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Validation of the educational effectiveness of a mobile learning app to improve knowledge about MR image quality optimisation and artefact reduction.

ABSTRACT: AIM:The aim was to design an app-based eLearning tool to provide radiographers with information about the physical basis of MR artefacts and practical elimination or/and minimisation strategies to optimise image quality, and to evaluate the impact of a smartphone app on radiographers' knowledge. METHODS:The study used the comparison-experimental approach (pre- and post-test). Thirty-five MR radiographers independently reviewed a prepared series of MR images (n?=?25). The participants were requested to identify image quality related errors, to specify error-correction strategies and to score how confident they were in their responses. Participants were then divided into experimental (n?=?19) and control cohorts (n?=?16). The app was provided to the experimental cohort for 3 months; after this period both cohorts re-reviewed the MR image datasets and repeated their identification of image quality errors. RESULTS:The results showed a statistically significant difference between control and experimental cohorts relative to participants' pre- to post-test knowledge level. For the experimental cohort, years of experience, qualification and type of hospital were not associated with radiographer knowledge level and confidence in recognising the presence of an image quality error, naming the error and specifying appropriate correction strategies (p?>?0.05). CONCLUSION:The study identified the potential of the smartphone app as an effective educational tool to support MR radiographers' knowledge in recognising and characterising MR image quality errors. KEY POINTS:• A high level of knowledge to optimise MR image quality is crucial. • Ongoing education in image quality optimisation is required. • The potential role of app as an effective educational tool is identified.

SUBMITTER: Alsharif W

PROVIDER: S-EPMC6206384 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Validation of the educational effectiveness of a mobile learning app to improve knowledge about MR image quality optimisation and artefact reduction.

Alsharif Walaa W Davis Michaela M Rainford Louise L Cradock Andrea A McGee Allison A

Insights into imaging 20180614 5

<h4>Aim</h4>The aim was to design an app-based eLearning tool to provide radiographers with information about the physical basis of MR artefacts and practical elimination or/and minimisation strategies to optimise image quality, and to evaluate the impact of a smartphone app on radiographers' knowledge.<h4>Methods</h4>The study used the comparison-experimental approach (pre- and post-test). Thirty-five MR radiographers independently reviewed a prepared series of MR images (n = 25). The participa ...[more]

PMID: 29949036

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Project description:BACKGROUND:Smartphone ownership is becoming ubiquitous among US adults, making the delivery of health interventions via a mobile app (ie, mobile health [mHealth]) attractive to many researchers and clinicians. Meditation interventions have become popular and have been delivered to study participants via mobile apps to improve a range of health outcomes in both healthy adults and those with chronic diseases. However, these meditation mHealth interventions have been limited by a lack of high-quality control groups. More specifically, these studies have lacked consistency in their use of active, time-matched, and attention-matched control groups. OBJECTIVE:The purpose of this study is to beta test a novel health education podcast control condition delivered via a smartphone app that would be a strong comparator to be used in future studies of app-based meditation interventions. METHODS:Patients with myeloproliferative neoplasm (MPN) cancer were recruited nationally. Upon enrollment, participants were informed to download the investigator-developed health education podcast app onto their mobile phone and listen to ~60 min/week of cancer-related educational podcasts for 12 weeks. The benchmarks for feasibility included ?70% of participants completing ?70% of the prescribed 60 min/week of podcasts, ?70% of participants reporting that they were satisfied with the intervention, and ?70% of participants reporting that they enjoyed the health education podcasts. RESULTS:A total of 96 patients with MPN were enrolled in the study; however, 19 never began the intervention. Of the 77 patients who participated in the intervention, 39 completed the entire study (ie, sustained participation through the follow-up period). Participation averaged 103.2 (SD 29.5) min/week. For 83.3% (10/12) of the weeks, at least 70% of participants completed at least 70% of their total prescribed use. Almost half of participants reported that they enjoyed the health education podcasts (19/39, 48.7%) and were satisfied with the intervention (17/39, 43.6%). There were no significant changes in cancer-related outcomes from baseline to postintervention. CONCLUSIONS:A 12-week, health education podcast mobile app was demanded but not accepted in a sample of patients with cancer. Using the mobile app was not associated with significant changes in cancer-related symptoms. Based on findings from this study, a health education podcast mobile app may be a feasible option as a time- and attention-matched control group for efficacy trials with more extensive formative research for the content of the podcasts and its acceptability by the specific population. TRIAL REGISTRATION:ClinicalTrials.gov NCT03907774; https://clinicaltrials.gov/ct2/show/NCT03907774.

Project description:mHealth interventions can help to improve the physical well-being of participants. Unfortunately, mHealth interventions often have low adherence and high attrition. One possible way to increase adherence is instructing participants to complete self-affirmation exercises. Self-affirmation exercises have been effective in increasing many types of positive behaviors. However, self-affirmation exercises often involve extensive essay writing, a task that is not easy to complete on mobile platforms.This study aimed to adapt a self-affirmation exercise to a form better suited for delivery through a mobile app targeting healthy eating behaviors, and to test the effect of differing self-affirmation doses on adherence to behavior change goals over time.We examined how varied self-affirmation doses affected behavior change in an mHealth app targeting healthy eating that participants used for 28 days. We divided participants into the 4 total conditions using a 2×2 factorial design. The first independent variable was whether the participant received an initial self-affirmation exercise. The second independent variable was whether the participant received ongoing booster self-affirmations throughout the 28-day study. To examine possible mechanisms through which self-affirmation may cause positive behavior change, we analyzed three aspects of self-affirmation effects in our research. First, we analyzed how adherence was affected by self-affirmation exercises. Second, we analyzed whether self-affirmation exercises reduced attrition rates from the app. Third, we examined a model for self-affirmation behavior change.Analysis of 3556 observations from 127 participants indicated that higher doses of self-affirmation resulted in improved adherence to mHealth intervention goals (coefficient 1.42, SE 0.71, P=.04). This increased adherence did not seem to translate to a decrease in participant attrition (P value range .61-.96), although our definition of attrition was conservative. Finally, we examined the mechanisms by which self-affirmation may have affected intentions of behavior change; we built a model of intention (R2=.39, P<.001), but self-affirmation did not directly affect final intentions (P value range .09-.93).Self-affirmations can successfully increase adherence to recommended diet and health goals in the context of an mHealth app. However, this increase in adherence does not seem to reduce overall attrition. The self-affirmation exercises we developed were simple to implement and had a low cost for both users and developers. While this study focused on an mHealth app for healthy eating, we recommend that other mHealth apps integrate similar self-affirmation exercises to examine effectiveness in other behaviors and contexts.

Project description:BackgroundThe postnatal period poses numerous challenges for new parents. Various educational programs are available to support new parents during this stressful period. However, the usefulness of educational programs must be evaluated to ascertain their credibility.ObjectiveThe aim of this descriptive, qualitative study was to explore the views of parents of newborns with regard to the content and delivery of a mobile health (mHealth) app-based postnatal educational program.MethodsA qualitative semistructured interview guide was used to collect data from 17 participants who belonged to the intervention group of a randomized controlled trial. The intervention, a 4-week-long access to a mHealth app-based educational program, was evaluated. The interviews were conducted in English and at the participants' homes. Thematic analysis was used to analyze the data. The Consolidated Criteria for Reporting Qualitative Research checklist was used to report the findings.ResultsThe interviews revealed 4 main themes: (1) positive features of the mHealth app, (2) advice from midwives, (3) experiences gained from using the mHealth app, and (4) recommendations for the future. The participants evaluated the educational program to be a good source of information that was tailored to the local context. The different modes of delivery, including audio and video, accentuated the accessibility of information. The parents evaluated that the facilitator of the featured communication platform, a midwife, provided trustworthy advice. Belongingness to a virtual community beyond the hospital endowed the parents the confidence that they were not alone and were supported by other parents and health care professionals.ConclusionsAccording to the parents, the mHealth app-based educational program was helpful in supporting a multi-ethnic sample of parents during the postnatal period. This insight indicates that the program could be implemented in a wide community of parents in the postnatal period. The helpfulness of the educational program is a testament of the potential benefits of using telemedicine among new parents postnatally. Resources can also be dedicated toward extending the duration of access to the app beyond 1 month and developing relevant content for parents across the perinatal period.

Project description:ObjectivesThe susceptibility of CT imaging to metallic objects gives rise to strong streak artefacts and skewed information about the attenuation medium around the metallic implants. This metal-induced artefact in CT images leads to inaccurate attenuation correction in PET/CT imaging. This study investigates the potential of deep learning-based metal artefact reduction (MAR) in quantitative PET/CT imaging.MethodsDeep learning-based metal artefact reduction approaches were implemented in the image (DLI-MAR) and projection (DLP-MAR) domains. The proposed algorithms were quantitatively compared to the normalized MAR (NMAR) method using simulated and clinical studies. Eighty metal-free CT images were employed for simulation of metal artefact as well as training and evaluation of the aforementioned MAR approaches. Thirty 18F-FDG PET/CT images affected by the presence of metallic implants were retrospectively employed for clinical assessment of the MAR techniques.ResultsThe evaluation of MAR techniques on the simulation dataset demonstrated the superior performance of the DLI-MAR approach (structural similarity (SSIM) = 0.95 ± 0.2 compared to 0.94 ± 0.2 and 0.93 ± 0.3 obtained using DLP-MAR and NMAR, respectively) in minimizing metal artefacts in CT images. The presence of metallic artefacts in CT images or PET attenuation correction maps led to quantitative bias, image artefacts and under- and overestimation of scatter correction of PET images. The DLI-MAR technique led to a quantitative PET bias of 1.3 ± 3% compared to 10.5 ± 6% without MAR and 3.2 ± 0.5% achieved by NMAR.ConclusionThe DLI-MAR technique was able to reduce the adverse effects of metal artefacts on PET images through the generation of accurate attenuation maps from corrupted CT images.Key points• The presence of metallic objects, such as dental implants, gives rise to severe photon starvation, beam hardening and scattering, thus leading to adverse artefacts in reconstructed CT images. • The aim of this work is to develop and evaluate a deep learning-based MAR to improve CT-based attenuation and scatter correction in PET/CT imaging. • Deep learning-based MAR in the image (DLI-MAR) domain outperformed its counterpart implemented in the projection (DLP-MAR) domain. The DLI-MAR approach minimized the adverse impact of metal artefacts on whole-body PET images through generating accurate attenuation maps from corrupted CT images.

Dataset Information

Validation of the educational effectiveness of a mobile learning app to improve knowledge about MR image quality optimisation and artefact reduction.

Publications

Validation of the educational effectiveness of a mobile learning app to improve knowledge about MR image quality optimisation and artefact reduction.

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