Fit & Strong! program is an evidence-based, multi-component program promoting physical activity among older adults, particularly those suffering from lower-extremity osteoarthritis. The primary purpose of the study is to examine if the Fit & Strong! program translated into a lay-leader model can produce comparable outcomes to the original program taught by physical therapists and/or certified exercise instructors. A single-group, pre–post study design was employed, and data were collected at the baseline (n = 136 participants) and the intervention conclusion (n = 71) with both baseline and post-intervention data. The measurements included socio-demographic information, health- and behavior-related information, and health-related quality of life. Various statistical tests were used for the program impact analysis and examination of the association between participant characteristics and program completion. As in the original study, there were statistically significant (p < 0.05) improvements in self-efficacy for exercise, aerobic capacity, joint stiffness, level of energy, and amount and intensity of physical activities. The odds of completing the program were significantly lower for the participants from rural areas and those having multiple chronic conditions. Successful adaptation of the Fit & Strong! program to a lay-leader model can increase the likelihood of program dissemination by broadening the selection pool of instructors and, hence, reducing the potential issue of resource limitation. However, high program attrition rates (54.1%) emphasize the importance of adopting evidence-based strategies for improving the retention of the participants from rural areas and those with multiple chronic conditions.The  Fit & Strong! is an example of one such multi-component physical activity program that combines guided aerobic, strength, and flexibility training with health education (Fit & Strong! has demonstrated efficacy to improve participants’: (1) self-efficacy (SE), or confidence, for exercise; (2) physical activity adherence; (3) aerobic capacity; and (4) lower-extremity joint pain and stiffness  and certified exercise instructors (CEIs) as a means to ensure safety and effectiveness in conducting the program  describe the characteristics of participants enrolled in the translated Fit & Strong! program; (2) examine factors associated with program attendance; and (3) assess changes in health-related outcomes among participants.Given the growth of other evidence-based programs delivered using train-the-trainer, lay-leader models through a variety of aging, public health, and health care organizations, questions arose about whether Fit & Strong! program is a multi-component physical activity and behavioral change program that is structured around two key components: (1) participation in group-facilitated (or guided) physical activity; and (2) group-based health education/problem-solving. Over an 8-week period, individuals participate in 24 total sessions, meeting 3 days each week for 90-min each session. Each session begins with 60-min of structured physical activity, which is then followed by a 30-min group-based discussion/problem-solving period  warm-up exercises (5–10 min); (2) low-impact aerobic conditioning  where participants begin with 10 min of activity and gradually work up to 30 min by the end of the program; (3) strength exercises (primarily lower-extremity) using ankle weights and resistance bands (15–20 min); and (4) cool-down and flexibility exercises (5–10 min) , 11, 17.Fit & Strong! in Central Texas involved two modifications: (1) a shift in the required instructor qualification from exercise-experts  to lay-leaders; and (2) a modification in the training protocol. In response to a shortage of qualified instructors in the targeted communities, especially in the rural sites, program implementers at the Texas A&M Program on Healthy Aging collaborated with the original program developers at the University of Illinois – Chicago to modify the existing qualification requirement for instructors and allow lay individuals to lead Fit & Strong! classes. For lay individuals to be qualified to lead Fit & Strong! classes, the lay-leaders needed to meet the following criteria: (1) be certified in another evidence-based class  and have experience and comfort leading group classes, if not already a PT or a CEI; (2) participate in the full instructor and supplemental lay-leader trainings conducted by the Master Trainers; and (3) adhere to fidelity standards by following the training manual in conducting the program  referring and identifying qualified/capable individuals; and (2) disseminating information  about the lay-leader training.Fit & Strong! program was conducted by Fit & Strong! staff, Master, and T-Trainers . Lay individuals as well as CEIs completed the mandatory instructor training, which lasted 8 h (in 1 day) and covered: (1) program background & development; (2) importance of fidelity; (3) roles/responsibilities of instructors in relation to other Fit & Strong! team members ; (4) Fit & Strong! exercise components (description and demonstration of various types of exercises used throughout the program); (5) Fit & Strong! group discussion/problem-solving component ; and (6) data collection, evaluation, and fidelity responsibilities of instructors  community support for hosting Fit & Strong! classes , (2) facility availability for Fit & Strong! classes, and (3) the presence of a sufficient number of older adults who could benefit from Fit & Strong! and who were interested in participating in the program. The number of participants in each class ranged from 16 to 25, which roughly paralleled the recommended 20–25 participant maximum ideal . As previously mentioned, participants were recruited by host agency members as well as Texas A&M program implementers. Participants were recruited through various sources, including print materials , community resources , family or friends (word of mouth), and health care providers. The majority of participants were recruited through print materials (43.4%) and family or friends (28.7%).Study participants included adults aged 47–94 years who enrolled in Data sources included a baseline survey at the beginning (first and second sessions of each class), a post-test survey at the end of the 8-week program , and an attendance log. Demographic data that was drawn from the baseline survey included age, gender, race/ethnicity, education, marital status, employment status, and annual household income. Several outcome measures were extracted and analyzed based upon these baseline and post-test surveys. Primary outcomes included: (1) SE for exercise and (2) level of physical activity related to aerobic capacity, flexibility, and strength. Secondary outcomes included: (1) self-rated health status; (2) joint pain and stiffness; and (3) level of energy (fatigue). Paralleling assessment protocols being utilized by the original program developers in their program dissemination phase , the meahigher self-efficacy. The scale value was set to “missing” if more than one item was missing .Self-efficacy for exercise was measured using four items. The items asked how confident participants are in performing different types of exercise , performing vigorous exercises, and performing exercise despite pain or symptoms. Each item is based on a 10-point scale ranging from “not at all confident” (score = 1) to “totally confident” (score = 10). The score for SE for exercise was the mean of the four items. Higher SE scores indicated  missing ; based oaerobic capacity.A slight adaptation of the rapid assessment of physical activity (RAPA) was used to measure the amount and intensity of participants’ physical activity . The adasedentary” and was scored 1; affirmative response to the item “(2)” represents “under-active” and was scored 2; affirmative response to the item “(3)” represents “under-active regular – light activities” and was scored 3; affirmative response to the item “(4)” represents “under-active regular” and was scored 4; and affirmative response to items “(5)” and/or “(6)” represents “active” and was scored 5. The highest score among the six items was selected for the aerobic capacity score .The six items were: (1) I rarely or never do any physical activity; (2) I do some light or moderate physical activities but not every week; (3) I do some light physical activity every week; (4) I do moderate physical activity every week; (5) I do 30 min or more per day of moderate physical activity, five or more days per week; and (6) I do 20 min or more per day of vigorous physical activities, three or more days per week. Each of the six items reflected a specific level of aerobic capacity. For example, affirmative response to the item “(1)” represents “ty score . The remhealth (poor = 1) and higher values indicating better health (excellent = 5).A single item was used to assess self-rated health , which hgreater pain; and the stiffness-scale ranged from 0 to 8 with higher values indicating greater stiffness. Internal reliabilities were high for both composite scales  (The Western Ontario and McMasters University Osteoarthritis Index (WOMAC) was used to measure lower-extremity pain and stiffness . The adoiffness) .worse health). The mean of the five items was used as the composite scale for the level of energy and fatigue. The scale ranged from 0 to 5 with higher values indicating a lower level of energy and a greater level of fatigue. Internal reliability was high for this composite scale (Cronbach’s α = 0.90).The level of energy and fatigue was measured using five items . Each itFit & Strong! sessions per class offering.Attendance was tracked via attendance logs for each session, and the attendance data were used to calculate the attendance and completion rates. “Completion” was defined as attending at least 18 out of the 24 total The recruitment flow from initial program enrollment is presented in Figure Fit & Strong! participants). Among the 181 potentially eligible participants, however, only 136 (75.1%) completed the baseline survey and were, therefore, eligible to be part of the initial participant comparison analyses. Only 71 participants (39.2%) of the 136 eligible participants completed both pre and post-test surveys and served as “impact study participants.”A total of 234 participants were enrolled in the program. Among this initial group, 181 (77.4%) individuals were potentially eligible for the outcomes study, 21 (9.0%) individuals did not meet study criteria , and 32 (13.7%) individuals were repeaters .Characteristics of those who completed both baseline and post-test surveys (matched surveys) were compared to the other participants  using χFit & Strong! participants  was 73.02 (SD = 9.16) years . The majority of participants were female (80.2%) and were of non-Hispanic White ethnicity (82.8%). Over 75% had more than a high school degree, and 62.2% were married. Among the four chronic conditions reported , hypertension was most frequently reported among the participants (47.3%). Of the 136 eligible participants who completed the baseline survey, 33.8% were from rural counties and 66.2% were from an urban county.As shown in Table n = 65), a significantly larger proportion of impact study participants (n = 71) were female . On average, SE at baseline for impact study participants (p = 0.033) was significantly higher relative to eligible participants omitted from the impact study; whereas, average self-rated health (p = 0.028) at baseline for participants included in the impact study was significantly higher than participants who were not included in the impact study. There were no significant differences by other socio-demographic characteristics and baseline levels of physical activity and illness symptomatology.Compared to eligible participants omitted from the impact study because of lack of matched data (completed baseline and post-tests) (p < 0.001) and had higher completion rates .As shown in Table p = 0.015). Those without any chronic conditions were also more likely to complete the program ; for every increase in number of chronic conditions, the odds of completing the class drops by 46.4%. There were no significant differences by other socio-demographic characteristics or baseline levels of physical activity, general health status, or illness symptomatology.As further seen in Table p = 0.020, d = 0.30) and aerobic capacity  from baseline to post-test. In terms of the magnitude of improvement at the individual level, there was an 8.1% improvement in SE for exercise and an 11.9% improvement in aerobic capacity. Furthermore, 54.8% of the sample reported an improvement in confidence to exercise and a 29.8% improvement in aerobic capacity. Additionally, there was a shift in the proportion of participants who met the Surgeon General’s recommended physical activity guidelines (According to the results illustrated in Table idelines . At basep < 0.05). The effect sizes for all secondary outcomes ranged from 0.05 to 0.59. The strongest effect sizes were observed for strength and flexibility scales (d = 0.59), then for the level of energy (d = 0.33), and then joint stiffness (d = 0.31). At the individual participant level, there was a 19.2% improvement in the degree of joint stiffness and an 11.7% improvement in the level of energy. Furthermore, 17.5% of the participants reported improvements in joint stiffness and 27.0% reported improvements in the level of energy. Over one-third of participants reported improvements in the degree of physical activities related to strength. 35.6% reported improvements in the degree of physical activities related to flexibility, and 48.3% reported improvements in the degree of physical activities related to both strength and flexibility.In terms of secondary outcomes, there were significant changes observed for joint stiffness, level of energy, and amount and intensity of physical activities related to strength and flexibility  was comparable with that found in other research studies using different time-bound evidence-based programs . It is nFit & Strong! classes on various outcome measures. Participants showed a significant improvement in their aerobic capacity, joint stiffness, level of energy/fatigue, and SE for exercise. Participants also reported greater participation in exercise types  such that more individuals met the Surgeon General’s recommendations of including exercises targeting flexibility and strength training. These findings are consistent with those of Hughes and colleagues  with guided instruction and ongoing feedback related to program administration as well as proper techniques for exercise progression. These modifications are essential for conducting the program and are seen as critical in allowing the Fit & Strong! program to be disseminated more broadly as a lay-led model.Although it is impossible to draw a definitive conclusion, these findings strongly suggest that the There is now a growing literature on factors affecting recruitment and strategies for boosting program retention . Given tFit & Strong! classes in terms of the magnitude of program impact and program fidelity. Also, lay-led Fit & Strong! classes should be evaluated/assessed in other settings to draw a more generalizable conclusion about the utility and effectiveness of varying levels of instructor expertise and training components.This pilot study also highlights the need for additional research. Future research should compare lay-led and professional-led Fit & Strong! program that relied on professional and experienced leaders (PTs and CEIs). The lay-led Fit & Strong! model produced outcomes that are consistent with the previous findings from the original intervention. Specifically, the program showed improvement in participants’ SE for exercise, aerobic capacity, engagement in strength, and flexibility exercises, while increasing energy levels and decreasing joint stiffness. The magnitude of program attrition in community-based exercise programs can be large; hence, creative strategies are needed to boost participant retention throughout the entire intervention period.Overall, utilizing a lay-led model was successfully adapted from the original The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.This paper is included in the Research Topic, “Evidence-Based Programming for Older Adults.” This Research Topic received partial funding from multiple government and private organizations/agencies; however, the views, findings, and conclusions in these articles are those of the authors and do not necessarily represent the official position of these organizations/agencies. All papers published in the Research Topic received peer review from members of the Frontiers in Public Health  panel of Review Editors. Because this Research Topic represents work closely associated with a nationwide evidence-based movement in the US, many of the authors and/or Review Editors may have worked together previously in some fashion. Review Editors were purposively selected based on their expertise with evaluation and/or evidence-based programming for older adults. Review Editors were independent of named authors on any given article published in this volume.


The prevalence of schizophrenia and depression in the United States is far higher among Medicaid recipients than in the general population. Individuals suffering from mental illness, including schizophrenia and depression, also have higher rates of emergency department utilization, which is costly and may not generate the positive health outcomes desired. Disease management programs strive to help individuals suffering from chronic illnesses better manage their condition(s) and seek health care in the appropriate settings. The objective of this manuscript is to estimate a dose–response impact on hospital inpatient and emergency room utilizations for any reason by Medicaid recipients with depression or schizophrenia who received disease management contacts.Multivariate regression analysis of panel data taken from administrative claims was conducted to test the hypothesis that increased contacts lower the likelihood of all-cause inpatient admissions and emergency room visits. Subjects included 6,274 members of Illinois’ non-institutionalized Medicaid-only aged, blind or disabled population diagnosed with depression or schizophrenia. The statistical measure is the odds ratio. The odds ratio association is between the monthly utilization indicators and the number of contacts (doses) a member had for each particular disease management intervention.Higher numbers of intervention contacts for Medicaid recipients diagnosed with depression or schizophrenia were associated with statistically significant reductions in all-cause inpatient admissions and emergency room utilizations.There is a high correlation between depression and schizophrenia disease management contacts and lowered all-cause hospital inpatient and emergency room utilizations. The presence of mental illness is associated with substantially higher per capita costs and all-cause hospitalization rates compared to the general population . Mental Between 1996 and 2006, the number of people with health care expenditures associated with mental disorders nearly doubled from 19.3 million to 36.2 million ,8 the prThe chronic care model (CCM) is a framework to guide organizational and quality changes in the health care delivery system to improve the quality of care to people with chronic conditions . The CCMDisease Management emerged as an application of the chronic care model in the mid-1990s, and is used as a strategy to help mitigate costs for health care, particularly the most costly services of inpatient hospital admissions and ED visits ,13. WhilThere is great variation between state Medicaid programs, and DM at the state level is no exception. State Medicaid programs differ in how they select potential DM participants and vary in organizational and structural design. For instance, the diagnosis codes used to identify conditions, the list of conditions to be managed, and the benefit design such as fee-for-service or managed care can vary greatly.For the Illinois Your Healthcare Plus program, individual health care goals were established in collaboration with participants and their medical homes or primary care physicians. The basic Medicaid benefit package and service access did not change for those participating in the program; medical services were still paid for through Medicaid. Where services such as Assertive Community Treatment were available or where there may have been a practice-based medical home Registered Nurse (RN) care coordinator available, the role of the DM RN was not to take the place of these services, but to support the member’s continued use of these services. It should be noted, however, that due to budgetary constraints for much of the state, often these services were unavailable. Therefore, the focus of the DM RN was to facilitate and support member engagement in any locally available and relevant behavioral and/or medical services.As many as 38 states have engaged in DM programs for a portion of their Medicaid population,  and manyThe key contribution of this study is to estimate a dose response effect for a DM program designed to improve both care to individuals and cost effective appropriate utilization among Illinois’ Medicaid-only recipients with behavioral health conditions. The literature review found no dose response studies related to a behavioral health DM program. This research is a beginning in filling that knowledge gap.Ethics approval/IRB approval was not necessary as the study was performed as part of a DM program using administrative claims data accessible to all authors.On July 1, 2006, the state of Illinois implemented the Your Healthcare Plus (YHP) DM program, which included the adult aged, blind, and disabled (ABD) Medicaid-only population living in the community and who were non-institutionalized. In fiscal year 2007, the entire Illinois ABD population enrolled in a DM program represented 4.41% of the state’s total Medicaid members, but accounted for over 18% of the state’s Medicaid expenditures, as calculated by Illinois administrative claims analysis.Subjects were selected based on administrative claims incurred between July 2005 and June 2009 for the ABD Medicaid population living in non-institutional settings. Additionally, subject inclusion criteria required members to have at least two different claims with a diagnosis of depression  or schizophrenia. Inclusion criteria for this study also require that subjects were continuously enrolled in Medicaid between July 2005 and June 2009. The total number of subjects with at least one of these conditions or diagnoses and eligible for the intervention was 6,274. Of that number, 1,738 individuals (27.7%) self-selected in to the DM program, were contacted by a nurse, and received interventions at some point between July 2006 and June 2009.Registered nurses began contacting identified subjects diagnosed with one or both behavioral health conditions (depression or schizophrenia) for DM program enrollment in July 2006. These nurses had at least 5 years of acute care experience and completed 6 weeks of training prior to launch of the DM program. This training included condition-specific information on national clinical guidelines for outpatient management standards, characteristics, and behaviors. Training also included practice in the use of motivational interviewing techniques and concepts  that helNurse-led interventions focused on enabling the individual to change their behaviors for improved health and well-being. Nurses provided support regarding lifestyle choices, appropriate use of a medical and/or behavioral therapy home, medication adherence, and participation in other behaviors and habits such as smoking or dietary patterns that may negatively impact well-being.Members were not randomized into an intervention or control group. For those members who chose to enroll and were able to be contacted, the YHP program customized a self-management intervention plan that included risk stratification, planned education and counseling sessions where nurses worked with participants to help them understand specific disease management skills and behaviors, 24-hour access to nurse counseling, and other sources of condition or symptom advice including telephonic support, printed action plans, and workbooks. In addition, participants received individualized assessment letters and reminders for medication compliance and vaccination. Physicians received alerts about critical signs and symptoms of decompensation and notification of gaps between participant-reported practices and guideline recommendations.Whether they participate in a DM program or not, health plan members very commonly present with multiple co-occurring physical and behavioral health conditions. The DM program interventions were designed to address both types of conditions by assessing each individual to determine what was most pressing ‘at the moment,’ recognizing the interplay between the conditions and the mind-body relationship.Participating members received multiple contacts throughout the course of the intervention period. A contact was any interaction related to the participant’s condition. Examples of contacts up to and including the month of measurement are described below and include (1) health assessments ; (2) monitoring and educational contacts; and (3) inbound symptomatic contacts by participants. Doses were defined as the number of cumulative contacts of any kind that a participant had up to, and including, the month of analysis.The nursing assessments  included gathering participating members’ self-reported information on areas such as medication use, adherence to their physician’s recommendations, barriers to care, knowledge of their condition(s) and symptoms, current self-management practices, use of a medical home, and recent ED or inpatient utilization. This self-reported information was included in each member’s record. Assessments were always conducted by an RN. While most assessments were done telephonically, some were conducted face-to-face at a mutually agreed upon location with the participating member. An assessment could have been completed in a single session or in more than one session, depending on the willingness of the member. In the case of an incomplete assessment, the nurse would schedule a follow-up session and gather the remaining information. Upon completion of the assessment, an individualized care plan was created.Assessments occurred initially upon enrollment of a member into the DM program and thereafter at six month intervals to update member status data and to update or revise the care plan and member goals. Providers received a summary letter of member self-reported data along with information on how to contact the DM nurse. Nursing staff also made regular visits to provider practices to collaborate on member management.The monitoring and education contacts were scheduled sessions that occurred in the interim period between assessments. Activities typical of this type of contact included addressing the participating member’s care plan challenges and any other pressing member concerns. Nursing staff used motivational interviewing techniques to facilitate needed behavior changes for the participant to be able to effectively self-manage their condition(s).Condition-specific education was provided and varied depending on the member’s specific needs. Educational content included information to enhance member understanding of their health condition(s), instructions on medication use, guidance on lifestyle choices, and direction on how to recognize symptoms of decompensation and the appropriate actions to take. To support communications between the member and their providers, mock scenarios were play acted between the nurse and members requiring this level of assistance.While the primary staff delivering interventions in the YHP program were Registered Nurses, other program staff included social workers, community health workers, and behavioral health specialists.Inbound symptomatic member contacts were unscheduled telephonic communications that occurred in addition to the scheduled monitoring and education contacts described above. These inbound contacts occurred when a participating member reported health symptoms, flagging the nurse to follow up the next day to ensure that the appropriate steps had been taken. This contact type was also used to address other member-reported issues that that were appropriate for follow-up prior to the next scheduled contact. Examples include follow-up with the member after a visit to a provider, and DM staff counseling when a participating member wanted to discuss current symptoms or other urgent needs.Panel/longitudinal data was used for this analysis. The unit of measurement was the individual Medicaid member month with each participating member measured for each calendar month between July 2005 and June 2009. All members in the analysis were continuously eligible for DM services between July 2005 and June 2009. Because the intervention began in July 2006, the 12 months of data between July 2005 and June 2006 did not have any DM program contacts for any members.Given the panel data structure of one observation for each member for each month of their Medicaid eligibility, a member could have switched from a non-intervention status to an intervention status for subsequent months. That is, since each member was continuously eligible for the entire 48 month period, if the member began receiving intervention contacts in month 37, then only months 37–48 were logged as months with an indicated contact. Once a member started receiving intervention contacts, every subsequent month was indicated as a month of contact, since the contacts in each month are the cumulative number of contacts a member has had up to that point.Multivariate regression analysis of panel data was used to evaluate the intervention and test the hypothesis that increased DM contacts lowered the odds of an inpatient admission or emergency room visit. The dependent variable was the dichotomous indicator as to whether or not the member had an inpatient admission in a given month, or had an ED visit for those separate regressions. For each monthly observation, an indicator of whether or not an inpatient admission or ED visit occurred during that month was calculated. The dichotomous formulation of this variable allows for a logistic regression to be estimated and odds ratios to be calculated.The independent or explanatory variables included (i) an identification indicator showing whether or not a member had been identified for a particular DM intervention or plan of care for their depression or schizophrenia, (ii) the cumulative number of contacts (doses) for each particular intervention a member has had, including doses squared and cubed, (iii) a high risk indicator, defined as members identified as having cancer, end stage renal disease, HIV, hemophilia, traumatic brain injury or a previous organ transplant, (iv) age/gender grouping, (v) the predictive model risk score prior to identification of each member for a DM program calculated by the predictive modeling company MEDai,  and (vi)Multivariate regression analysis allowed for simultaneous estimation of several explanatory variables influencing a participating member either at the same or different times. That is, if a member had both depression and schizophrenia, the number of contacts related to each condition was used as an explanatory variable. In cases such as this, there was one explanatory variable for the cumulative number of depression contacts up to that month, as well as one explanatory variable for the cumulative number of schizophrenia contacts up to that month.To calculate the dose response effect, a cubic representation was chosen to allow for a flexible functional representation. This flexibility allowed for a quadratic and linear dose response relationship as a special case. Contact or dose variables were calculated for each condition. For instance, the number of depression contacts, the number of depression contacts squared, and the number of depression contacts cubed was calculated. The coefficients of each of these three variables were then estimated in the regression analysis and used to estimate the odds ratio for each level of contact by exponentiation of the coefficient estimates.Table The dose response impact is considered statistically significant (rejecting the null hypothesis that the dose variable odds ratio is 1.0) if Pearson’s chi-square test p-value is less than the critical value. Figures For participating members with depression, all-cause inpatient admissions showed statistically significant impact from one to nine contacts with odds ratios between 0.8 and 0.62. Depression DM program impact on emergency room utilization was lower than the program impact on inpatient admissions. Results were statistically significant from two to four contacts, with odds ratios ranging from 0.89 to 0.86.For schizophrenia, all-cause inpatient admissions showed statistically significant impact from four to eleven contacts with odds ratios between 0.75 and 0.34. Similar to the above finding for the depression DM program, schizophrenia DM program impact on emergency room utilization was also lower than the program impact on inpatient admissions. Results were statistically significant from six to twelve contacts, with odds ratios ranging from 0.78 to 0.68.For both depression and schizophrenia, the increased number of contacts showed greater impact on reductions in inpatient admissions than it did on the number of ED visits. This indicates that the intervention had less influence in reducing ED visits as compared to inpatient admissions. For participating members with schizophrenia, more contacts were required to show a statistically significant reduction in ED and inpatient admissions as compared to participating members with depression, where impacts were seen with fewer contacts.Figures One limitation of this study may be its generalizability. This study was specific to a state Medicaid population, and translating these findings to either another Medicaid population or to a commercially insured population may not be valid. This, however, suggests an area for future research. Another limitation is the quasi-experimental study design. Selection bias may exist because members may choose whether to participate in the program for reasons that may not be controlled for with the observable variables. For example, certain members may be more motivated for self-care or have different complexities related to their disease(s). Predictive risk adjustment may be a possible control. As well, even though members agree to participate in the DM program, the level and number of contacts are determined through risk assessment. Apart from an experimental design, the magnitude of this potential bias cannot be determined. A further limitation is restricting the sample to continuously enrolled members. Medicaid members do churn in and out of eligibility; however, continuously enrolled members were chosen to ensure that each person was equally represented and had complete data. Finally, dose timing was not examined here but might provide further insights into the effect of the intervention. Future analyses could examine the differential effects of 12 monthly doses versus 12 doses received over the course of 2 years.Program staff cited that the most difficult challenges in delivering effective DM programs stem from the participating populations’ burden of illness, dysfunctional patterns of health behaviors, and years of barriers to access to care that have contributed to unfortunate decision making in their own health care and lifestyle choices. Due to both the varied nature of human behavior and the complexity of chronic health problems, achieving a positive impact on health outcomes and behaviors must rely on a multipronged approach that focuses on supporting and motivating the individual member. For members with chronic mental illnesses, a caring, individualized approach provides the attention that they may not otherwise perceive as being provided.This study has evaluated a multipronged approach focusing on supporting and motivating the individual member. A thorough analysis of the program data found statistically significant reductions of all-cause inpatient hospitalizations and ED visits for Medicaid members diagnosed with depression or schizophrenia. These reductions were based on the number of contacts and were not the result of denying inpatient utilization or restricting treatment.This analytical work has been valuable for Illinois Medicaid and the Your Healthcare Plus program by providing data-driven information that may be used in making program modifications and staffing adjustments. Program changes suggested by these results include altering the schedule of contacts and ensuring both regular and ad hoc contacts to meet participating member needs. It is important that resources be allocated for interventions where the best opportunities for improvements in DM program outcomes may exist. As this is the first dose response study related to a non-drug DM intervention for behavioral health in the literature, additional future studies are needed in this area to determine further methods of improving health outcomes.WM and MM have no conflict of interest. GDB, SD, and KW work for McKesson Corporation, which provided services to the state of Illinois.GDB and SD have made substantial contributions to conception and design. GDB and SD performed the data analysis. GDB, SD, KW, WM and MM were involved with interpretation of data. GDB, SD, KW, WM and MM have been involved in drafting the manuscript or revising it critically for important intellectual content. GDB, SD, KW, WM and MM have all given final approval of the version to be published. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6963/14/288/prepub


In late 2012 and in conjunction with South Sudan’s Ministry of Health – National Malaria Control Program, PSI  conducted a comprehensive mapping exercise to assess geographical coverage of its integrated community case management (iCCM) program and consider scope for expansion. The operational research was designed to provide evidence and support for low–cost mapping and monitoring systems, demonstrating the use of technology to enhance the quality of programming and to allow for the improved allocation of resources through appropriate and need–based deployment of community–based distributors (CBDs).The survey took place over the course of three months and program staff gathered GPS  data, along with demographic data, for over 1200 CBDs and 111 CBD supervisors operating in six counties in South Sudan. Data was collated, cleaned and quality assured, input into an Excel database, and subsequently uploaded to geographic information system (GIS) for spatial analysis and map production.The mapping results showed that over three–quarters of CBDs were deployed within a five kilometer radius of a health facility or another CBD, contrary to program planning and design. Other characteristics of the CBD and CBD supervisor profiles  were more closely matched with other regional programs.The results of this mapping exercise provided a valuable insight into the contradictions found between a program “deployment plan” and the realities observed during field implementation. It also highlighted an important need for program implementers and national–level strategy makers to consider the natural and community–driven diffusion of CBDs, and take into consideration the strength of the local health facilities when developing a deployment plan. South Sudan has one of the highest childhood mortality rates in the world, with an infant mortality rate of 105 per 1000 live births  of context–appropriate improvements to the intervention and maximize coverage and uptake.” [The visual mapping of CBDs indicates that coverage by community health programming is ultimately affected by local recruitment and selection/nomination proceedings. As recommended by Jacobs et al., “uptake.” . This inUnderserved communities – where are all the CBDs? The red circles on the maps indicate large swaths of each county that, at the time of mapping, were not supported by a CBD. This showcases a gap in program implementation and depending on the community needs, these villages should have been prioritized in the reallocation and subsequent round of CBD selection and training. This component of the exercise highlights the need for precise and comprehensive program planning, along with a strong coordinated effort from the county level health officials and the implementing partners. Improved coordination between these two groups can improve coverage of potentially underserved areas.Outdated maps of states and counties of South Sudan mean scant information about the “operational status” of primary health care units and centers  as well as real–time land use by populations. It is also possible that some of the more traditional nomadic communities were not included in the mapping exercise, though every attempt was made to include these mobile groups. Population distribution at the time of the exercise relied on information from the South Sudan National Bureau of Statistics. This meant that repositioning/redeployment of CBDs required additional time, surveying and coordination to ensure that they were being chosen in the areas of greatest need.Lessons learned and study methods from such a deployment case study are practical and useful for others wishing to conduct similar operational research, but it must be noted that results and conclusions may not be widely generalizable.GPS mapping can and has worked in other contexts, but regulations and policies developed as a result need to be tailored to the area of operation.The programmatic decision to reduce CBDs in some areas, and increase in other areas was highly sensitive, despite the clear inequity in access, imbalance in CBD density, redistribution of resources, and potential to improve the quality of programming. Re–deployment also meant the training of new CBDs, establishment of new relationships with health facilities and this may have been interpreted as a step backwards programmatically, despite improvements to the quality of the program.The current political situation also presents challenges to the continuation of many public health programs, including iCCM. Although these community–based programs are the principle mechanism of reaching remote and underserved areas, they rely heavily on the national supply chain for commodity management, in addition to program implementing agencies for funding and monitoring. Internal displacement and high rates of attrition add to the challenges of this already complex program.mHealth solutions. This initial mapping exercise was the first step in shifting towards a more advanced and practical data management system. Further down the line, as mobile network coverage improves across the country, it will be feasible to create a mobile health  system that collects caseload data instantly from CBDs. This data can be sent to a centralized server by SMS and seamlessly uploaded into DHIS2, where constant monitoring can alert implementing teams, as well as national level decision makers, to make evidence–based policy and action plans.The mapping of CBD and use of mapping technology fits in very nicely with other mHealth programming, particularly in the areas of data access and monitoring and compliance . When paImproved quality of programming through retention. Quality Assurance programming should be implemented and management decisions should be driven by caseload data and quality of care provided by the CBD. Program quality can also be improved by maintaining the strong individuals among the CBD and CBD Supervisor cadre through varying types of motivation. Young men are primarily CBD supervisors, and yet they often leave the cadre in search of higher paying work, to the detriment of the program.The results of this mapping exercise provided a valuable insight into the contradictions found between a program “deployment plan” and field implementation. It also highlights an important need for program implementers and national–level strategy makers to consider the natural and community–driven diffusion of CBDs, and take into consideration the strength of the local health facilities when establishing catchment areas.The results from this mapping exercise demonstrate that the process can be employed as a low–cost management tool for the distribution and deployment of community health workers or health services, for many different disease areas in addition to malaria, as it simple to use, affordable and accessible through different global funding sources, and it can aid in both effective implementation and planning.The system can improve allocation of scarce resources, and with additional technology and quality assurance measurements, it increases the ability to “keep” high performing CBDs and CBDs that are valued in the community for providing a quality service to their patients.Additional research and funding will be required to go to scale in South Sudan, but there are organizations investing heavily in building the capacity of the community health worker, by equipping them appropriately, using adult training techniques and providing a network of supportive supervision and continuous training that will give CBDs the confidence to do their jobs well.


Clinicians need innovative educational programs to enhance their capacity for using research evidence to inform clinical decision-making. This paper and its companion paper introduce the Physical therapist-driven Education for Actionable Knowledge translation (PEAK) program, an educational program designed to promote physical therapists’ integration of research evidence into clinical decision-making. This, second of two, papers reports a mixed methods feasibility study of the PEAK program among physical therapists at three university-based clinical facilities.A convenience sample of 18 physical therapists participated in the six-month educational program. Mixed methods were used to triangulate results from pre-post quantitative data analyzed concurrently with qualitative data from semi-structured interviews and focus groups. Feasibility of the program was assessed by evaluating change in participants’ attitudes, self-efficacy, knowledge, skills, and self-reported behaviors in addition to their perceptions and reaction to the program.All 18 therapists completed the program. The group experienced statistically significant improvements in evidence based practice self-efficacy and self-reported behavior (p < 0.001). Four themes were supported by integrated quantitative and qualitative results: 1. The collaborative nature of the PEAK program was engaging and motivating; 2. PEAK participants experienced improved self-efficacy, creating a positive cycle where success reinforces engagement with research evidence; 3. Participants’ need to understand how to interpret statistics was not fully met; 4. Participants believed that the utilization of research evidence in their clinical practice would lead to better patient outcomes.The PEAK program is a feasible educational program for promoting physical therapists’ use of research evidence in practice. A key ingredient seems to be guided small group work leading to a final product that guides local practice. Further investigation is recommended to assess long-term behavior change and to compare outcomes to alternative educational models. The World Confederation for Physical Therapy asserts that physical therapists have a responsibility to integrate research evidence into practice as a foundation of patient care . While mIn the companion to this paper  we descrThe 6-month PEAK program consists of four consecutive and interdependent components described in detail in the companion paper and illustrated in Figure A mixed methods “triangulation design model”  was adopTherapists practicing in one of three geographically dispersed USC patient care centers  were invited to participate through staff meetings and individual email. Therapists were required to have a minimum of 6 months clinical experience, be providing patient care at USC at least 20 hours per week, be able to attend both days of an introductory workshop, and be willing to commit to study activities at least 1 hour per month for 6 months. Therapists that also served as onsite clinic managers were included as long as they met the inclusion criteria. The study was approved by the USC Health Science Campus Institutional Review Board (HS-10-00593) and all participants gave consent to participate.The PEAK program, described in detail in this paper’s companion manuscript , was 6 mAll components of the PEAK program supported a participant-driven learning experience: to work as a group to generate a Best Practice List around a common, participant-selected clinical area. The Best Practices List is a locally generated list of evidence-based, actionable behaviors that participants agreed (as a group) to implement in their clinical practice. Participants self-organized into small groups to review literature and generate evidence-based actionable behaviors. The actionable behaviors were reviewed and revised through a process of peer and expert review until all participants felt that they could implement the Best Practices List in practice .The Classification Rubric for EBP Assessment Tools in Education (CREATE) model  Figure  providedParticipants completed four standardized assessments collated into one computer-based survey immediately before and after participating in the educational program. This evaluation will focus on changes observed immediately following the PEAK program.Attitudes toward EBP were assessed using the attitudes items from the EBP Beliefs Scale. The 16-item EBP Beliefs Scale measures EBP attitudes and self-efficacy and has demonstrated construct and criterion validity among practicing nurses . To exclParticipants were asked to rate their participation in developing the Best Practices List and to rate the educational value of 11 elements of the PEAK program  on a 5-point likert-type scale. The complete assessment consisted of 70 individual items and was expected to take 60-75 minutes. Scoring was computerized with the exception of the mFT. A trained, blinded assessor scored the mFT.Participants attended either a face-to-face semi-structured interview (1 participant:1 interviewer) or focus group (3-4 participants:1 interviewer) within 2 weeks of completing the PEAK program. A common interview template was developed to explore the range of experiences and subjective reactions to the PEAK program (Appendix A). Questions addressed all seven categories of the CREATE framework. Participants were initially asked to describe their own engagement in, and reaction to, the PEAK program. They were then facilitated to describe the impact of the program on their EBP attitudes, self-efficacy, knowledge, skills and practice behaviors. They were also asked to consider, from their professional experience, whether the program provided a benefit to patients. Finally, they were asked to comment on the feasibility of transferring the PEAK program to other clinical settings (e.g. other institutions and/or patient populations).Individual interviews and focus groups were conducted by an independent and experienced investigator. The investigator explained to participants that the purpose of the interview was to understand the feasibility of the program and that both supportive and critical comments were welcome. Clinic manager participants were not interviewed with non-manager participants. Interviews and focus groups were conducted at USC. Individual interviews averaged 39 minutes  and focus groups averaged 66 minutes . All interview sessions were audio taped and transcribed in full. Participants were assigned ID numbers and identifying comments were removed from transcripts to ensure participant anonymity.Using a triangulation design model, quantitative and qualitative data were first analyzed independently . This parallel analysis was followed by integrated discussion and analysis between two authors  to achieve concurrent triangulation . All anaChange in standardized quantitative assessments was assessed using paired two-tailed t-tests for normally distributed data (Shapiro-Wilk of p > 0.05). When the normality assumption was met, parametric tests were used for likert-type scales  based on the fact that each scale represents an underlying continuous concept and has relatively equal intervals . Alpha wAll participants were sent their own electronic transcripts for clarification and validation and no changes were requested. Text from all interviews was read and allocated to the appropriate categories on the CREATE framework. The initial coding process was piloted independently by two authors  across four transcripts. Differences in coding were discussed and a final coding system agreed upon that included additional topics beyond the CREATE framework. All transcripts were independently coded by two authors using NVivo software . Coding differences were resolved through discussion. It was noted that for the last few interviews, no new ideas emerged, suggesting that saturation was reached after 11 individual interviews and 3 focus groups.Following independent analyses, an iterative process of comparison and further analysis was conducted to integrate the quantitative and qualitative data sets. Preliminary qualitative themes suggested that there might be important differences in participant responses on the standardized questionnaires depending on which of the 5 EBP steps a particular item addressed. We anticipated that item-by-item analysis of standardized assessments that showed overall statistical change would provide exploratory information about participants’ responses across the 5 steps of EBP. Hence, post-hoc Wilcoxon signed-rank tests were conducted for individual items when a statistically significant change in the entire assessment was observed . With this new quantitative information, concurrent thematic analysis of qualitative data continued consistent with recommendations by Pope and colleagues . ExplanaBoth investigators responsible for analyzing the qualitative data have academic appointments with responsibility for teaching and facilitating the implementation of EBP and KT. One investigator (JKT) was the primary developer of the PEAK program. Both investigators used their experience educating healthcare practitioners in EBP to inform the analysis. They remained open to identifying positive and negative feedback and to identifying expected and unexpected explanations. Authors were sensitive to the complexity of this educational program and recognized that negative feedback provided opportunity for improvement.Eighteen physical therapists met inclusion criteria and agreed to participate Table . ParticiTen of eleven elements of the PEAK program were rated as having ‘good’ or ‘excellent’ value by at least 80% of participants. Of note, ‘Local Skype™ access with webcams’ was rated as having ‘good’ or ‘excellent’ value by just 47% of participants.Participants had consistently positive descriptions of their reaction to the PEAK program. They particularly enjoyed the collaborative nature of the small group work.“I think it was good to collaborate with each other and [for] the final product we were going through each of the behaviors and discussing that as a group. I think that was the most beneficial.” (Participant [P] 18)They described the opportunity to collaborate with people from different settings and with diverse expertise as empowering and motivating. They emphasized the benefits of working with their group toward the common goal of providing consistent and high quality patient care.“I think we all felt accountability for each other that we had this group project that we had to work together for that was going to improve the way we deliver care. And I feel like pretty much everyone was dedicated to that.” (P 10)Participants were invested in the ultimate goal of developing the Best Practices List and this was seen to be an important unifying feature of the program.“Developing a best practices guideline, going through the literature, looking at clinical guidelines, and actually every single person agreeing that these are good, these need to be used.” (P1)The use of an online collaboration tool  to set and achieve group goals was seen as important.“Backpack was almost like Facebook, where it kind of provides us this medium to see what everybody else is thinking and sharing information.” (P14)A small number of participants, without experience using similar online tools, reported that they either struggled to learn them or simply chose not to try.“I didn't participate as much in this group as much as I think I could have otherwise… I don't have the time to keep up with that technology.” (P8)Additionally, there was common frustration  about frequent technical problems during monthly Skype™ video conferences.“And those Skype meetings made it really easy although we had a lot of Skype™ problems. … We spent an hour trying to get everybody online. So that was a little frustrating.” (P17)Despite these technical challenges, monthly large group meetings provided a venue for regular progress reports and accountability.“It’s great to see how all the different clinicians across the board can get together and actually focus on clinical practice. And it’s great to see how therapists in different settings can contribute.” (P1)Most participants did PEAK work from home or before or after work and many reported struggling to find time to meet with group members. They found it frustrating and burdensome, but felt that the time spent was an important investment in the quality of care provided to patients.“It takes more time and its more energy and all that. But really, you know, you're doing the best thing for your patients. ” (P4)Finally, when questioned about whether any components of the program were superfluous, participants were unanimous that all components were necessary. In addition, participants valued the high levels of managerial support. While it was difficult for managers to allocate specific time for this program, they were seen as understanding of the needs of the program and willing to give participating clinicians a degree of flexibility in their work practices.“The managers here definitely are supportive of implementing this [but] from a time perspective, are challenged.” (P5)The PEAK program was not associated with a statistically significant change in knowledge and skills as measured by the mFT The PEAK program was associated with a significant increase in self-efficacy for EBP Participants also reported new confidence making decisions about how to integrate research evidence into patient care. This seemed to come from a combination of personal success in identifying relevant research and from opportunities to discuss the integration of research evidence with their peers. Participants also described feeling more confident sharing study results with patients.“I think confidence is a huge validation that you're doing the right thing, this is a huge piece in making sure the patient gets better and certainly, if I feel confident in the way I'm treating you you're going to feel more like you're in the right place to be treated.” (P21)Participants were disappointed that they did not gain more confidence in their ability to understand statistical methods. Low self-efficacy around this topic was troubling to participants and many expressed a desire to pursue further education.“I would have to say that my ability to look at statistics is not as strong as I would like it to be even still.” (P20)The PEAK program was not associated with any change in knowledge and skills as measured by the mFT With regard to skills, participants described the PEAK program as providing a valuable opportunity to improve through practice. For example, participants described learning to ask better questions as a basis for their searching.“Oh, I'm so much better. I've revised all the way I've done the clinical questions. And so that's been kind of a learning opportunity for me.” (F5)Participants also described identifying better search terms, using newly learned features in PubMed, and cross-referencing results from more than one search engine.“…learning how to narrow my questions…initially I thought I was missing out on a lot, but now it’s very focused.” (P19)Other new skills included searching specifically for clinical practice guidelines and systematic reviews.“My approach has changed in that often I'm trying to find clinical guidelines in different areas of treatment to get a broad array of, what are the approaches that current experts in the field are taking?” (F15)They identified a pattern where, as skills improve, confidence increases and skills are further enhanced through more practice.“Being able to go onto a database like Pub Med and have a more effective, efficient search was very helpful…the more efficient you are at something, the less time you spend doing it, the more apt you are to do it again.” (P14)Participants also described developing their skills of critical appraisal as they expressed new insights into the strengths and weaknesses associated with different study designs.“Being a more savvy consumer of research, looking at methods and results, is this a reliable and valid study, does it match my patient population, is their sample size large enough, how did they select, include or exclude their test subjects, to be able to look at a strength of a study based on that, and then to make my independent assessment of the results without having the author's coloring of the data, I think has been very nice.” (P20)The PEAK program was associated with a 20.4% increase in mean score for 13 items of the EBP Implementation Scale Participants also described discussing ways in which they could integrate research evidence into their clinical practice more with their peers.“I see people sitting around the table discussing articles, sharing articles, and people that weren't involved in that previously are now involved.” (P2)Lastly, participants described active reflection about the process of using evidence in their clinical decision-making.“Let me figure out why I haven't done that with my last patient and see if there's a reason. So now I can check myself and see, am I doing those things, am I not doing those things, why or why not and what is the evidence that supports it?” (F7)Although patient-oriented outcomes were not assessed quantitatively, participants described feeling that the process of developing the Best Practices List would result in more accurate and evidence-based clinical decisions for patients with lumbar spine conditions. Ultimately, they believed that patients would experience better outcomes.“You’re putting the patients’ needs first, you’re using your own past experience just to manage the patient, and then you're using the top evidence. So I think that would be the key and I think then your average patient would want to participate or have a clinician treating and using those standards” (P14)Participants also believed that inclusion of a common set of outcome measures in the Best Practices List would support evidence based clinical decision-making, improve patient education, and enhance communication with other healthcare professionals.“We’ve started kind of monthly in-services and having discussion. What’s the latest within the literature, what are the recommended outcome measures we should be using for these various patient populations?” (P3)Participants selected the topic of ‘Lumbar Spine Conditions’ with five sub-topics  for which they reviewed the literature and identified locally relevant, actionable, evidence-based behaviors that should be implemented in their practice. At the conclusion of the program, participants had created a Best Practices List consisting of 38 evidence-based behaviors , a larger sample size with a subset used for the qualitative analysis would have been a stronger design. Second, the participant population lacked diversity in that they all worked at USC. There is a selection bias among individuals who pursue, and get the opportunity to, work at a university teaching hospital or clinic. While previous studies have established that physical therapists routinely report strongly positive attitudes about EBP ,15,34, oFinally, it is important to note that the PEAK program was designed to influence one component of clinical decision-making—the integration of research evidence. Clinical decision-making is influenced by a complex host of issues  and often involves tensions between scientific reason and social reality . While tThe multifaceted, learner-centered PEAK program is a feasible educational program for promoting physical therapists’ use of research evidence in clinical decision-making. Four themes informing feasibility of the program relate to 1) the collaborative nature of the program; 2) improved self-efficacy for using research evidence to inform practice; 3) the need for greater learning around statistics; and 4) participant expectation that the list of evidence-based practices developed and agreed to by the group would lead to better patient outcomes. A key ingredient seems to be guided small group work leading to a final product (Best Practices List) that guides local practice. Further investigation is recommended to assess long-term behavior change and to compare outcomes to alternative educational models.Introduction, Reaction to the EBP Experience:What small group were you in?How was the Fellowship?What aspects were most helpful to you?How has your work environment impacted on your ability to engage in the Fellowship?How have you used the librarian and library resources?Knowledge about EBP principles:Has the Fellowship influenced the way you think about and interpret research?Has there been a change in how you use clinical guidelines in your practice?Skills for performing EBP:Has the Fellowship taught you new skills?[if YES] How did the Fellowship help you learn these skills?How do you keep up to date with the research evidence?Behavior as part of patient care:Has this Fellowship impacted on the way you work with your patients?Has the Fellowship influenced the way you work with your colleagues?Self-Efficacy for conducting EBP:In what way has this Fellowship changed your ability to use research evidence (ask across 5 steps)?What will you do differently, as a result of participating in this Fellowship?Benefits to patients associated with EBP training:Have there been any benefits for your patients, from your involvement in the Fellowship?Attitudes about EBP:Does EBP impact on the way you work with your patients?What is the most important aspect of EBP in your clinical practice?Future Thinking:If you could change one thing in your current work to improve EBP, what would it be?If this Fellowship were to be expanded to over 100 people, what would you do differently?The authors declare that they have no competing interests.JKT designed the PEAK program, developed the overarching study design, collected and analyzed quantitative data, and had primary responsibility for drafting the manuscript; SM developed the qualitative study design, conducted qualitative interviews, directed analysis of qualitative data, and made major contributions to drafting the manuscript; JCS, MZ, JMD, and RH made substantial contributions to the study design and acquisition of data and critically reviewed the manuscript for important intellectual content. All authors gave final approval of the manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6920/14/126/prepubBest Practices List generated by PEAK participants: “University of Southern California Best Practices List: Physical Therapy for Lumbar Spine Conditions”.Click here for file


The objectives of this paper are to describe the planned implementation and evaluation of the Building on Existing Tools to Improve Chronic Disease Prevention and Screening in Primary Care (BETTER 2) program which originated from the BETTER trial. The pragmatic trial, informed by the Chronic Care Model, demonstrated the effectiveness of an approach to Chronic Disease Prevention and Screening (CDPS) involving the use of a new role, the prevention practitioner. The desired goals of the program are improved clinical outcomes, reduction in the burden of chronic disease, and improved sustainability of the health-care system through improved CDPS in primary care.The BETTER 2 program aims to expand the implementation of the intervention used in the original BETTER trial into communities across Canada . This proactive approach provides at-risk patients with an intervention from the prevention practitioner, a health-care professional. Using the BETTER toolkit, the prevention practitioner determines which CDPS actions the patient is eligible to receive, and through shared decision-making and motivational interviewing, develops a unique and individualized ‘prevention prescription’ with the patient. This intervention is 1) personalized; 2) addressing multiple conditions; 3) integrated through linkages to local, regional, or national resources; and 4) longitudinal by assessing patients over time. The BETTER 2 program brings together primary care providers, policy/decision makers and researchers to work towards improving CDPS in primary care. The target patient population is adults aged 40–65. The reach, effectiveness, adoption, implementation, maintain (RE-AIM) framework will inform the evaluation of the program through qualitative and quantitative methods. A composite index will be used to quantitatively assess the effectiveness of the prevention practitioner intervention. The CDPS actions comprising the composite index include the following: process measures, referral/treatment measures, and target/change outcome measures related to cardiovascular disease, diabetes, cancer and associated lifestyle factors.integration not creation). The program evaluation is designed to provide an understanding of issues impacting the implementation of an effective approach for CDPS within primary care that may be adapted to become sustainable in the non-research setting.The BETTER 2 program is a collaborative approach grounded in practice and built from existing work (i.e.,  The purpose of this paper is to describe the planned implementation and evaluation of the Building on Existing Tools to Improve Chronic Disease Prevention and Screening in Primary Care (BETTER 2) program. We will describe the unique approach the BETTER 2 program takes to address chronic disease prevention and screening (CDPS) in primary care settings including the frameworks and evaluation metrics that will be used to measure the effectiveness of the program in achieving its goals.The paper is divided into the following three broad sections: 1) the ‘Background’ section provides an overview of CDPS in primary care and discusses the BETTER approach to CDPS and the integral role that the prevention practitioner plays in promoting CDPS. This section further reviews the effectiveness of the Building on Existing Tools to Improve Chronic Disease Prevention and Screening in Family Practice (BETTER) trial and describes the evolution of the BETTER 2 program including the specific objectives it seeks to accomplish. 2) The ‘Methods/Design’ section explains the prevention practitioner role and how the BETTER 2 program will expand and adapt to novel jurisdictions. This section further includes a description of the planned evaluation of the BETTER 2 program, which involves qualitative, quantitative, and health economic components. 3) Lastly, the ‘Discussion’ section highlights the unique approach utilized for knowledge integration and the potential outcomes of the proposed program evaluation.The prevalence of chronic disease is steadily increasing ,2. TheseThe BETTER trial, a pragmatic randomized controlled trial, previously demonstrated the effectiveness of an approach to CDPS that involved 1) a clinical working group (CWG) which identified and harmonized the evidence for CDPS actions in patients aged 40–65 .Figure 1The prevention practitioner was a new role introduced to the primary care practice setting . In the The original BETTER trial demonstrated that a prevention practitioner ‘can improve the implementation of clinically important prevention and screening for chronic diseases in a cost-effective manner’  in urbanThe overarching aims of the BETTER 2 program are to improve clinical outcomes, reduce the burden of chronic disease, and improve the sustainability of the health-care system through improved CDPS in primary care. The program targets those chronic diseases that have strong evidence for prevention and screening, specifically cancer, cardiovascular disease, diabetes, and their associated lifestyle risk factors in patients aged 40–65.To introduce the BETTER approach to new jurisdictions and deepen the impact in the two original participating provincesTo adapt, revise, and tailor the BETTER individual patient-level CDPS intervention including the survey, tools, actions, and resources for uptake in a variety of settings including remote, rural, aboriginal populations, and disadvantaged populationsTo synthesize BETTER knowledge products for translation, dissemination and exchange through the development of a web resource with updated CDPS tools, actions, and training resourcesTo evaluate the adapted BETTER intervention using a mixed methods approachSpecific objectives of the BETTER 2 program are the following:Experts in research, practice, and policy worked together to develop the BETTER 2 program with the aim of addressing CDPS from a policy and practice perspective including the needs of the end users, such as health-care professionals, a key to integrated knowledge translation –11. SincWe propose to introduce an intervention that is proactive at both the population and an individual patient-level. The patient intervention consists of a dedicated prevention visit with the prevention practitioner . Activated communities and partners will be the ones to support policies and organizational structures and engage in the BETTER 2 program.Informed, activated individuals and families through patient participation. The sites will select and identify at-risk populations such as adult patients of the participating primary care providers who are between the ages of 40 and 65, and data will be obtained from those who provide written informed consent to participate. We chose this age group because this is the age group for which most of the CDPS actions can be applied [Prepared proactive practice teams including primary care providers, researchers, and policy/decision makers who will work together with the BETTER 2 team to develop a comprehensive source of tools aimed to transform practice through a patient-level intervention by a health-care professional within the practice, the prevention practitioner.The target populations include  did not attend any visits . In our The number of patients approached to participate in the program (denominator)The number of patients who agree to participate and who follow-through to actually have a visit with the prevention practitionerThe number of primary care providers/sites approached and educated about BETTER and the proportion that have adopted the tools or adapted the prevention practitioner role into their practicesDemographic information (if available) of participating patients including ethnicity, gender, age, socioeconomic status, and primary care siteThe reach of the program will include the following:The effectiveness of the program will be evaluated using the explicitly defined summary statistics defined below as well as via the use of a more comprehensive composite index .The proportion of individual CDPS activities achieved at any point in time. We will identify specific CDPS action information that we can capture in the population at six-month intervals . We will track the proportion of patients completing these maneuvers longitudinally at each of these observation points.An evaluation of the CDPS achieved using a composite index  developeSpecifically, effectiveness measures will include the following:The composite index is comprised of process outcome measures , referral and treatment measures , and target/change outcomes . The CDPS actions in each patient eligible to complete as part of the program are determined at baseline, and the achievement of these eligible actions is assessed at follow-up. Where possible, a composite index for each patient will be calculated at baseline and then at six-month intervals. If a patient is ineligible (up to date) for all CDPS maneuvers, then his/her contribution to the aggregate composite index is non-calculable as the denominator (the number of eligible items at baseline) is zero.The composite index will allow for measures of change over time, that is, a gross indicator of effectiveness. The composite index essentially provides an average measure of effectiveness over all people enrolled in the study, and suggests the proportion of conditionally eligible maneuvers (as defined at baseline) which are achieved at follow-up. It acts to average over the heterogeneity of the patients and the maneuvers they are specifically eligible for; hence, it is referred to as a gross measure of effectiveness. For any given patient, the composite index can also act as a benchmark of how the patient is doing with respect to CDPS at any given moment in time. We will use the composite index as a comprehensive measure of CDPS to describe the proportion of eligible actions achieved at six-month intervals, and it will also allow researchers the opportunity to investigate certain comparisons of CDPS effectiveness between group factors, such as jurisdiction, age, sex, etc.Adoption will be assessed by describing the proportion, characteristics, and representativeness of Canadian settings approached and those willing to participate in the BETTER 2 program. We will describe the national/provincial/territorial characteristics of the sites, including their similarities and differences relative to other communities in a larger geographic area or region.Implementation of BETTER 2 in the diverse primary care settings will be captured by detailed description of the contextual adaptations of the program, the time required for intervention, and the costs of the intervention. In addition, the ‘Qualitative Evaluation’ section details the qualitative approach aimed to capture implementation information including process, facilitators and barriers, and contextual issues.A description of the implementation cost of the intervention in various settings including the amount of time the prevention practitioners spend with individual patients and the improvement in CDPS activities achieved by the various patient populations in the intense intervention groups.We will explore the impact of the different levels of interventions on the costs of the program and examine patterns in costs over time.An economic assessment will include the following:We will explore the extent that the program becomes part of a routine practice and policy over time including how and if the program can be delivered over the long term. Most decisions about maintaining a program are influenced not only by the overall impact of a treatment but also by its costs . The timThe proportion of primary care providers introduced to the BETTER 2 approach who use the BETTER tools/activities calculated at three time points  with participants (as identified above) by a trained researcher at approximately three time periods throughout the project   national and provincial (macro), 2) regional or program (meso), and 3) site or clinic (micro) levels . There aThe TDF is a comprehensive framework that includes all of the important constructs of implementation ,29. SincThe BETTER 2 is a program evaluation and not a research trial. The BETTER 2 program has received approval for the various research components of the program evaluation through the University of Alberta, and St. John’s Newfoundland ethics boards. The program also has a scientific research license with the Northwest Territories. The registration number of the original RCT BETTER trial was ISRCTN07170460.The prevalence of chronic disease is steadily increasing ,2, and tOur ultimate long-term outcomes are improved clinical outcomes and reduced burden of chronic disease through improved early detection of cancers ; lower incidence of cancers through modification of lifestyle ; and reduction of chronic disease through prevention . Cost-effective analyses of the original BETTER trial demonstrated that a prevention practitioner ‘can improve the implementation of clinically important prevention and screening for chronic diseases in a cost-effective manner’ . We expe


Lupus is a chronic disease for which health education is an integral part of the treatment. Health education consists in teaching the patients how to manage everyday life with their illness.This study aimed to evaluate the benefits of health education programs among patients with lupus.This experimental study was conducted by two doctors experienced in health education programs, one intern in paediatrics, and two trainee nurses. The health education program was offered to ten young women suffering from systemic lupus erythematosus, aged between 13 and 17 years old. It did not receive any financial support or benefit from additional human resources, and relied exclusively on the internal organization within the board and among the research team members.Seven of the ten women involved accepted to be part of the program. Every patient was first administered an educational assessment test, that was meant to evaluate the patients' already acquired skills as well as their expectations from the teaching. The program was designed to develop such skills as a better knowledge of the disease, adequate self-care and other psychosocial skills To this day, two group sessions have already taken place and it has been our main concern to build them along with the patients. These sessions, of a duration of 2 hours and 45 minutes each, were scheduled some time away from the assessment session. Some educational tools were created during the sessions . Additional tools are in the process of being built. They include a tryptic about the disease , which is intended to the use of other young patients.The patients' satisfaction with the program was assessed individually and collectively.This experimental program will be running until the end of 2014. It has been submitted to the Regional Health Agency for accreditation.By revealing the patients' demand for and satisfaction with the proposed health education sessions, this study has greatly encouraged us to continue this experimental health education program, which will soon be extendedNone declared.