Ignite Creation Date:
2024-06-16 @ 11:48 AM
Last Modification Date:
2024-10-26 @ 3:30 PM
Study NCT ID:
NCT06433414
Status:
NOT_YET_RECRUITING
Last Update Posted:
2024-06-05
First Post:
2024-05-13
Brief Title:
PAUSE Sick Day Medication Management Mobile App Study
Sponsor:
University of Alberta
Organization:
University of Alberta
Study Overview
Official Title:
Preventing Medication Complications During AcUte Illness Through Symptom Evaluation and Sick Day Guidance Mobile Application PAUSE
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-08
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
PAUSE
Brief Summary:
Diabetes heart disease and kidney disease have high morbidity and costs of care Medications used to treat these conditions are effective Yet some have the risk of preventable adverse events when people are sick with the flu or stomach bug These events include low blood sugar and acute kidney injury which can lead to extended hospital stays or death Sick day medication guidance SDMG recommends stopping these medications temporarily when sick and restarted after symptoms subside Unfortunately many patients are not aware of these recommendations or find them hard to follow
The investigators previous research has shown that there is a lack of SDMG education and patient resources Research on the development implementation usability and efficacy of these resources is also limited In developing a SDMG tool the investigators surveyed patients who expressed interest in an electronic health eHealth tool As a result the PAUSE App provides a timely and innovative way to provide continuity of care to patients that is linked to each patients unique pharmacy record
In the present pilot randomized control trial the investigators will examine the outcomes of the PAUSE Initiative consisting of the PAUSE App and a SDMG educational handout Approximately 16 LoblawShoppers Drug Mart pharmacies across Alberta will take part Patients of these pharmacies who take high-risk medications will be invited to participate Each pharmacy will be randomized to provide their patients usual care ie SDMG handout or the intervention ie PAUSE App handout Approximately 320 participants 20 per pharmacy are expected to be recruited The expected trial length is 9 months from recruitment to analysis
A simulated sick day survey will be used to assess the fidelity and efficacy of the PAUSE Initiative Feasibility of the study processes ie recruitment onboarding will be assessed to inform a full-scale trial The usability and acceptability of the PAUSE App will also be investigated Pharmacists and participants will complete questionnaires and qualitative interviews to assess these outcomes Additionally PAUSE App user metrics will be collected All participants will receive an honorarium for their time
The investigators previous research has shown that there is a lack of SDMG education and patient resources Research on the development implementation usability and efficacy of these resources is also limited In developing a SDMG tool the investigators surveyed patients who expressed interest in an electronic health eHealth tool As a result the PAUSE App provides a timely and innovative way to provide continuity of care to patients that is linked to each patients unique pharmacy record
In the present pilot randomized control trial the investigators will examine the outcomes of the PAUSE Initiative consisting of the PAUSE App and a SDMG educational handout Approximately 16 LoblawShoppers Drug Mart pharmacies across Alberta will take part Patients of these pharmacies who take high-risk medications will be invited to participate Each pharmacy will be randomized to provide their patients usual care ie SDMG handout or the intervention ie PAUSE App handout Approximately 320 participants 20 per pharmacy are expected to be recruited The expected trial length is 9 months from recruitment to analysis
A simulated sick day survey will be used to assess the fidelity and efficacy of the PAUSE Initiative Feasibility of the study processes ie recruitment onboarding will be assessed to inform a full-scale trial The usability and acceptability of the PAUSE App will also be investigated Pharmacists and participants will complete questionnaires and qualitative interviews to assess these outcomes Additionally PAUSE App user metrics will be collected All participants will receive an honorarium for their time
Detailed Description:
Our previous research surveyed healthcare providers from Alberta on factors affecting clinicians decision to provide sick day medication guidance to patients with diabetes and CKD to prevent adverse events Our results identified 75 of primary health providers were aware of sick day medication guidance but just 56 knew where to find guidelines and resources An overwhelming majority of respondents 97 were supportive of enrolling patients in a study evaluating alternative innovations for providing sick day medication guidance
In a recent scoping review summarizing existing interventions our research team found the majority of published SDMG documents were aimed towards healthcare providers with few patient-targeted documents These were mainly in the form of handouts wallet-sized cards webpages or telephone support There is limited primary research on the development implementation or evaluation of current SDMG interventions Most were reported to be challenging to follow and identification of sick days or qualifying medication without error was low This survey and review highlight the need to develop and to evaluate new solutions for providing SDMG to patients Our previous work also found that seniors in Canada were receptive to the use of electronic means of communication and several patients have expressed interest in using electronic health eHealth tools for sick day self-management
Participants receiving the intervention will receive access to the PAUSE App a self-management tool for SDMG intended for patients to use during an acute illness Users LoblawShoppers Drug Mart pharmacy records are electronically linked to the PAUSE App within the Presidents Choice PC Health app allowing for up-to-date recommendations based on current prescribed medications The app asks users a series of questions regarding signs and symptoms that identify a qualifying sick day illness and screens for red flags that would require emergency or healthcare provider or urgent care referral and help patients identify which of their medications they should temporarily withhold or adjusted tailored to a patients current medication list This aims to provide patients with interactive support for managing medication during a sick day event As part of the intervention patients will also receive a SDMG patient handout The intervention addresses the previously identified challenges of identifying qualifying signs and symptoms that warrant SDMG and which medications qualify via an interactive and individualized electronic application designed to facilitate provision of SDMG The usual care group will receive a SDMG patient handout which outlines SDMG and addresses which medications qualify for SDMG
Based on preliminary data the investigators assume an absolute difference of 30 50 with the PAUSE app vs 20 without the PAUSE app in the proportion of participants who complete a simulated sick day without error Using a two-sided alpha of 005 80 power and an interclass correlation coefficient of 01 between pharmacy clusters a sample size of 280 participants will be required To account for a 10 loss to follow-up the investigators will aim to recruit a total of 320 participants in the trial The investigators plan to recruit 16 pharmacies that will recruit 20 participants each
Data Analysis Participant baseline data including sociodemographics comorbidities and active prescriptions will be analyzed using descriptive statistics Feasibility and fidelity outcomes will be reported using descriptive statistics with numbers and percentages Comparisons of outcomes between groups eg PAUSE App vs usual care will be reported using unadjusted and adjusted generalized estimating equations to determine mean differences and risk differences between groups Descriptive statistics will be used as appropriate to evaluate group differences following the follow-up period Associations between key variables and study outcomes will be analyzed using appropriate univariate multivariate and mixed model analyses Exploratory analyses of Google Analytics data will be performed to report user behaviour insights Analyses of routinely collected health data over a 5-year extended follow-up period will be used to determine the effect if any of the intervention on health outcomes
The simulated sick day evaluations will be scored and analyzed according to predefined scorecards based on scenarios used by Doerfler et al measuring correct usage of SDMG during acute illness Log-binomial regression models will be used to directly estimate the risk ratios RRs and 95 confidence intervals for the outcome of error free completion of the simulated sick day as well as for correct completion of each of the 3 individual components of the sick day simulation Random effects will be used to account for clustering by pharmacies Unadjusted and adjusted models will be fit including fixed effects for individual participant characteristics including age sex demographics diabetes other comorbidities number of qualifying medications and any other significant confounding variables from univariate analyses Additionally data collected from participants on the usefulness of the PAUSE App andor SDMG patient handout in managing a simulated sick day and overall acceptability of the interventions will be used to further assess the fidelity of the intervention All statistical analysis will be completed in R
Selected participants patients and pharmacists will be invited to be interviewed following their simulated sick day scenario evaluation based on the purposive sampling strategy One-on-one semi-structured interviews will be conducted with participants and pharmacists ranging from 30-60 minutes in duration Interview questions and analysis will be iterative throughout the study to allow for emerging or irregular themes to be examined in later interviews Qualitative interviews will be audio-recorded transcribed verbatim and examined using multiple phases of inductive thematic analysis Collected field notes and transcriptions from interviews will be analyzed using NVIVO qualitative analysis software Analysis of data will begin immediately following the conclusion of the first participant interview Data will be coded by two researchers independently and then codes will be compared after the first interview to draft the coding manual for subsequent interviews
In a recent scoping review summarizing existing interventions our research team found the majority of published SDMG documents were aimed towards healthcare providers with few patient-targeted documents These were mainly in the form of handouts wallet-sized cards webpages or telephone support There is limited primary research on the development implementation or evaluation of current SDMG interventions Most were reported to be challenging to follow and identification of sick days or qualifying medication without error was low This survey and review highlight the need to develop and to evaluate new solutions for providing SDMG to patients Our previous work also found that seniors in Canada were receptive to the use of electronic means of communication and several patients have expressed interest in using electronic health eHealth tools for sick day self-management
Participants receiving the intervention will receive access to the PAUSE App a self-management tool for SDMG intended for patients to use during an acute illness Users LoblawShoppers Drug Mart pharmacy records are electronically linked to the PAUSE App within the Presidents Choice PC Health app allowing for up-to-date recommendations based on current prescribed medications The app asks users a series of questions regarding signs and symptoms that identify a qualifying sick day illness and screens for red flags that would require emergency or healthcare provider or urgent care referral and help patients identify which of their medications they should temporarily withhold or adjusted tailored to a patients current medication list This aims to provide patients with interactive support for managing medication during a sick day event As part of the intervention patients will also receive a SDMG patient handout The intervention addresses the previously identified challenges of identifying qualifying signs and symptoms that warrant SDMG and which medications qualify via an interactive and individualized electronic application designed to facilitate provision of SDMG The usual care group will receive a SDMG patient handout which outlines SDMG and addresses which medications qualify for SDMG
Based on preliminary data the investigators assume an absolute difference of 30 50 with the PAUSE app vs 20 without the PAUSE app in the proportion of participants who complete a simulated sick day without error Using a two-sided alpha of 005 80 power and an interclass correlation coefficient of 01 between pharmacy clusters a sample size of 280 participants will be required To account for a 10 loss to follow-up the investigators will aim to recruit a total of 320 participants in the trial The investigators plan to recruit 16 pharmacies that will recruit 20 participants each
Data Analysis Participant baseline data including sociodemographics comorbidities and active prescriptions will be analyzed using descriptive statistics Feasibility and fidelity outcomes will be reported using descriptive statistics with numbers and percentages Comparisons of outcomes between groups eg PAUSE App vs usual care will be reported using unadjusted and adjusted generalized estimating equations to determine mean differences and risk differences between groups Descriptive statistics will be used as appropriate to evaluate group differences following the follow-up period Associations between key variables and study outcomes will be analyzed using appropriate univariate multivariate and mixed model analyses Exploratory analyses of Google Analytics data will be performed to report user behaviour insights Analyses of routinely collected health data over a 5-year extended follow-up period will be used to determine the effect if any of the intervention on health outcomes
The simulated sick day evaluations will be scored and analyzed according to predefined scorecards based on scenarios used by Doerfler et al measuring correct usage of SDMG during acute illness Log-binomial regression models will be used to directly estimate the risk ratios RRs and 95 confidence intervals for the outcome of error free completion of the simulated sick day as well as for correct completion of each of the 3 individual components of the sick day simulation Random effects will be used to account for clustering by pharmacies Unadjusted and adjusted models will be fit including fixed effects for individual participant characteristics including age sex demographics diabetes other comorbidities number of qualifying medications and any other significant confounding variables from univariate analyses Additionally data collected from participants on the usefulness of the PAUSE App andor SDMG patient handout in managing a simulated sick day and overall acceptability of the interventions will be used to further assess the fidelity of the intervention All statistical analysis will be completed in R
Selected participants patients and pharmacists will be invited to be interviewed following their simulated sick day scenario evaluation based on the purposive sampling strategy One-on-one semi-structured interviews will be conducted with participants and pharmacists ranging from 30-60 minutes in duration Interview questions and analysis will be iterative throughout the study to allow for emerging or irregular themes to be examined in later interviews Qualitative interviews will be audio-recorded transcribed verbatim and examined using multiple phases of inductive thematic analysis Collected field notes and transcriptions from interviews will be analyzed using NVIVO qualitative analysis software Analysis of data will begin immediately following the conclusion of the first participant interview Data will be coded by two researchers independently and then codes will be compared after the first interview to draft the coding manual for subsequent interviews
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None