Comparing Three Ways to Improve Quality of Life for Patients with Kidney Disease and Their Caregivers

Background

Chronic kidney disease (CKD) and end-stage renal disease (ESRD) are significant global health problems. In 2016, the incidence of ESRD in the United States was 124 675 people, and the prevalence reached 726 331, among the highest rates in the world.¹ In addition to substantial mortality, morbidity, and cost, CKD has significant negative effects on quality of life (QOL)^2,3 that is, in turn, correlated with mortality among CKD patients.⁴

CKD is a general term for a variety of disorders that affect the structure and function of the kidneys. CKD is characterized by kidney damage, as defined by either abnormal urinary albumin excretion or decreased kidney function, or both, for >3 months. Based on severity of reduction in kidney function, CKD is classified into 5 stages. The complications and chance of progression to the need for kidney replacement therapy (dialysis or transplant) are more likely in severe CKD (stages 4 and 5). Patients with stage 5 CKD, who require kidney replacement therapy for continued survival, are defined as having ESRD.^5,6 Patients with stage 1 or 2 CKD progress to more advanced stages at approximately 0.5% per year, and patients with stage 3 or 4 disease progress to stage 5 or ESRD at a rate of 1.5% per year.⁷

Patient and Family Partner Program

Since 2004, the Kidney Foundation of Central Pennsylvania (KFCP) has operated a comprehensive patient engagement and empowerment program (Patient and Family Partner Program [PFPP]). Through the PFPP, trained patients with CKD and their family partners serve as volunteer peer mentors to other patients with CKD and their caregivers to help them engage successfully in their own care.⁵⁴ Information about the impact of trained mentors on the care of patients with CKD is lacking. More specifically, a peer-led CKD intervention has not been assessed for its impact on patient activation, patient QOL, and caregiver burden.

To address this issue, we examined the impact of a formal PM program on patient QOL, patient activation, and caregiver burden among patients with CKD and their caregivers. Knowledge gained from this randomized clinical trial (RCT) can potentially be extrapolated to other chronic disease states. We hypothesized, as depicted in Figure 1, that a formal structured mentoring program, based on information needs of patients and caregivers, would lead to improved patient QOL (aim 1) and decreased caregiver burden (aim 2). We also hypothesized that such educational programs would lead to activation of patients to participate in their own care (aim 3).

Figure 1

Conceptual Model of the Effect of a PM Program for Patients with CKD and Their Caregivers.

The specific aims of this RCT were as follows:

1. To determine whether PM leads to improved QOL among patients with CKD.
2. To determine whether PM leads to improved caregiver burden among caregivers of patients with CKD.
3. To determine whether PM leads to improved patient activation among patients with CKD.

Patient and Stakeholder Engagement

Patients, caregivers, and other stakeholders were involved in various stages of the study. The PFPP, initially named Patient-Partner Program (PPP), was envisioned by a kidney transplant recipient who was a registered nurse and a member of the KFCP board of directors.⁵⁵ She partnered with a renal social worker and a transplant coordinator to develop a PM program for patients with ESRD. Following a thorough literature and internet search, as well as personal contacts, the founding group developed a preliminary curriculum for the PPP. Based on input from renal physicians and social workers, the curriculum and the program has undergone periodic modification.⁵⁵ Acknowledging the vital role of caregivers and family members in the care of patients with CKD, the founding group, consisting of a patient, a renal social worker, and a transplant coordinator, changed the name of the program to PFPP in 2011.

By 2011, approximately 80 mentors and >200 mentoring partnerships had been established. Review of mentor and mentee evaluations pointed toward relative success of the program. Mentored patients noted improved adherence with treatment and overall satisfaction with the improved adherence; mentors reported satisfaction with their roles; and both groups perceived the bonding experience as a very positive influence.

To assess the performance of the program and to discuss its future direction, we convened a focus group in August 2011. Participants in the focus group included the 3 original founders (a patient, a renal social worker, and a transplant coordinator), a dialysis patient, spouse of a transplanted patient, a social worker, director of the KFCP, the program coordinator, and the medical director of the PFPP. The current study is congruent with the 2 main themes identified in the focus group as the proposed goals of expansion of the PFPP: (1) Creating an online version of the program was proposed as a possible strategy to expand the PFPP to include rural areas; and (2) expanding the PFPP to include patients with pre-ESRD was deemed essential to the overall success of the program.

Figure 2 presents the governance of the research team. For this study, the research team organized 3 advisory groups and engaged 3 individual advisors (see the Acknowledgments section) as follows:

Figure 2

Governance of Research Team.

• Patient and Caregiver Advisory Group consisted of 12 members (7 patients and 5 caregivers) and provided the patients' and caregivers' perspectives about the content of the interventions, strategies for recruiting participants, and interpreting and disseminating findings. We recruited Patient and Caregiver Advisory Group members from among previously trained PFPP mentors, through a general call for volunteers on the KFCP website, and nomination by the KFCP and PFPP program coordinator.
• Provider Advisory Group consisted of 12 members and included nephrologists (2), nephrology advanced practice providers (1), primary care providers (1), dialysis social workers (2), transplant social workers (2), dialysis nurses (2), and transplant coordinators (2). The research team identified members of the Provider Advisory Group through professional networks in the community.
• Community Advisory Board consisted of the 15-member volunteer board of directors of the KFCP. The membership of the board of directors included patients (2), caregivers (2), community nephrologist (1), academic nephrologist (1), transplant surgeon (1), senior medical director of a major health care network and a primary care physician (1), vice president of another health system (1), dialysis social workers (2), members of the community (3), and an attorney (1). Some board members have been trained as mentors. The Community Advisory Board provided insights for developing the proposed research and oversight during the study. The principal investigator, a member of the board of directors since 2011, presented updates on the project at the quarterly meetings to seek input on recruitment, study flow, evaluation, and dissemination of findings. The Community Advisory Board's partnership with the KFCP board provided an opportunity to present preliminary results at the KFCP annual symposium on kidney disease.
• Individual advisors included an experienced renal social worker who was one of the original founders of the PFPP. She is highly regarded in the renal social work community of central Pennsylvania and, to improve recruitment, assisted in the description of the PFPP for potential participants and health care providers. The 2 patient advisors had previously completed training as mentors in the PFPP. Collectively, the 3 advisors provided insight into creating interventions; they particularly assisted in developing the content of the online PM intervention.

In addition to their roles within the advisory groups, patients, caregivers, and stakeholders played key roles in implementing the study. During training of peer mentors, stakeholders delivered parts of the content. The interventions (face-to-face [FTF] and online PM) were carried out entirely by patients and caregivers. Each year, a panel of patients and caregivers who were involved in the study presented updates and their personal experiences at the KFCP annual symposium on kidney disease. During the second year of the study, the Community Engagement Studio, organized by the Penn State Clinical and Translational Science Institute Comparative Effectiveness Research Center, was convened to present results and to explore options for public dissemination.

Methods

Study Overview

We conducted an RCT to test the effectiveness of mentoring by trained peers on QOL for patients with CKD as measured by the Kidney Disease Quality of Life-36 (KDQOL-36) instrument (specific aim 1), caregiver burden as measured by the Zarit Burden Interview (ZBI) (specific aim 2), and patient activation as measured by the Patient Activation Measure (PAM) (specific aim 3). We randomly assigned patients with CKD and their caregivers into 3 groups: (1) FTF PM plus informational textbook about CKD; (2) online PM plus informational textbook about CKD; and (3) textbook only (participants received the informational textbook about CKD but no PM). We used the PFPP, described earlier, as the mentoring program for the PM intervention groups.

Patients with CKD and their caregivers assigned to FTF intervention groups received 6 months of PM and the informational textbook of the PFPP. Participants assigned to the online intervention group also received 6 months of PM through an interactive online platform and the informational textbook of the PFPP. The textbook-only group received the informational textbook of the PFPP to review independently.

At baseline, 12 months, and 18 months, the patients completed the KDQOL-36 instrument and the PAM survey; the caregivers completed the ZBI. Figure 3 presents the study design.

Figure 3

Design of the Trial: Recruitment, Enrollment, Interventions, and Assessments.

Results

Patient Populations and Demographics

The CONSORT diagram (Figure 4) describes the flow of patient participants through the study interventions. We assessed 236 patients with stage 4 or 5 CKD for eligibility. We excluded 81 patients. Of these, 73 did not meet inclusion criteria (no internet access, n = 61; previous mentoring for CKD, n = 12), and 8 eligible patients declined to participate after receiving more information about the study because of the perceived burden of participating in the study.

Figure 4

CONSORT Diagram for Flow of Patients Through the Trial.

Using permuted block randomization with 1:1:1 allocation, we randomly assigned 155 patients to 3 groups. All patients began participation in the interventions and completed the baseline assessment. Of these, 117 patients completed the interventions and the 18-month assessment. Assessments consisted of completion of the KDQOL-36 instrument (specific aim 1) and PAM (specific aim 3).

Table 1 presents the baseline characteristics of the patients. Baseline demographic characteristics did not significantly differ statistically among the 3 groups (Appendix D documents the details of patients' demographic characteristics).

Table 1

Baseline Characteristics of Patients by Intervention Group.

Among the FTF group, 45 patients completed the allocated intervention and the 12-month assessment; 40 patients completed the 18-month assessment. Among the online PM group, 45 patients completed the allocated intervention and the 12-month assessment; 42 patients completed the 18-month assessment. Among the textbook-only group, 40 patients completed the allocated intervention and the 12-month assessment; 35 patients completed the 18-month assessment. We included 117 patients in the final analyses.

Patient Outcomes: Aims 1 and 3

The key outcomes for aim 1 and aim 3 were KDQOL-36 score and PAM score, respectively.

Aim 1: KDQOL-36 scores: main analyses

In Table 2, we document the unadjusted mean scores of the EKD, BKD, SPKD, and SF-12 PCS and MCS of the KDQOL-36 in the 3 groups at baseline, at 12 months, and at 18 months.

Table 2

Mean Unadjusted Domain Scores of the KDQOL-36 Throughout the Study Period by Intervention Group (ITT Analysis).

Table 3 shows the changes in mean (∆) scores from the KDQOL-36 over the study period. We note the following findings:

Table 3

Unadjusted Changes in Mean Scores of Domains of the KDQOL-36 Throughout the Study Period by Intervention Group (ITT Analysis).

• Online PM was associated with a statistically significant increase in 4 mean scores at 12 months:
  • BKD score (∆, 11.7; 95% CI, 0.4-23.0 [P = .04])
  • SPKD score (∆, 9.0; 95% CI, 1.9-16.1 [P = .01])
  • SF-12 PCS score (∆, 7.8; 95% CI, 4.1-11.6 [P = .0001])
  • SF-12 MCS score (∆, 6.5; 95% CI, 2.4-10.6 [P = .002])

• Online PM was associated with a statistically significant increase in 5 mean scores at 18 months:
  • EKD score (∆, 9.8; 95% CI, 0.2-19.4 [P = .04]).
  • BKD score (∆, 13.4; 95% CI, 1.0-25.8 [P = .03]).
  • SPKD score (∆, 8.0; 95% CI, 0.8-15.2 [P = .03]).
  • SF-12 PCS score (∆, 5.8; 95% CI, 1.8-9.8 [P = .005]).
  • SF-12 MCS score (∆, 7.7; 95% CI, 3.7-11.7 [P = .0002]).

• FTF PM was associated with a statistically significant increase in 2 mean scores at 12 months:
  • SPKD score (∆, 8.3; 95% CI, 1.5-15.1 [P = .01]).
  • SF-12 MCS score (∆, 6.2; 95% CI, 1.8-10.6 [P = .006]).

FTF PM was not associated with any significant change in the mean scores of any of the KDQOL-36 domains at 18 months.

Among the textbook-only group, no significant changes were found in the mean scores of any of the KDQOL-36 domains at 12 months or 18 months.

Table 4 shows changes in the scores from the KDQOL-36 over the study period, adjusted for demographic variables (race/ethnicity, sex, age quartile, marital status, attended or completed college, employment status, and rural/urban location), using ITT analysis. The slope estimates represent change in scores in standard points over 18 months.

Table 4

Changes in Scores on the KDQOL-36 Instrument Throughout the Study Period Among Groups (ITT Analysis).

Online PM was associated with a statistically significant increase in 5 slope scores:

• EKD score (slope estimate, 4.13; 95% CI, 0.87-7.4 [P = .01]).
• BKD score (slope estimate, 5.44; 95% CI, 1.24-9.64 [P = .01]).
• SPKD score (slope estimate, 6.00; 95% CI, 3.09-8.91 [P = .006]).
• SF-12 PCS score (slope estimate, 2.50; 95% CI, 0.95-4.06 [P = .001]).
• SF-12 MCS score (slope estimate, 3.46; 95% CI, 1.78-5.13 [P = .0001]).

Compared with textbook only, online PM was associated with a statistically significant increase in 4 comparisons of slope scores:

• In the BKD domain (6.43; 95% CI, 0.27-12.58 [P = .04]).
• In the SPKD domain (8.71; 95% CI, 5.35-12.07 [P = .003]).
• In the PCS domain (3.20; 95% CI, 0.95-5.48 [P = .01]).

Compared with textbook only, FTF PM was associated with a statistically significant increase in the slope score for the SPKD domain (6.12; CI, 2.62-9.38 [P = .04]).

Aim 1: KDQOL-36 Scores: Subgroup Analyses

We performed exploratory analyses to investigate HTEs. We used type 3 fixed-effects models to identify statistically significance (P ≤ .05) [demographic variable × KDQOL domain score] to include in the multivariable model. Results appear in Table 5.

Table 5

Type 3 Tests of Fixed Effects for Demographic Variables and Scores of KDQOL-36 Domains.

Exploratory subgroup analyses displayed in Tables 6 and 7 (for EKD and BKD, respectively) showed heterogeneity for the effects of being employed on the association between FTF PM and increased EKD (P value of slope estimate = .002) and BKD (P value of slope estimate = .04) scores and the association between online PM and increased EKD score (P value of slope estimate = .02). When comparing FTF PM with textbook only, employment had a positive effect on the difference between the slopes of change of EKD (P value of slope estimate = .02) (Table 6) and BKD (P value of slope estimate = .03) (Table 7) in the FTF group vs the textbook-only group. All analyses were by ITT.

Table 6

Change in EKD Scores Among the Groups by Employment Status (ITT Analysis).

Table 7

Change in BKD Scores Among the Groups by Employment Status (ITT Analysis).

In exploratory subgroup analyses shown in Table 8, “not employed” status had a positive effect on the association between online PM and increased SF-12 PCS score (P = .01). We also detected a positive effect of “not employed” status on the difference between slopes of change in SF-12 PCS score in the online PM group vs the textbook-only group (P = .04). All analyses were by ITT.

Table 8

Change in SF-12 PCS Score Among Groups by Employment Status (ITT Analysis).

Being male (shown in Table 9) had a positive effect on the association between online PM and increased SF-12 MCS score (P < .0001). We also saw a positive effect of male sex on the difference between slopes of change in the SF-12 MCS score in the FTF PM group vs the online PM group (P = .02) and between slopes of change in SF-12 MCS score in the online PM group vs the textbook-only group (P = .02).

Table 9

Change in SF-12 MCS Score Among Groups by Sex (ITT Analysis).

Aim 3: PAM scores: main analyses

Patient activation highlights patients' preparedness and ability to take independent actions to manage their health and care and is a component of patient engagement. An improvement of 4 points in the PAM score is considered an MCID.⁹⁷ The mean PAM scores in the 3 groups at baseline, 12 months, and 18 months are shown in Table 10. The mean baseline scores were not statistically different among the 3 groups.

Table 10

Mean PAM Scores Throughout the Study Period by Intervention Group (ITT Analysis).

Table 11 shows the changes in mean PAM scores from baseline to 12 months and 18 months. Online PM was associated with a statistically significant increase in the mean PAM score at 12 months (∆, 10.5; 95% CI, 4.5-16.6 [P = .0009]).

Table 11

Changes in Mean PAM Scores Throughout the Study Period by Intervention Group (ITT Analysis).

Online PM was also associated with a statistically significant increase in mean PAM score at 18 months (∆, 11.0; 95% CI, 4.7-17.3 [P = .0008]).

FTF PM and textbook only were not associated with statistically significant changes in mean PAM scores at 12 months or at 18 months.

Table 12 shows changes in PAM scores from baseline to 18 months adjusted for demographic variables (race/ethnicity, sex, age quartile, marital status, attended or completed college, employment status, and rural/urban location). The online PM group experienced a significant increase in the PAM score in the study period (slope estimate, 5.66; 95% CI, 2.79-8.52 [P = .0001]). The difference between the slopes of the FTF PM PAM score and the online PM PAM score was also statistically significant (SE, −4.70 ± 2.07; 95% CI, −8.79 to −0.62 [P = .02]). The difference between the slopes of the online PM PAM score and textbook-only PAM score was also statistically significant (SE, −5.63 ± 2.12; 95% CI, 1.44-9.83 [P = .01]).

Table 12

Change in PAM Scores Throughout the Study Period Among Groups (ITT Analysis).

Subgroup analyses: PAM scores: aim 3

We performed exploratory analyses to investigate HTEs. We used type 3 fixed-effects models to identify statistical significance (P ≤ .05) [demographic variable × PAM score] to include in the multivariable model (Table 13).

Table 13

Type 3 Tests of Fixed Effects for Demographic Variables and PAM Scores.

In exploratory subgroup analyses documented in Table 14, we detected a positive effect of male sex on the association between online PM and increased PAM score (P < .0001). Female sex had a positive effect on the association between FTF PM and increased PAM score (P < .01). With respect to the comparisons, being male had a positive effect on the difference between slopes of change in PAM score for the FTF PM group vs the online PM group (P < .0001) and between the online PM group and the textbook-only group (P < .0001).

Table 14

Change in PAM Among Groups by Sex (ITT Analysis).

In other exploratory subgroup analyses of marital status (Table 15), being married had a positive effect on the association between online PM and PAM score (P = .003). In contrast, “not married” status had a positive effect on the association between FTF PM and increased PAM score (P = .03). “Not married” status also had a positive effect on the association between online PM and increased PAM score (P = .008). In addition, we found a positive effect of “married” status on the differences in slopes of change for PAM score in the FTF PM group vs the online PM group (P = .0008) and in the online PM group vs the textbook-only group (P = .02).

Table 15

Change in PAM Among Groups by Marital Status (ITT Analysis).

Aim 2: Caregiver Outcomes

The CONSORT diagram in Figure 5 describes the flow of caregiver participants through the study interventions. This focus involves specific aim 2 and completion of the ZBI. We assessed 122 caregivers of patients with stage 4 or 5 CKD for the caregivers' eligibility. We excluded 36 caregivers: 22 did not meet inclusion criteria (no internet access, n = 18; previous mentoring experience, n = 4), and 14 eligible caregivers declined to participate after receiving more information about the study owing to the perceived burden.

Figure 5

CONSORT Diagram for Flow of Caregivers Through the Trial.

We randomly assigned 86 caregivers to the 3 groups. All caregivers completed the baseline assessment and started participation in the interventions. Seventy caregivers completed the interventions and the 18-month assessment. Fifteen caregivers were lost to follow-up before the 12-month assessment, and 1 caregiver withdrew from the trial after completing the 12-month assessment.

Table 16 documents the primary baseline characteristics of the caregivers. Appendix E presents details of caregiver demographics. Baseline demographic characteristics did not significantly differ statistically across the 3 groups.

Table 16

Baseline Characteristics of Caregivers According to Intervention Group.

Aim 2: Caregiver burden scores

The mean ZBI scores in the 3 groups at baseline, 12 months, and 18 months are shown in Table 17. The mean baseline scores did not statistically differ among the 3 groups.

Table 17

Unadjusted Mean ZBI Scores Throughout the Study Period Among Intervention Groups (ITT Analysis).

Table 18 shows the changes in mean scores from the ZBI over the study period.

Table 18

Changes in Unadjusted Mean ZBI Scores Throughout the Study Period by Intervention Group (ITT Analysis).

FTF PM was associated with a statistically significant decrease (improvement) in the mean ZBI score at 12 months (∆, −5.1; 95% CI, −10.0 to −0.2 [P = .04]). FTF PM was also associated with a statistically significant improvement in the ZBI score at 18 months (∆, −7.1; 95% CI, −12.2 to −2.1 [P = .007]). Online PM was associated with a statistically significant improvement in the mean ZBI score at 12 months (∆, −7.1; 95% CI, −13.3 to −0.9 [P = .03]). Online PM was also associated with a statistically significant improvement in the ZBI score at 18 months (∆, −8.4; 95% CI, −14.9 to −1.9 [P = .01]).

Table 19 shows changes in caregivers' ZBI scores over the study period, adjusted for demographic variables (race/ethnicity, sex, age quartile, marital status, education, employment status, and rural/urban location). ZBI scores decreased significantly in the study period in the online PM group (SE, −3.44; 95% CI, −6.31 to −0.57 [P = .002]). ZBI scores did not significantly change among the FTF PM group or the textbook-only group.

Table 19

Change in Adjusted ZBI Score Throughout the Study Period Among Groups (ITT Analysis).

No demographic variables had a significant independent effect on the change in ZBI scores among groups.

Discussion

Conclusions

The fundamental aim of this RCT was to evaluate the effect of a PM program on patient QOL and patient activation among patients with CKD and on burden of care among their caregivers. We compared 2 forms of PM, 1 delivered in FTF meetings and the other by online contact only, with a control group whose only intervention was access to an informational textbook on kidney disease. Online PM was associated with increased QOL as measured by the KDQOL-36 and patient activation as measured by the PAM among patients with CKD. Online PM was also associated with decreased burden of care among caregivers of patients with CKD as measured by the ZBI. In contrast, FTF PM was associated with improvement in fewer outcomes than online PM and only in subgroups identified in exploratory analyses. We conclude that online PM is an effective strategy for patient activation that leads to improved QOL and reduced caregiver burden with potential for use in other chronic disease states.

References

1.

Saran R, Robinson B, Abbott KC, et al. US Renal Data System 2018 Annual Data Report: Epidemiology of Kidney Disease in the United States. Am J Kidney Dis. 2019;73(3 Suppl 1):A7-A8. [PMC free article: PMC6620109] [PubMed: 30798791]

2.

Kalender B, Ozdemir AC, Dervisoglu E, Ozdemir O. Quality of life in chronic kidney disease: effects of treatment modality, depression, malnutrition and inflammation. Int J Clin Pract. 2007;61(4):569-576. [PubMed: 17263698]

3.

Walters BAJ, Hays RD, Spritzer KL, Fridman M, Carter WB. Health-related quality of life, depressive symptoms, anemia, and malnutrition at hemodialysis initiation. Am J Kidney Dis. 2002;40(6):1185-1194. [PubMed: 12460037]

4.

Tsai YC, Hung CC, Hwang SJ, et al. Quality of life predicts risks of end-stage renal disease and mortality in patients with chronic kidney disease. Nephrol Dial Transplant. 2010;25(5):1621-1626. [PubMed: 20037172]

5.

Levey AS, Eckardt K-U, Tsukamoto Y, et al. Definition and classification of chronic kidney disease: a position statement from Kidney Disease: Improving Global Outcomes (KDIGO). Kidney int. 2005;67(6):2089-2100. [PubMed: 15882252]

6.

Levey AS, Coresh J. Chronic kidney disease. Lancet. 2012;379(9811):165-180. [PubMed: 21840587]

7.

Hsu C-y, Vittinghoff E, Lin F, Shlipak MG. The Incidence of end-stage renal disease is increasing faster than the prevalence of chronic renal insufficiency. Ann Intern Med. 2004;141(2):95-101. [PubMed: 15262664]

8.

DeCuir JR. Praying for the power of patience (POP)—life is great, even with kidney failure! Adv Ren Replace Ther. 1998;5(3):252-253. [PubMed: 9686636]

9.

Hedman H. Patient compliance in renal replacement therapy: whose problem is it? The patient's perspective. EDTNA ERCA J. 1998;24(4):15-16. [PubMed: 10222907]

10.

Waterman AD, Robbins ML, Peipert JD. Educating prospective kidney transplant recipients and living donors about living donation: practical and theoretical recommendations for increasing living donation rates. Curr Transplant Rep. 2016;3(1):1-9. [PMC free article: PMC4918088] [PubMed: 27347475]

11.

Waterman AD, Morgievich M, Cohen DJ, et al. Living donor kidney transplantation: improving education outside of transplant centers about live donor transplantation—recommendations from a consensus conference. Clin J Am Soc Nephrol. 2015;10(9):1659-1669. [PMC free article: PMC4559502] [PubMed: 26116651]

12.

LaPointe Rudow D, Hays R, Baliga P, et al. Consensus conference on best practices in live kidney donation: recommendations to optimize education, access, and care. Am J Transplant. 2015;15(4):914-922. [PMC free article: PMC4516059] [PubMed: 25648884]

13.

Cassidy BP, Getchell LE, Harwood L, Hemmett J, Moist LM. Barriers to education and shared decision making in the chronic kidney disease population: a narrative review. Can J Kidney Health Dis. 2018;5:2054358118803322. https://www​.ncbi.nlm​.nih.gov/pmc/articles/PMC6236635/ [PMC free article: PMC6236635] [PubMed: 30542621]

14.

Lopez-Vargas PA, Tong A, Howell M, Craig JC. Educational interventions for patients with CKD: a systematic review. Am J Kidney Dis. 2016;68(3):353-370. [PubMed: 27020884]

15.

Hsu C-K, Lee C-C, Chen Y-T, et al. Multidisciplinary predialysis education reduces incidence of peritonitis and subsequent death in peritoneal dialysis patients: 5-year cohort study. PLoS One. 2018;13(8):e0202781. doi:10.1371/journal.pone.0202781 [PMC free article: PMC6107258] [PubMed: 30138478] [CrossRef]

16.

Moss AH. Ethical principles and processes guiding dialysis decision-making. Clin J Am Soc Nephrol. 2011;6(9):2313. https://cjasn​.asnjournals​.org/content/6/9/2313 [PubMed: 21896833]

17.

Tuso P. Choosing wisely and beyond: shared decision making and chronic kidney disease. Perm J. 2013;17(4):75-78. [PMC free article: PMC3854813] [PubMed: 24361024]

18.

Covic A, Bammens B, Lobbedez T, et al. Educating end-stage renal disease patients on dialysis modality selection. NDT Plus. 2010;3(3):225-233. [PMC free article: PMC5477971] [PubMed: 28657058]

19.

Finkelstein FO, Story K, Firanek C, et al. Perceived knowledge among patients cared for by nephrologists about chronic kidney disease and end-stage renal disease therapies. Kidney Int. 2008;74(9):1178-1184. [PubMed: 18668024]

20.

Whaley-Connell A, Shlipak MG, Inker LA, et al. Awareness of kidney disease and relationship to end-stage renal disease and mortality. Am J Med. 2012;125(7):661-669. [PMC free article: PMC3383388] [PubMed: 22626510]

21.

Iles-Smith H. Perceptions and experiences of pre-dialysis patients. EDTNA ERCA J. 2005;31(3):130-133. [PubMed: 16363411]

22.

Ameling JM, Auguste P, Ephraim PL, et al. Development of a decision aid to inform patients' and families' renal replacement therapy selection decisions. BMC Med Inform Decis Mak. 2012;12:140. [PMC free article: PMC3560257] [PubMed: 23198793]

23.

Morton RL, Howard K, Webster AC, Snelling P. Patient Information about Options for Treatment (PINOT): a prospective national study of information given to incident CKD stage 5 patients. Nephrol Dial Transplant. 2011;26(4):1266-1274. [PubMed: 20819955]

24.

McIntyre NJ, Fluck R, McIntyre C, Taal M. Treatment needs and diagnosis awareness in primary care patients with chronic kidney disease. Br J Gen Pract. 2012;62(597):e227-232. doi:10.3399/bjgp12X636047 [PMC free article: PMC3310028] [PubMed: 22520909] [CrossRef]

25.

Ormandy P. Information topics important to chronic kidney disease patients: a systematic review. J Ren Care. 2008;34(1):19-27. [PubMed: 18336519]

26.

O'Donnell A, Tucker L. Pre-dialysis education. A change in clinical practice. How effective is it? EDTNA ERCA J. 1999;25(2):29-32. [PubMed: 10531879]

27.

Juhnke J, Curtin RB. New study identifies ESRD patient education needs. Nephrol News Issues. 2000;14(6):38-39. [PubMed: 11249457]

28.

Schatell D, Ellstrom-Calder A, Alt PS, Garland JS. Survey of CKD patients reveals significant gaps in knowledge about kidney disease. Part 2. Nephrol News Issues. 2003;17(6):17-19. [PubMed: 12778612]

29.

Coupe D. Making decisions about dialysis options: an audit of patients' views. EDTNA ERCA J. 1998;24(1):25-26, 31. [PubMed: 9873281]

30.

Klang B, Bjorvell H, Clyne N. Predialysis education helps patients choose dialysis modality and increases disease-specific knowledge. J Adv Nurs. 1999;29(4):869-876. [PubMed: 10215978]

31.

Tweed AE, Ceaser K. Renal replacement therapy choices for pre-dialysis renal patients. Br J Nurs. 2005;14(12):659-664. [PubMed: 16010217]

32.

Groome PA, Hutchinson TA, Tousignant P. Content of a decision analysis for treatment choice in end-stage renal disease: who should be consulted? Med Decis Making. 1994;14(1):91-97. [PubMed: 8152362]

33.

Niccum KJ, Perez D. Wheels within wheels: creating a circle of knowledge through communication. Adv Ren Replace Ther. 2000;7(4 Suppl 1):S56-S64. [PubMed: 11053588]

34.

Bass EB, Jenckes MW, Fink NE, et al. Use of focus groups to identify concerns about dialysis. Choice study. Med Decis Making. 1999;19(3):287-295. [PubMed: 10424835]

35.

Whittaker AA, Albee BJ. Factors influencing patient selection of dialysis treatment modality. ANNA J. 1996;23(4):369-375; discussion 376-367. [PubMed: 8900682]

36.

Wilkinson H. Pre-dialysis information: how effective is it? EDTNA ERCA J. 1998;24(1):32-34. [PubMed: 9873284]

37.

Orsino A, Cameron JI, Seidl M, Mendelssohn D, Stewart DE. Medical decision-making and information needs in end-stage renal disease patients. Gen Hosp Psychiatry. 2003;25(5):324-331. [PubMed: 12972223]

38.

Harwood L, Locking-Cusolito H, Spittal J, Wilson B, White S. Preparing for hemodialysis: patient stressors and responses. Nephrol Nurs J. 2005;32(3):295-302; quiz 303. [PubMed: 16035471]

39.

Burns T, Fernandez R, Stephens M. The experiences of adults who are on dialysis and waiting for a renal transplant from a deceased donor: a systematic review. JBI Database System Rev Implement Rep. 2015;13(2):169-211. [PubMed: 26447040]

40.

DePasquale N, Ephraim PL, Ameling J, et al. Selecting renal replacement therapies: what do African American and non-African American patients and their families think others should know? A mixed methods study. BMC Nephrol. 2013;14:9. doi:10.1186/1471-2369-14-9 [PMC free article: PMC3565884] [PubMed: 23317336] [CrossRef]

41.

Wuerth DB, Finkelstein SH, Schwetz O, Carey H, Kliger AS, Finkelstein FO. Patients' descriptions of specific factors leading to modality selection of chronic peritoneal dialysis or hemodialysis. Perit Dial Int. 2002;22(2):184-190. [PubMed: 11990402]

42.

Tong A, Sainsbury P, Carter SM, et al. Patients' priorities for health research: focus group study of patients with chronic kidney disease. Nephrol Dial Transplant. 2008;23(10):3206-3214. [PubMed: 18445638]

43.

Bath J, Tonks S, Edwards P. Psychological care of the haemodialysis patient. EDTNA ERCA J. 2003;29(2):85-88. [PubMed: 14598951]

44.

Heisler M. Different models to mobilize peer support to improve diabetes self-management and clinical outcomes: evidence, logistics, evaluation considerations and needs for future research. Fam Pract. 2010;27:I23-I32. [PMC free article: PMC2902359] [PubMed: 19293400]

45.

Dennis C-L. Peer support within a health care context: a concept analysis. Int J Nurs Stud. 2003;40(3):321-332. [PubMed: 12605954]

46.

Lucksted A, McNulty K, Brayboy L, Forbes C. Initial evaluation of the peer-to-peer program. Psychiatr Serv. 2009;60(2):250-253. [PubMed: 19176421]

47.

Wright L, Pennington JJ, Abbey S, Young E, Haines J, Ross H. Evaluation of a mentorship program for heart transplant patients. J Heart Lung Transplant. 2001;20(9):1030-1033. [PubMed: 11557200]

48.

Mandalia PK, Stone MA, Davies MJ, Khunti K, Carey ME. Diabetes self-management education: acceptability of using trained lay educators. Postgrad Med J. 2014;90(1069):638-642. [PubMed: 25258417]

49.

Jerson B, D'Urso C, Arnon R, et al. Adolescent transplant recipients as peer mentors: a program to improve self-management and health-related quality of life. Pediatr Transplant. 2013;17(7):612-620. [PubMed: 23905874]

50.

Hibbard MR, Cantor J, Charatz H, et al. Peer support in the community: initial findings of a mentoring program for individuals with traumatic brain injury and their families. J Head Trauma Rehabil. 2002;17(2):112-131. [PubMed: 11909510]

51.

Ahola Kohut S, Stinson JN, Ruskin D, et al. iPeer2Peer program: a pilot feasibility study in adolescents with chronic pain. Pain. 2016;157(5):1146-1155. [PubMed: 26808145]

52.

Doyle M. Peer support and mentorship in a US rare disease community: findings from the Cystinosis in Emerging Adulthood study. Patient. 2015;8(1):65-73. [PubMed: 25231828]

53.

Perry E, Swartz J, Brown S, Smith D, Kelly G, Swartz R. Peer mentoring: a culturally sensitive approach to end-of-life planning for long-term dialysis patients. Am J Kidney Dis. 2005;46(1):111-119. [PubMed: 15983964]

54.

Ghahramani N, Asick R, Hodge JM. Patient and Family Partner Program. Accessed December 27, 2013. http://www.kfcp.org/WhatWeDo/PatientandFamilyPartnerProgram.aspx

55.

Pierce P, Asick R, Weaver M. Trained ESRD mentors: an important resource for total patient care. Nephrol News Issues. 2009;23(3):36, 38, 40 passim. [PubMed: 19353975]

56.

Hoffmann TC, Glasziou PP, Boutron I, et al. Better reporting of interventions: Template for Intervention Description and Replication (TIDieR) checklist and guide. BMJ. 2014;348:g1687. doi:10.1136/bmj.g1687 [PubMed: 24609605] [CrossRef]

57.

McKay HG, King D, Eakin EG, Seeley JR, Glasgow RE. The diabetes network internet-based physical activity intervention: a randomized pilot study. Diabetes Care. 2001;24(8):1328-1334. [PubMed: 11473065]

58.

Tate DF, Jackvony EH, Wing RR. Effects of Internet behavioral counseling on weight loss in adults at risk for type 2 diabetes: a randomized trial. JAMA. 2003;289(14):1833-1836. [PubMed: 12684363]

59.

Zrebiec JF. Internet communities: do they improve coping with diabetes? Diabetes Educ. 2005;31(6):825-836. [PubMed: 16288090]

60.

Lorig KR. Internet-based chronic disease self-management: a randomized trial. Med Care. 2006;44(11):964-971. [PubMed: 17063127]

61.

Hays RD, Kallich JD, Mapes DL, Coons SJ, Carter WB. Development of the kidney disease quality of life (KDQOL) instrument. Qual Life Res. 1994;3(5):329-338. [PubMed: 7841967]

62.

Peipert JD, Bentler PM, Klicko K, Hays RD. Psychometric properties of the Kidney Disease Quality of Life 36-Item Short-Form Survey (KDQOL-36) in the United States. Am J Kidney Dis. 2018;71(4):461-468. [PubMed: 29128411]

63.

Rao S, Carter WB, Mapes DL, et al. Development of subscales from the symptoms/problems and effects of kidney disease scales of the Kidney Disease Quality of Life instrument. Clin Ther. 2000;22(9):1099-1111. [PubMed: 11048907]

64.

Korevaar JC, Merkus MP, Jansen MA, Dekker FW, Boeschoten EW, Krediet RT. Validation of the KDQOL-SF: a dialysis-targeted health measure. Qual Life Res. 2002;11(5):437-447. [PubMed: 12113391]

65.

Fukuhara S, Lopes AA, Bragg-Gresham JL, et al. Health-related quality of life among dialysis patients on three continents: the Dialysis Outcomes and Practice Patterns Study. Kidney Int. 2003;64(5):1903-1910. [PubMed: 14531826]

66.

Wight JP, Edwards L, Brazier J, Walters S, Payne JN, Brown CB. The SF36 as an outcome measure of services for end stage renal failure. Qual Health Care. 1998;7(4):209-221. [PMC free article: PMC2483621] [PubMed: 10339023]

67.

Mapes DL, Bragg-Gresham JL, Bommer J, et al. Health-related quality of life in the Dialysis Outcomes and Practice Patterns Study (DOPPS). Am J Kidney Dis. 2004;44(5 Suppl 2):54-60. [PubMed: 15486875]

68.

Bakewell AB, Higgins RM, Edmunds ME. Quality of life in peritoneal dialysis patients: decline over time and association with clinical outcomes. Kidney Int. 2002;61(1):239-248. [PubMed: 11786106]

69.

Vazquez I, Valderrabano F, Fort J, et al. Psychosocial factors and health-related quality of life in hemodialysis patients. Qual Life Res. 2005;14(1):179-190. [PubMed: 15789952]

70.

Manns B, Johnson JA, Taub K, Mortis G, Ghali WA, Donaldson C. Quality of life in patients treated with hemodialysis or peritoneal dialysis: what are the important determinants? Clin Nephrol. 2003;60(5):341-351. [PubMed: 14640240]

71.

Lopes AA, Bragg-Gresham JL, Satayathum S, et al. Health-related quality of life and associated outcomes among hemodialysis patients of different ethnicities in the United States: the Dialysis Outcomes and Practice Patterns Study (DOPPS). Am J Kidney Dis. 2003;41(3):605-615. [PubMed: 12612984]

72.

Mapes DL, Lopes AA, Satayathum S, et al. Health-related quality of life as a predictor of mortality and hospitalization: the Dialysis Outcomes and Practice Patterns Study (DOPPS). Kidney Int. 2003;64(1):339-349. [PubMed: 12787427]

73.

Peipert JD, Nair D, Klicko K, Schatell DR, Hays RD. Kidney Disease Quality of Life 36-Item Short Form Survey (KDQOL-36) normative values for the United States dialysis population and new single summary score. J Am Soc Nephrol. 2019;30(4):654-663. [PMC free article: PMC6442334] [PubMed: 30898868]

74.

Tong A, Sainsbury P, Craig JC. Support interventions for caregivers of people with chronic kidney disease: a systematic review. Nephrol Dial Transplant. 2008;23(12):3960-3965. [PubMed: 18658178]

75.

Zarit SH, Toseland RW. Current and future direction in family caregiving research. Gerontologist. 1989;29(4):481-483. [PubMed: 2521107]

76.

Bocquet H, Pous J, Charlet JP, Grand A. Measuring the burden for carers of dependent elderly with the Zarit inventory [in French]. Rev Epidemiol Sante Publique. 1996;44(1):57-65. [PubMed: 8851943]

77.

Shimoyama S, Hirakawa O, Yahiro K, Mizumachi T, Schreiner A, Kakuma T. Health-related quality of life and caregiver burden among peritoneal dialysis patients and their family caregivers in Japan. Perit Dial Int. 2003;23(Suppl 2):S200-S205. [PubMed: 17986549]

78.

Yoo R, Yeom J, Kim GH, et al. A multicenter, randomized clinical trial to assess the efficacy of a therapeutic intervention program for caregivers of people with dementia. J Clin Neurol. 2019;15(2):235-242. [PMC free article: PMC6444149] [PubMed: 30938110]

79.

Gratão ACM, Brigola AG, Ottaviani AC, et al. Brief version of Zarit Burden Interview (ZBI) for burden assessment in older caregivers. Dement Neuropsychol. 2019;13(1):122-129. [PMC free article: PMC6497029] [PubMed: 31073389]

80.

Gutiérrez-Maldonado J, Caqueo-Urízar A. Effectiveness of a psycho-educational intervention for reducing burden in Latin American families of patients with schizophrenia. Qual Life Res. 2007;16(5):739-747. [PubMed: 17286192]

81.

Sharif F, Shaygan M, Mani A. Effect of a psycho-educational intervention for family members on caregiver burdens and psychiatric symptoms in patients with schizophrenia in Shiraz, Iran. BMC Psychiatry. 2012;12:48. doi:10.1186/1471-244X-12-48 [PMC free article: PMC3441201] [PubMed: 22632135] [CrossRef]

82.

Yu Y, Liu Z-W, Zhou W, et al. Assessment of burden among family caregivers of schizophrenia: psychometric testing for short-form Zarit Burden Interviews. Front Psychol. 2018;9:2539. doi:10.3389/fpsyg.2018.02539 [PMC free article: PMC6305706] [PubMed: 30618960] [CrossRef]

83.

Hu X, Peng X, Su Y, Huang W. Caregiver burden among Chinese family caregivers of patients with lung cancer: a cross-sectional survey. Eur J Oncol Nurs. 2018;37:74-80. [PubMed: 30473054]

84.

Carrilho PEM, Rodrigues MA, de Oliveira BCR, et al. Profile of caregivers of Parkinson's disease patients and burden measured by Zarit Scale Analysis of potential burden-generating factors and their correlation with disease severity. Dement Neuropsychol. 2018;12(3):299-305. [PMC free article: PMC6200152] [PubMed: 30425794]

85.

Yu Y, Liu Z-W, Zhou W, et al. Cutoff of the Zarit Burden Interview in predicting depression and anxiety. Qual Life Res. 2019;28(9):2525-2533. [PubMed: 31089989]

86.

Hibbard JH, Stockard J, Mahoney ER, Tusler M. Development of the Patient Activation Measure (PAM): conceptualizing and measuring activation in patients and consumers. Health Serv Res. 2004;39(4 Pt 1):1005-1026. [PMC free article: PMC1361049] [PubMed: 15230939]

87.

Munson GW, Wallston KA, Dittus RS, Speroff T, Roumie CL. Activation and perceived expectancies: correlations with health outcomes among veterans with inflammatory bowel disease. J Gen Intern Med. 2009;24(7):809-815. [PMC free article: PMC2695514] [PubMed: 19444526]

88.

Stepleman L, Rutter MC, Hibbard J, Johns L, Wright D, Hughes M. Validation of the patient activation measure in a multiple sclerosis clinic sample and implications for care. Disabil Rehabil. 2010;32(19):1558-1567. [PubMed: 20590506]

89.

Skolasky RL, Mackenzie EJ, Wegener ST, Riley LH III. Patient activation and adherence to physical therapy in persons undergoing spine surgery. Spine (Phila Pa 1976). 2008;33(21):E784-E791. https://www​.ncbi.nlm​.nih.gov/pmc/articles/PMC6153437/ [PMC free article: PMC6153437] [PubMed: 18827683]

90.

Skolasky RL, Green AF, Scharfstein D, Boult C, Reider L, Wegener ST. Psychometric properties of the patient activation measure among multimorbid older adults. Health Serv Res. 2011;46(2):457-478. [PMC free article: PMC3064914] [PubMed: 21091470]

91.

Hung M, Carter M, Hayden C, et al. Psychometric assessment of the patient activation measure short form (PAM-13) in rural settings. Qual Life Res. 2013;22(3):521-529. [PubMed: 22466721]

92.

Rademakers J, Nijman J, van der Hoek L, Heijmans M, Rijken M. Measuring patient activation in The Netherlands: translation and validation of the American short form Patient Activation Measure (PAM13). BMC Public Health. 2012;12:577. doi:10.1186/1471-2458-12-577 [PMC free article: PMC3490810] [PubMed: 22849664] [CrossRef]

93.

Steinsbekk A. Patient Activation Measure [in Swedish]. Tidsskr Nor Laegeforen. 2008;128(20):2316-2318. [PubMed: 19096487]

94.

Hibbard JH, Mahoney ER, Stock R, Tusler M. Do increases in patient activation result in improved self-management behaviors? Health Serv Res. 2007;42(4):1443-1463. [PMC free article: PMC1955271] [PubMed: 17610432]

95.

Mosen DM, Schmittdiel J, Hibbard J, Sobel D, Remmers C, Bellows J. Is patient activation associated with outcomes of care for adults with chronic conditions? J Ambul Care Manage. 2007;30(1):21-29. [PubMed: 17170635]

96.

Hibbard JH, Mahoney ER, Stockard J, Tusler M. Development and testing of a short form of the patient activation measure. Health Serv Res. 2005;40(6 Pt 1):1918-1930. [PMC free article: PMC1361231] [PubMed: 16336556]

97.

Anderson JK, Wallace LM. Evaluation of uptake and effect on patient-reported outcomes of a clinician and patient co-led chronic musculoskeletal pain self-management programme provided by the UK National Health Service. Br J Pain. 2018;12(2):104-112. [PMC free article: PMC5958511] [PubMed: 29796262]

98.

Knox L, Huff J, Graham D, et al. What peer mentoring adds to already good patient care: implementing the Carpeta Roja Peer Mentoring Program in a well-resourced health care system. Ann Fam Med. 2015;13(suppl 1):S59-S65. [PMC free article: PMC4648130] [PubMed: 26304973]

99.

Sandhu S, Veinot P, Embuldeniya G, et al. Peer-to-peer mentoring for individuals with early inflammatory arthritis: feasibility pilot. BMJ Open. 2013;3(3):e002267. doi:10.1136/bmjopen-2012-002267 [PMC free article: PMC3612764] [PubMed: 23457326] [CrossRef]

100.

Maslow GR, Chung RJ. Systematic review of positive youth development programs for adolescents with chronic illness. Pediatrics. 2013;131(5):e1605-18. doi:10.1542/peds.2012-1615 [PubMed: 23610201] [CrossRef]

101.

Embuldeniya G, Veinot P, Bell E, et al. The experience and impact of chronic disease peer support interventions: a qualitative synthesis. Patient Educ Couns. 2013;92(1):3-12. [PubMed: 23453850]

102.

Enriquez M, Conn VS. Peers as facilitators of medication adherence interventions: a review. J Prim Care Community Health. 2016;7(1):44-55. [PMC free article: PMC5695224] [PubMed: 26303976]

103.

Joshi A, Arutagi V, Nahar N, Tiwari S, Singh D, Sethia S. Diabetes ongoing sustainable care and treatment (DOST): a strategy for informational deliverance through visual dynamic modules sustained by near peer mentoring. J Family Med Prim Care. 2016;5(2):270-275. [PMC free article: PMC5084546] [PubMed: 27843826]

104.

Wenzel L, Huang HQ, Monk BJ, Rose PG, Cella D. Quality-of-life comparisons in a randomized trial of interval secondary cytoreduction in advanced ovarian carcinoma: a Gynecologic Oncology Group study. J Clin Oncol. 2005;23(24):5605-5612. [PMC free article: PMC4920482] [PubMed: 16110020]

105.

WWAMI. RUCA data: using RUCA data. University of Washington. Accessed July 11, 2014. http://depts​.washington​.edu/uwruca/ruca-uses.php

106.

Zrebiec JF, Jacobson AM. What attracts patients with diabetes to an internet support group? A 21-month longitudinal website study. Diabet Med. 2001;18(2):154-158. [PubMed: 11251681]

107.

Zrebiec JF. Internet communities. Diabetes Educ. 2005;31(6):825-836. [PubMed: 16288090]

108.

McKay HG, King D, Eakin EG, Seeley JR, Glasgow RE. The Diabetes Network internet-based physical activity intervention. Diabetes Care. 2001;24(8):1328. doi:10.2337/diacare.24.8.1328 [PubMed: 11473065] [CrossRef]

109.

Lorig KR, Ritter PL, Laurent DD, Plant K. Internet-based chronic disease self-management: a randomized trial. Med Care. 2006;44(11):964-971. [PubMed: 17063127]

110.

Lorig KR, Ritter PL, Dost A, Plant K, Laurent DD, McNeil I. The Expert Patients Programme online, a 1-year study of an Internet-based self-management programme for people with long-term conditions. Chronic Illn. 2008;4(4):247-256. [PubMed: 19091933]

111.

Tate DF, Jackvony EH, Wing RR. A randomized trial comparing human e-mail counseling, computer-automated tailored counseling, and no counseling in an internet weight loss program. JAMA Int Med. 2006;166(15):1620-1625. [PubMed: 16908795]

112.

Tate DF, Jackvony EH, Wing RR. Effects of internet behavioral counseling on weight loss in adults at risk for type 2 diabetes: a randomized trial. JAMA. 2003;289(14):1833-1836. [PubMed: 12684363]

113.

Eysenbach G, Powell J, Englesakis M, Rizo C, Stern A. Health related virtual communities and electronic support groups: systematic review of the effects of online peer to peer interactions. BMJ. 2004;328(7449):1166. doi:10.1136/bmj.328.7449.1166 [PMC free article: PMC411092] [PubMed: 15142921] [CrossRef]

114.

Foster G, Taylor SJC, Eldridge S, Ramsay J, Griffiths CJ. Self-management education programmes by lay leaders for people with chronic conditions. Cochrane Database Syst Rev. 2007;(4):CD005108. doi:10.1002/14651858.CD005108.pub2 [PubMed: 17943839] [CrossRef]

115.

Guise J-M, Butler ME, Chang C, Viswanathan M, Pigott T, Tugwell P. AHRQ series on complex intervention systematic reviews—paper 6: PRISMA-CI extension statement and checklist. J Clin Epidemiol. 2017;90:43-50. [PubMed: 28720516]

116.

Moore GF, Audrey S, Barker M, et al. Process evaluation of complex interventions: Medical Research Council guidance. BMJ. 2015;350:h1258. doi:10.1136/bmj.h1258 [PMC free article: PMC4366184] [PubMed: 25791983] [CrossRef]

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Acknowledgments

We wish to acknowledge the following individuals for their assistance in various stages of the study:

• All patients and caregivers who participated in the study as peer mentors or as study participants. Without their active and enthusiastic participation, this study would not have been possible.
• The KFCP and members of its board of directors who served as members of the Community Advisory Board (Larry Carroll, MD; Luann Cartwright; John Eyster; Carole Fair; Cheryl Green, PA-C; Nancy J. Hammonds, FHFMA; Karen Lehman; Tammie Loper; Nina Myers; Seth Narins, MD; Nicole J. Radziewicz, Esq; M. Kris Srinivasan, MD, MBA, MHSE; Barbara Terry; and Kristy Washinger, CRNP). The Community Advisory Board played a major role in development of the protocol, implementation of the study, dissemination of interim results to the community, and engagement of the patients and other stakeholders in the study at various stages. In addition, the KFCP board members provided continuous supervision for the PFPP. We extend special thanks to patient and caregiver members of the KFCP board of directors for providing patient/caregiver perspectives.
• Members of the advisory groups, particularly for assistance in recruitment and retention.

  –

  Patient and Caregiver Advisory Group (Raymond Thorn, Anthony Baltimore, Dennis Adams, Kenneth Ellison, Pamela Jester, Francine Citrone, Sally Anderson, David Deibler, Tim Derrick, Donald Hartzel, Mark Maines, and Cynthia Godanis)

  –

  Provider Advisory Group (Teresa Bruno, MSW; William Curry, MD; Umar Farooq, MD; Gail Flannery, MSW; Phyllis Hicks, RN, BSN; Julia Hix, RN; Hillary Hoover, MSW; Rajiv Sharma, MD; Elizabeth Snyder, PA-C; Cynthia Royer, MSW; Sandra Ryan, RN, MSN; and Eileen Swartz, RN, BSN)

• Robin Asick (consultant) for her guidance in recruitment and for oversight of the mentoring program. Her leadership role as a highly regarded renal social worker in central Pennsylvania played a critical role in advocating for participation in the study.
• Patient representatives Michelle Bentley Mullikin and Mark Barnhardt for providing the patient perspective.
• Research coordinator Tara Sofia Liaghat for her invaluable assistance in recruitment, delivery of assessments, facilitating focus group discussions, and data entry.
• PFPP coordinator Tabitha Semancik for her dedication to the mentorship program and for regular monitoring of the online platform. We extend special thanks for her role in dissemination of the interim results to the community and for organizing and facilitating the panel discussions by the study participants at the annual KFCP conferences.

Research reported in this report was funded through a Patient-Centered Outcomes Research Institute® (PCORI®) Award (#CDR-1310-07055). Further information available at: [https://www.pcori.org/research-results/2014/comparing-three-ways-improve-quality-life-patients-kidney-disease-and-their]

Appendices