Peer crowd affiliation,adherence,perceived support,and metabolic control in T1DM youth

Abstract

Youth with type 1 diabetes (T1DM) can face many challenges when adhering to their complex regimen in the context of their peer crowd. The aim of this study was to determine if peer crowd affiliation was associated with adolescents’ adherence behaviors, perceived peer support, and metabolic control. A sample of 128 adolescents with T1DM completed the Peer Crowd Questionnaire, Diabetes Social Support Questionnaire–Friends Version, and Self-Care Inventory–Revised, and HbA1c readings were collected during their clinic visit. Results from this study suggest that adherence behaviors mediated the relationship between Jock peer crowd affiliation and metabolic control. Results also suggested that perceived peer support did not mediate the relationship between peer crowd affiliation and metabolic control through adherence behaviors. When examining the path coefficients for the purposed models, results showed a positive relationship between adherence and metabolic control, and more perceived peer support was related to worse metabolic control. Adolescents who identified mostly with the Jock peer crowd may hold lifestyle values that are consistent with the diabetes regimen which may make their diabetes management easier. In general, adolescents with T1DM may have a more multifaceted aspect of one’s identity and therefore, their peer crowd affiliation is not as salient.

Keywords

Adherence behavior adolescents metabolic control peer crowd affiliation peer support type 1 diabetes

Introduction

Older youth display more mismanagement of type 1 diabetes (T1D) regimen than younger children (DiMatteo, 2004; Kovacs et al., 1992). In fact, age is related to a linear decline in metabolic control (Helgeson et al., 2009). Adherence and good metabolic control can be difficult to achieve when adolescents are confronted with conformity and peer pressure (Hanson, 1990; La Greca, 1990; Seiffge-Krenke and Stemmler, 2003). The demands of development can conflict with the demands of the regimen pressuring the adolescent to choose between them (Burroughs et al., 1997; Delamater et al., 2003). They often violate their adherence behaviors in the context of peer influence in order to assimilate into the crowd (Burroughs et al., 1997; Thomas et al., 1997). Therefore, understanding how peers influence youths’ adherence behaviors is imperative for facilitating positive health outcomes.

Research has shown that adolescents depend on their peers for support; however, the support that they receive from their peers can either help or hinder their adherence behaviors. Furthermore, the type of support they receive from friends, such as tangible or companionship support, varies across the domains of self-care (Charron-Prochownik et al., 1991; La Greca et al., 2002). The relationship between support and adherence has implications for metabolic control, although previous research findings suggest the nature of that relationship is complex.

Adolescent peer crowd affiliation may be an important factor to understand when considering the relationship among peer support and adherence behaviors. Peer crowds are composed of a large group of adolescents that share or are identified by common characteristics (Brown, 1989a, Brown, 1990), and provide an important source of social support (O’Brien and Bierman, 1988). Belonging to a group of peers can cultivate a sense of inclusion which may produce positive effects on the youth’s self-esteem, thereby gaining approval and shared group identity (Furman and Robbins, 1985). Two major functions of crowds are to structure social interactions and foster the development of identity (Brown et al., 1994). Each crowd represents a unique identity or identity prototype where adolescents either choose (Urberg et al., 2000) or get placed based on their reputation and personal characteristics (Brown, 1990). Some adolescents have one, partial, or multiple crowd affiliations that can vary in intensity. Others may not identify with any crowds (Brown et al., 1994) and just consider themselves as ‘normal or average’ (La Greca and Prinstein, 1999). Adolescents may or may not want to exclusively identify to one crowd, but may interact with several throughout the day. The crowd depicts adolescents’ identity and the adolescent may or may not want to be associated with that chosen identity (Brown et al., 1994).

Reputation-based peer crowds are associated with increases in certain health-risk behaviors. Distinct peer crowds are known for deviant and health-risk taking behavior and are referred to in a variety of ways within the literature, such as ‘Burnouts, Nonconformists, Alternatives, Druggies, Dirts and Greasers’ (Brown et al., 1993; La Greca et al., 2001; Sussman et al., 1990; Urberg et al., 2000). Other crowds, such as ‘Brains and Eggheads’, are seen as academically orientated and the least likely to engage in health-risk behaviors (Brown et al., 1993; La Greca et al., 2001). Yet, ‘Populars and Jocks’ may not always display consistent characteristics in regard to health-risk behaviors as they can have both positive and negative influences (La Greca et al., 2001; Sussman et al., 1990).

One way to gain more insight on how peers influence and support adherence behaviors is through examining these relationships in the context of the peer crowd. While a few studies examined the relationships between peer crowds and health-risk behaviors (La Greca et al., 2001), no studies have examined the relationship among peer crowds, adherence, and metabolic control in youth with T1DM.

Aim

This study investigated whether peer crowd affiliation was associated with adolescents’ adherence behaviors, perceived peer support, and metabolic control. It was hypothesized that peer crowd affiliation would be related to metabolic control and that this relationship would be mediated by adherence behaviors. We further hypothesized the effect of peer crowd on adherence behaviors will be mediated by perceived peer support.

Methods

Participant selection

Participants were patients at the diabetes clinic who are seen for management and treatment of their diabetes in an outpatient setting. Participants were recruited by two methods. First, the principle investigator or a graduate student in the MS program in Counseling Psychology briefly introduced the study to families and verbally inquired if they were interested in hearing more about the study. If the families were interested, then they met with the principal investigator or graduate student to review the project in more detail and sign written consent forms.

Second, the principal investigator called families who volunteered for the Pediatric Diabetes Behavioral Health Cohort Registry. This registry was recently developed by a group of psychologists affiliated with the collaborating diabetes clinic and approved by their institutional review board (IRB). The purpose of this registry is to create a list of families of children with diabetes who are willing to be contacted by researchers for future behavioral health research projects. The principal investigator verbally reviewed the consent forms and project in detail over the telephone to interested families.

Data collection procedure

Once parents and youths provided written consent and verbal accent to participate, youths recruited during their diabetes clinic appointment had an option of completing the survey online or filling out paper and pencil versions. Under both conditions, the participants were able to complete the instruments at their convenience. These measures took approximately 20 minutes to complete. If they chose the paper and pencil format, participants filled out the questionnaires on their own during their clinic visit or they completed the instruments at home and mailed them back (postage paid, return envelope was provided).

For the internet option, participants were given an internet address to link them with a Midwestern university campus survey instrument. Participants were given an ID and password for access to all of the study materials. No personal identifying information was entered online. The software was powered by Qualtrics, a nationally respected survey instrument software company. Survey creation and distribution, data collection and storage, and reporting were hosted by Qualtrics.

For those who were contacted from the Pediatric Diabetes Behavioral Health Cohort Registry, the paper and pencil version was offered. Participants were sent through mail the consent forms and measures. Participants completed their measures and written consent forms at home and then mailed them back (postage paid, return envelope was provided).

All participants who consented and returned their questionnaires received in the mail a five-dollar gift certificate to one of two retailers in return. Participants included their mailing addresses and retailer preference at the time of consent of the project. This study was reviewed and approved by an IRB.

Instrumentation/measures

Demographic information

The questionnaire asked youths to report their age, gender, race/ethnicity, and duration of illness.

Peer crowd affiliation

The Peer Crowd Questionnaire (PCQ) (La Greca et al., 2001) assesses the extent adolescents identify with a selection of common peer crowd affiliations. Based on the nine-question, self-report, revised version of the PCQ (Mackey and La Greca, 2007), a modified version was used for this study. Adolescents were asked (Yes or No) to verify if the listed common peer crowds existed in their school. Common peer crowds included Jocks (athletic, on a school team), Brains (do well in school, enjoy academics), Burnouts (skip school, get into trouble), Populars/Preps (social leaders, involved in activities, concerned about image), Alternatives (rebel against the norm in clothing or ideas, attempt not to conform to social ideals), Loners (keep to themselves, do not belong to any particular peer group), and Averages (no crowd affiliation or just ‘typical’). Adolescents were then asked how much they identify with each peer crowd on a 5-point Likert scale (1= Not at all, 5 = Very much). Finally, adolescents were asked which group they most identify with. Studies have shown adolescents are accurate in identifying their placement in the peer crowd (Brown and Lohr, 1987), with a 93% inter-rater reliability (Sussman et al., 1990). Strong associations were reported between the adolescents’ primary peer crowd affiliation and their ratings of the degree to which they identified with that peer crowd (La Greca and Harrison, 2005).

Peer support

Peer support was examined by the Diabetes Social Support Questionnaire–Friends Version (DSSQ) (Bearman and La Greca, 2002), a 28-item self-report measure of friends’ support for diabetes care. Items involve the support for diabetes management in the areas of insulin shots, blood glucose testing, meal plan, exercise, and emotional support. First, adolescents were asked to rate the frequency of support (‘How does a friend…?’) for each item (1 = never, 5 = at least once a day). Second, adolescents were asked to rate perceived supportiveness (‘How does this make you feel?’ or ‘How would you feel?’) for each item (−1 = unhelpful or not supportive, 3 = very supportive). The scale can be independently scored for either frequency or as a combined rating (frequency × support). The combined rating takes into account both frequency and supportiveness of behavior as perceived by the adolescent and ranges from −5 (unsupportive behavior that occurs frequently) to 15 (very supportive and very frequent behavior). The DSSQ has been normed on youth that are predominately Caucasian, with an age range of 11–18. Internal consistency for total scores (Cronbach’s α > .70) and test–retest reliability (range = .78–.94, all ps < .001) are generally high. Furthermore, The DSSQ is reported to have good concurrent validity as total scores are highly correlated to other measures of support from friends (Bearman and La Greca, 2002).

Adherence

Adherence was examined by the Self-Care Inventory–Revised (SCI-R) (Weinger et al., 2005), a 15-item measure assessing perceived adherence to treatment recommendations for the diabetes regimen during the past one to two months. The SCI-R has a 5-point Likert scale (1 = never; 5 = always), with items addressing six areas of diabetes care, such as diet, glucose monitoring, medication administration, exercise, low glucose levels, and preventative/routine aspects of care. However, factorial analysis identified only one, large general factor rather than specific diabetes self-care areas. Mean adherence scores are calculated with higher scores reflecting better treatment adherence. The SCI-R yields adequate internal consistency (α = .87), concurrent validity (r = .63), and construct validity. The revised SCI-R norming sample consisted of Caucasian adults 18 years and older, and the recommended reading level is sixth grade (Weinger et al., 2005).

Metabolic control

HbA1c provides an average measure of blood glucose over the past two to three months, with the goal of having the value less than 7% (American Diabetes Association, 2006). HbA1c readings were collected from the youths’ lab results from their clinic visit.

Results

Participants

Participants were adolescents between the ages of 14 and 18 (M = 15.35, SD = 1.282) who received outpatient treatment at a Midwest Children’s Hospital. A total of 251 participants were recruited: 199 from the diabetes outpatient clinic and 52 from the Pediatric Diabetes Behavioral Health Cohort Registry. Five Diabetes Behavioral Health Cohort Registry and 123 diabetes clinic patients actually returned their instruments. The final sample consisted of 128 adolescents of which 64 (50%) were female. The total sample consisted of 111 (86.7%) Caucasians, 9 (7%) African Americans, 6 (4.7%) Hispanics, 1 (0.8%) Asian, and 1 (0.8%) self-identified as ‘Other’. The mean HbA1c reading was 8.8 (SD = 1.68, Min = 5.2, Max = 14.0). The average duration of illness in months for the sample was 80.89 (SD = 46.75). These youths received treatment and management for their diabetes at the clinic approximately three to four times per year. Patients were excluded due to non-English-speaking, developmental delay, or thought disorder based upon clinical judgment and the patient’s history. No youth were excluded from the study based on race, ethnicity, or gender.

Data analysis and significance

Analysis of variance (ANOVA) was used to assess the association between adolescents’ primary peer crowd affiliation and their ratings of the degree to which they identify with that peer crowd. That is, seven ANOVAs examined the seven peer crowds compared to the degree to which adolescents identify with each peer crowd. When the results were significant, post hoc analyses were conducted that compared the identified peer crowd of the youth to the combined responses of all other peer crowds. For example, when identification with Jocks was investigated, the response of Jocks was compared to responses of all other peer crowd identifications.

Second, in order to test the mediation model, hierarchical linear regression analysis was conducted. Metabolic control was the outcome variable and all demographic variables were entered on the first step: age, gender (girls = 1), ethnicity (dummy coded in reference to Caucasian adolescents), and duration of illness. The seven items assessing identification with each peer crowd was entered on the second step. Thus, for the mediation model, while controlling for age, gender, race/ethnicity, and duration of illness, the seven peer crowds were entered, followed by perceived support and adherence behaviors.

Descriptive statistics and reliabilities

The frequencies of sample characteristics broken down by peer crowd affiliation can be found in Table S1.

The means and standard deviations of duration of illness in months for peer crowd affiliation are found in Table S2.

Internal consistency for the total DSSQ in this study was excellent (Cronbach’s α = .94) and for the SCI-R (Cronbach’s α = .77) was acceptable.

Also, as an initial validity check, the percentages of responses indicating the presence of each peer crowd in the schools of participants were examined. Adolescents reported percentages of each peer crowd affiliation within their school were greater than 75%.

Analysis of variance (ANOVA)

A set of seven ANOVAs were used as a validity check to assess the association between adolescents’ primary peer crowd affiliation and their ratings of the degree to which they identify with each peer crowd. An ANOVA found that average scores on the level of identification with each peer crowd differed by primary peer crowd affiliation. The ANOVAs were significant for all seven validity questions regarding the level of identification with peer crowd affiliation: Jocks (F_(6,122) = 13.008, p < .001); Brains (F_(6,122) = 6.23, p < .001); Burnouts (F_(6,121) = 3.85, p < .001); Populars (F_(6,120) = 8.02, p < .001), Alternatives (F_(6,121) = 6.17, p < .001), Loners (F_(6,122) = 7.76, p < .001), and Averages (F_(6,122) = 10.03, p < .001).

Seven post hoc analyses were completed by comparing mean identification with each peer crowd by group. In the post hoc analyses, groups were combined to compare only the group of interest to all other groups. For instance, the Jocks rated their identification with the Jock crowd affiliation (M = 4.58, SD = 0.51) significantly higher than all other crowd affiliations combined (M = 2.44, SD = 1.21). Table 1 presents these means, standard deviations, and F-ratios for each peer crowd affiliation post hoc test. All post hoc tests were found to be significant in the direction expected.

Table 1.

ANOVA means, standard deviations, and F-ratios for peer crowd affiliations.

	Group of interest (n = 1)		All other groups (n = 6)		F
	Mean	SD	Mean	SD	F
Jocks***	4.58	0.51	2.44	1.21	62.93
Brains***	4.46	0.66	2.84	1.07	26.85
Burnouts***	3.50	1.92	1.44	0.81	21.57
Populars***	4.32	0.75	2.73	1.26	27.55
Alternatives***	4.14	1.07	1.85	1.03	26.93
Loners***	4.00	1.55	1.62	0.88	36.32
Averages***	4.30	0.89	2.91	1.19	56.19

Note: n = 128.

* p < .05; **p < .01; ***p < .001.

Hypothesized model

Originally, peer crowd affiliation was hypothesized to be related to metabolic control and that relationship would be mediated by adherence behaviors. Please refer to Figure S1 (Supplemental material) for a review of the full model.

Three relationships were tested: the total effect of peer crowd affiliation on metabolic control, the direct effect of peer crowd affiliation on adherence behaviors, and the direct effect of adherence behaviors on metabolic control. Overall, it was found that adherence behaviors mediate the relationship between Jock peer crowd affiliation and metabolic control (see Table S3).

In addition to the original model, peer crowd affiliation was hypothesized to be related to metabolic control and that relationship would be mediated by perceived support and adherence behaviors. In order to test this model, the effects of the demographic controls variables on metabolic control, perceived support, and adherence behaviors were examined independently using linear regression (see Table S4).

Results indicated that demographic variables significantly predict metabolic control; as a result, they were included in future analysis. Age, gender, race/ethnicity, and duration of illness accounted for approximately 24% of the variance in predicting metabolic control (F_(7,96) = 4.39, p < .001). An examination of the beta weights shows that the age, gender, and duration of illness effects did not have a significant effect on metabolic control, so they were excluded from further analysis; however, ethnicity did significantly predict metabolic control so it was retained. Specifically, identification as Hispanic, compared to Caucasian was found to significantly predict metabolic control (B = 2.50, t = 4.05, p < .001).

When examining the effects of demographic variables on adherence behaviors, results indicated that demographic variables did not significantly predict adherence. Age, gender, race/ethnicity, and duration of illness only accounted for 3.4% of the variance in predicting adherence and were eliminated from further analysis investigating the direct effect of perceived support and peer crowd affiliation on adherence behaviors (F_(7,97) = 0.49, p = .838).

When examining the effects of demographic variables on perceived support, results indicated that demographic variables did not significantly explain enough variance (10.6%) in perceived support overall (F_(7,103) = 1.74, p = .109). It was found that duration of illness in months was the only sole significant predictor of perceived support, which needed to be controlled for in further analysis investigating the direct effect of peer crowd affiliation on perceived support (B = 0.07, t = 2.08, p = .040). When examining the mediation model, the nonsignificant demographic control variables were removed for each step in the analysis as mentioned above.

In addition to the three relationships tested in the original model, the extended model examined seven additional relationships (see Figure S2).

Again, the total effect of peer crowd affiliation on metabolic control (path C) was not significant for any of the peer crowd affiliations (see Table S5, step 1).

The requirement for mediation in step 1 was not met. However, even though there was not a significant total effect of peer crowd affiliation on metabolic control, the mediation effect can still be tested (Kenny et al., 1998). A cancellation effect occurs when some unknown mediator complements the direction (positive or negative) of the relationship, leading the total effect to cancel out to zero (Hayes, 2009; MacKinnon et al., 2000).

Following these hypothesized steps for testing mediation, the path coefficients C, D, and F were examined (see Table S6).

Race/ethnicity accounted for approximately 20% of the variance in predicting metabolic control (F_(4,100) = 6.17, p < .001). When controlling for demographic variables, peer crowd affiliation, perceived support, and adherence behaviors accounted for a significant amount of additional variance (R² change = .15) in predicting metabolic control (F_(9,91) = 2.32, p = .021). The direct effect of perceived support on metabolic control (path C) was significant, indicating more perceived support is related to worse metabolic control (B = 0.02, t = 2.37, p = .020). The path coefficient for the direct effect of adherence behaviors on metabolic control (path D) was significant with a negative relationship indicating that the more that adolescents adhere to their diabetes regimen, the better their metabolic control (B = −0.05, t = −2.72, p = .008). Next path F was investigated, or the direct effect of peer crowd affiliation on metabolic control. This path was investigated for all seven peer crowd affiliation questions. None of path coefficients were significant, indicating that, in the presence of perceived support and adherence behaviors, metabolic control cannot be predicted by peer crowd affiliation (refer to Table S6 for regression coefficients).

For step 2, peer crowd affiliation and perceived support accounted for 13.7% of the variance in predicting adherence behaviors and this effect was marginally significant (F_(8,97) = 1.92, p = .065). The path coefficient associated with the effect of peer crowd affiliation on adherence behaviors (path E) was significant for the Jock peer crowd affiliation (B = 1.91, t = 2.56, p = .012). This means that the more adolescents identified with the Jock peer crowd, the better they will adhere to their diabetes regimen. No other paths for the remaining six peer crowd affiliations on metabolic control were significant, which can be referenced in Table S6. Path B, or the direct effect of perceived support on adherence behaviors, was not found to be significant (B = −0.01, t = −0. 21, p = .836).

Finally for step 3, the seven peer crowd affiliations did not account for a significant amount of variation in adherence behaviors above and beyond duration of illness (R² change = .06, F_(7,103) = 1.01, p = .426). The actual nonsignificant path coefficients can be referenced in Table S6.

The indirect effect of A × C was not tested because path A was not significant, as mentioned before. Also, the indirect effect of A × B × D was not tested because paths A and B were not significant. The only indirect effect which can be investigated is the indirect effect of E × D. In order to test the mediation effect, the Sobel test for mediation was utilized (Sobel, 1982). Results from the Sobel test showed that the mediation model or indirect effect of peer crowd affiliation with Jocks on metabolic control through adherence behaviors was not significant (Sobel test statistic = −1.866, p = .062). The Sobel test indicated no mediation effect; however, a better estimate of a mediating effect can be derived using the Empirical M-test, because it calculates an asymmetric confidence interval (Mackinnon et al., 2004). Because 0 was not in the confidence interval, the Empirical M-test indicated a mediation effect was present (−.20737, −.01432). A significant Empirical M-test and a moderately significant Sobel test statistic, indicate that a mediation effect is present in the model from Jock peer crowd affiliation to metabolic control through adherence behaviors. The indirect effect was calculated to be −.05 using the product-of-coefficient approach. Therefore, the indirect effect of Jock peer crowd affiliation on metabolic control through adherence behaviors accounts for 14.4% (−.05/−.094) of the total explained variance of the total effect of Jock peer crowd on metabolic control. As stated in the original hypothesized model, the indirect effect cannot be considered reliable because the total effect was not significant (refer back to Table S6, step 1, Supplemental material). For a full a review of the full model, please refer to Figure 1.

Figure 1.

Model with perceived support and adherence behaviors as mediators. Path E coefficient was significant with how much the adolescents identified with the Jock peer crowd affiliation. All other paths were nonsignificant regarding the remaining peer crowd affiliations.

Follow-up analyses

Because only Jock peer crowd affiliation was found to be a significant predictor, the model was reexamined with only Jock peer crowd affiliation present for the follow-up analyses (see Table S7).

It was found that the same paths were significant as the previous examination consistent with earlier findings, the total effect of Jock crowd affiliation on metabolic control was not significant; therefore, the indirect effect cannot be interpreted. For a full review of the full model, please refer to Figure 2.

Figure 2.

Model with Jock peer crowd affiliation as the predictor and perceived support and adherence behaviors as mediators.

Discussion

Overall, results suggested that identification with the Jock peer crowd was related to higher reports of adherence behaviors and that better adherence was associated with better metabolic control. Adolescents who mostly identified with the Jocks had lifestyle values that were consistent with the diabetes regimen; such as exercise and diet, that might have made diabetes management easier. These adolescents with T1DM may have had peers that were more supportive of the Jock life style, which in turn may have helped them keep up with their adherence behaviors and stay in better control. Results of this study further found that adolescents’ higher reports of adherence behaviors were associated with better metabolic control.

This study further suggested that perceived peer support did not mediate the relationship between peer crowd affiliation and metabolic control through adherence behaviors. However, results showed a significant relationship between the more perceived support and the worse metabolic control in adolescents with T1DM. It may be that the peers of adolescents with T1DM did not know how to best support their friend, especially if the adolescents had been going through tough times in their relationships with others. With the exception of Jocks, the lack of relationships associated with peer crowd affiliation could be due to the majority of adolescents identifying with the Average peer crowd. These Average adolescents may not have viewed themselves as integrated in other peer crowd groups. According to practitioners’ views of youth with chronic pain, adolescents typically reported concerns unrelated to their medical condition around their engagement in high risk behaviors, common peer, and intimate relationship issues. Furthermore, peer concerns involved small social networks, specific, and general peers (Fleischman et al., 2011). Just like youth with chronic pain, adolescents with T1DM may have had a more multifaceted identity and therefore, their peer crowd affiliation was not the only salient part of their identity, directly associated with peer support or only one peer or group.

The lack of the relationship between peer crowd affiliation and the associated variables may have been related to the way adolescents responded to the PCQ. The majority of adolescents (60%) in this sample identified with the ‘Average’ peer crowd affiliation, similar to previous research (Daddis, 2010), which has a number of potential implications. As previously stated, the lack of relationships associated with peer crowd affiliation as a predictor variable could be due to the majority of adolescents identifying more with the Average peer crowd and possibly leading to a more inconsistent identification with other peer crowds. Again, these adolescents might have identified more as Average for the social desirability of linking positive and/or negative connotations to each peer crowd affiliation. These adolescents identifying to a greater extent as Average may not have viewed themselves as integrated in other peer crowd groups, rather, they may have diversified themselves within other crowds. Yet, the Average peer crowd identification may have been an accurate representation of youth with T1DM.

In conclusion, adolescents who mostly identified with the Jocks had lifestyle values that were consistent with the diabetes regimen. Adolescents with T1DM may have had a more multifaceted identity than only their peer crowd affiliation, medical condition, and adherence-related behaviors.

Implications for practice

Adolescents who identified with the jock peer crowd may perceive benefits from improved health through their peer crowd’s support of a healthy lifestyle. It may be useful to identify the self-management strategies of these adolescents to facilitate self-regulation in the social context for all adolescents with T1DM. Identification and training of these skills may help prepare all adolescents with T1DM to effectively solve problems when faced with peer pressure (Grey et al., 1998).

Limitations

There are a number of limitations associated with this study that may limit this study’s generalizations and conclusions. First, results from this study may be limited to Caucasian adolescents with T1DM. Very few participants (13.3%) endorsed identifying with any other ethnic identity, an average smaller than the 25% average found in previous research.

A second limitation to this study included a small sample size given the large number of predictor variables examined. While the overall sample size was large enough to find a medium effect for a regression analysis based on the a priori power analysis, most of the peer crowd predictor and ethnic group sample sizes were very small which could have precluded the generalization of these results (Hood et al., 2009).

A third limitation in this study could be the methodology used to measure peer crowds through only the use of the adolescents’ self-report (Brown, 1989b; Brown et al., 1993; Prinstein and La Greca, 2002; Urberg et al., 2000). The fact that so many adolescents in the current study reported that they mostly identify with the Average crowd could suggest a bias self-report.

Future directions

Future research may further expand on the previous hypothesized models. For instance, to measure peer crowd affiliations, researchers may want to hold a focus group of adolescents with T1DM to determine a list of peer crowds that are in their school and then utilize this list for all adolescents’ self-report of peer crowds. This may allow greater validity of the peer crowds represented in the sample. Other replication studies of the hypothesized model may want to investigate if specific types of adherence behaviors mediate the relationship between peer crowd affiliations and metabolic control. In addition, to discover if there is a relationship among adolescents’ peer crowd affiliation, with the type of support they most appreciate, and how that type of support relates to certain adherence behaviors. Finally, research should focus on a broader range of ethnic group populations with T1DM in order to better understand the above-mentioned relationships among variables for all groups. This study will hopefully continue to foster interest in examining the influence of peers and how to facilitate better peer functioning, especially in relation to self-care in the social context.

Supplemental material

Supplemental Material, sj-tif-1-chc-10.1177_1367493520924875 - Peer crowd affiliation, adherence, perceived support, and metabolic control in T1DM youth

Supplemental Material, sj-tif-1-chc-10.1177_1367493520924875 for Peer crowd affiliation, adherence, perceived support, and metabolic control in T1DM youth by Katie Fleischman and Anthony A Hains in Journal of Child Health Care

Supplemental material

Supplemental Material, sj-tif-2-chc-10.1177_1367493520924875 - Peer crowd affiliation, adherence, perceived support, and metabolic control in T1DM youth

Supplemental Material, sj-tif-2-chc-10.1177_1367493520924875 for Peer crowd affiliation, adherence, perceived support, and metabolic control in T1DM youth by Katie Fleischman and Anthony A Hains in Journal of Child Health Care

Supplemental material

sj-pdf-1-chc-10.1177_1367493520924875 - Peer crowd affiliation, adherence, perceived support, and metabolic control in T1DM youth

sj-pdf-1-chc-10.1177_1367493520924875 for Peer crowd affiliation, adherence, perceived support, and metabolic control in T1DM youth by Katie Fleischman and Anthony A Hains in Journal of Child Health Care

Footnotes

Author’s contribution

The manuscript is adapted from the dissertation completed by Katie Fleischman under the supervision of Anthony Hains. Katie Fleischman made contributions to the study conceptualization and design, collection of data, all data analyses, and interpretation of the data as part of the larger dissertation. She wrote the entire dissertation and made the first effort to reduce the document to manuscript size. Finally, she made final approval of the manuscript to be published. Anthony Hains made significant contributions to conception and design of the study, made critical revisions to the initial manuscript draft, and then redrafted the manuscript and developed the final version for publication.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Katie Fleischman

Supplemental material

Supplemental material for this article is available online.

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