Background: Patient activation is associated with better outcomes and lower costs. Although the concept is widely investigated, little attention was given to patient activation and its predictors in patients undergoing hemodialysis. Hence, we aimed to investigate the level of patient activation and aimed to determine patient- and treatment- related predictors of activation in patients undergoing hemodialysis. Methods: This cross-sectional observational study recruited patients undergoing hemodialysis in three Flemish hospitals. Participants were questioned about patient characteristics (i.e., age, sex, education, employment, children, social support, leisure-time, living condition, and care at home), treatment- and health-related characteristics (i.e., hospital, time since first dialysis, transplantation, self-reported health (EQ-VAS) and depressive symptoms (PHQ-2)), and patient activation (PAM-13). Univariate and multiple linear regression analyses with dummy variables were conducted to investigate the associations between the independent variables and patient activation. Results: The average patient activation-score was 51. Of 192 patients, 44% patients did not believe they had an important role regarding their health. Multiple linear regression showed that older patients, who reported being in bad health, treated in a particular hospital, without leisure-time activities, and living in a residential care home, had lower patient activation. These variables explained 31% of the variance in patient activation. Based on literature, we found that activation of patients on hemodialysis is low, compared to that of other chronic patient groups. Conclusion: It could be useful to implement patient activation monitoring, since the level of activation is low in patients undergoing hemodialysis. Older patients, who reported being in bad health, treated in a particular hospital, without leisure-time activities, living in a residential care home, are at higher risk for lower activation. Keywords: End-stage renal disease, Hemodialysis, Patient activation, Personalized interventions Background therapy, i.e., hemodialysis, peritoneal dialysis, or renal Chronic kidney disease (CKD) is defined as structural transplantation . or functional abnormalities of the kidneys, present for The number of patients in need for a renal replace- more than three months . When renal function ment therapy is increasing rapidly. In the United States further deteriorates, patients develop end-stage renal in 2013, the prevalent dialysis population consisted of disease (ESRD) with need for renal replacement 466,607 patients . This population has increased by 63.2% since 2000. On the other hand, dialysis treatments are very expensive. Hence, along with the increasing * Correspondence: email@example.com 1 population, the cost of providing dialysis and transplant- KU Leuven, Department of Public Health and Primary Care, Academic Centre for Nursing and Midwifery, Kapucijnenvoer 35 (box 7001), B-3000 ation continue to escalate [2, 3]. Leuven, Belgium Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Van Bulck et al. BMC Nephrology (2018) 19:126 Page 2 of 9 One way to influence health-care costs on the Methods long-term could be to focus on patient activation . Design and study population Driven by the person-centered approach, patient activa- In this quantitative, observational, cross-sectional, and tion specifies the level of patients’ involvement with their questionnaire-based study, convenience sampling was health care and refers to the extent to which they have used. Participants were recruited in three Flemish dialy- the knowledge, belief, motivation, confidence, and skills sis units based on following criteria: (1) diagnosis of to manage a chronic disease . There is a growing body ESRD; (2) older than 18 years; (3) Dutch-speaking; (4) of literature indicating that activated patients make cognitively able to understand difficult concepts (at more effective use of healthcare services, have better examiner’s discretion: first question contained the word self-management behaviors , better patient outcomes, ‘responsibility’ and the examination stopped when the better care experiences , and lower health-care costs  participant did not understand that word correctly); (5) in chronic patients. Hence, to enhance patient outcomes dialysis treatment longer than three months. Systematic- at lower costs, the level of activation should be optimized. ally, all hemodialysis patients that were treated in the In previous research, patients with end stage renal hospital on the day of the investigation and that were disease (stage 5), both with and without dialysis, had the eligible, were asked to participate. lowest patient activation scores (average: 58) of all chronic To determine the sample size, the number of variables kidney patients . Due to the intensive dialysis treatment was examined. In order to achieve sufficient statistical and proximity of healthcare workers, the patient activation power, 50 patients and an additional 8 patients for each of patients undergoing hemodialysis might be even lower. variable had to be enrolled . Because of the 15 However, an assessment of the level of activation in a par- variables in this study, the sample should thus consist of ticular relevant growing population of expensive patients at least 170 patients. undergoing hemodialysis is lacking. The level of activation can be improved using tailored Data collection and ethics coaching . In order to be able to identify patients at Data were collected in February and March 2016. The high risk of low activation and in the context of the de- questionnaires were completed independently or velopment of tailored interventions, an understanding of together with the interviewer. The same interviewer all patient- and treatment related factors associated with (LVB) was available on request for all patients. patient activation is needed . Based on the Society to Self-reported questionnaires were used to measure all Cells Resilience framework , Gleason et al.  have variables. Hence, data were gathered from the patient’s shown that patient activation in an older adult popula- perspective [see Additional file 1]. tion with functional difficulties was related to age, family Approval for the study was given by the Independent support, difficulties with activities of daily living, depres- Commission for Medical Ethics of the UZ/KU Leuven, sive symptoms, self-reported health, and living situ- Medical Ethics Committee of hospital Imelda, and ation, among other factors. It is still unclear whether Medical Ethics Committee of hospital Sint-Trudo, all these factors are also predictive for patient activation located in Belgium. Procedures were in accordance with in patients undergoing hemodialysis. In addition, the declaration of Helsinki . Oral and written infor- treatment-related factors, specifically for dialysis, have mation about purpose, duration, and risks of study par- not yet been associated with patient activation. ticipation was given to patients before they were asked Therefore, the present study was guided by two to participate. All participants provided written informed objectives. First, we determined the level of activation of consent. patients undergoing hemodialysis. It was hypothesized that the average patient activation score would be lower Outcome measure than 58. In order to be able to interpret this score, an The primary outcome was patient activation, which was additional aim was to compare the level of activation of measured by ‘Patient Activation Measure-13’ (PAM-13, patients undergoing hemodialysis with the level of activa- Dutch version) . This instrument is a non-disease tion of other chronic populations. Second, we aimed to in- specific scale that shows involvement of patients in their vestigate the patient- and treatment-related characteristics health. This 5-item Guttman scale has following possible associated with activation in patients with hemodialysis. It answers: ‘disagree strongly’ (1), ‘disagree’ (2), ‘agree’ was hypothesized that better self-reported health, higher (3), ‘agree strongly’ (4), and ‘not applicable’ (5). Raw education level, and good social support would be associ- scores range from 13 to 52. No score was calculated ated with higher PAM. In addition, we hypothesized that if no answer or ‘not applicable’ was chosen more than higher age, no job, no hobbies, use of multiple home care three times. Raw scores were converted to a theoret- services, and depressive symptoms would be associated ical score on a scale of 0 to 100. Higher scores indi- with lower patient activation. cate a higher level of patient activation. Patients were Van Bulck et al. BMC Nephrology (2018) 19:126 Page 3 of 9 divided into four levels based on cut-off scores. In mean ± standard deviation for continuous variables and level 1 (score: ≤ 47.0), patients do not believe they have number and percentage for categorical variables. an important role regarding their health. In level 2 (score: Firstly, univariable linear regression procedures were 47.1–55.1), patients have lack confidence or knowledge to conducted to examine associations between activation take action. In level 3 (score: 55.2–67.0), patients start to and all determinants. In advance dummy variables were take action. In level 4 (score: ≥ 67.1), patients are main- created for all categorical determinants. Secondly, a mul- taining active behavior . The Dutch version of tiple linear regression analysis with a stepwise exclusion PAM-13 has been shown to be a reliable instrument . method was conducted with all continuous and dummy Insignia Health provided a license. variables. Determinants that seemed relevant for predic- tion of activation, were kept in the model (p < 0.05). In order to detect existence and extent of multicollinear- Predictor variables ity in the final model, tolerance and variance inflation Patient-, treatment- and health-related variables were mea- factor (VIF) were calculated . By using histograms and sured though self-reported open and multiple-choice ques- scatter plots the assumptions of linearity, homoscedastic- tions [see Additional file 1]. ity and normality were checked and approved. Outliers Basic patient-related characteristics included age, sex, were identified using Cook’sdistance. highest degree of education, employment status, and living situation. Employment status was questioned as Results follows: “Do you currently work in paid employment”, Patient characteristics with possible answers ‘no’, ‘part-time’, and ‘full-time’.In A total of 214 patients undergoing hemodialysis were order to gain information on the level of activation in approached in this study of which 197 were willing and daily life, leisure-time activities, amount of home care able to participate (response rate: 92%) (Fig. 1). Of these services, presence or absence of children, and perception patients, 3 patients generated incomplete PAM scores of social support, were measured. “Do you receive suffi- and another 2 patients were excluded because of cient support from your environment?” with possible outlying results that affected the results of the analysis. answers ‘yes’ or ‘no’, was asked to measure the percep- Ultimately, data from 192 patients were used in the final tion of social support. Leisure-time activities were ques- tioned as follows: “Do you have a hobby, do you do any sport, or are you a member of any organization?” Treatment- and health-related factors included in this study were time since first dialysis, history of one or more renal transplantations, depressive symptoms, and self-reported health. Depressive symptoms were mea- sured by the “Patient Health Questionnaire-2” (PHQ-2, Dutch version). This instrument is the short version of the PHQ-9 . It questions frequency of a depressed mood and anhedonia (no interest in activities) during the last 2 weeks before the day of the study . Answer possibilities were ‘Not at all’ (0), ‘Several days’ (1), ‘More than half the days’ (2), and ‘Nearly every day’ (3). The maximum score was 6. Our study used a cut-off score of 3, because sensitivity is 87% and specificity is 78% for major depression for this cut-off score . Self-reported health was measured by the EuroQol Visual Analogue Scale (EQ-VAS, Dutch version) . This 20-cm long vis- ual analog scale is a part of the standardized EQ-5D. A scale of 0 to 100 was displayed. A score of 0 represented the worst health, and 100 the best health that could be imagined. A license from EuroQol Research Foundation was obtained. Statistical analysis Analyses were performed using statistical package SPSS Fig. 1 Flowchart of recruitment (version 23). Patient characteristics are presented as Van Bulck et al. BMC Nephrology (2018) 19:126 Page 4 of 9 analysis. Of these patients, 117 (61%) were male and age important role regarding their health. The high number range was 20–95 years with a mean age of 72 ± 14 of 73% of the patients did not take charge of their own (Table 1). In these categories, the sample was representa- health. The difficult combination of diet and fluid tive for the population of patients undergoing dialysis in restrictions, strict medication regime, intensive dialysis Flanders (Table 2). Patient characteristics and PAM treatment, comorbidities, and the proximity of health- scores are descripted in Table 1. A total of 138 (72%) care workers three times a week, might explain the low participants completed the questionnaire together with activation scores in this population. Although the aver- the interviewer. age activation score of 51 may be overestimated because people with cognitive impairment were excluded in the Identification variables associated with patient activation sample, activation of patients undergoing hemodialysis Table 3 shows the results of univariable and multiple seems to be low and healthcare workers should be rec- analysis with activation as a dependent variable associ- ommended to measure patient activation and intervene ated with all independent variables. upon low levels of activation. To further increase comprehensibility of the activation Univariable linear regression scores in our study, a literature search was performed High activation scores correlate with lower age and high about activation of other chronic patient groups which self-reported health. Patients in hospital 2 had signifi- face similar challenges due to their disease. It appeared cantly lower activation scores compared to patients in that patients with hypertension, depression, asthma, and hospital 1. Higher activation scores were found in partic- diabetes have a higher average activation score com- ipants with a non-university higher degree or university pared to the patients undergoing hemodialysis in the degree compared to participants with only a primary present study [10, 20–30]. In the study of Bos-Touwen education degree. Participants who worked full-time or et al. a comparison was made between the average acti- part-time had higher activation scores, compared to vation scores of patients with diabetes (55.3), chronic participants who did not work. Having leisure-time obstructive pulmonary disease (54.7), chronic heart fail- activities and having children were related to patient ure (53.6), and chronic kidney disease (51.4) . The activation. Patients who lived alone or with someone kidney patients had the lowest activation score . In had a better level of activation compared to patients liv- the literature only one activation score was found to be ing in a residential care home. Patients who used more lower than the average score measured in our study, than one home care service had lower activation scores namely the average activation score (50) of patients with then patients who used no services. Participants with a osteoarthritis in South Korea . Because of the vari- history of kidney transplant had higher activation scores. ous sample characteristics [22, 26], different countries of Activation score was not significantly related to sex, origin, cultural backgrounds, access to and cost of health perception of social support, time since first dialysis, and care for patients [15, 22, 31], it can only be assumed and depressive symptoms. not determined with certainty that patients undergoing hemodialysis in Flanders have lower activation scores Adjusting for age compared to other chronic patients. Age is a confounder for the relationship between activa- tion and level of education, employment status, and Multivariate analysis renal transplant. The second aim was to identify patient- and treatment-related factors associated with activation. Age, Multiple linear regression self-reported health, hospital, leisure-time activities, and The variance (R ) of the reduced multivariable linear living situation were associated with activation in model was 31%. When adjusted for all the other vari- multiple analysis. ables, age, self-reported health, hospital, leisure-time ac- The R of the model was 0.306. Around 31% of tivities, and living situation were still associated with variance of activation could be explained by these patient activation. Direction of the associations did not five variables. This R washigherthaninprevious change, compared to univariable regression. models [10, 32]. It was demonstrated in our study and in many other Discussion studies that higher activation correlates with lower age Level of activation [12, 15, 21, 22, 26, 30]. Explanations may be that older One of the aims of this study was to investigate the level patients have lower self-efficacy , lower health liter- of activation of patients undergoing hemodialysis. The acy , seem to be less compliant , and are more average activation score was 51 (± 10). Of the 192 accustomed to a paternalistic healthcare system  patients, 44% patients did not believe they had an compared to younger people. Van Bulck et al. BMC Nephrology (2018) 19:126 Page 5 of 9 Table 1 Patient- and treatment-related characteristics and Table 1 Patient- and treatment-related characteristics and patient activation scores of the sample patient activation scores of the sample (Continued) Variable Totalnn (%) Mean, SD Variable Totalnn (%) Mean, SD Hospital 192 EQ-VAS (0–100) 191 63 ± 17 Hospital 1 (university hospital) 92 (48) PHQ-2 192 Hospital 2 (regional hospital) 46 (24) No depressive symptoms (0–2) 168 (87.5) Hospital 3 (regional hospital) 54 (28) Depressive symptoms (3–6) 24 (12.5) Sex, man 192 117 (61) Patient Activation Measure (0–100) 192 51 ± 10 Age, years 192 72 ± 14 Level 1 85 (44) Level of education 192 Level 2 56 (29) Primary education 68 (35) Level 3 42 (22) Lower secondary education 29 (15) Level 4 9 (5) Higher secondary education 55 (29) Non-university higher education 32 (17) University education 8 (4) As in our study, previous literature showed that patients who reported being in good health, have Employment status 192 higher patient activation [10, 15, 21, 26, 35]. This is No work 175 (91) not surprising, since patients who are more active, Full-time work 3 (2) report better skills and better knowledge, confidence Part-time work 14 (7) and behavior needed to manage their health condi- Perception social support 191 tion [21, 26]. Good social support 171 (90) In our study, a significant lower level of activation was found in patients treated in hospital 2. This Lacking social support 20 (10) could be due to differences in the predialysis training, Leisure-time activities 192 since previous research has shown that predialysis Yes 83 (43) education can lead to higher levels of knowledge , No 109 (57) and better self-management skills, such as better fluid Children 192 balance . However, we were unable to investigate Yes 149 (78) this in our study, since no individual data about pre- dialysis education was available. It would be interest- No 43 (22) ing in future studies to investigate the association Living condition 192 between organizational features of a hospital, accom- Alone 49 (26) paniment of patients before and during dialysis treat- With someone 135 (70) ment, and patient activation. Residential care home 8 (4) When looking at functional disability and level of acti- Care at home 192 vation in daily life, previous research showed that pa- tients who were more active, had no difficulties with None 63 (33) activities of daily living , which could explain why 1 service 47 (24) active patients are more likely to have leisure time 2 or more services 82 (43) Time since first dialysis 192 3 months - 6 months 18 (9) Table 2 Comparison sample and population Flanders > 6 months - 1 year 25 (13) Age Sample Flanders  > 1 year 149 (78) Mean age (y) 72 72 Renal transplantation 192 Mean age men (y) 73 72 Yes 17 (9) Mean age women (y) 70 73 No 175 (91) Sex Male 61% 59% Female 39% 41% Legend: y = years Van Bulck et al. BMC Nephrology (2018) 19:126 Page 6 of 9 Table 3 Univariable and multiple regression Univariable linear regression Multiple linear regression R : 0.306 Variable N β p-value β p-value Age 192 −0.330 < 0.001* − 0.284 < 0.001* Self-reported health 191 0.328 < 0.001* 0.278 < 0.001* Hospital 192 Hospital 1 Hospital 2 −0.165 0.033* − 0.145 0.019* Hospital 3 −0.016 0.833 Sex, woman 192 −0.024 0.739 Level of education 192 Primary Lower secondary 0.083 0.285 Higher secondary 0.088 0.275 Non-university higher 0.219 0.005* University 0.212 0.004* Employment status 192 No work Part-time 0.189 0.008* Full-time 0.152 0.033* Perception social support, good social support 191 −0.091 0.211 Leisure-time activities, yes 192 0.331 < 0.001* 0.206 0.002* Children, yes 192 −0.220 0.002* Living condition 192 Alone 0.325 0.047* With someone 0.486 0.003* 0.141 0.025* Residential care Care at home 192 No services 1 service −0.010 0.900 More than one service −0.291 < 0.001* Time since first dialysis 192 3 month – 6 month −0.059 0.513 > 6 month – 1 year > 1 year −0.084 0.357 Transplantation, yes 192 0.155 0.031* Depressive symptoms, yes 192 −0.135 0.063 Legend: *: significant (p < 0.05) β = standardized beta activities in our study. Although, Fowles et al. did not In our study, age was a confounder in the associ- find a significant association between activation and ation between patient activation and educational level, membership of a health club . Patients who live in a employment status, and history of renal transplant. residential care home are less activated compared to The average 70-year-old population is less educated patients living with someone. It was previously showed compared to a young population, more than half of that people who live in their own house or apartment the patients were on retirement, and older people were significantly more activated than those living else- with comorbidities might have lower chances to get where . on the transplant list. Van Bulck et al. BMC Nephrology (2018) 19:126 Page 7 of 9 Strengths and limitations personalized interventions. Furthermore, the conse- To the best of our knowlegde, this study is the first quences of low activation should be further investigated study that has investigated patient activation specifically in the particular population of patients undergoing in the population of patients with hemodialysis. hemodialysis. Strengths of the study are a fairly large sample and a high response rate. Furthermore, the sample is represen- Conclusion tative for the Flemish hemodialysis population and par- The average activation score of patients undergoing ticipants were recruited in three hospitals. International hemodialysis in Flanders was 51. Multiple linear regres- validated questionnaires were used. Finally, because of sion revealed that age, self-reported health, hospital, the clinically meaningful outcome, the study provides in- leisure-time activities, and living situation explained 31% formation that is useful for practice. of the variance in activation. It seems that the average Furthermore, the study has several limitations. First, activation score of patients undergoing hemodialysis in no information about the directionality of the relation- Flanders is lower than the average activation score of ships could be obtained, due to the observational patients with hypertension, asthma, depression, and cross-sectional design. Second, data were measured diabetes. using a self-reported questionnaire, which can induce re- Healthcare workers could be already recommended to call bias or telescoping. However, only two questions measure the patient activation, and to take initiatives in had a recall timeframe. Third, certain possibly interest- order to increase it. ing variables were not included in this study, such as motivation, health literacy, hope, external control, cogni- Additional file tive impairment, genetics, life events, (the amount of) comorbidities, predialysis education on individual level, Additional file 1: Questionnaire used in the study. The survey questioned and organization features. Future research should take demographic, social and illness-related information. Moreover, the these variables into account. Fourth, in our sample, questionnaire on patient activation was also included. The participants have completed the Dutch translation of this questionnaire. (DOC 331 kb) patients that were cognitively unable to understand diffi- cult words were excluded from the study. In addition, no randomization techniques were used. These two factors Abbreviation PAM: patient activation measure might reduce the generalizability of our findings and might have created selection bias, which could have Acknowledgements affected the results of the study. The authors would like to thank the patients, the nephrologists and the dialysis nurses of the participating hospitals for their contribution to this study. Recommendations Availability of data and materials Nearly 73% of the patients did not take charge of their Unfortunately, we are not able to publish the dataset, due to privacy own health, which has shown to negatively influence concerns. Demographic, social, and clinical data of the patients were health outcomes and costs. Hence, practitioners and collected and due to the risk of small cells, anonymity cannot be guaranteed, even though the dataset is encrypted. The dataset can be made other healthcare workers should be recommended to available on request. measure activation of patients and if needed to intervene upon low levels of patient activation. Authors’ contributions Because older patients, who reported being in bad LVB and GVP drafted the research protocol, performed the statistical analysis, interpreted the results and drafted the manuscript. LVB has arranged the health, treated in a particular hospital, without ethical duties and has interviewed all patients. KD and AH assisted with the leisure-time activities, and living in a residential care development of the questionnaire. KC, KD, AH, SJ, SS and GVP provided home, had significantly lower patient activation, patients critical feedback at various times throughout the process and have made substantial intellectual contributions. All authors read and approved the final at high risk can be identified using these screening manuscript. Hence, all authors have met the four criteria of the ICMJE factors. Furthermore, on the long term, provided that guidelines for authorship. longitudinal studies show similar results and provide in- formation on the directionality of the associations, these Ethics approval and consent to participate Approval for the study was given by the Independent Commission for factors can help healthcare providers to develop person- Medical Ethics of the UZ/KU Leuven, Medical Ethics Committee of hospital alized interventions to improve patient activation. Imelda, and Medical Ethics Committee of hospital Sint-Trudo, all located in Hence, improving self-reported health and/or encour- Belgium. The study is in accordance with the principles of the Declaration of Helsinki. Oral and written information about purpose, duration, and risks of aging patients to be more active in daily living could be study participation was given to patients before they were asked to part of these interventions. participate. All participants provided written informed consent. Future research should investigate patient activation and its predictors in longitudinal research and provide Competing interests information on the usefulness of these predictors in The authors declare they have no competing interests. Van Bulck et al. BMC Nephrology (2018) 19:126 Page 8 of 9 Publisher’sNote 16. Insignia Health. Patient activation measure (PAM) 13 - license materials. Springer Nature remains neutral with regard to jurisdictional claims in 2014. published maps and institutional affiliations. 17. Löwe B, Kroenke K, Gräfe K. Detecting and monitoring depression with a two-item questionnaire (PHQ-2). J Psychosom Res. 2005;58(2):163–71. Author details 18. Brooks R. EuroQol: the current state of play. Health Policy. 1996;37(1):53–72. KU Leuven, Department of Public Health and Primary Care, Academic 19. O’brien RM. A caution regarding rules of thumb for variance inflation Centre for Nursing and Midwifery, Kapucijnenvoer 35 (box 7001), B-3000 factors. Quality & Quantity. 2007;41(5):673–90. https://doi.org/10.1007/ Leuven, Belgium. University Hospitals Leuven, Department of Nephrology s11135-006-9018-6. and Renal Transplantation, Herestraat 49, B-3000 Leuven, Belgium. KU 20. Ryvicker M, Feldman PH, Chiu YL, Gerber LM. The role of patient activation Leuven, Department of Microbiology and Immunology, Laboratory of in improving blood pressure outcomes in black patients receiving home Nephrology, Minderbroederstraat 10 (box 1030), B-3000 Leuven, Belgium. care. Med Care Res Rev. 2013;70(6):636–52. https://doi.org/10.1177/ Imeldaziekenhuis, Nephrology, Imeldalaan 9, B-2820 Bonheiden, Belgium. Sint-Trudo Ziekenhuis, Nephrology, Diestersteenweg 100, B-3800 21. Hibbard JH, Mahoney ER, Stockard J, Tusler M. Development and testing of Sint-Truiden, Belgium. KU Leuven, Department of Public Health and Primary a short form of the patient activation measure. Health Serv Res. 2005;40(6): Care, Academic Centre for General Practice, Kapucijnenvoer 33 (box 7001), 1918–30. B-3000 Leuven, Belgium. 22. Graffigna G, Barello S, Bonanomi A, Lozza E, Hibbard J. Measuring patient activation in Italy: translation, adaptation and validation of the Italian version Received: 28 March 2017 Accepted: 14 May 2018 of the patient activation measure 13 (PAM13-I). BMC Med Inform Decis Mak. 2015;15:109. https://doi.org/10.1186/s12911-015-0232-9. 23. Wagner PJ, Dias J, Howard S, Kintziger KW, Hudson MF, Seol YH, et al. Personal health records and hypertension control: a randomized trial. J Am References Med Inform Assoc. 2012;19(4):626–34. https://doi.org/10.1136/amiajnl-2011- 1. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis. 2002;39:S1–266. 24. Young HN, Larson TL, Cox ED, Moreno MA, Thorpe JM, MacKinnon NJ. The 2. United States Renal Data System. Annual Data Report 2015: Volume 2, active patient role and asthma outcomes in an underserved rural Chapter 1: Incidence, Prevalence, Patient Characteristics, and Treatment community. J Rural Health. 2014;30(2):121–7. https://doi.org/10.1111/jrh. Modalities. Available at: https://www.usrds.org/2015/view/v2_01.aspx. 3. Lysaght MJ. Maintenance dialysis population dynamics: current trends and 25. Rask KJ, Ziemer DC, Kohler SA, Hawley JN, Arinde FJ, Barnes CS. Patient long-term implications. J Am Soc Nephrol. 2002;13(S1):S37–40. activation is associated with healthy behaviors and ease in managing 4. Greene J, Hibbard JH, Sacks R, Overton V, Parrotta CD. When patient diabetes in an indigent population. Diabetes Educ. 2009;35(4):622–30. activation levels change, health outcomes and costs change, too. Health Aff https://doi.org/10.1177/0145721709335004. (Millwood). 2015;34(3):431–7. https://doi.org/10.1377/hlthaff.2014.0452. 26. Hendriks M, Rademakers J. Relationships between patient activation, 5. Hibbard JH, Stockard J, Mahoney ER, Tusler M. Development of the patient disease-specific knowledge and health outcomes among people with activation measure (PAM): conceptualizing and measuring activation in diabetes; a survey study. BMC Health Serv Res. 2014;14:393. https://doi.org/ patients and consumers. Health Serv Res. 2004;39:1005–26. https://doi.org/ 10.1186/1472-6963-14-393. 10.1111/j.1475-6773.2004.00269.x. 27. Chubak J, Anderson ML, Saunders KW, Hubbard RA, Tuzzio L, Liss DT, 6. Hibbard JH, Greene J. What the evidence shows about patient activation: et al. Predictors of 1-year change in patient activation in older better health outcomes and care experiences; fewer data on costs. Health adults with diabetes mellitus and heart disease. J Am Aff (Millwood). 2013;32(2):207–14. https://doi.org/10.1377/hlthaff.2012.1061. Geriatr Soc. 2012;60(7):1316–21. https://doi.org/10.1111/j.1532-5415. 7. Greene J, Hibbard JH, Sacks R, Overton V. When seeing the same physician, 2012.04008.x. highly activated patients have better care experiences than less activated 28. Maindal HT, Sokolowski I, Vedsted P. Translation, adaptation and patients. Health Aff (Millwood). 2013;32(7):1299–305. https://doi.org/10.1377/ validation of the American short form patient activation measure hlthaff.2012.1409. (PAM13) in a Danish version. BMC Public Health. 2009;9:209. 8. Johnson ML, Zimmerman L, Welch JL, Hertzog M, Pozehl B, Plumb T. Patient https://doi.org/10.1186/1471-2458-9-209. activation with knowledge, self-management and confidence in chronic 29. Rygg LØ, Rise MB, Grønning K, Steinsbekk A. Efficacy of ongoing group kidney disease. J Ren Care. 2016;42(1):15–22. https://doi.org/10.1111/jorc. based diabetes self-management education for patients with type 2 diabetes mellitus. A randomised controlled trial. Patient Educ Couns. 2012; 9. Hibbard JH, Greene J, Tusler M. Improving the outcomes of disease 86(1):98–105. management by tailoring care to the patient's level of activation. 30. Begum N, Donald M, Ozolins IZ, Dower J. Hospital admissions, emergency Am J Manag Care. 2009;15(6):353–60. department utilisation and patient activation for self-management among 10. Bos-Touwen I, Schuurmans M, Monninkhof EM, Korpershoek Y, Spruit- people with diabetes. Diabetes Res Clin Pract. 2011;93(2):260–7. Bentvelzen L, Ertugrul-van Der Graaf I, et al. Patient and disease https://doi.org/10.1016/j.diabres.2011.05.031. characteristics associated with activation for self-management in patients 31. Ahn YH, Yi CH, Ham OK, Kim BJ. Psychometric properties of the Korean with diabetes, chronic obstructive pulmonary disease, chronic heart failure version of the "patient activation measure 13" (PAM13-K) in patients with and chronic renal disease: a cross-sectional survey study. PLoS ONE. 2015; osteoarthritis. Eval Health Prof. 2015;38(2):255–64. https://doi.org/10.1177/ 10(5). https://doi.org/10.1371/journal.pone.0126400. 11. Szanton SL, Gill JM. Facilitating resilience using a society-to-cells framework: 32. Fowles JB, Terry P, Xi M, Hibbard J, Bloom CT, Harvey L. Measuring self- a theory of nursing essentials applied to research and practice. ANS Adv management of patients' and employees' health: further validation of the Nurs Sci. 2010;33(4):329–43. https://doi.org/10.1097/ANS.0b013e3181fb2ea2. patient activation measure (PAM) based on its relation to employee 12. Gleason KT, Tanner EK, Boyd CM, Saczynski JS, Szanton SL. Factors characteristics. Patient Educ Couns. 2009;77(1):116–22. https://doi.org/10. associated with patient activation in an older adult population with 1016/j.pec.2009.02.018. functional difficulties. Patient Educ Couns. 2016;99(8):1421–6. 33. Schillinger D, Grumbach K, Piette J, Wang F, Osmond D, Daher C, et al. https://doi.org/10.1016/j.pec.2016.03.011. Association of health literacy with diabetes outcomes. JAMA. 2002;288(4): 13. Polit DF, Beck CT. Nursing research: Generating and assessing evidence for 475–82. nursing practice. 9th ed. Lippincott Williams & Wilkins. 2012. 34. Mollaoğlu M, Kayataş M. Disability is associated with nonadherence to diet 14. World Medical Association. World Medical Association Declaration of and fluid restrictions in end-stage renal disease patients undergoing Helsinki: ethical principles for medical research involving human subjects. maintenance hemodialysis. Int Urol Nephrol. 2015;47:1863–70. JAMA. 2013;310(20):2191–4. 15. Rademakers J, Nijman J, van der Hoek L, Heijmans M, Rijken M. Measuring 35. Lubetkin EI, Lu WH, Gold MR. Levels and correlates of patient activation in patient activation in the Netherlands: translation and validation of the health center settings: building strategies for improving health outcomes. American short form patient activation measure (PAM13). BMC Public J Health Care Poor Underserved. 2010;21(3):796–808. https://doi.org/10. Health. 2012;12:577. https://doi.org/10.1186/1471-2458-12-577. 1353/hpu.0.0350. Van Bulck et al. BMC Nephrology (2018) 19:126 Page 9 of 9 36. Van den Bosch J, Warren DS, Rutherford PA. Review of predialysis education programs: a need for standardization. Patient Prefer Adherence. 2015;9: 1279–91. https://doi.org/10.2147/ppa.s81284. 37. Hall G, Bogan A, Dreis S, Duffy A, Greene S, Kelley K, et al. New directions in peritoneal dialysis patient training. Nephrol Nurs J. 2004;31(2):149–154, 59–63. 38. Dutchspeaking Belgian Society of Nephrology - Nederlandstalige Belgische Vereniging voor Nefrologie. 1 January 2016. Data not published.
– Springer Journals
Published: Jun 1, 2018