, and may not be representative of the larger population due to self-selection (Braithwaite, Emery, De Lusignan, & Sutton, 2003; Duffy, 2002; Eysenbach & Wyatt, 2002). Furthermore, because the questionnaire was completed at the leisure of the participant in this study, there was no control over the environment in which it was completed, possibly allowing random factors or events to influence the respondent. However, this issue is a concern with mailed surveys as well and can only be controlled via in-person interviews, which presents a large burden on participant and investigator (Duffy, 2002; Nosek et al., 2002). There was also the possibility of multiple responses by a single individual (Bowen, Daniel, Williams, & Baird, 2008; Duffy, 2002; Nosek et al., 2002). Nevertheless, collection of specific demographic datas, including respondents' and their children's birth dates, allowed for identification and exclusion of multiple responses (Nosek et al., 2002), and restriction of multiple responses by IP address, or the individual identifier of each computer, also prevented multiple responses (Bowen et al., 2008). Lastly, using the Internet for administration of the questionnaire limits its psychometric evaluation only to administration using the Internet or a computer.

Finally, self-report data provided by the participants for the height and weight of their children yielded such an abnormal distribution that these data were unusable. For example, the data provided by the parents suggested a prevalence of children below the 5th percentile and above the 97th percentile of body mass index (BMI) for age that far exceeded the U.S. population norms. This suggests the need for collection of these data by trained data collectors or healthcare professionals.

FUTURE RESEARCH AND IMPLICATIONS

  1. Top of page
  2. Abstract
  3. METHODS AND PROCEDURES
  4. RESULTS
  5. DISCUSSION
  6. FUTURE RESEARCH AND IMPLICATIONS
  7. CONCLUSION
  8. How might this information affect nursing practice?
  9. Acknowledgements
  10. References

The future directions and implications for this instrument are varied and will add to the growing arsenal of tools to be used in the fight against the obesity pandemic. The first step in future research for this questionnaire will include further testing of the psychometric properties of this instrument in a broader and more diverse demographic sample. In particular, the target sample will focus on participants who are non-Caucasian races and Hispanic or Latino ethnicity. Variability among other demographic factors, such as marital status, SES, and educational level, will also be sought. This will require recruitment in communities with a higher prevalence of these demographic subgroups. Administration of the questionnaire via the Internet or computer will also limit its utility. Examination of the psychometric properties using paper copies of the questionnaire will be necessary. Increasing the utility of the questionnaire will require investigation of its utility with parents with children in different age groups, such as 2–5-year olds or 12–17-year olds. However, this will require changes in the questionnaire items to reflect the different developmental stages of these age groups. Finally, examination of the questionnaire's sensitivity to change over time will be addressed.

The use of the “parent” label for the questionnaire should be reconsidered or given an expanded definition. Future iterations of the questionnaire and its testing may change this to “caregiver” or define “parent” as the person most responsible for the dietary and physical activity behaviors of the children. Therefore, the individual most responsible for the healthy behaviors in the child will be targeted, whether this is a parent, grandparent, aunt or uncle, guardian, or even an older sibling.

Additional testing for the relationships between this questionnaire and behaviors is planned. Future studies will include measures of dietary intake (i.e., 24-hr diet recall), physical activity (i.e., physical activity recall surveys or accelerometers), and body weight status (i.e., BMI, weight, waist–hip ratio). This will allow examination of the relationship between scores on this questionnaire and the actual behaviors or body weight status of the child.

Following refinement and further extensive examination of the psychometric properties of the questionnaire, translation into other languages commonly found in the United States, such as Spanish or Creole, may be warranted to increase its utility and understandability among a broader range of minority populations. This process will require that the translated scale demonstrate conceptual, item, semantic, operational, and measurement equivalence to the original scale (Streiner & Norman, 2003). The translated scale would then be back-translated into English and compared with the original scale for equivalence. Once the translation process has been completed, the psychometric properties of the translated instrument will need to be tested in the target sample.

The overarching goal of the development and psychometric testing of this questionnaire is for its use in interventional research aimed at increasing caregiver self-efficacy for promoting these healthy behaviors in their children. Following further refinement and psychometric evaluation, this questionnaire can serve as a tool for assessing change or improvement in parental self-efficacy from pre- to post-intervention and fills in a previous gap in the arsenal.

Another area of potential use for this questionnaire is for research investigating the relationships between factors that play a role in childhood overweight and obesity. Researchers may use parent scores on this questionnaire to examine relationships with other parental or child measures, such as dietary intake, physical activity participation, and measures of fatness (i.e., BMI, weight, waist–hip ratio). This will allow further examination of the relationship between parental self-efficacy for promoting these healthy behaviors in their children and actual behaviors and weight status. Finally, if this questionnaire is valid and reliable for use with parents with children of other ages, comparisons of parental self-efficacy can be assessed between parents with children in different age groups, perhaps assessing for changes in parental self-efficacy throughout their child's lifespan.

CONCLUSION

  1. Top of page
  2. Abstract
  3. METHODS AND PROCEDURES
  4. RESULTS
  5. DISCUSSION
  6. FUTURE RESEARCH AND IMPLICATIONS
  7. CONCLUSION
  8. How might this information affect nursing practice?
  9. Acknowledgements
  10. References

This questionnaire to assess parental self-efficacy for promoting healthy dietary and physical activity behaviors in their children demonstrates potential to be a useful tool. It consists of two separate subscales, composed of items related either to diet or physical activity behaviors. The content and face validity of the questionnaire were deemed acceptable and valid by eight independent content experts. Lastly, internal consistency and test–retest reliability of the total measure and its two subscales were strong. These psychometric properties support the need for further examination and refinement of this questionnaire.

How might this information affect nursing practice?

  1. Top of page
  2. Abstract
  3. METHODS AND PROCEDURES
  4. RESULTS
  5. DISCUSSION
  6. FUTURE RESEARCH AND IMPLICATIONS
  7. CONCLUSION
  8. How might this information affect nursing practice?
  9. Acknowledgements
  10. References

This study shows the initial development and psychometric evaluation of a new questionnaire to assess parental self-efficacy for promoting healthy dietary and physical activity behaviors in their children ages 6–11 years. The results demonstrate that this questionnaire shows promise for future use. However, further refinement and psychometric evaluation of the questionnaire is necessary and warranted. With further testing, this questionnaire may provide an additional tool in the fight against the childhood obesity pandemic.

References

  1. Top of page
  2. Abstract
  3. METHODS AND PROCEDURES
  4. RESULTS
  5. DISCUSSION
  6. FUTURE RESEARCH AND IMPLICATIONS
  7. CONCLUSION
  8. How might this information affect nursing practice?
  9. Acknowledgements
  10. References
  • Anderson, S. E., Economos, C. D., & Must, A. (2008). Active play and screen time in US children aged 4 to 11 years in relation to sociodemographic and weight status characteristics: A nationally representative cross-sectional analysis. BMC Public Health, 8. doi:10.1186/1471-2458-8-366
  • Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W. H. Freeman.
  • Bandura, A. (2006). Guide for constructing self-efficacy scales. In F. Pajares & T. C. Urdan (Eds.), Self-efficacy beliefs of adolescents (pp. 307337). Greenwich, CT: Information Age.
  • Baranowski, T., Cullen, K. W., & Baranowski, J. (1999). Psychosocial correlates of dietary intake: Advancing dietary intervention. Annual Review of Nutrition, 19(1), 1740. doi:10.1146/annurev.nutr.19.1.17
  • Bois, J. E., Sarrazin, P. G., Brustad, R. J., Trouilloud, D. O., & Cury, F. (2005). Elementary schoolchildren's perceived competence and physical activity involvement: The influence of parents' role modelling behaviours and perceptions of their child's competence. Psychology of Sport and Exercise, 6(4), 381397. doi:10.1016/j.psychsport.2004.03.003
  • Boone, J. E., Gordon-Larsen, P., Adair, L. S., & Popkin, B. M. (2007). Screen time and physical activity during adolescence: Longitudinal effects on obesity in young adulthood. International Journal of Behavioral Nutrition and Physical Activity, 4(26), doi:10.1186/1479-5868-4-26
  • Bowen, A. M., Daniel, C. M., Williams, M. L., & Baird, G. L. (2008). Identifying multiple submissions in internet research: Preserving data integrity. AIDS and Behavior, 12(6), 964973. doi:10.1007/s10461-007-9352-2
  • Braithwaite, D., Emery, J., De Lusignan, S., & Sutton, S. (2003). Using the Internet to conduct surveys of health professionals: A valid alternative? Family Practice, 20(5), 545551. doi:10.1093/fampra/cmg509
  • Britten, P., Haven, J., & Davis, C. (2006). Consumer research for development of educational messages for the MyPyramid Food Guidance System. Journal of Nutrition Education and Behavior, 38(6, Suppl.), S108S123. doi:10.1016/j.jneb.2006.08.006
  • DeVon, H. A., Block, M. E., Moyle-Wright, P., Ernst, D. M., Hayden, S. J., Lazzara, D. J., . . . Kostas-Polston, E. (2007). A psychometric toolbox for testing validity and reliability. Journal of Nursing Scholarship, 39(2), 155164.
  • DiLorenzo, T. M., Stucky-Ropp, R. C., Vander Wal, J. S., & Gotham, H. J. (1998). Determinants of exercise among children: II. A longitudinal analysis. Preventive Medicine, 27(3), 470477. doi:10.1006/pmed.1998.0307
  • Duffy, M. E. (2002). Methodological issues in web-based research. Journal of Nursing Scholarship, 34(1), 8388. doi:10.1111/j.1547-5069.2002.00083.x
  • Eysenbach, G., & Wyatt, J. (2002). Using the internet for surveys and health research. Journal of Medical Internet Research, 4(2), e13. doi:10.2196/jmir.4.2.e13
  • Folta, S. C., Lichtenstein, A. H., Seguin, R. A., Goldberg, J. P., Kuder, J. F., & Nelson, M. E. (2009). The StrongWomen-Healthy Hearts program: Reducing cardiovascular disease risk factors in rural sedentary, overweight, and obese midlife and older women. American Journal of Public Health, 99(7), 12711277. doi:10.2105/AJPH.2008.145581
  • Fricker, R. D., & Schonlau, M. (2002). Advantages and disadvantages of internet research surveys: Evidence from the literature. Field Methods, 14(4), 347367. doi:10.1177/152582202237725
  • Goldberg, J. P., Belury, M. A., Elam, P., Finn, S. C., Hayes, D., Lyle, R., . . . Hellwig, J. P. (2004). The obesity crisis: Don't blame it on the pyramid. Journal of the American Dietetic Association, 104(7), 11411147. doi:10.1016/j.jada.2004.04.026
  • Hagler, A. S., Norman, G. J., Radick, L. R., Calfas, K. J., & Sallis, J. F. (2005). Comparability and reliability of paper- and computer-based measures of psychosocial constructs for adolescent fruit and vegetable and dietary fat intake. Journal of the American Dietetic Association, 105(11), 17581764. doi:10.1016/j.jada.2005.08.010
  • Hart, K. H., Herriot, A., Bishop, J. A., & Truby, H. (2003). Promoting healthy diet and exercise patterns amongst primary school children: A qualitative investigation of parental perspectives. Journal of Human Nutrition and Dietetics, 16(2), 8996. doi:10.1046/j.1365-277X.2003.00429.x
  • Heerwegh, D. (2006). An investigation of the effect of lotteries on web survey response rates. Field Methods, 18(2), 205220. doi:10.1177/1525822X05285781
  • Hesketh, K., Waters, E., Green, J., Salmon, L., & Williams, J. (2005). Healthy eating, activity and obesity prevention: A qualitative study of parent and child perceptions in Australia. Health Promotion International, 20(1), 1926. doi:10.1093/heapro/dah503
  • Hodges, E. A. (2003). A primer on early childhood obesity and parental influence. Pediatric Nursing, 29(1), 1316.
  • Ievers-Landis, C. E., Burant, C., Drotar, D., Morgan, L., Trapl, E. S., & Kwoh, C. K. (2003). Social support, knowledge, and self-efficacy as correlates of osteoporosis preventive behaviors among preadolescent females. Journal of Pediatric Psychology, 28(5), 335345. doi:10.1093/jpepsy/jsg023
  • Jenkins, S., & Horner, S. D. (2005). Barriers that influence eating behaviors in adolescents. Journal of Pediatric Nursing, 20(4), 258267. doi:10.1016/j.pedn.2005.02.014
  • Kaiser, H. (1974). An index of factorial simplicity. Psychometrika, 39(1), 3136. doi:10.1007/BF02291575
  • Kelder, S. H., Perry, C. L., Klepp, K. I., & Lytle, L. L. (1994). Longitudinal tracking of adolescent smoking, physical activity, and food choice behaviors. American Journal of Public Health, 84(7), 11211126. doi:10.2105/AJPH.84.7.1121
  • Lynn, M. R. (1986). Determination and quantification of content validity. Nursing Research, 35(6), 382385.
  • McCaffree, J. (2003). Childhood eating patterns: The roles parents play. Journal of the American Dietetic Association, 103(12), 1587. doi:10.1016/j.jada.2003.10.031
  • Moore, L. L., Lombardi, D. A., White, M. J., Campbell, J. L., Oliveria, S. A., & Ellison, R. C. (1991). Influence of parents' physical activity levels on activity levels of young children. Journal of Pediatrics, 118(2), 215219.
  • Nestle, M. (1998). In defense of the USDA Food Guide Pyramid. Nutrition Today, 33(5), 189197.
  • Nosek, B. A., Banaji, M. R., & Greenwald, A. G. (2002). E-research: Ethics, security, design, and control in psychological research on the internet. Journal of Social Issues, 58(1), 161176.
  • Nothwehr, F., & Peterson, N. A. (2005). Healthy eating and exercise: Strategies for weight management in the rural Midwest. Health Education & Behavior, 32(2), 253263. doi:10.1177/1090198104272328
  • Nothwehr, F., & Stump, T. (2002). Weight control behaviors of low-income, African American women. Health Promotion Practice, 3(2), 207216. doi:10.1177/152483990200300216
  • Oliveria, S. A., Ellison, R. C., Moore, L. L., Gillman, M. W., Garrahie, E. J., & Singer, M. R. (1992). Parent-child relationships in nutrient intake: The Framingham Children's Study. American Journal of Clinical Nutrition, 56(3), 593598.
  • Oman, K., Krugman, M. E., & Fink, R. M. (2003). Nursing research secrets. Philadelphia: Hanley & Belfus.
  • Ornelas, I. J., Perreira, K. M., & Ayala, G. X. (2007). Parental influences on adolescent physical activity: A longitudinal study. International Journal of Behavioral Nutrition and Physical Activity, 4(3), doi:10.1186/1479-5868-4-3
  • Povey, R., Conner, M., Sparks, P., James, R., & Shepherd, R. (1998). Interpretations of healthy and unhealthy eating, and implications for dietary change. Health Education Research, 13(2), 171183. doi:10.1093/her/13.2.171
  • Resnicow, K., Jackson, A., Wang, T., De, A. K., McCarty, F., Dudley, W. N., & Baranowski, T. (2001). A motivational interviewing intervention to increase fruit and vegetable intake through Black churches: Results of the Eat for Life trial. American Journal of Public Health, 91(10), 16861693.
  • Resnicow, K., McCarty, F., & Baranowski, T. (2003). Are precontemplators less likely to change their dietary behavior? A prospective analysis. Health Education Research, 18(6), 693705. doi:10.1093/her/cyf052
  • Sallis, J. F., Pinski, R. B., Grossman, R. M., Patterson, T. L., & Nader, P. R. (1988). The development of self-efficacy scales for health-related diet and exercise behaviors. Health Education Research, 3(3), 283292. doi:10.1093/her/3.3.283
  • Stevenson, C., Doherty, G., Barnett, J., Muldoon, O. T., & Trew, K. (2007). Adolescents' views of food and eating: Identifying barriers to healthy eating. Journal of Adolescence, 30(3), 417434. doi:10.1016/j.adolescence.2006.04.005
  • Streiner, D. L., & Norman, G. R. (2003). Health measurement scales: A practical guide to their development and use (3rd ed.). Oxford: Oxford University Press.
  • Tershakovec, A. M. (2004). Psychological considerations in pediatric weight management. Obesity Research, 12(10), 15371538. doi:10.1038/oby.2004.192
  • Trudeau, F., Laurencelle, L., & Shephard, R. J. (2004). Tracking of physical activity from childhood to adulthood. Medicine and Science in Sports and Exercise, 36(11), 19371943. doi:10.1249/01.MSS.0000145525.29140.3B
  • United States Department of Agriculture. (2008a). MyPyramid.gov. Retrieved from http://www.mypyramid.gov/
  • United States Department of Agriculture. (2008b). MyPyramid.gov: For kids. Retrieved from http://www.mypyramid.gov/kids/index.html
  • United States of America: Internet usage and broadband usage report . (2007). Retrieved from http://www.internetworldstats.com/am/us.htm
  • Walker, S. N., Pullen, C. H., Hertzog, M., Boeckner, L., & Hageman, P. A. (2006). Determinants of older rural women's activity and eating. Western Journal of Nursing Research, 28(4), 449468; discussion 469–474. doi:10.1177/0193945906286613
  • Waltz, C. F., Strickland, O., & Lenz, E. R. (2005). Measurement in nursing and health research (3rd ed.). New York: Springer.
  • Wang, G., & Dietz, W. H. (2002). Economic burden of obesity in youths aged 6 to 17 years: 1979–1999. Pediatrics, 109(5), e81. doi:10.1542/peds.109.5.e81
  • White, M. A., Martin, P. D., Newton, R. L., Walden, H. M., York-Crowe, E. E., Gordon, S. T., . . . Williamson, D. A. (2004). Mediators of weight loss in a family-based intervention presented over the internet. Obesity Research, 12(7), 10501059. doi:10.1038/oby.2004.132
  • Wilson, A. M. (2002). Pilot testing. The Westburn Dictionary of Marketing. Retrieved from http://www.westburnpublishers.com/marketing-dictionary/p/pilot-testing.aspx
  • Wyatt, J. C. (2000). When to use web-based surveys. Journal of the American Medical Informatics Association, 7(4), 426429.
  • Zabinski, M. F., Daly, T., Norman, G. J., Rupp, J. W., Calfas, K. J., Sallis, J. F., & Patrick, K. (2006). Psychosocial correlates of fruit, vegetable, and dietary fat intake among adolescent boys and girls. Journal of the American Dietetic Association, 106(6), 814821. doi:10.1016/j.jada.2006.03.014