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Ethics approval | This study was performed in line with the principles of the Declaration of Helsinki and approved by the Human Research Ethics Committees at Edith Cowan University (Ref. no. 2019-00311-SCHOFIELD, 07/05/2019), Sir Charles Gairdner Hospital (Ref. no. 03094, 20/12/2019), and St John of God Hospital, Subiaco (Ref. no. 1516, 12/04/2019). | PMC10132425 | ||
Consent to participate | All participants provided written informed consent. | PMC10132425 | ||
Competing interests | The authors declare no competing interests. | PMC10132425 | ||
References | PMC10132425 | |||
2. Materials and Methods | This pilot study used a prospective descriptive design. Institutional review board approval was obtained from the University. Study inclusion criteria for women are (1) between the ages of 18 and 42, (2) menstrual cycle length of 21 to 42 days, (3) had not used depot medroxyprogesterone acetate over the past 12 months, (4) had no history of oral or subdermal contraceptives for the past 3 months, (5) had at least three cycles past breastfeeding weaning, and (6) have no known fertility problems.The study participants were recruited through email using the Marquette NFP teacher network throughout North America. The teachers shared the study information with their clients, and the interested clients contacted the researchers. Women were pre-screened with questions based on the inclusion criteria using a simple survey. Thirty participants were recruited by this snowball method. After receiving informed consent, the participant was assigned a study ID and then randomized 1:1 into either the Premom LH test group or the EAH LH test group. All study participants were currently using the CBFM to track their ovulation to avoid pregnancy. Each Premom LH test group participant was provided with 60 Premom LH test strips and each EAH LH test group participant was provided 60 EAH LH test strips. Information was provided to the participants on how to download and use the Premom Ovulation Tracker app and urine testing. Participants were instructed to start testing their first morning urine void on day six of their menstrual cycle. They continued morning urine testing for 20 days with each menstrual cycle and for a total of three cycles. On the test morning, urine was collected in a clean container and the test strips from each monitoring system were placed in the urine for 15s consecutively (i.e., first the CBFM test strip and then right after the Premom or EAH test strip based on the group allocation). The participants recorded the low, high, and peak results for the CFBM and the quantitative results for the Premom LH test strip or the ratio result from the EAH LH test strip on their charting sheet.At the end of the third menstrual cycle, the participants completed a survey to evaluate user acceptability and satisfaction using the electronic fertility monitor. The survey was developed by Severy et al. [Descriptive statistics were calculated for participant demographics and the characteristics of the menstrual cycles. Independent t-tests were used to determine if there were differences between the Premom LH and EAH LH groups. Pearson correlation analysis was used to assess the correlation of the Premom peak LH level with the first CBFM peak day and the correlation of the EAH peak LH level with the first CBFM peak day. Paired t-tests were used to compare differences in ease of use and satisfaction for the CBFM, the Premom LH, or the EAH LH testing. | PMC9960263 | ||
3. Results | PMC9960263 | |||
3.1. Participant Demographics | A total of 30 women consented to participate. The mean age was 33.47 (range 26–42, SD 4.76). All participants were Caucasian and had some college education. All 30 participants initiated the testing and charting. One participant achieved a desired pregnancy after the first cycle and withdrew from the study. The participant did complete the satisfaction survey and agreed that her first cycle data and survey data could be used for the study. The demographics of the 30 participants can be found in | PMC9960263 | ||
3.2. Cycle Data and Ovulation Detection | The 30 participants contributed 84 menstrual cycles. The average cycle length was 28.82 days (range 24–38, SD 3.62). The LH surge was detected in 94% of the cycles by the CBFM, 82% by the Premom LH test strips, and 95% by EAH LH test strips. The correlation of the Premom peak day with the CBFM first peak was r = 0.99; The estimated day of ovulation for the CBFM was defined as the second peak day on the CBFM. | PMC9960263 | ||
3.3. User Satisfaction and Ease-of-Use Rating | Overall, the participants reported significantly higher satisfaction and ease-of-use ratings for the CBFM compared to the Premom LH testing or the EAH LH testing. For the Premom LH group, the mean score for the CBFM was 51.29 (SD 4.56) vs. 43.07 (SD 11.82) for the Premom testing (t = 2.66; df = 13; When comparing the individual satisfaction and ease-of-use items, the only items that were significantly different for the Premom LH group were Item 5, “To what extent, if at all, do you think using the NFP fertility monitoring system has increased your ability to avoid pregnancy?” and Item 8, “At this point in time, how do you like the fertility monitoring system?” (Qualitative feedback from the participants showed mixed feelings toward the Premom Ovulation Tracker app and its LH testing kits. One study participant shared, “I really like the premom numeric rating given to the picture of the test, it makes interpretation so much easier. My only frustration was that sometimes it was difficult to get a good picture due to an old phone and poor light.” Another participant stated, “Overall, I found the system easy to use and read, but I dislike having to time the test myself. The Clearblue monitor is much easier to use because I can walk away while the test is reading. Much easier for busy mornings.” | PMC9960263 | ||
4. Discussion | In the 1980s, scientists determined that one of the best predictors of the beginning of the fertile phase was the rise in estrogen and the urine metabolite E3G [Both monitoring systems are intended for women who want to become pregnant and target the most fertile days of the menstrual cycle. Premom and EAH LH testing have a tighter estimate of two days. The Premom and EAH LH peak days correlate strongly with the peak days of the CBFM. These two days have the greatest probability of fertility during the six-day fertile window [Although we hypothesized that there would be no differences in the satisfaction and ease of use of the compared fertility monitoring systems, the two LH monitoring systems (i.e., Premom and EAH) had significantly lower scores. The lower scores make sense since all the participants had been using the CBFM for family planning purposes and were familiar with its use. The CBFMs were their personal use monitors, whereas the Premom and EAH LH testing were new to them. A fairer comparison of the CBFM with the Premom and EAH for ease of use and satisfaction would be to randomize women users who have not used either system into a CBFM, Premom, and EAH group. Since we only used current CBFM users, there most likely was an obvious bias to that system.More women are interested in utilizing mobile health tools and apps in their fertility and reproductive health self-care [ | PMC9960263 | ||
5. Conclusions | There is a boom in reproductive mobile health monitoring systems and more women are exploring these options for fertility monitoring or family planning purposes [ | PMC9960263 | ||
Author Contributions | Conceptualization, Q.M. and R.J.F.; methodology, Q.M. and R.J.F.; software, Q.M.; formal analysis, Q.M. and R.J.F.; data curation, Q.M.; writing—original draft preparation, Q.M.; writing—review and editing, Q.M. and R.J.F. All authors have read and agreed to the published version of the manuscript. | PMC9960263 | ||
Institutional Review Board Statement | The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Marquette University on January 28, 2020 (Approval number: HR-3553). | PMC9960263 | ||
Informed Consent Statement | Informed consent was obtained from all subjects involved in the study. | PMC9960263 | ||
Data Availability Statement | Not applicable. | PMC9960263 | ||
Conflicts of Interest | The authors declare no conflict of interest. | PMC9960263 | ||
References | Comparison of the demographic parameters between the two study groups.Abbreviations: BMI, body mass index; LH, luteinizing hormone.Menstrual cycle parameters for the two study groups based on the CBFM.Abbreviations: CBFM, Clearblue Fertility Monitor; LH, luteinizing hormone.Premom quantitative LH levels in relation to the pre- and post-second CBFM peak day.Abbreviations: CBFM, Clearblue Fertility Monitor. Note: second CBFM peak day is defined as the estimated day of ovulation.Easy@Home ratio LH levels in relation to the pre- and post-second CBFM peak day.Abbreviations: CBFM, Clearblue Fertility Monitor. Note: second CBFM peak day is defined as the estimated day of ovulation.Ease of use and satisfaction for Premom LH testing versus CBFM.Abbreviations: LH, luteinizing hormone; CBFM, Clearblue Fertility Monitor.Ease of use and satisfaction for Easy@Home LH monitoring versus CBFM.Abbreviations: LH, luteinizing hormone; CBFM, Clearblue Fertility Monitor. | PMC9960263 | ||
Background | While audit & feedback (A&F) is an effective implementation intervention, the design elements which maximize effectiveness are unclear. Partnering with a healthcare quality advisory organization already delivering feedback, we conducted a pragmatic, 2 × 2 factorial, cluster-randomized trial to test the impact of variations in two factors: (A) the benchmark used for comparison and (B) information framing. An embedded process evaluation explored hypothesized mechanisms of effect. | PMC10173488 | ||
Methods | SECONDARY | Eligible physicians worked in nursing homes in Ontario, Canada, and had voluntarily signed up to receive the report. Groups of nursing homes sharing physicians were randomized to (A) physicians’ individual prescribing rates compared to top-performing peers (the top quartile) or the provincial median and (B) risk-framed information (reporting the number of patients prescribed high-risk medication) or benefit-framed information (reporting the number of patients not prescribed). We hypothesized that the top quartile comparator and risk-framing would lead to greater practice improvements. The primary outcome was the mean number of central nervous system-active medications per resident per month. Primary analyses compared the four arms at 6 months post-intervention. Factorial analyses were secondary. The process evaluation comprised a follow-up questionnaire and semi-structured interviews. | PMC10173488 | |
Results | Two hundred sixty-seven physicians (152 clusters) were randomized: 67 to arm 1 (median benchmark, benefit framing), 65 to arm 2 (top quartile benchmark, benefit framing), 75 to arm 3 (median benchmark, risk framing), and 60 to arm 4 (top quartile benchmark, risk framing). There were no significant differences in the primary outcome across arms or for each factor. However, engagement was low (27–31% of physicians across arms downloaded the report). The process evaluation indicated that both factors minimally impacted the proposed mechanisms. However, risk-framed feedback was perceived as more actionable and more compatible with current workflows, whilst a higher target might encourage behaviour change when physicians identified with the comparator. | PMC10173488 | ||
Conclusions | Risk framing and a top quartile comparator have the potential to achieve change. Further work to establish the strategies most likely to enhance A&F engagement, particularly with physicians who may be most likely to benefit from feedback, is required to support meaningfully addressing intricate research questions concerning the design of A&F. | PMC10173488 | ||
Trial registration | ClinicalTrials.gov, | PMC10173488 | ||
Supplementary Information | The online version contains supplementary material available at 10.1186/s13012-023-01271-6. | PMC10173488 | ||
Keywords |
Head-to-head trials investigating ways to optimize audit and feedback impact are lacking but feasible within organizations already delivering feedbackThis head-to-head trial was unable to fully determine the effect of theory- and evidence-based variations in (i) the benchmark used for comparison (median vs. top quartile) and (ii) information framing (risk vs. benefit-framing), due to a lack of physician engagementThe process evaluation indicated that emphasizing the risk of patient harms and using a benchmark more closely aligned with high-quality care is worth further exploration in contexts with high physician engagementThis work demonstrates a successful partnership between researchers and health system stakeholders already delivering feedback at scale | PMC10173488 | ||
Background | ’ | Audit and Feedback (A&F) involves measuring a provider’s practice, comparing it to a benchmark, and relaying this information back to the provider to encourage change [In A&F reports, provider data are often compared to an average: for example, the median prescribing rate for physicians is a specific region. However, a higher benchmark might better represent high-quality care, and the level of the benchmark may impact motivation. Goal-Setting Theory predicts that setting specific goals, which are difficult but achievable, will have a greater impact on behaviour by increasing the effort made towards achieving the goal [The impact of information framing on providers’ and patients’ health-related decision-making and behavior has a long history of study [A&F comparative effectiveness research may be best achieved by implementation scientists partnering with organizations already delivering A&F to create “implementation science laboratories” [The A&F intervention we evaluated aimed to support the appropriate prescribing of high-risk medications in nursing homes. Almost half of nursing home residents receive potentially inappropriate medications [ | PMC10173488 | |
Methods | Our methods are described in detail in the published protocol [ | PMC10173488 | ||
Trial design | This was a 2 × 2 factorial, pragmatic, cluster-randomized trial with an embedded process evaluation. The trial is registered on ClinicalTrials.gov (NLM identifier: NCT02979964). | PMC10173488 | ||
Setting | This trial took place in the province of Ontario, Canada. Ontario Health (OH—formerly Health Quality Ontario at the time of the study), the provincial advisor on quality in healthcare, supports quality improvement through various initiatives. One such initiative is their “Practice Reports,” whereby confidential, aggregate feedback is offered to physicians across the province, combined with | PMC10173488 | ||
Participants | Eligible physicians were those working in the nursing home sector in Ontario who had (i) voluntarily signed up to receive their report prior to randomization and (ii) consistently had > 5 residents that they cared for in the nursing home setting (to allow for adequate data capture). The participating research ethics boards approved a waiver of consent with the provision of opt-out opportunities. | PMC10173488 | ||
Interventions and mechanisms of action | Full details of the history of the report and its re-design in preparation for this trial were reported previously [ | PMC10173488 | ||
Manipulated feature 1—The benchmark | Previously, the report compared physicians’ data to provincial and regional averages. OH felt that a benchmark of the top 10% of peers used in previous research [ | PMC10173488 | ||
Manipulated feature 2—Information framing | We developed a “risk-framed” and a “benefit-framed” version of the report. The risk-framed version focused on the proportion of residents prescribed high-risk medication. Risk-framing was presented visually (a graph demonstrating the percentage of patients at risk, with red colouring), and in text form (“Thus, four variants of the report were developed (excerpts included in Additional file Proposed theory-informed mechanisms of action of the two factors varied in the Audit & Feedback report | PMC10173488 | ||
Outcomes | SECONDARY | The primary outcome was the mean number of central nervous system (CNS)-active medications per resident per month, with the primary endpoint for analysis being 6 months post-intervention. CNS-active medications included antipsychotics, opioids, benzodiazepines, and antidepressants (including tricyclic antidepressants and trazodone), consistent with the indicator used in the OH report. We selected this as the primary outcome to enable us to capture any prescribing changes directly influenced by the report indicators. We planned to assess antipsychotic and benzodiazepine prescriptions as secondary outcomes, as well as statin prescriptions (as a non-targeted control or “tracer outcome” [ | PMC10173488 | |
Data collection | depression, pain | In this pragmatic trial, we used provincial health administrative data to assess baseline characteristics and outcomes. Data were compiled from (1) the Ontario Drug Benefits database, which covers nearly all prescriptions in nursing homes; (2) the Canadian Institute for Health Information databases covering all inpatient hospitalizations and emergency department visits; (3) the Ontario Health Insurance Plan database, covering physician billings; (4) the Registered Persons Database covering demographic information; and (5) the Continuing Care Reporting System database for clinical and demographic information on nursing home residents collected using the Resident Assessment Instrument (RAI). A full RAI assessment completed by nursing home staff is legislatively mandated within 14 days of admission and updated annually or with a change in status; a quarterly RAI assessment is required every 92 days. RAI data were used to identify dates of admission and discharge to define the appropriate set of residents contributing to each time period. For each 3-month period under investigation, residents were assigned to a most responsible physician according to previously defined algorithms [We used the RAI for demographic and clinical characteristics of residents, including clinical assessment scores (e.g., function scale, pain scale, depression rating score, aggressive behaviour score). We used Ontario Health Insurance Plan data to determine whether residents had a specialist consultation in the prior year by a geriatrician or psychiatrist. We used the Canadian Institute for Health Information datasets to assess whether residents had an emergency department visit in the prior year (using the National Ambulatory Care Reporting System database) and whether residents had a hospital admission in the prior year (using the Discharge Abstract Database). These databases provide complete population-level data for the variables of interest. | PMC10173488 | |
Randomization | To prevent contamination due to physicians working across multiple homes, the unit of randomization was groups of one or more nursing homes sharing physicians. All eligible physicians were included in the clusters. An independent statistician randomized these clusters independently to the two factors (resulting in four experimental conditions), stratifying by a total number of nursing home beds in the cluster [ | PMC10173488 | ||
Sample size | We anticipated having approximately 160 clusters, with an average of 350 beds per cluster. In a 2 × 2 factorial design assuming no interaction and similar effects for each factor, a test of each intervention at 6 months in an ANCOVA design would achieve 90% power to detect an absolute mean difference of 0.3 in the primary outcome (i.e., a difference in the mean number of CNS-active medications per month of 3 versus 2.7). Based on previous data, we assumed a standard deviation of 4, an intracluster correlation coefficient of 0.05, a cluster autocorrelation of 0.8, and an individual autocorrelation of 0.9 [ | PMC10173488 | ||
Blinding | BLIND | Participants were not explicitly blinded, but the risks of this were felt to be minimal, given that the physician were not aware of the variations being tested nor the outcome measures. The analysts were blind to allocation status. | PMC10173488 | |
Data analysis | REGRESSION, SECONDARY | Descriptive characteristics of nursing home residents included variables assessed as part of the RAI assessment: therefore, only those residents for whom a recent RAI assessment had been completed were included in the analysis of resident characteristics at baseline. Primary outcome analyses included the broader population of included physicians’ residents. All primary analyses were by intention-to-treat and compared the four arms. The primary outcome was analyzed using a general linear mixed effects regression model; time was specified as a continuous variable, and a common secular trend was imposed across all study arms with the effect of the intervention modelled as a slope deviation from the trend. The analysis adjusted for the size of each home (number of beds) as a fixed effect. A random intercept and slope for time were specified for the unit of randomization (group of homes). The primary comparison between the arms at 6 months post-intervention was estimated using least square mean differences, together with 95% confidence intervals. Factorial analyses were conducted as a secondary analysis because the traditional approach (i.e., an interaction test followed by dropping the interaction term if non-significant) can lead to bias in factorial trials [ | PMC10173488 | |
Process evaluation | Physicians who downloaded their report were sent an email invitation to complete a questionnaire which assessed the proposed mechanisms of action outlined in Fig. Questionnaire participants who indicated interest were invited to take part in a telephone interview. The interview topic guide focused on report use and ideas for improvement; prioritization of behavior change in relation to the prescribing indicators in the report; and the hypothesized mechanisms of action. Interviews were audio-recorded, then transcribed verbatim by an external third party. Analysis was conducted in NVIVO 10 and informed by the framework analysis method [ | PMC10173488 | ||
Results | PMC10173488 | |||
Recruitment | Cluster and participant flow through the study is presented in Additional file | PMC10173488 | ||
Report engagement | Of the 266 physicians analyzed, 76 (28.6%) in 60 clusters downloaded their report at the December 2016 release (19 (28.4%) physicians (13 clusters) in arm 1, 17 (26.6%) physicians (15 clusters) in arm 2, 23 (30.7%) physicians (16 clusters) in arm 3, and 17 (28.3%) physicians (16 clusters) in arm 4). | PMC10173488 | ||
Baseline resident characteristics | Baseline characteristics of nursing home residents for whom a recent RAI assessment had been completed in each arm and overall (December 1, 2016) are summarized in Table Baseline resident characteristicsAnalyses of baseline characteristics restricted to nursing home residents for whom a recent RAI assessment had been completed | PMC10173488 | ||
Effects of A&F variants on monthly number of CNS-active medications prescribed | REGRESSION | Primary outcome analyses included the broader population of included physicians’ residents (i.e., not only those with a recent RAI assessment). The mean number of CNS-active medications prescribed per resident at the baseline month (December 1, 2016) and at the last follow-up time point (July 1, 2017) are displayed in Table CNS-active medications per resident at baseline and follow-upThe general linear mixed effects regression model indicated there were no significant deviations from the secular trend in the monthly mean number of CNS-active medications in any of the arms (Table Results of general linear mixed effects regression model predicting deviations from the secular trend in the monthly mean number of CNS-active medicationsMean number of CNS-active medications prescribed across trial arms at 6 months post-intervention: pairwise comparisons | PMC10173488 | |
Participants’ intentions to adjust their prescribing were high before receiving the report | All participants highlighted their pre-existing intention to review medications and appropriately adjust their prescribing. Some also acknowledged that the report may have enhanced this. This corresponded to the CFIR domain “Yeah so whether I look at this report or not I know for myself that I need to minimize the use of antipsychotic drugs, that’s there all the time.” (LTC4, risk, median)“I think the idea of the report is for me a really good one because I think it’s just, this is your performance and, you know, can you do better? Like it sort of makes you look at it and think yeah can I do better… is there 1 or 2 people that I can get off these medications?” (LTC5, benefit, top quartile) | PMC10173488 | ||
A comparator representing a higher target has the potential to influence prescribing behaviour change, if physicians identify with it | Participants indicated that comparing their performance to others was a key motivation for using the report. Participants who received the median comparator and those that received the top quartile comparator (a higher target) indicated that they aimed to achieve similar prescribing rates to the comparator. It also appeared that efforts to adjust prescribing were reduced when the comparator was reached or was close: this indicates a potential coasting effect (if physicians are close to the comparator, they may not prioritise it).
“When I’m at the 75th percentile or better, you know, I maybe don’t put as much emphasis on it” (LTC1, benefit, top quartile) “The useful information for me is that either I am using less or I’m using the same as others in the, in Ontario… that’s good enough” (LTC4, risk, median) However, problems with identification with the comparator may negatively impact this. Participants emphasised that their prescribing rates should be considered in the context of the behavioural profile of their residents and that generalized comparators are not always appropriate. Participants discussed at length how their prescribing rates are reflective of the interaction between individual patient characteristics and the facility in which they reside. Various alternative comparators were suggested: for example, comparators based on the proportion of residents with certain cognitive/behavioural scale scores, or similar units (such as the presence of a secure unit). This corresponded to the CFIR domain “My ratio of aggressive behaviours double everybody else’s… so my antipsychotic use is a little higher, which isn’t surprising… Then the comments are, how do you de-prescribe? Well you know what I have a different unit is what my answer is… you can’t rate a percentage of antipsychotic use unless you’re looking at the population I’m dealing with.” (LTC2, benefit, top quartile) | PMC10173488 | ||
Benefit-framed feedback is not immediately actionable and impedes report usability | Those receiving the benefit-framed report were vocal about the framing and found it difficult and time-consuming to visualize and interpret their data. They preferred a risk-framed report as this format matches other reports they receive and is viewed as more practical. Benefit framing therefore appears to decrease report usability. This corresponded to the CFIR domain “So how many of my residents are safe from the risks of falls associated with benzos? …you have to think about it a little bit more… if my percentage is lower that’s not good… I almost prefer the other way… because that’s the way it’s reported in our PAC meetings and it’s reported in CIHI that way… I think the negative has more impact… it’s a little bit easier to visualize.” (LTC5, benefit, top quartile) | PMC10173488 | ||
Indicator selection may have hindered behaviour change efforts | Participants stated that of the three indicators, responding to the antipsychotic medication indicator by appropriately adjusting their prescribing was more of a priority than responding to the benzodiazepine indicator. Prioritisation of the “three or more specified CNS-active medications” indicator (i.e., the trial primary outcome) was rarely discussed. Participants expressed challenges with interpreting this indicator, specifically with identifying which medications were included. This precluded their ability to make sense of the data in order to influence behaviour change. This corresponded to CFIR domains “The 3 or more specified I have to admit I don’t know where it’s specified. I don’t know which drugs they’re talking about.” (LTC2, benefit, top quartile) | PMC10173488 | ||
Physicians value the report and suggested enhancements to help them monitor and discuss progress on improvement efforts at specific facilities | dementia | Participants noted that in summarizing prescribing data over a period of time, the report provides data not otherwise available and complements individual resident data already available. Some noted that the report informs discussions with other nursing home team members by “armoring” them with information. However, all participants practiced in more than one nursing home, and it was felt that discussions would be better facilitated if the report included data for all the participants’ facilities separately. This would allow for tailoring of prescribing adjustment efforts to different facilities (for example, taking the presence of a locked unit into account), and therefore enhance their ability to monitor progress with these efforts, and to apply the lessons learned and progress made at one facility to another. This corresponded to CFIR domains
“Well I think it gives me some ammunition… the reports let me know what’s going on, how I’m comparing with the community and, sometimes pharmacists come up with ideas that are based on statistics… I like to have my own statistics… it’s a way to stimulate discussion.” (LTC3, risk, median)
“We have a protocol at (PRACTICE 2) where we are actually trying to discontinue or decrease the use of antipsychotic drugs in dementia patients… And I would love to know what my practice is there… But I don’t have that information… I could show them at (PRACTICE 2) that is why I’m using less there, because of the process we have in place.” (LTC4, risk, median) | PMC10173488 | |
Discussion | PMC10173488 | |||
Summary of findings | We investigated the impact of variations in the benchmark used for comparison, and in information framing, on the effectiveness of A&F. In accordance with theory and evidence, we hypothesized that greater improvements in practice would be achieved when feedback recipients were compared to the top quartile rather than the median of their peers and when information was risk-framed rather than benefit-framed. There were no significant differences in the monthly mean number of CNS-active medications prescribed per resident over 6 months pre- and post-intervention in any of the four arms, or between arms at 6 months post-intervention. However, engagement with the report amongst those who signed up was poor, such that fewer physicians than anticipated were exposed to the design feature variations. In addition, the mean number of CNS-active medications was relatively low across all arms at baseline (ranging from 1.1 to 1.3), indicating that there was little room for improvement in the primary outcome. As a result, we did not proceed with some of the planned analyses, including the economic evaluation.Importantly, the process evaluation revealed that both factors minimally impacted the proposed underlying mechanisms, which may also help to explain the lack of effects. However, benefit-framed feedback was not perceived as actionable, and physicians described aiming to align their practice with the top quartile, indicating that risk framing and a top quartile comparator still have the potential to achieve change. | PMC10173488 | ||
Interpretation and implications for the design and delivery of A&F | fits | Around 35% of eligible physicians signed up for the report. However, under 30% of the physicians who signed up subsequently downloaded their reports. We therefore could not answer our relatively intricate research questions due to a lack of engagement. Previous work with this report showed that antipsychotic prescribing was reduced only for those who signed up for and downloaded their report [Further research to establish strategies most likely to enhance engagement, particularly with physicians who may be most likely to benefit from feedback, would advance the science and practice of A&F. Clinical Performance Feedback Intervention Theory (CP-FIT) proposes that “pushing” A&F to providers rather than requiring the “pulling” of A&F, clearly demonstrating the potential benefits of the A&F, and targeting providers with positive attitudes to feedback all serve to enhance engagement and thereby A&F effectiveness [It is also worth noting that whilst most participants did not download their report, they did demonstrate some initial engagement by voluntarily signing up for the report in the first place. Whilst the strategies suggested above may help to sustain their engagement, there may also be other factors making it difficult for them to integrate the use of A&F into their day-to-day practice. In this case, it may be worthwhile to investigate the impact of supportive goal setting, action planning, problem-solving, and habit formation strategies designed to encourage continued review and use of feedback. In sum, future research should focus on evaluating the effectiveness of a range of strategies aiming to enhance both initial and sustained engagement with A&F interventions. In accordance with Goal Setting Theory [A key hypothesis of CP-FIT is that feedback is more effective when it fits alongside existing ways of working [The process evaluation also indicated that both factors minimally impacted our proposed mechanisms of effect. Most participants across all groups intended to adjust their prescribing, were confident in doing so, prioritized adjusting their prescribing, and believed that doing so would avoid unnecessary risks to their residents’ health. This may indicate that these constructs were not the key barriers preventing change [Since this trial, OH has updated the design and delivery of its report. The report is now directly emailed to physicians. The information is risk-framed, focusing on the number of residents prescribed high-risk medications. The median prescribing rate is provided as the comparator, and in addition, physicians’ prescribing rates are described in reference to their peers. Specifically, prescribing rates higher than the 60th percentile are noted and highlighted in red; prescribing rates between the 25th and 60th percentiles are noted and highlighted in yellow and prescribing rates lower than the 75th percentile are noted and highlighted in green. Prescribing rates are reported overall and for each home a physician works in. The CNS-active medications indicator is still included but no longer features on the summary page as a “headline” indicator. The impact of the updated report on prescribing rates will be the subject of future work. | PMC10173488 | |
Strengths and limitations | This trial demonstrates a successful partnership between researchers and health systems stakeholders to pragmatically investigate the impact of variations in the design of A&F focused on a topic of clinical importance in our context and already delivered at scale. This is the first in a series of studies aimed at optimizing A&F being conducted within the Ontario Health Implementation Laboratory (OHIL) which is also part of the international Audit and Feedback MetaLab initiative aiming to develop a more cumulative science to better inform A&F practice [ | PMC10173488 | ||
Conclusions | fits | This head-to-head trial of A&F delivered at scale found no impact of variations in either the benchmark used for comparison, or the framing of information, on physician prescribing of CNS-active medications to nursing home residents. However, we could not fully answer our research questions due to a lack of engagement with the report. The process evaluation indicated that a comparator representing a higher target can encourage behaviour change if physicians identify with it. In addition, feedback framed to emphasize the potential risk to patients is more actionable and more compatible with current workflows. Those designing and delivering A&F should consider the actionability of their indicators, how their report fits with current workflows, and the use of a comparator which may more likely represent a difficult but achievable goal. A&F researchers should explore the impact on A&F effectiveness of strategies for enhancing engagement, different types of tailored comparators, and co-interventions to support behavior change within healthcare teams. | PMC10173488 | |
Acknowledgements | We are grateful to all of the participants for taking part in this research. We would also like to thank the staff at Ontario Health Quality for their work on the development and implementation of the intervention and Yingbo Na for running the trial analysis. | PMC10173488 | ||
Authors’ contributions | JP | NMI led the development of the protocol, obtained the funding, led the development of the study design, and contributed to all other aspects of the study. NMc contributed to the process evaluation methods; led the process evaluation data collection, analysis, and interpretation; contributed to the interpretation of the trial results; and drafted the manuscript. LD and JP contributed to the design of the intervention, developed the process evaluation, and contributed to process evaluation data analysis and results interpretation. HOW contributed to the design of the intervention and developed the process evaluation. MT developed the trial analysis plan and contributed to the trial data analysis. CR contributed to the process evaluation design and interpretation of process evaluation results and supported the trial data analysis. SL contributed to process evaluation data analysis and interpretation of process evaluation results. KT developed the economic evaluation plan. GD, CLM, and JMCL play key roles in the Practice Reports program at OH, and for this study contributed to the design of the benchmark and information framing variations, advised on aspects of the study design, oversaw report delivery, tracked numbers randomized, and facilitated the data acquisition. JG contributed to all aspects of the study design. The authors reviewed the manuscript draft and read and approved the final manuscript. | PMC10173488 | |
Funding | This study was funded by the Ontario SPOR SUPPORT Unit, which is supported by the Canadian Institutes of Health Research and the Province of Ontario. The funding source had no role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication. HOW is supported by a Research Scholar Junior 2 Career Development award from the Fonds de Recherché du Québec—Santé and a Canada Research Chair (Tier 2) in Human-Centred Digital Health. JMG is supported by a Tier 1 Canada Research Chair in Health Knowledge Transfer and Uptake and a CIHR Foundation grant. NMI is supported by a Canada Research Chair (Tier 2) in the Implementation of Evidence-Based Practice, and a Clinician Scholar Award from the University of Toronto Department of Family and Community Medicine. | PMC10173488 | ||
Availability of data and materials | Trial data access is governed by the policies at ICES. Process evaluation data are available from the corresponding author on reasonable request. | PMC10173488 | ||
Declarations | PMC10173488 | |||
Ethics approval and consent to participate | Ethics approval was granted by the University of Toronto Research Ethics Board (#00032455) and the Women’s College Hospital Research Ethics Board (#2016–0122-E) for the trial and the Ottawa Health Science Network Research Ethics Board (#20160934-01H) for the process evaluation. Ethics approval included approval of a departure from the general principles of consent, in a manner concordant with the minimal risks involved. Further rationale for this is provided in the published trial protocol. For physicians who had already consented to receive the audit and feedback reports, upon their usual sign-in to download their report, a notice explaining the general goals of this program appeared, with an invitation to seek further information by contacting the program team. This met the Canadian Tri-Council Policy Statement-2 on Ethical Conduct for Research Involving Humans requirements for departures from the general principles of consent procedures and is aligned with the Ottawa Guidance on Ethics for Cluster Trials. Process evaluation participants were informed that by responding that they would like to participate in the process evaluation, they were giving their consent for participation, as approved in our original protocol. | PMC10173488 | ||
Consent for publication | Not applicable. | PMC10173488 | ||
Competing interests | JP | JP is an Associate Editor of Implementation Science, and JMG and NMI are members of the editorial board. They were not involved in any decisions made about this manuscript. The authors declare that they have no other competing interests. | PMC10173488 | |
References | PMC10173488 | |||
Background: | RLS, neurological disease | RESTLESS LEGS SYNDROME (RLS), NEUROLOGICAL DISEASE | Restless legs syndrome (RLS) is a common neurological disease that has a significant impact on daily activities and quality of life, for which there is often no satisfactory therapy. Complementary medicine, such as acupressure and hydrotherapy, is used to treat patients with RLS; however, the clinical evidence is unclear. This study aims to investigate the effects and feasibility of self-administered hydrotherapy and acupressure in patients with RLS. | PMC10313283 |
Methods: | Anxiety, restless-legs syndrome, RLS-severity, RLS, Depression | DISEASE, COLD | This is a randomized, controlled, open-label, exploratory, clinical study with 3 parallel arms, comparing both self-applied hydrotherapy (according to the German non-medical naturopath Sebastian Kneipp) and acupressure in addition to routine care in comparison to routine care alone (waiting list control) in patients with RLS. Fifty-one patients with at least moderate restless-legs syndrome will be randomized. Patients in the hydrotherapy group will be trained in the self-application of cold knee/lower leg affusions twice daily for 6 weeks. The acupressure group will be trained in the self-application of 6-point-acupressure therapy once daily for 6 weeks. Both interventions take approximately 20 minutes daily. The 6-week mandatory study intervention phase, which is in addition to the patient preexisting routine care treatment, is followed by a 6-week follow-up phase with optional interventions. The waitlist group will not receive any study intervention in addition to their routine care before the end of week 12. Outcome parameters including RLS-severity, disease and health-related quality of life (RLS-QoL, SF-12), Hospital Anxiety and Depression Score in German version, general self-efficacy scale, and study intervention safety will be measured at baseline and after 6 and 12 weeks. The statistical analyses will be descriptive and exploratory. | PMC10313283 |
Conclusion: | RLS | In the case of clinically relevant therapeutic effects, feasibility, and therapeutic safety, the results will be the basis for planning a future confirmatory randomized trial and for helping to develop further RLS self-treatment concepts. | PMC10313283 | |
1. Introduction | PMC10313283 | |||
1.1. Background and rationale | loss of social networks, comorbidity, Willis-Ekbom disease, WED, anxiety, sleep disorders, RLS/WED, neurological disorder, inflammation, autoimmune diseases, metabolic diseases, depressive, polyneuropathy, RLS-, RLS, depression, cardiac and renal diseases, sleep-related | DISORDER, WILLIS-EKBOM DISEASE, NEUROLOGICAL DISORDER, PARKINSON DISEASE, DIABETES MELLITUS, AUTOIMMUNE DISEASES, NEURODEGENERATIVE DISEASES, INFLAMMATION, DISEASE, METABOLIC DISEASES, DISORDERS, RESTLESS LEGS SYNDROME (RLS), WED, IRON DEFICIENCY, MULTIPLE SCLEROSIS, POLYNEUROPATHY, COLD, DISEASES | Restless legs syndrome (RLS), also known as Willis-Ekbom disease (WED), is a common neurological disorder characterized by agonizing sensations in the legs, occurrence at rest, and compelling urge to move the legs (or arms or sometimes other body parts).RLS symptoms of any frequency and severity occur in 5% to 10% of the general population in Western industrialized countries.RLS affects and interacts with many comorbidities, and may lead to missed work, loss of social networks, and even early retirement.Patients with RLS also show more anxiety and depression symptoms, psychopathological symptoms, and lower well-being than control subjects without sleep disorders. There is a relationship between RLS severity, anxiety, and depressive symptoms. Under psychological stress, RLS/WED symptoms are more severe.RLS is a complex disorder in which predisposing genetic factors, environmental factors, and comorbidities contribute to the expression of the disorder. Patients with a high burden of comorbidity have a consistently higher prevalence of RLS than healthy individuals do. Additionally, an increasing number of publications have reported associations between RLS and multiple diseases, including metabolic diseases (diabetes mellitus and iron deficiency), cardiac and renal diseases, autoimmune diseases (e.g., multiple sclerosis), polyneuropathy, neurodegenerative diseases (e.g., Parkinson disease), and illnesses associated with inflammation and depression.In many cases, RLS remains incurable; therefore, available treatments focus on alleviating the symptoms of the disease.Dopamine agonists may also lead to impulse control disorders in up to 12.4% of patients taking the dopamine agonist Pramipexole).Effective non-drug symptom reduction in RLS would be clinically helpful and relevant because RLS-medications with potential side effects, such as augmentation, could be avoided or reduced. State of the art reviews on RLSFor the treatment of mild and intermittent forms of RLS, clinical guidelines often recommend relaxation methods and other non-pharmacological therapies.Up to 65% of RLS patients regularly use complementary medical interventions to relieve their symptoms and improve well-being.Hydrotherapy according to Kneipp and acupressure are methods of complementary and integrative medicine that can be performed by the patients themselves and at a low cost. To date, no randomized controlled trials have examined the effectiveness, safety, and feasibility of self-administered acupressure and hydrotherapy according to Kneipp in patients with RLS.Two systematic reviews in 2019 and 2021 showed that acupuncture was significantly superior to control interventions for RLS in terms of severity and reported significant clinical effects.In a pilot study on the efficacy of acupressure in dialysis patients with RLS, a reduction in RLS severity was reported.Hydrotherapy according to Sebastian Kneipp (1821–1897) is characterized by serial, mostly cold-water applications (e.g., affusions, compresses, washes) and has been known in German-speaking countries since the 19There are indications that cold water applications can reduce RLS symptoms in pregnant women,Self-administered acupressure and hydrotherapy, according to the German non-medical naturopath and priest Sebastian Kneipp, showed little to no side effects.As there is good evidence for the effectiveness of acupuncture in patients with RLS, and some pilot studies have shown that acupressure and cold applications can reduce RLS- and sleep-related symptoms, we hypothesized that self-administered acupressure and Kneipp hydrotherapy with cold affusions could be a potential therapeutic option for patients with RLS. | PMC10313283 |
1.2. Study aims | RLS | The aim of this clinical study is to assess the feasibility and effects of self-administered acupressure and Kneipp hydrotherapy in patients with RLS, and to gather preliminary information on both study interventions as a basis for conducting a high-quality confirmatory trial including a valid sample size calculation. | PMC10313283 | |
2. Methods | PMC10313283 | |||
2.1. Study design | RESTLESS LEGS SYNDROME | The HYDRAC study (HYDRotherapy and ACupressure for Restless legs syndrome) is a prospective, parallel, 3-armed, interventional, exploratory clinical study. The study will be conducted for 12 weeks per patient, including a self-treatment phase of 6 weeks and a follow-up phase for another 6 weeks with optional self-treatment (Fig. Design of the HYDRAC study. HYDRAC = Hydrotherapy and Acupressure for Restless legs syndrome.Patients will be enrolled at the Charité Outpatient Clinic for Complementary and Integrative Medicine in Berlin-Mitte. After training on how to self-perform the respective interventions, the study interventions are to be carried out independently by the patients at home.To ensure high-quality evidence, we will conduct this study in accordance with Standard Protocol Items: Recommendations for Interventional Trials (Table A standard protocol items: recommendation for interventions for trials (SPIRIT). | PMC10313283 | |
2.2. Patients and recruitment | long-coronavirus disease syndrome, Raynaud disease, psoriasis, RLS, dermatological disease | ACUTE INFECTION, VIRUS, RAYNAUD DISEASE, CIRCULATORY DISORDERS, DISEASE, ATOPIC DERMATITIS, RECRUITMENT, PSORIASIS, CORONAVIRUS, HEART, SEVERE ACUTE RESPIRATORY SYNDROME, DERMATOLOGICAL DISEASE | A total of 51 patients (n = 17 in each arm) included in the trial. Patients who meet the following inclusion criteria will be included in the study: age between 18 and 75 years; diagnosis of RLS confirmed by a trained medical specialist (“Facharzt”) in accordance with the diagnostic criteria,Patients who meet any of the following criteria are excluded: indication for iron substitution therapy (exceptions are iron substitution that has already been carried out without sufficient symptom improvement or refusal of the iron substitution therapy by the patient); regular use of RLS triggering/exacerbating medications (such as mirtazapine, mianserin, clozapine, olanzapine, risperidone, haloperidol, sulpiride, promethazine); use of hydrotherapy, acupuncture, or acupressure in the last 4 weeks prior to inclusion or planned in the next 12 weeks; acute infection with severe acute respiratory syndrome coronavirus type 2 virus or presence of long-coronavirus disease syndrome; for women: pregnant or breastfeeding; presence of a serious acute and/or chronic organic or serious mental illness that does not allow participation in the study intervention (e.g., advanced cardio/pulmonary disease New York Heart Association classification/COLD III + IV); known Raynaud disease and advanced circulatory disorders of the extremities; inadequately treated dermatological disease in the therapeutic area (large wounds, severe atopic dermatitis, severe psoriasis, etc); abuse of medicines, drugs and/or alcohol; existing treatment with opioids; participation in an intervention study during the same period of the study or in the last 3 months prior to the start of the study for this condition; dependence on the study site (e.g., employee).The recruitment of study patients will be carried out primarily by advertising on public transport, newspaper advertisements, newsletters of medical institutions, the internet, flyers at general practitioners’ clinics, and neurological specialist practices. If patients meet the study criteria, they must sign an informed consent form before participating in the study or before randomization. The study physician obtains informed consent and signs a written informed consent form. The consent form contains the agreement to participate in the study, as well as the collection and storage of study data and personal data of the patient. | PMC10313283 |
2.3. Study physicians | acupuncture/acupressure | Both study physicians (JS and JK) fulfill the following requirements: knowledge of data protection, ICH-good clinical practice guidelines, content and procedure of the study, and instructions for the study interventions. They received detailed instruction and supervision by long-standing experts in hydrotherapy according to Kneipp and acupuncture/acupressure. The study physicians all had specialist medical training (German: “Facharztausbildung”) with at least 10 years of professional experience. | PMC10313283 | |
2.4. Study interventions | PMC10313283 | |||
2.4.1. Hydrotherapy according to Kneipp | RESTLESS LEGS SYNDROME, COLD | Patients in the hydrotherapy group will be trained by the study physicians on the day of randomization and will receive an illustrated manual. Hydrotherapy should be performed semi-standardized with obligatory and optional affusions. The patients perform the treatments twice daily for 6 weeks. After week 6, further treatment is optional. Recommended affusions are 2 cold affusions up to the knees daily for 30 to 60 seconds or 3 to 6 minutes if performed as alternating warm/cold affusions. (Fig. General rules for hydrotherapy applications according to Kneipp (as handed out to patients).°C = degree celsius.HYDRAC study: Scheme of the study intervention hydrotherapy according to Kneipp. HYDRAC = Hydrotherapy and Acupressure for Restless legs syndrome. | PMC10313283 | |
2.4.2. Acupressure | RESTLESS LEGS SYNDROME | Patients in the acupressure group will be trained by the study physicians after inclusion and randomization, and will also receive an illustrated manual. Acupressure should be performed in a standardized manner with a 6 acupuncture point set according to the rules and principles of Chinese Medicine,HYDRAC study: Scheme of the study intervention acupressure. HYDRAC = Hydrotherapy and Acupressure for Restless legs syndrome. | PMC10313283 | |
2.4.3. Waiting list group | Patients in the waitlist group continue to take their routine medication and will not receive any additional training during the intervention and follow-up periods. They have the option to receive free hydrotherapy or acupressure training by one of the study physicians after completing the study.Medically necessary treatments during the study are allowed for all groups. Changes in RLS medication are recorded in patients’ diaries. | PMC10313283 | ||
2.4.5. Blinding procedures | Due to the nature of Kneipp hydrotherapy and acupressure, patients and study physicians cannot be blinded. To minimize bias, all outcomes at baseline and 6 and 12 weeks after randomization will be evaluated by the same independent, experienced investigators blinded to the group assignment. After the completion of the statistical analyses, the group assignment will be announced by the research associate for biometrics. | PMC10313283 | ||
2.4.6. Randomization | Randomization will be performed centrally based on a computer-generated randomization list (generated using R software (version 4.1.2)) and will be block randomization with variable block length. Allocation to the 3 groups will be in a 1:1:1 ratio. Randomization will be performed at the end of the inclusion examination by the study physician using an administrative database. This will happen after informing about the study, providing written informed consent, and reviewing the inclusion and exclusion criteria. The last name, first name, date of birth, and sex will be entered into the administrative database by the study physician. The system performs automated randomization, and randomization confirmation is generated and printed using a button click. All further personal data will then be completed by the study nurse. The result of the randomization is communicated to the patients when the baseline questionnaires have been completed. Subsequently, training in the respective interventions will take place. | PMC10313283 | ||
2.5. Sample size calculation | RLS | As this is an exploratory study to generate the first results and further queries about the effect of hydrotherapy and acupressure in patients with RLS, we did not make a sample size estimation. As we expect about 10% of the patients to drop out of the study before week 12; 17 randomized patients per group (51 randomized patients in total) are planned and seem logistically feasible at the study center. Thus, we expect to have at least 15 patients aged 18 to 75 years per group at week 6. | PMC10313283 | |
2.6. Attendance and drop-outs | The attendance of each patient is recorded by the return and evaluation of diaries and questionnaires. Dropouts and reasons for dropout will be recorded in the study database. | PMC10313283 | ||
2.7. Statistical methods | REGRESSION | All data collected are analyzed descriptively: means, standard deviation, median and quartile, or frequencies and percentages (overall and separated by intervention group). Endpoints are analyzed graphically and in an exploratory manner, and depending on the scale, performed using analysis of covariance or logistic regression (in each case, the treatment group and (if available and in case of relevant group differences) the respective baseline values are adjusted for as covariates). Adjusted means or odds ratios with 95% confidence intervals and p-values for the group comparisons are reported. All | PMC10313283 | |
2.8. Data collection and management | The personally identifiable data are entered and managed in a password-protected MS Access database and serve as a re-identification list. The database resides on a separate project drive, with limited accessibility. Data from the survey instruments are entered in pseudonymized form into a SoSci-Survey online database programmed by the Institute of Social Medicine, Epidemiology, and Health Economics at Charité Universitätsmedizin Berlin, via an HTTPS connection (SSL encryption) with an existing Internet connection to the server, which is located within the Charité IT infrastructure. The data received from the server can be exported for further processing, and this can only be performed by the data manager via a password-protected login. The exported data are stored on a specially protected drive within the server of the Institute of Social Medicine, Epidemiology, and Health Economics.The questionnaires are entered into the online database in a pseudonymous form by 1 or more persons known by name and authorized by the principal investigator. The entry of the questionnaires is also password protected. After checking for correctness and plausibility, the data are transferred to SPSS data format. | PMC10313283 | ||
2.9. Withdrawal criteria and management | massive deterioration | ADVERSE EVENT | Patients may or must withdraw from the study if the continuation of the study imposes an unreasonable burden on the patient because of the patient condition, if a serious adverse event occurs and a causal relationship is established by the study director, if patients request withdrawal from the study and/or there is a lack of willingness to cooperate or comply, and/or if circumstances arise that make continued participation unreasonable, such as massive deterioration in health due to serious illness.Reasons and timing of withdrawal will be recorded in standard case report forms and stored in the study database. | PMC10313283 |
2.10. Oversight and monitoring | PMC10313283 | |||
2.10.1. Safety monitoring | EVENTS, ADVERSE EVENT | Together with the study documents, all patients will receive the necessary contact information to report any serious events directly to the Institute of Social Medicine, Epidemiology and Health. Serious Adverse Events must be reported by the study physicians to the study management within 24 hours of being known. | PMC10313283 | |
2.10.2. Dissemination plans | The authors intend to publish the results of this study in peer-reviewed journals and present them at local and international conferences. A summary of the results can be provided to the patients who participated at their request. | PMC10313283 | ||
3. Discussion | Restless Legs Syndrome, RLS/WED, anxiety, RLS-severity, RLS, depression | RECRUITMENT, RESTLESS LEGS SYNDROME | The present exploratory RCT will be the first to investigate whether there is evidence for the effects of self-applied Kneipp hydrotherapy and acupressure in patients with RLS and whether it is reasonable to conduct a subsequent confirmatory study in a larger patient population.Our requirement is that the inclusion and exclusion criteria be comparable to those of other high-quality studies on RLS. Thus, the study inclusion and exclusion criteria were based on the RLS/WED diagnostic criteria according to the updated International Restless Legs Syndrome Study Group (IRLSSG) consensus criteria of 2014.We want to investigate not only a non-drug approach but also an intervention that can be performed independently by the patient. Self-applicable therapies empower patients, but rely on motivation. To promote motivation and adherence to the intervention, we use diaries and contact patients by telephone 1 and 3 weeks after randomization. Problems regarding implementation and the need for further training will be ascertained through telephone interviews. This also allows for immediate optimization of the intervention and (if necessary) improves the implementation of the intervention and the intervention itself in a future full-scale study.All patients in the intervention groups also receive a handout with the corresponding intervention instructions in the text and pictures at the beginning of the study.This study uses a well-defined acupressure protocol that focuses on 6 specific acupuncture points, some of which have already been shown to be effective in previous acupuncture and acupressure studies in RLSKneipp hydrotherapy was chosen because it is a well-known and recognized treatment concept in Germany that can be carried out independently at home without additional equipment and without much time expenditure, and has shown good acceptance in previous studies.A twice-daily application was chosen because a previous study on Kneipp hydrotherapy in patients with menopausal symptoms showedBy recruiting patients broadly, both online, in public transportation, and in physicians’ offices and through self-help groups, the study aims to find a representative group of patients in terms of socioeconomic status, age distribution, and comorbidities. This study uses valid, reliable, and internationally accepted assessment tools to measure changes in RLS-severity, including sleep quality, depression, anxiety, and QoL. If effects are detectable, the data obtained can help determine recruitment potential, subsequent sample size calculation, reasonable number and frequency of interventions, and further parameters needed to successfully design a subsequent full-scale study. | PMC10313283 |
3.1. Strength and limitations of the study | RLS | This study is the first to investigate 2 non-drug therapy options for RLS that could expand the current treatment spectrum through self-applicable, openly accessible, and cost-effective measures. On the one hand, both therapies can be implemented in addition to the existing routine therapy, and, on the other hand, they can potentially limit or prevent the side effects of the existing drug therapy by helping to reduce the dose. After instruction, patients are largely independent of therapists in the implementation of the intervention and gain self-efficacy in the best case.Limitations arise from the fact that this is an exploratory study designed to investigate feasibility and assess potential impact, but not a study with a design that allows the confirmation of a formal hypothesis, a primary outcome based on a valid sample size calculation. In this study, routine care alone is the control condition. This means that patients assigned to the control group do not receive additional intervention in their routine care, as is the case with the hydrotherapy and acupressure groups. This means that there is no comparison group for possible specific effects of acupressure or hydrotherapy, such as sham procedures, on both study interventions. Furthermore, the study physicians and patients are not blinded to the interventions, whereas the data analysts are. Therefore, if there are clinically relevant effects of the interventions, we cannot say that they are due to them. It could also be due to other factors, such as “doing something,” “taking time for oneself,” “relaxing,” and “participating in a study.” Patients in the control group could be frustrated about being assigned to a “waiting group” and could be influenced in their outcome evaluation by negative affect.However, self-applied acupressure and hydrotherapy are not easily blinded, making the sham control of the 2 study interventions difficult.Because outcome parameters are collected solely from patients and through subjective measures, behavioral change and dissatisfaction may occur if patients do not receive the desired intervention. These patients are also more likely to be lost to follow-up.Another critical point could be the training of the patients. Live training for the study interventions will occur only once at the beginning of the intervention, and there will be no personal visual inspection by the study physicians during the study to ensure that the applications are performed correctly. This can lead to a situation in which the possible effects of the interventions do not take effect because they were performed incorrectly. Therefore, in the case of missing effects, it is not possible to say with certainty whether this is due to incorrect execution or the lack of effect of the study intervention itself.Moreover, patients in the acupressure group have some freedom in choosing the quality of the pressure to be applied (e.g., constant pressure, pulsating massage), which could lead to different results according to the theory of Chinese medicine.This exploratory study can provide a better understanding of the effects of complementary and self-administered interventions in patients with RLS. This may bring new questions to light so that future research questions in the field of RLS therapy can be refined. This study can also be used to formulate hypotheses about the causal relationships between the study interventions and their impact on RLS severity and quality of life in patients with RLS. Another advantage of this study is that we can see how both interventions work in their natural environment, what problems occur, and whether it is safe to do them at home. | PMC10313283 | |
Acknowledgments | We would like to thank the entire study team, including Margit Cree and Katharina Kleinsteuber (members of the HYDRAC study secretary) for their outstanding work on this study. | PMC10313283 | ||
Abbreviations: | RESTLESS LEGS SYNDROME | restless-legs-syndromerestless-legs-syndrome quality of life questionnairestatistical package for social sciencesWillis-Ekbom diseaseMT and JS contributed equally to this work.Trial registration: This study was registered in the German Clinical Trial Registry (number DRKS00029960) on August 09, 2022.Ethics approval and consent to participate: This study was reviewed by the ethics committee of Charité-Universitätsmedizin Berlin (reference number: EA2/132/22). Written informed consent is obtained from all patients. All procedures in this study are in accordance with the Declaration of Helsinki.Consent for publication: Not applicable.The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.The authors have no conflicts of interest to disclose.Funding for this study was provided in part by the Karl and Veronica Carstens Foundation, which has made grants available as part of the Young Clinician Scientists grant program (scholarship recipient: Dr. med. Julia Siewert). The foundation had no influence on the design, methodology, conduct, analysis, or publication of the study.How to cite this article: Kubasch J, Ortiz M, Binting S, King R, Dietzel J, Nögel R, Hummelsberger J, Willich SN, Brinkhaus B, Teut M, Siewert J. Hydrotherapy and acupressure in restless legs syndrome: A randomized, controlled, 3-armed, explorative clinical trial. Medicine 2023;102:26(e34046). | PMC10313283 | |
References | PMC10313283 | |||
Background | EMA | Ecological momentary assessments (EMAs) are short, repeated surveys designed to collect information on experiences in real-time, real-life contexts. Embedding periodic bursts of EMAs within cohort studies enables the study of experiences on multiple timescales and could greatly enhance the accuracy of self-reported information. However, the burden on participants may be high and should be minimized to optimize EMA response rates. | PMC10623229 | |
Objective | EMA | We aimed to evaluate the effects of study design features on EMA response rates. | PMC10623229 | |
Methods | EMA | Embedded within an ongoing cohort study (Health@NUS), 3 bursts of EMAs were implemented over a 7-month period (April to October 2021). The response rate (percentage of completed EMA surveys from all sent EMA surveys; 30-42 individual EMA surveys sent/burst) for each burst was examined. Following a low response rate in burst 1, changes were made to the subsequent implementation strategy (SMS text message announcements instead of emails). In addition, 2 consecutive randomized controlled trials were conducted to evaluate the efficacy of 4 different reward structures (with fixed and bonus components) and 2 different schedule lengths (7 or 14 d) on changes to the EMA response rate. Analyses were conducted from 2021 to 2022 using ANOVA and analysis of covariance to examine group differences and mixed models to assess changes across all 3 bursts. | PMC10623229 | |
Results | Participants (N=384) were university students (n=232, 60.4% female; mean age 23, SD 1.3 y) in Singapore. Changing the reward structure did not significantly change the response rate ( | PMC10623229 | ||
Conclusions | EMA | Small changes to the implementation strategy (SMS text messages instead of emails) may have contributed to increasing the response rate over time. Changing the available rewards did not lead to a significant difference in the response rate, whereas changing the schedule length did lead to a significant difference in the response rate. Our study provides novel insights on how to implement EMA surveys in ongoing cohort studies. This knowledge is essential for conducting high-quality studies using EMA surveys. | PMC10623229 | |
Trial Registration | ClinicalTrials.gov NCT05154227; https://clinicaltrials.gov/ct2/show/NCT05154227 | PMC10623229 | ||
Methods | EMA | This study evaluated participants’ response rates to the first 3 bursts of EMA surveys nested within an ongoing prospective cohort study, Health@NUS (ClinicalTrials.gov NCT05154227). | PMC10623229 | |
Health@NUS | EMA | Full details of Health@NUS are available elsewhere [The schedule for the first burst of Health@NUS EMA surveys was designed based on the available literature [ | PMC10623229 |
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